3b-model-basic-comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import gensim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "\n",
    "ROOT = os.path.dirname(os.getcwd())\n",
    "#path_data = os.path.join(ROOT, 'data')\n",
    "path_data = 'C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\data'\n",
    "sys.path.insert(0, ROOT)\n",
    "sys.path.insert(0, \"C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\spec2vec_gnps_data_analysis\\\\custom_functions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading processed query, library and found_matches data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\training_query_library_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "# load training dataset, for making these see notebook 1b\n",
    "\n",
    "outfile = os.path.join(path_data, 'training_query_library_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        training_query_library_s2v_2dec = pickle.load(inf)\n",
    "        old_and_unique_documents_query_s2v_2dec, old_and_unique_documents_library_s2v_2dec = training_query_library_s2v_2dec\n",
    "else:\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        training_query_library_s2v_2dec = (old_and_unique_documents_query_s2v_2dec, old_and_unique_documents_library_s2v_2dec)\n",
    "        pickle.dump(training_query_library_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\testing_query_library_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "#loading test dataset\n",
    "\n",
    "outfile = os.path.join(path_data, 'testing_query_library_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        testing_query_library_s2v_2dec = pickle.load(inf)\n",
    "        new_and_unique2_documents_query_s2v_2dec, new_and_unique2_documents_library_s2v_2dec = testing_query_library_s2v_2dec\n",
    "else:\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        testing_query_library_s2v_2dec = (new_and_unique2_documents_query_s2v_2dec, new_and_unique2_documents_library_s2v_2dec)\n",
    "        pickle.dump(testing_query_library_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_found_matches_s2v_2dec.pickle\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_and_unique2_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "# load/make found_matches for both query and test set\n",
    "\n",
    "# define models\n",
    "path_models = os.path.join(path_data, \"trained_models\")\n",
    "model_file2 = os.path.join(path_models, \"spec2vec_library_testing_4000removed_2dec.model\")\n",
    "\n",
    "# check if output exists, if not: query models\n",
    "outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        old_and_unique_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    # load model\n",
    "    model2 = gensim.models.Word2Vec.load(model_file2)\n",
    "    print(model2)\n",
    "    old_and_unique_found_matches_s2v_2dec = library_matching(old_and_unique_documents_query_s2v_2dec,\n",
    "                                                             old_and_unique_documents_library_s2v_2dec,\n",
    "                                                             model2,\n",
    "                                                             presearch_based_on=[\"spec2vec-top200\"],\n",
    "                                                             ignore_non_annotated=True,\n",
    "                                                             intensity_weighting_power=0.5,\n",
    "                                                             allowed_missing_percentage=100,\n",
    "                                                             cosine_tol=0.005,\n",
    "                                                             mass_tolerance=1.0)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(old_and_unique_found_matches_s2v_2dec, outf)\n",
    "\n",
    "outfile = os.path.join(path_data, 'new_and_unique2_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        new_and_unique2_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    # load model\n",
    "    model2 = gensim.models.Word2Vec.load(model_file2)\n",
    "    print(model2)\n",
    "    new_and_unique2_found_matches_s2v_2dec = library_matching(new_and_unique2_documents_query_s2v_2dec,\n",
    "                                                             new_and_unique2_documents_library_s2v_2dec,\n",
    "                                                             model2,\n",
    "                                                             presearch_based_on=[\"spec2vec-top200\"],\n",
    "                                                             ignore_non_annotated=True,\n",
    "                                                             intensity_weighting_power=0.5,\n",
    "                                                             allowed_missing_percentage=100,\n",
    "                                                             cosine_tol=0.005,\n",
    "                                                             mass_tolerance=1.0)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(new_and_unique2_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max parent mass: 13418.370894192036\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get max parent mass in dataset\n",
    "all_q_masses = [doc._obj.get(\"parent_mass\") for doc in old_and_unique_documents_query_s2v_2dec]\n",
    "all_l_masses = [doc._obj.get(\"parent_mass\") for doc in old_and_unique_documents_library_s2v_2dec]\n",
    "max_parent_mass = max(all_q_masses + all_l_masses)\n",
    "print(\"Max parent mass:\", max_parent_mass)\n",
    "plt.hist(all_q_masses + all_l_masses, bins = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem, DataStructs\n",
    "\n",
    "def find_info_matches(matches, documents_library, documents_query,\n",
    "                      add_label = True, add_tanimoto_sim = True, add_cols = False,\n",
    "                      add_num_matches_transform = True, add_mass_transform = True,\n",
    "                      max_parent_mass = False, add_mass_similarity = True):\n",
    "    '''\n",
    "    To each df in matches, add more info like tanimoto scores vs query etc.\n",
    "    \n",
    "    A matching inchikey gets label 1 and non-matching 0.\n",
    "\n",
    "    matches: list of pandas DataFrames, library matching result of query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    documents_query: list of SpectrumDocuments, spectra in query set.\n",
    "        Indices should correspond to indices of matches.\n",
    "    bins: list of int/float, the cutoff for the mass change, will result in boolean columns,\n",
    "        i.e. if a cutoff is 2 there will be a column with 1 (true) or 0 (false) for if the mass change\n",
    "        is within 2\n",
    "    calc_change: bool, instead of bins add a percentage change of the parent mass difference\n",
    "    add_label: bool, add a label for inchikey match or not\n",
    "    add_tanimoto_sim: bool, add tanimoto similarity or not\n",
    "    add_cols: bool/list of str, add other info present in metadata such as parent_mass, adduct\n",
    "        default: False\n",
    "    add_num_matches_transform: bool, transform cosine and mod_cosine matches to a number 0-1.\n",
    "        both matches are transformed to between 0-1 by doing 1-0.93^num_matches\n",
    "    add_mass_transform: bool, add transform of the parent masses to a fraction of the maximal parent mass\n",
    "    max_parent_mass: int/float, the maximum parent mass in the dataset, default = False\n",
    "    add_mass_similarity: bool, add similarity of parent mass to the query as a scaled number from 0-1\n",
    "        where The similarity in dalton is calculated and transformed into a value 0 - 1 by doing\n",
    "        1 - base_num^diff_in_dalton\n",
    "    \n",
    "    Output\n",
    "    matches_with_info: list of pandas DataFrames, library matching result of query\n",
    "        on library with matching labels\n",
    "    '''\n",
    "    matches_with_info = []\n",
    "    if add_mass_transform and not max_parent_mass:\n",
    "        print(\"If you want to transform the masses, please provide a max_parent_mass\")\n",
    "        return\n",
    "    else:\n",
    "        print('Max parent mass:', max_parent_mass)\n",
    "\n",
    "    for query_id in range(len(documents_query)):\n",
    "        match = matches[query_id].copy()\n",
    "        if add_label:\n",
    "            query_inchi = documents_query[query_id]._obj.get(\"inchikey\")[:14]\n",
    "            match = find_inchikey_match(match, documents_library, query_inchi)\n",
    "        if add_cols:\n",
    "            match = find_basic_info(match, documents_library, add_cols)\n",
    "        if add_tanimoto_sim:\n",
    "            query_smiles = documents_query[query_id]._obj.get(\"smiles\")\n",
    "            match = find_tanimoto_sim(match, documents_library, query_smiles)\n",
    "        if add_num_matches_transform:\n",
    "            match = transform_num_matches(match)\n",
    "        if add_mass_transform:\n",
    "            #add parent_mass if its not there already\n",
    "            match = find_basic_info(match, documents_library, add_cols = ['parent_mass'])\n",
    "            match['parent_mass'] = [cur_pm/max_parent_mass for cur_pm in match['parent_mass']]\n",
    "        if add_mass_similarity:\n",
    "            if 'mass_match' in match:\n",
    "                match.drop(['mass_match'], axis=1, inplace=True)\n",
    "            q_mass = documents_query[query_id]._obj.get(\"parent_mass\")\n",
    "            match = find_mass_similarity(match, documents_library, q_mass, base_num = 0.8)\n",
    "        \n",
    "        matches_with_info.append(match)\n",
    "    return matches_with_info\n",
    "\n",
    "def find_inchikey_match(matches, documents_library, query_inchi):\n",
    "    '''To each match in the matches df, add label for matching inchikey (1) or non-matching (0)\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    query_inchi: str, first 14 symbols of query inchikey\n",
    "    df: pandas DataFrame, library matching result of query on library with matching labels\n",
    "    '''\n",
    "    matches_with_labels = []\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    labels = []\n",
    "    for lib_id in library_ids:\n",
    "        lib_inchi = documents_library[lib_id]._obj.get(\"inchikey\")[:14]\n",
    "        lab = 0\n",
    "        if query_inchi == lib_inchi:\n",
    "            lab = 1\n",
    "        labels.append(lab)\n",
    "    df['label'] = labels\n",
    "    return df\n",
    "\n",
    "def find_mass_matches(matches, documents_library, query_mass, bins = [2, 5], calc_change = False):\n",
    "    '''\n",
    "    To each match in matches df, add the mass change of query to match in bins or a percentage change\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    query_mass: float, parent mass of query\n",
    "    bins: list of int/float, the cutoff for the mass change, will result in boolean columns,\n",
    "        i.e. if a cutoff is 2 there will be a column with 1 (true) or 0 (false) for if the mass change\n",
    "        is within 2\n",
    "    calc_change: bool, instead of bins add a percentage change of the parent mass difference\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with mass matches\n",
    "    '''\n",
    "    range_bins = range(len(bins)) #calc once\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    masses = [[] for _ in range_bins] #initialise\n",
    "    mass_changes = []\n",
    "    for lib_id in library_ids:\n",
    "        lib_mass = documents_library[lib_id]._obj.get(\"parent_mass\")\n",
    "        if calc_change: #calculate a percentage change in parent mass instead of discrete bins\n",
    "            perc_change = abs(lib_mass - query_mass) / query_mass * 100\n",
    "            mass_changes.append(perc_change)\n",
    "        else:\n",
    "            for bin_i in range_bins:\n",
    "                cutoff = bins[bin_i]\n",
    "                lab = 0\n",
    "                if abs(query_mass - lib_mass) < cutoff:\n",
    "                    lab = 1\n",
    "                masses[bin_i].append(lab)\n",
    "    #add to df\n",
    "    if calc_change:\n",
    "        df['perc_mass_change'] = mass_changes\n",
    "    else:\n",
    "        for bin_i in range_bins:\n",
    "            df['mass_match_' + str(bins[bin_i])] = masses[bin_i]\n",
    "    return df\n",
    "\n",
    "def find_basic_info(matches, documents_library, add_cols = ['parent_mass']):\n",
    "    '''\n",
    "    To each match in matches df, add the info from add_cols entries\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with added info\n",
    "    '''\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    if add_cols:\n",
    "        for col in add_cols:\n",
    "            col_data = []\n",
    "            for lib_id in library_ids:\n",
    "                lib_data = documents_library[lib_id]._obj.get(col)\n",
    "                col_data.append(lib_data)\n",
    "            df[col] = col_data\n",
    "    return df\n",
    "\n",
    "def find_tanimoto_sim(matches, documents_library, query_smiles):\n",
    "    '''To each match in matches df, add the tanimoto similarity between query and match\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with tanimoto similarities\n",
    "    '''\n",
    "    df = matches.copy()\n",
    "    sims = []\n",
    "    library_ids = df.index.values\n",
    "    \n",
    "    if not query_smiles or query_smiles == \"None\": # check that query smiles exist\n",
    "        df['similarity'] = [0] * len(library_ids) #default to all 0 if it doesnt exist\n",
    "        return df\n",
    "    ms_q = Chem.MolFromSmiles(query_smiles)\n",
    "    if not ms_q: #in case something is wrong with smiles\n",
    "        df['similarity'] = [0] * len(library_ids) #default to all 0 if it doesnt exist\n",
    "        return df\n",
    "    \n",
    "    fp_q = Chem.RDKFingerprint(ms_q)\n",
    "    for lib_id in library_ids:\n",
    "        smiles_lib = documents_library[lib_id]._obj.get(\"smiles\")\n",
    "        if smiles_lib and smiles_lib != \"None\":\n",
    "            ms_lib = Chem.MolFromSmiles(smiles_lib)\n",
    "            if ms_lib:\n",
    "                fp_lib = Chem.RDKFingerprint(ms_lib)\n",
    "                score = DataStructs.FingerprintSimilarity(fp_q, fp_lib)\n",
    "            else: #in case something is wrong with smiles\n",
    "                score = 0\n",
    "        else: #in case it doesnt have smiles\n",
    "            score = 0\n",
    "        sims.append(score)\n",
    "    df['similarity'] = sims\n",
    "    return df\n",
    "\n",
    "def transform_num_matches(input_df, exp = 0.93):\n",
    "    '''Transform the cosine_matches and mod_cosine_matches to between 0-1\n",
    "    \n",
    "    input_df: pandas DataFrame, spec2vec matches for one query\n",
    "    exp: int, the base for the exponential, default: 0.93\n",
    "    \n",
    "    Both matches are transformed to between 0-1 by doing 1-0.93^num_matches\n",
    "    '''\n",
    "    df = input_df.copy() #otherwise it edits the df outside the function\n",
    "    df['cosine_matches'] = [(1-0.93**i) for i in df['cosine_matches']]\n",
    "    df['mod_cosine_matches'] = [(1-0.93**i) for i in df['mod_cosine_matches']]\n",
    "    return df\n",
    "\n",
    "def find_mass_similarity(matches, documents_library, query_mass, base_num = 0.8):\n",
    "    '''\n",
    "    To each match in matches df, add a scaled value for how similar the parent_mass is to the query\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    query_mass: float, parent mass of query\n",
    "    base_num: float, the base for the exponent\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with mass sims\n",
    "    \n",
    "    The similarity in dalton is calculated and transformed into a value 0 - 1 by doing\n",
    "    1 - base_num^diff_in_dalton\n",
    "    '''\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    scaled_mass_sims = []\n",
    "    for lib_id in library_ids:\n",
    "        lib_mass = documents_library[lib_id]._obj.get(\"parent_mass\")\n",
    "        mass_diff = abs(lib_mass - query_mass)\n",
    "        scaled_mass_sim = base_num ** mass_diff\n",
    "        scaled_mass_sims.append(scaled_mass_sim)\n",
    "\n",
    "    #add to df\n",
    "    df['mass_sim'] = scaled_mass_sims\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get top20\n",
    "old_and_unique_found_matches_s2v_2dec_top20 = []\n",
    "\n",
    "for matches in old_and_unique_found_matches_s2v_2dec:\n",
    "    old_and_unique_found_matches_s2v_2dec_top20.append(\n",
    "    matches.sort_values(\"s2v_score\", ascending=False).iloc[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_training_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_prep_training_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_prep_training_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_prep_training_found_matches_s2v_2dec = find_info_matches(old_and_unique_found_matches_s2v_2dec_top20,\n",
    "                                                                old_and_unique_documents_library_s2v_2dec,\n",
    "                                                                old_and_unique_documents_query_s2v_2dec,\n",
    "                                                                max_parent_mass=max_parent_mass)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_prep_training_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get top20\n",
    "new_and_unique2_found_matches_s2v_2dec_top20 = []\n",
    "\n",
    "for matches in new_and_unique2_found_matches_s2v_2dec:\n",
    "    new_and_unique2_found_matches_s2v_2dec_top20.append(\n",
    "    matches.sort_values(\"s2v_score\", ascending=False).iloc[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_testing_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_prep_testing_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_prep_testing_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_prep_testing_found_matches_s2v_2dec = find_info_matches(new_and_unique2_found_matches_s2v_2dec_top20,\n",
    "                                                                new_and_unique2_documents_library_s2v_2dec,\n",
    "                                                                new_and_unique2_documents_query_s2v_2dec,\n",
    "                                                                max_parent_mass=max_parent_mass)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_prep_testing_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22101</th>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.909346</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>0.999777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20538</th>\n",
       "      <td>0.856661</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.856661</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.859138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.584506</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034744</td>\n",
       "      <td>0.999554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>0.336160</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.337487</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.303197</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581808</td>\n",
       "      <td>0.037051</td>\n",
       "      <td>0.000997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "22101      0.928457        0.663299          0.928457            0.663299   \n",
       "20538      0.856661        0.549896          0.856661            0.549896   \n",
       "22454      0.857507        0.549896          0.857507            0.549896   \n",
       "1366       0.389183        0.549896          0.389183            0.549896   \n",
       "5998       0.336160        0.070000          0.337487            0.195643   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass  mass_sim  \n",
       "22101   0.909346      1    1.000000     0.034743  0.999777  \n",
       "20538   0.859138      1    1.000000     0.034743  1.000000  \n",
       "22454   0.674490      1    1.000000     0.034743  1.000000  \n",
       "1366    0.584506      1    1.000000     0.034744  0.999554  \n",
       "5998    0.303197      0    0.581808     0.037051  0.000997  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_prep_testing_found_matches_s2v_2dec[2].iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add matches together for each query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add all the found matches together in one big df\n",
    "nn_training_found_matches_s2v_2dec = nn_prep_training_found_matches_s2v_2dec[0].append(\n",
    "    nn_prep_training_found_matches_s2v_2dec[1:])\n",
    "nn_training_found_matches_s2v_2dec = nn_training_found_matches_s2v_2dec.sample(frac=1)\n",
    "\n",
    "nn_testing_full_found_matches_s2v_2dec = nn_prep_testing_found_matches_s2v_2dec[0].append(\n",
    "    nn_prep_testing_found_matches_s2v_2dec[1:])\n",
    "nn_testing_full_found_matches_s2v_2dec = nn_testing_full_found_matches_s2v_2dec.sample(frac=1)\n",
    "\n",
    "\n",
    "# take 300 (randomly chosen) query matches for validation set\n",
    "np.random.seed(42)\n",
    "first_half = list(np.random.choice(range(0,1000), 250, replace=False))\n",
    "np.random.seed(42)\n",
    "second_half = list(np.random.choice(range(1000,2000), 250, replace=False))\n",
    "val_set = first_half + second_half\n",
    "nn_val_found_matches_s2v_2dec = pd.DataFrame()\n",
    "nn_testing_found_matches_s2v_2dec = pd.DataFrame()\n",
    "\n",
    "for i in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "    if i in val_set:\n",
    "        nn_val_found_matches_s2v_2dec = nn_val_found_matches_s2v_2dec.append(nn_prep_testing_found_matches_s2v_2dec[i])\n",
    "    else:\n",
    "        nn_testing_found_matches_s2v_2dec = nn_testing_found_matches_s2v_2dec.append(nn_prep_testing_found_matches_s2v_2dec[i])\n",
    "\n",
    "nn_val_found_matches_s2v_2dec = nn_val_found_matches_s2v_2dec.sample(frac=1)\n",
    "nn_testing_found_matches_s2v_2dec = nn_testing_found_matches_s2v_2dec.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nn_training_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'training set')\n",
    "plt.hist(nn_testing_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'test subset')\n",
    "plt.hist(nn_val_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'validation set')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = nn_training_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_train = nn_training_found_matches_s2v_2dec['similarity']\n",
    "X_test = nn_testing_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_test = nn_testing_found_matches_s2v_2dec['similarity']\n",
    "X_val = nn_val_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_val = nn_val_found_matches_s2v_2dec['similarity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE 0.049800809392068046\n",
      "Validation MSE 0.05047421650180994\n",
      "Variable: s2v_score            Importance: 0.38\n",
      "Variable: mass_sim             Importance: 0.23\n",
      "Variable: parent_mass          Importance: 0.21\n",
      "Variable: mod_cosine_matches   Importance: 0.14\n",
      "Variable: cosine_matches       Importance: 0.03\n",
      "Variable: cosine_score         Importance: 0.0\n",
      "Variable: mod_cosine_score     Importance: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train rf\n",
    "num_est = 250\n",
    "rf = RandomForestRegressor(n_estimators = num_est, random_state = 42, max_depth = 5)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# predict on train\n",
    "rf_train_predictions = rf.predict(X_train)\n",
    "mse_train_rf = mean_squared_error(y_train, rf_train_predictions)\n",
    "print('Training MSE', mse_train_rf)\n",
    "\n",
    "# predict on test\n",
    "rf_predictions = rf.predict(X_val)\n",
    "mse_rf = mean_squared_error(y_val, rf_predictions)\n",
    "print('Validation MSE', mse_rf)\n",
    "\n",
    "# get feature importances\n",
    "importances = list(rf.feature_importances_)\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(X_train.columns, importances)]\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimising RF parameters\n",
    "- training and val loss seem very far apart -> overfitting in some way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find good max_depth -> 5 (see plot)\n",
    "to_try = range(1, 21)\n",
    "num_est = 40\n",
    "\n",
    "train_mse = []\n",
    "val_mse = []\n",
    "for try_maxd in to_try:\n",
    "    rf2 = RandomForestRegressor(n_estimators = num_est, random_state = 42, max_depth = try_maxd)\n",
    "    rf2.fit(X_train, y_train)\n",
    "\n",
    "    # predict on train\n",
    "    rf_train_predictions2 = rf2.predict(X_train)\n",
    "    mse_train_rf2 = mean_squared_error(y_train, rf_train_predictions2)\n",
    "#     print('Training MSE', mse_train_rf2)\n",
    "    train_mse.append(mse_train_rf2)\n",
    "    # predict on test\n",
    "    rf_predictions2 = rf2.predict(X_val)\n",
    "    mse_rf2 = mean_squared_error(y_val, rf_predictions2)\n",
    "#     print('Validation MSE', mse_rf2)\n",
    "    val_mse.append(mse_rf2)\n",
    "\n",
    "plt.plot(to_try, train_mse, \"b\", label=\"Train MSE\")\n",
    "plt.plot(to_try, val_mse, \"r\", label=\"Val MSE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"Tree depth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 9.832176208496094\n",
      "100 19.46282410621643\n",
      "250 59.83411455154419\n",
      "500 132.64440441131592\n",
      "1000 308.5162515640259\n",
      "1500 184.54904007911682\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find good nodes\n",
    "import time\n",
    "to_try = [50,100,250,500,1000,1500]\n",
    "\n",
    "train_mse = []\n",
    "val_mse = []\n",
    "for try_ns in to_try:\n",
    "    start = time.time()\n",
    "    rf2 = RandomForestRegressor(n_estimators = try_ns, random_state = 42, max_depth = 5)\n",
    "    rf2.fit(X_train, y_train)\n",
    "    end = time.time()\n",
    "    elapsed = end-start\n",
    "    print(try_ns, elapsed)\n",
    "    # predict on train\n",
    "    rf_train_predictions2 = rf2.predict(X_train)\n",
    "    mse_train_rf2 = mean_squared_error(y_train, rf_train_predictions2)\n",
    "#     print('Training MSE', mse_train_rf2)\n",
    "    train_mse.append(mse_train_rf2)\n",
    "    # predict on test\n",
    "    rf_predictions2 = rf2.predict(X_val)\n",
    "    mse_rf2 = mean_squared_error(y_val, rf_predictions2)\n",
    "#     print('Validation MSE', mse_rf2)\n",
    "    val_mse.append(mse_rf2)\n",
    "\n",
    "plt.plot(to_try, train_mse, \"b\", label=\"Train MSE\")\n",
    "plt.plot(to_try, val_mse, \"r\", label=\"Val MSE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"N estimators\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_rf_predict_found_matches_s2v_2dec = []\n",
    "for ID in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "    current_match = nn_prep_testing_found_matches_s2v_2dec[ID].copy()\n",
    "    predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "    predictions = rf.predict(predict_on)\n",
    "    current_match['rf_predictions'] = predictions\n",
    "    nn_rf_predict_found_matches_s2v_2dec.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>rf_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18625</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.306735</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.069334</td>\n",
       "      <td>2.747835e-68</td>\n",
       "      <td>0.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18631</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.303958</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.034521</td>\n",
       "      <td>5.104856e-23</td>\n",
       "      <td>0.311354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7299</th>\n",
       "      <td>0.355064</td>\n",
       "      <td>0.1351</td>\n",
       "      <td>0.355068</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.292372</td>\n",
       "      <td>0</td>\n",
       "      <td>0.414251</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>1.897516e-03</td>\n",
       "      <td>0.182476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18626</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.290061</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.068143</td>\n",
       "      <td>9.705796e-67</td>\n",
       "      <td>0.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18627</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.274596</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.067774</td>\n",
       "      <td>2.933035e-66</td>\n",
       "      <td>0.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92968</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.119211</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64763</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18628</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.262923</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.033253</td>\n",
       "      <td>2.280202e-21</td>\n",
       "      <td>0.311354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58180</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255365</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19223</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.169412</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.252203</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267964</td>\n",
       "      <td>0.018184</td>\n",
       "      <td>8.961955e-02</td>\n",
       "      <td>0.258783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90526</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.252028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.117347</td>\n",
       "      <td>0.013497</td>\n",
       "      <td>8.968766e-06</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19527</th>\n",
       "      <td>0.392147</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.392147</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.245704</td>\n",
       "      <td>0</td>\n",
       "      <td>0.306294</td>\n",
       "      <td>0.020657</td>\n",
       "      <td>5.461553e-05</td>\n",
       "      <td>0.295133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82058</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.000822</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.242076</td>\n",
       "      <td>0</td>\n",
       "      <td>0.248347</td>\n",
       "      <td>0.014165</td>\n",
       "      <td>6.627308e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24340</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240455</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069481</td>\n",
       "      <td>0.010738</td>\n",
       "      <td>2.317539e-09</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68124</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.079702</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.235435</td>\n",
       "      <td>0</td>\n",
       "      <td>0.266990</td>\n",
       "      <td>0.024001</td>\n",
       "      <td>2.442311e-09</td>\n",
       "      <td>0.293433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.234369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.156800</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>3.939133e-01</td>\n",
       "      <td>0.184220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58178</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.233790</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58181</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.233335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90527</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.230278</td>\n",
       "      <td>0</td>\n",
       "      <td>0.117347</td>\n",
       "      <td>0.013497</td>\n",
       "      <td>8.968766e-06</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65686</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.229389</td>\n",
       "      <td>0</td>\n",
       "      <td>0.336756</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>6.912263e-14</td>\n",
       "      <td>0.297047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "18625      0.093364          0.0700          0.093364            0.070000   \n",
       "18631      0.093364          0.0700          0.093364            0.070000   \n",
       "7299       0.355064          0.1351          0.355068            0.195643   \n",
       "18626      0.093364          0.0700          0.093364            0.070000   \n",
       "18627      0.093364          0.0700          0.093364            0.070000   \n",
       "92968      0.000000          0.0000          0.000000            0.000000   \n",
       "64763      0.000000          0.0000          0.000000            0.000000   \n",
       "18628      0.093364          0.0700          0.093364            0.070000   \n",
       "58180      0.000000          0.0000          0.000000            0.000000   \n",
       "19223      0.000000          0.0000          0.169412            0.070000   \n",
       "90526      0.000000          0.0000          0.000000            0.000000   \n",
       "19527      0.392147          0.0700          0.392147            0.070000   \n",
       "82058      0.000002          0.0700          0.000822            0.135100   \n",
       "24340      0.000000          0.0000          0.000000            0.000000   \n",
       "68124      0.000000          0.0000          0.079702            0.070000   \n",
       "6978       0.000000          0.0000          0.000043            0.070000   \n",
       "58178      0.000000          0.0000          0.000000            0.000000   \n",
       "58181      0.000000          0.0000          0.000000            0.000000   \n",
       "90527      0.000000          0.0000          0.000172            0.195643   \n",
       "65686      0.093364          0.0700          0.093364            0.070000   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  rf_predictions  \n",
       "18625   0.306735      0    0.323934     0.069334  2.747835e-68        0.358800  \n",
       "18631   0.303958      0    0.323934     0.034521  5.104856e-23        0.311354  \n",
       "7299    0.292372      0    0.414251     0.015286  1.897516e-03        0.182476  \n",
       "18626   0.290061      0    0.323934     0.068143  9.705796e-67        0.358800  \n",
       "18627   0.274596      0    0.323934     0.067774  2.933035e-66        0.358800  \n",
       "92968   0.272369      0    0.119211     0.014083  5.186006e-05        0.181458  \n",
       "64763   0.272369      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "18628   0.262923      0    0.323934     0.033253  2.280202e-21        0.311354  \n",
       "58180   0.255365      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "19223   0.252203      0    0.267964     0.018184  8.961955e-02        0.258783  \n",
       "90526   0.252028      0    0.117347     0.013497  8.968766e-06        0.181458  \n",
       "19527   0.245704      0    0.306294     0.020657  5.461553e-05        0.295133  \n",
       "82058   0.242076      0    0.248347     0.014165  6.627308e-05        0.181458  \n",
       "24340   0.240455      0    0.069481     0.010738  2.317539e-09        0.181458  \n",
       "68124   0.235435      0    0.266990     0.024001  2.442311e-09        0.293433  \n",
       "6978    0.234369      0    0.156800     0.017067  3.939133e-01        0.184220  \n",
       "58178   0.233790      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "58181   0.233335      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "90527   0.230278      0    0.117347     0.013497  8.968766e-06        0.181458  \n",
       "65686   0.229389      0    0.336756     0.027499  6.912263e-14        0.297047  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_rf_predict_found_matches_s2v_2dec[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nn function\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "def train_nn(X_train, y_train, X_test, y_test, layers = [12, 12, 12, 12, 12, 1],\n",
    "             model_loss = 'binary_crossentropy', activations = 'relu',\n",
    "             last_activation = 'sigmoid', model_epochs = 20, model_batch_size = 16,\n",
    "             save_name = False):\n",
    "    '''Train a keras deep NN and test on test data, returns (model, history, accuracy, loss)\n",
    "    \n",
    "    X_train: matrix like object like pd.DataFrame, training set\n",
    "    y_train: list like object like np.array, training labels\n",
    "    X_test: matrix like object like pd.DataFrame, test set\n",
    "    y_test: list like object like np.array, test labels\n",
    "    layers: list of ints, the number of layers is the len of this list while the elements\n",
    "        are the amount of neurons per layer, default: [12, 12, 12, 12, 12, 1]\n",
    "    model_loss: str, loss function, default: binary_crossentropy\n",
    "    activations: str, the activation of the layers except the last one, default: relu\n",
    "    last_activation: str, activation of last layer, default: sigmoid\n",
    "    model_epochs: int, number of epochs, default: 20\n",
    "    model_batch_size: int, batch size for updating the model, default: 16\n",
    "    save_name: str, location for saving model, optional, default: False\n",
    "    \n",
    "    Returns:\n",
    "    model: keras sequential\n",
    "    history: dict, training statistics\n",
    "    accuracy: float, accuracy on test set\n",
    "    loss, float, loss on test set\n",
    "    \n",
    "    If save_name is not False and save_name exists this function will load existing model\n",
    "    '''\n",
    "    if os.path.exists(save_name) and save_name:\n",
    "        print('\\nLoading existing model')\n",
    "        nn_model = load_model(save_name)\n",
    "        with open(save_name + '_train_hist.pickle', 'rb') as hist_inf:\n",
    "            history = pickle.load(hist_inf)\n",
    "    else:\n",
    "        # define the keras model\n",
    "        nn_model = Sequential()\n",
    "        #add first layer\n",
    "        nn_model.add(Dense(layers[0], input_dim = X_train.shape[1], activation = activations))\n",
    "        #add other layers\n",
    "        for i in range(1,len(layers)-1): #skip first and last one\n",
    "            nn_model.add(Dense(layers[i], activation = activations))\n",
    "        #add last layer\n",
    "        nn_model.add(Dense(layers[-1], activation = last_activation))\n",
    "        # compile the keras model\n",
    "        nn_model.compile(loss = model_loss, optimizer='adam', metrics=['accuracy'])\n",
    "        # fit the keras model on the dataset\n",
    "        hist = nn_model.fit(X_train, y_train, epochs = model_epochs, batch_size = model_batch_size)\n",
    "        history = hist.history\n",
    "    \n",
    "    #training set\n",
    "    print('Training loss: {:.4f}\\n'.format(history['loss'][-1]))\n",
    "    \n",
    "    #test set    \n",
    "    loss, accuracy = nn_model.evaluate(X_test, y_test)\n",
    "    print('Test accuracy: {:.2f}'.format(accuracy*100))\n",
    "    print('Test loss: {:.4f}'.format(loss))\n",
    "    \n",
    "    if save_name and not os.path.exists(save_name):\n",
    "        print('Saving model at:', save_name)\n",
    "        nn_model.save(save_name)\n",
    "        with open(save_name + '_train_hist.pickle', 'wb') as hist_outf:\n",
    "            pickle.dump(history, hist_outf)\n",
    "    \n",
    "    return nn_model, history, accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading existing model\n",
      "Training loss: 0.0493\n",
      "\n",
      "30000/30000 [==============================] - 2s 54us/sample - loss: 0.0502 - accuracy: 0.1053\n",
      "Test accuracy: 10.53\n",
      "Test loss: 0.0502\n"
     ]
    }
   ],
   "source": [
    "test_layers = [10,24,1]\n",
    "#model_name = os.path.join(path_data, 'nn_2000_queries_top20_1')\n",
    "model_name = os.path.join(path_data, 'nn_2000_queries_top20_layers_opt_1') #this one tested best in notebook 3\n",
    "\n",
    "# X_tanimoto_top20 = nn_training_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "# y_tanimoto_top20 = nn_training_found_matches_s2v_2dec['similarity']\n",
    "# X_tanimoto_top20_test = nn_testing_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "# y_tanimoto_top20_test = nn_testing_found_matches_s2v_2dec['similarity']\n",
    "\n",
    "nn_2000_queries_top20_1 = train_nn(X_train, y_train, X_test, y_test, layers = test_layers,\n",
    "         model_loss = 'mean_squared_error', activations = 'relu', last_activation = None,\n",
    "         model_epochs = 50, model_batch_size = 16, save_name = model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential, load_model\n",
    "\n",
    "best_model_name = os.path.join(path_data, 'nn_2000_queries_trimming_simple_10.hdf5')\n",
    "best_model = load_model(best_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000/30000 [==============================] - 1s 43us/sample - loss: 0.0488 - mae: 0.1706\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04878429824113846, 0.17055081]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying test set on model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_tested_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_tested_found_matches_s2v_2dec.pickle')\n",
    "#query and library of the test data can be found (as a tuple of (query, library)) in testing_query_library_s2v_2dec.pickle\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_tested_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_tested_found_matches_s2v_2dec = []\n",
    "    for ID in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "        current_match = nn_prep_testing_found_matches_s2v_2dec[ID].copy()\n",
    "        predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "        predictions = nn_2000_queries_top20_1[0].predict(predict_on)\n",
    "        current_match['sim_predictions'] = predictions\n",
    "        nn_tested_found_matches_s2v_2dec.append(current_match)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_tested_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_best_model_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_best_model_found_matches_s2v_2dec.pickle')\n",
    "#query and library of the test data can be found (as a tuple of (query, library)) in testing_query_library_s2v_2dec.pickle\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_best_model_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_best_model_found_matches_s2v_2dec = []\n",
    "    for ID in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "        current_match = nn_prep_testing_found_matches_s2v_2dec[ID].copy()\n",
    "        predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "        predictions = best_model.predict(predict_on)\n",
    "        current_match['sim_predictions'] = predictions\n",
    "        nn_best_model_found_matches_s2v_2dec.append(current_match)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_best_model_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>sim_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.440418</td>\n",
       "      <td>0.481348</td>\n",
       "      <td>0</td>\n",
       "      <td>0.618732</td>\n",
       "      <td>0.109013</td>\n",
       "      <td>1.326203e-39</td>\n",
       "      <td>0.450711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.471357</td>\n",
       "      <td>0</td>\n",
       "      <td>0.483736</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>2.963293e-19</td>\n",
       "      <td>0.440107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>0.016219</td>\n",
       "      <td>0.353010</td>\n",
       "      <td>0.016285</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.456992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.635606</td>\n",
       "      <td>0.073968</td>\n",
       "      <td>2.023195e-07</td>\n",
       "      <td>0.477650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26600</th>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.022880</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.439868</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670978</td>\n",
       "      <td>0.085979</td>\n",
       "      <td>1.187375e-09</td>\n",
       "      <td>0.442850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.439756</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383291</td>\n",
       "      <td>0.066811</td>\n",
       "      <td>9.974635e-17</td>\n",
       "      <td>0.449107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "1639       0.000052        0.251948          0.000107            0.440418   \n",
       "3005       0.000169        0.070000          0.000169            0.070000   \n",
       "4364       0.016219        0.353010          0.016285            0.549896   \n",
       "26600      0.000075        0.135100          0.022880            0.195643   \n",
       "6633       0.001208        0.070000          0.001212            0.135100   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  \\\n",
       "1639    0.481348      0    0.618732     0.109013  1.326203e-39   \n",
       "3005    0.471357      0    0.483736     0.093364  2.963293e-19   \n",
       "4364    0.456992      0    0.635606     0.073968  2.023195e-07   \n",
       "26600   0.439868      0    0.670978     0.085979  1.187375e-09   \n",
       "6633    0.439756      0    0.383291     0.066811  9.974635e-17   \n",
       "\n",
       "       sim_predictions  \n",
       "1639          0.450711  \n",
       "3005          0.440107  \n",
       "4364          0.477650  \n",
       "26600         0.442850  \n",
       "6633          0.449107  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_tested_found_matches_s2v_2dec[0].iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>sim_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.440418</td>\n",
       "      <td>0.481348</td>\n",
       "      <td>0</td>\n",
       "      <td>0.618732</td>\n",
       "      <td>0.109013</td>\n",
       "      <td>1.326203e-39</td>\n",
       "      <td>0.482210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.471357</td>\n",
       "      <td>0</td>\n",
       "      <td>0.483736</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>2.963293e-19</td>\n",
       "      <td>0.500722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4364</th>\n",
       "      <td>0.016219</td>\n",
       "      <td>0.353010</td>\n",
       "      <td>0.016285</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.456992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.635606</td>\n",
       "      <td>0.073968</td>\n",
       "      <td>2.023195e-07</td>\n",
       "      <td>0.514977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26600</th>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.022880</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.439868</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670978</td>\n",
       "      <td>0.085979</td>\n",
       "      <td>1.187375e-09</td>\n",
       "      <td>0.469464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.439756</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383291</td>\n",
       "      <td>0.066811</td>\n",
       "      <td>9.974635e-17</td>\n",
       "      <td>0.430324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "1639       0.000052        0.251948          0.000107            0.440418   \n",
       "3005       0.000169        0.070000          0.000169            0.070000   \n",
       "4364       0.016219        0.353010          0.016285            0.549896   \n",
       "26600      0.000075        0.135100          0.022880            0.195643   \n",
       "6633       0.001208        0.070000          0.001212            0.135100   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  \\\n",
       "1639    0.481348      0    0.618732     0.109013  1.326203e-39   \n",
       "3005    0.471357      0    0.483736     0.093364  2.963293e-19   \n",
       "4364    0.456992      0    0.635606     0.073968  2.023195e-07   \n",
       "26600   0.439868      0    0.670978     0.085979  1.187375e-09   \n",
       "6633    0.439756      0    0.383291     0.066811  9.974635e-17   \n",
       "\n",
       "       sim_predictions  \n",
       "1639          0.482210  \n",
       "3005          0.500722  \n",
       "4364          0.514977  \n",
       "26600         0.469464  \n",
       "6633          0.430324  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_best_model_found_matches_s2v_2dec[0].iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine with rf score\n",
    "nn_scores_rf_nn_found_matches_s2v_2dec = []\n",
    "for ID in range(len(nn_best_model_found_matches_s2v_2dec)):\n",
    "    current_match = nn_best_model_found_matches_s2v_2dec[ID].copy()\n",
    "    current_match[\"rf_predictions\"] = nn_rf_predict_found_matches_s2v_2dec[ID][\"rf_predictions\"]\n",
    "    nn_scores_rf_nn_found_matches_s2v_2dec.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>sim_predictions</th>\n",
       "      <th>rf_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22101</th>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.909346</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>9.997769e-01</td>\n",
       "      <td>0.972707</td>\n",
       "      <td>0.971482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20538</th>\n",
       "      <td>0.856661</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.856661</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.859138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.989312</td>\n",
       "      <td>0.972299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.919803</td>\n",
       "      <td>0.763664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.584506</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034744</td>\n",
       "      <td>9.995538e-01</td>\n",
       "      <td>0.976083</td>\n",
       "      <td>0.753079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>0.336160</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.337487</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.303197</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581808</td>\n",
       "      <td>0.037051</td>\n",
       "      <td>9.972265e-04</td>\n",
       "      <td>0.266289</td>\n",
       "      <td>0.362811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8329</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.638535</td>\n",
       "      <td>0.479589</td>\n",
       "      <td>0.301578</td>\n",
       "      <td>0</td>\n",
       "      <td>0.477106</td>\n",
       "      <td>0.029002</td>\n",
       "      <td>3.415633e-08</td>\n",
       "      <td>0.337801</td>\n",
       "      <td>0.415669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80614</th>\n",
       "      <td>0.229810</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.229817</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.285593</td>\n",
       "      <td>0</td>\n",
       "      <td>0.624545</td>\n",
       "      <td>0.030273</td>\n",
       "      <td>1.536262e-06</td>\n",
       "      <td>0.259401</td>\n",
       "      <td>0.322322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64254</th>\n",
       "      <td>0.086992</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.108168</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.277985</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187068</td>\n",
       "      <td>0.018644</td>\n",
       "      <td>1.159416e-21</td>\n",
       "      <td>0.203914</td>\n",
       "      <td>0.276579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65357</th>\n",
       "      <td>0.090625</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.108395</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.277905</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180175</td>\n",
       "      <td>0.018644</td>\n",
       "      <td>1.159416e-21</td>\n",
       "      <td>0.203538</td>\n",
       "      <td>0.276579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8850</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444915</td>\n",
       "      <td>0.353010</td>\n",
       "      <td>0.273831</td>\n",
       "      <td>0</td>\n",
       "      <td>0.518172</td>\n",
       "      <td>0.032134</td>\n",
       "      <td>4.042025e-04</td>\n",
       "      <td>0.315131</td>\n",
       "      <td>0.363247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65932</th>\n",
       "      <td>0.144903</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.151255</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.267550</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180175</td>\n",
       "      <td>0.018644</td>\n",
       "      <td>1.159416e-21</td>\n",
       "      <td>0.204137</td>\n",
       "      <td>0.276579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64253</th>\n",
       "      <td>0.130122</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.138346</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.264920</td>\n",
       "      <td>0</td>\n",
       "      <td>0.187068</td>\n",
       "      <td>0.018644</td>\n",
       "      <td>1.159416e-21</td>\n",
       "      <td>0.204502</td>\n",
       "      <td>0.276579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58292</th>\n",
       "      <td>0.001670</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.031879</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.249914</td>\n",
       "      <td>0</td>\n",
       "      <td>0.185958</td>\n",
       "      <td>0.020208</td>\n",
       "      <td>1.253959e-19</td>\n",
       "      <td>0.199834</td>\n",
       "      <td>0.295351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58298</th>\n",
       "      <td>0.001622</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.032836</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.249500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.185958</td>\n",
       "      <td>0.020208</td>\n",
       "      <td>1.253959e-19</td>\n",
       "      <td>0.199799</td>\n",
       "      <td>0.295351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70571</th>\n",
       "      <td>0.167742</td>\n",
       "      <td>0.353010</td>\n",
       "      <td>0.167742</td>\n",
       "      <td>0.353010</td>\n",
       "      <td>0.248224</td>\n",
       "      <td>0</td>\n",
       "      <td>0.624339</td>\n",
       "      <td>0.043613</td>\n",
       "      <td>2.922606e-12</td>\n",
       "      <td>0.296114</td>\n",
       "      <td>0.331398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>0.336160</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.336169</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.246883</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581808</td>\n",
       "      <td>0.037051</td>\n",
       "      <td>9.972265e-04</td>\n",
       "      <td>0.257647</td>\n",
       "      <td>0.362811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74598</th>\n",
       "      <td>0.294558</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.294558</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.245294</td>\n",
       "      <td>0</td>\n",
       "      <td>0.440842</td>\n",
       "      <td>0.027876</td>\n",
       "      <td>1.175511e-09</td>\n",
       "      <td>0.248199</td>\n",
       "      <td>0.300675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82637</th>\n",
       "      <td>0.254920</td>\n",
       "      <td>0.304312</td>\n",
       "      <td>0.254932</td>\n",
       "      <td>0.353010</td>\n",
       "      <td>0.242649</td>\n",
       "      <td>0</td>\n",
       "      <td>0.177861</td>\n",
       "      <td>0.027736</td>\n",
       "      <td>7.718830e-10</td>\n",
       "      <td>0.245901</td>\n",
       "      <td>0.300675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65362</th>\n",
       "      <td>0.086466</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.127300</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.240240</td>\n",
       "      <td>0</td>\n",
       "      <td>0.180175</td>\n",
       "      <td>0.018644</td>\n",
       "      <td>1.159416e-21</td>\n",
       "      <td>0.200963</td>\n",
       "      <td>0.276579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58291</th>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.251948</td>\n",
       "      <td>0.239969</td>\n",
       "      <td>0</td>\n",
       "      <td>0.185958</td>\n",
       "      <td>0.020208</td>\n",
       "      <td>1.253959e-19</td>\n",
       "      <td>0.195892</td>\n",
       "      <td>0.295351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "22101      0.928457        0.663299          0.928457            0.663299   \n",
       "20538      0.856661        0.549896          0.856661            0.549896   \n",
       "22454      0.857507        0.549896          0.857507            0.549896   \n",
       "1366       0.389183        0.549896          0.389183            0.549896   \n",
       "5998       0.336160        0.070000          0.337487            0.195643   \n",
       "8329       0.000008        0.135100          0.638535            0.479589   \n",
       "80614      0.229810        0.195643          0.229817            0.251948   \n",
       "64254      0.086992        0.195643          0.108168            0.304312   \n",
       "65357      0.090625        0.195643          0.108395            0.304312   \n",
       "8850       0.000000        0.000000          0.444915            0.353010   \n",
       "65932      0.144903        0.195643          0.151255            0.304312   \n",
       "64253      0.130122        0.195643          0.138346            0.304312   \n",
       "58292      0.001670        0.070000          0.031879            0.251948   \n",
       "58298      0.001622        0.070000          0.032836            0.251948   \n",
       "70571      0.167742        0.353010          0.167742            0.353010   \n",
       "5999       0.336160        0.070000          0.336169            0.135100   \n",
       "74598      0.294558        0.304312          0.294558            0.304312   \n",
       "82637      0.254920        0.304312          0.254932            0.353010   \n",
       "65362      0.086466        0.195643          0.127300            0.251948   \n",
       "58291      0.000421        0.070000          0.006916            0.251948   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  \\\n",
       "22101   0.909346      1    1.000000     0.034743  9.997769e-01   \n",
       "20538   0.859138      1    1.000000     0.034743  1.000000e+00   \n",
       "22454   0.674490      1    1.000000     0.034743  1.000000e+00   \n",
       "1366    0.584506      1    1.000000     0.034744  9.995538e-01   \n",
       "5998    0.303197      0    0.581808     0.037051  9.972265e-04   \n",
       "8329    0.301578      0    0.477106     0.029002  3.415633e-08   \n",
       "80614   0.285593      0    0.624545     0.030273  1.536262e-06   \n",
       "64254   0.277985      0    0.187068     0.018644  1.159416e-21   \n",
       "65357   0.277905      0    0.180175     0.018644  1.159416e-21   \n",
       "8850    0.273831      0    0.518172     0.032134  4.042025e-04   \n",
       "65932   0.267550      0    0.180175     0.018644  1.159416e-21   \n",
       "64253   0.264920      0    0.187068     0.018644  1.159416e-21   \n",
       "58292   0.249914      0    0.185958     0.020208  1.253959e-19   \n",
       "58298   0.249500      0    0.185958     0.020208  1.253959e-19   \n",
       "70571   0.248224      0    0.624339     0.043613  2.922606e-12   \n",
       "5999    0.246883      0    0.581808     0.037051  9.972265e-04   \n",
       "74598   0.245294      0    0.440842     0.027876  1.175511e-09   \n",
       "82637   0.242649      0    0.177861     0.027736  7.718830e-10   \n",
       "65362   0.240240      0    0.180175     0.018644  1.159416e-21   \n",
       "58291   0.239969      0    0.185958     0.020208  1.253959e-19   \n",
       "\n",
       "       sim_predictions  rf_predictions  \n",
       "22101         0.972707        0.971482  \n",
       "20538         0.989312        0.972299  \n",
       "22454         0.919803        0.763664  \n",
       "1366          0.976083        0.753079  \n",
       "5998          0.266289        0.362811  \n",
       "8329          0.337801        0.415669  \n",
       "80614         0.259401        0.322322  \n",
       "64254         0.203914        0.276579  \n",
       "65357         0.203538        0.276579  \n",
       "8850          0.315131        0.363247  \n",
       "65932         0.204137        0.276579  \n",
       "64253         0.204502        0.276579  \n",
       "58292         0.199834        0.295351  \n",
       "58298         0.199799        0.295351  \n",
       "70571         0.296114        0.331398  \n",
       "5999          0.257647        0.362811  \n",
       "74598         0.248199        0.300675  \n",
       "82637         0.245901        0.300675  \n",
       "65362         0.200963        0.276579  \n",
       "58291         0.195892        0.295351  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_scores_rf_nn_found_matches_s2v_2dec[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making well known performance plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if top hit is above 0.6 similarity\n",
    "#look at top20\n",
    "test_tan_nn_top20 = []\n",
    "test_sim_mcos = []\n",
    "test_sim_s2v = []\n",
    "ideal_case_top20 = []\n",
    "test_tan_rf_top20 = []\n",
    "test_nn_rf_top20 = []\n",
    "\n",
    "pred_thresh = 0.6\n",
    "mass_sim_thresh = 0.000705\n",
    "restr_nn = False # do mass+spec2vec restricitons on nn models\n",
    "highest_pred = False # take highest prediction instead of first prediction over threshold\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "sim_threshold = 0.6\n",
    "mc_matches_thresh = 0.35 # mod cosine matches threshold corresponds to min match 6\n",
    "\n",
    "for ID in range(len(nn_scores_rf_nn_found_matches_s2v_2dec)):\n",
    "    if ID in val_set:\n",
    "        continue\n",
    "    # mass + mod cosine\n",
    "    current_match = nn_scores_rf_nn_found_matches_s2v_2dec[ID]\\\n",
    "                        .sort_values(by=['mod_cosine_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['mod_cosine_score'] > threshold)\n",
    "                                  & (current_match['mod_cosine_matches'] > mc_matches_thresh)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['mod_cosine_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_mcos.append(best_bet)\n",
    "\n",
    "    # Scenario 1: mass + Spec2Vec + threshold\n",
    "    current_match = nn_scores_rf_nn_found_matches_s2v_2dec[ID]\\\n",
    "                        .sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    #inchikey_query = documents_query_s2v[ID]._obj.get(\"inchikey\")[:14]\n",
    "    #select top20\n",
    "    #current_match = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_s2v.append(best_bet)\n",
    "    \n",
    "    # Scenario 3: (Spec2Vec + threshold) after filtering for non-similar matches with nn\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['sim_predictions'] >= pred_thresh)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['sim_predictions'] >= pred_thresh)\n",
    "                                      & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        if highest_pred:\n",
    "            #look at highest predicted above prediction threshold\n",
    "            sim = df_select.iloc[np.argmax(df_select['sim_predictions'])]['similarity']\n",
    "        else:\n",
    "            #look at similarity of top hit\n",
    "            sim = df_select.iloc[0]['similarity']\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_tan_nn_top20.append(best_bet)\n",
    "    \n",
    "    # ideal case: how many queries have an actual match above s2v threshold and >0.6 tanimoto\n",
    "    df_select = current_match[(current_match['s2v_score'] > threshold)]\n",
    "    best_bet = -1\n",
    "    if df_select.shape[0] > 0:\n",
    "        if any(df_select['similarity'] > 0.6):\n",
    "            best_bet = 1\n",
    "    ideal_case_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario RF: filter with trained random forest model\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['rf_predictions'] >= pred_thresh)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['rf_predictions'] >= pred_thresh)\n",
    "                                      & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        if highest_pred:\n",
    "            #look at highest predicted above prediction threshold\n",
    "            sim = df_select.iloc[np.argmax(df_select['rf_predictions'])]['similarity']\n",
    "        else:\n",
    "            #look at similarity of top hit\n",
    "            sim = df_select.iloc[0]['similarity']\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_tan_rf_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario NN+RF: filter with both models\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['rf_predictions'] >= pred_thresh)\n",
    "                                      & (current_match['sim_predictions'] >= pred_thresh)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['rf_predictions'] >= pred_thresh)\n",
    "                                      & (current_match['s2v_score'] > threshold)\n",
    "                                      & (current_match['sim_predictions'] >= pred_thresh)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        if highest_pred:\n",
    "            #look at highest predicted above prediction threshold\n",
    "            sim = df_select.iloc[np.argmax(df_select['rf_predictions'])]['similarity']\n",
    "        else:\n",
    "            #look at similarity of top hit\n",
    "            sim = df_select.iloc[0]['similarity']\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_nn_rf_top20.append(best_bet)\n",
    "    \n",
    "    \n",
    "test_tan_nn_top20_arr = np.array(test_tan_nn_top20)\n",
    "test_sim_mcos_arr = np.array(test_sim_mcos)\n",
    "test_sim_s2v_arr = np.array(test_sim_s2v)\n",
    "ideal_case_top20_arr = np.array(ideal_case_top20)\n",
    "test_tan_rf_top20_arr = np.array(test_tan_rf_top20)\n",
    "test_nn_rf_top20_arr = np.array(test_nn_rf_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Compare if top hit is >= 0.6 tanimoto similarity\\n(2000 query dataset)')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n",
       "<svg height=\"297.198475pt\" version=\"1.1\" viewBox=\"0 0 761.978375 297.198475\" width=\"761.978375pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       " <metadata>\r\n",
       "  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n",
       "   <cc:Work>\r\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n",
       "    <dc:date>2020-11-27T10:20:28.394602</dc:date>\r\n",
       "    <dc:format>image/svg+xml</dc:format>\r\n",
       "    <dc:creator>\r\n",
       "     <cc:Agent>\r\n",
       "      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\r\n",
       "     </cc:Agent>\r\n",
       "    </dc:creator>\r\n",
       "   </cc:Work>\r\n",
       "  </rdf:RDF>\r\n",
       " </metadata>\r\n",
       " <defs>\r\n",
       "  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\r\n",
       " </defs>\r\n",
       " <g id=\"figure_1\">\r\n",
       "  <g id=\"patch_1\">\r\n",
       "   <path d=\"M 0 297.198475 \r\n",
       "L 761.978375 297.198475 \r\n",
       "L 761.978375 0 \r\n",
       "L 0 0 \r\n",
       "z\r\n",
       "\" style=\"fill:#ffffff;\"/>\r\n",
       "  </g>\r\n",
       "  <g id=\"axes_1\">\r\n",
       "   <g id=\"patch_2\">\r\n",
       "    <path d=\"M 278.471875 257.7066 \r\n",
       "L 613.271875 257.7066 \r\n",
       "L 613.271875 40.2666 \r\n",
       "L 278.471875 40.2666 \r\n",
       "z\r\n",
       "\" style=\"fill:#e5e5e5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"matplotlib.axis_1\">\r\n",
       "    <g id=\"xtick_1\">\r\n",
       "     <g id=\"line2d_1\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 257.7066 \r\n",
       "L 278.471875 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_2\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L 0 3.5 \r\n",
       "\" id=\"mb4a43bffc2\" style=\"stroke:#555555;stroke-width:0.8;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_1\">\r\n",
       "      <!-- 0 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(275.290625 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 31.78125 66.40625 \r\n",
       "Q 24.171875 66.40625 20.328125 58.90625 \r\n",
       "Q 16.5 51.421875 16.5 36.375 \r\n",
       "Q 16.5 21.390625 20.328125 13.890625 \r\n",
       "Q 24.171875 6.390625 31.78125 6.390625 \r\n",
       "Q 39.453125 6.390625 43.28125 13.890625 \r\n",
       "Q 47.125 21.390625 47.125 36.375 \r\n",
       "Q 47.125 51.421875 43.28125 58.90625 \r\n",
       "Q 39.453125 66.40625 31.78125 66.40625 \r\n",
       "z\r\n",
       "M 31.78125 74.21875 \r\n",
       "Q 44.046875 74.21875 50.515625 64.515625 \r\n",
       "Q 56.984375 54.828125 56.984375 36.375 \r\n",
       "Q 56.984375 17.96875 50.515625 8.265625 \r\n",
       "Q 44.046875 -1.421875 31.78125 -1.421875 \r\n",
       "Q 19.53125 -1.421875 13.0625 8.265625 \r\n",
       "Q 6.59375 17.96875 6.59375 36.375 \r\n",
       "Q 6.59375 54.828125 13.0625 64.515625 \r\n",
       "Q 19.53125 74.21875 31.78125 74.21875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-48\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_2\">\r\n",
       "     <g id=\"line2d_3\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 320.986161 257.7066 \r\n",
       "L 320.986161 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_4\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"320.986161\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_2\">\r\n",
       "      <!-- 200 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(311.442411 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 19.1875 8.296875 \r\n",
       "L 53.609375 8.296875 \r\n",
       "L 53.609375 0 \r\n",
       "L 7.328125 0 \r\n",
       "L 7.328125 8.296875 \r\n",
       "Q 12.9375 14.109375 22.625 23.890625 \r\n",
       "Q 32.328125 33.6875 34.8125 36.53125 \r\n",
       "Q 39.546875 41.84375 41.421875 45.53125 \r\n",
       "Q 43.3125 49.21875 43.3125 52.78125 \r\n",
       "Q 43.3125 58.59375 39.234375 62.25 \r\n",
       "Q 35.15625 65.921875 28.609375 65.921875 \r\n",
       "Q 23.96875 65.921875 18.8125 64.3125 \r\n",
       "Q 13.671875 62.703125 7.8125 59.421875 \r\n",
       "L 7.8125 69.390625 \r\n",
       "Q 13.765625 71.78125 18.9375 73 \r\n",
       "Q 24.125 74.21875 28.421875 74.21875 \r\n",
       "Q 39.75 74.21875 46.484375 68.546875 \r\n",
       "Q 53.21875 62.890625 53.21875 53.421875 \r\n",
       "Q 53.21875 48.921875 51.53125 44.890625 \r\n",
       "Q 49.859375 40.875 45.40625 35.40625 \r\n",
       "Q 44.1875 33.984375 37.640625 27.21875 \r\n",
       "Q 31.109375 20.453125 19.1875 8.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-50\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_3\">\r\n",
       "     <g id=\"line2d_5\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 363.500446 257.7066 \r\n",
       "L 363.500446 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_6\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"363.500446\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_3\">\r\n",
       "      <!-- 400 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(353.956696 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 37.796875 64.3125 \r\n",
       "L 12.890625 25.390625 \r\n",
       "L 37.796875 25.390625 \r\n",
       "z\r\n",
       "M 35.203125 72.90625 \r\n",
       "L 47.609375 72.90625 \r\n",
       "L 47.609375 25.390625 \r\n",
       "L 58.015625 25.390625 \r\n",
       "L 58.015625 17.1875 \r\n",
       "L 47.609375 17.1875 \r\n",
       "L 47.609375 0 \r\n",
       "L 37.796875 0 \r\n",
       "L 37.796875 17.1875 \r\n",
       "L 4.890625 17.1875 \r\n",
       "L 4.890625 26.703125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-52\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_4\">\r\n",
       "     <g id=\"line2d_7\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 406.014732 257.7066 \r\n",
       "L 406.014732 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_8\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"406.014732\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_4\">\r\n",
       "      <!-- 600 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(396.470982 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 33.015625 40.375 \r\n",
       "Q 26.375 40.375 22.484375 35.828125 \r\n",
       "Q 18.609375 31.296875 18.609375 23.390625 \r\n",
       "Q 18.609375 15.53125 22.484375 10.953125 \r\n",
       "Q 26.375 6.390625 33.015625 6.390625 \r\n",
       "Q 39.65625 6.390625 43.53125 10.953125 \r\n",
       "Q 47.40625 15.53125 47.40625 23.390625 \r\n",
       "Q 47.40625 31.296875 43.53125 35.828125 \r\n",
       "Q 39.65625 40.375 33.015625 40.375 \r\n",
       "z\r\n",
       "M 52.59375 71.296875 \r\n",
       "L 52.59375 62.3125 \r\n",
       "Q 48.875 64.0625 45.09375 64.984375 \r\n",
       "Q 41.3125 65.921875 37.59375 65.921875 \r\n",
       "Q 27.828125 65.921875 22.671875 59.328125 \r\n",
       "Q 17.53125 52.734375 16.796875 39.40625 \r\n",
       "Q 19.671875 43.65625 24.015625 45.921875 \r\n",
       "Q 28.375 48.1875 33.59375 48.1875 \r\n",
       "Q 44.578125 48.1875 50.953125 41.515625 \r\n",
       "Q 57.328125 34.859375 57.328125 23.390625 \r\n",
       "Q 57.328125 12.15625 50.6875 5.359375 \r\n",
       "Q 44.046875 -1.421875 33.015625 -1.421875 \r\n",
       "Q 20.359375 -1.421875 13.671875 8.265625 \r\n",
       "Q 6.984375 17.96875 6.984375 36.375 \r\n",
       "Q 6.984375 53.65625 15.1875 63.9375 \r\n",
       "Q 23.390625 74.21875 37.203125 74.21875 \r\n",
       "Q 40.921875 74.21875 44.703125 73.484375 \r\n",
       "Q 48.484375 72.75 52.59375 71.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-54\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_5\">\r\n",
       "     <g id=\"line2d_9\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 448.529018 257.7066 \r\n",
       "L 448.529018 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_10\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"448.529018\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_5\">\r\n",
       "      <!-- 800 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(438.985268 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 31.78125 34.625 \r\n",
       "Q 24.75 34.625 20.71875 30.859375 \r\n",
       "Q 16.703125 27.09375 16.703125 20.515625 \r\n",
       "Q 16.703125 13.921875 20.71875 10.15625 \r\n",
       "Q 24.75 6.390625 31.78125 6.390625 \r\n",
       "Q 38.8125 6.390625 42.859375 10.171875 \r\n",
       "Q 46.921875 13.96875 46.921875 20.515625 \r\n",
       "Q 46.921875 27.09375 42.890625 30.859375 \r\n",
       "Q 38.875 34.625 31.78125 34.625 \r\n",
       "z\r\n",
       "M 21.921875 38.8125 \r\n",
       "Q 15.578125 40.375 12.03125 44.71875 \r\n",
       "Q 8.5 49.078125 8.5 55.328125 \r\n",
       "Q 8.5 64.0625 14.71875 69.140625 \r\n",
       "Q 20.953125 74.21875 31.78125 74.21875 \r\n",
       "Q 42.671875 74.21875 48.875 69.140625 \r\n",
       "Q 55.078125 64.0625 55.078125 55.328125 \r\n",
       "Q 55.078125 49.078125 51.53125 44.71875 \r\n",
       "Q 48 40.375 41.703125 38.8125 \r\n",
       "Q 48.828125 37.15625 52.796875 32.3125 \r\n",
       "Q 56.78125 27.484375 56.78125 20.515625 \r\n",
       "Q 56.78125 9.90625 50.3125 4.234375 \r\n",
       "Q 43.84375 -1.421875 31.78125 -1.421875 \r\n",
       "Q 19.734375 -1.421875 13.25 4.234375 \r\n",
       "Q 6.78125 9.90625 6.78125 20.515625 \r\n",
       "Q 6.78125 27.484375 10.78125 32.3125 \r\n",
       "Q 14.796875 37.15625 21.921875 38.8125 \r\n",
       "z\r\n",
       "M 18.3125 54.390625 \r\n",
       "Q 18.3125 48.734375 21.84375 45.5625 \r\n",
       "Q 25.390625 42.390625 31.78125 42.390625 \r\n",
       "Q 38.140625 42.390625 41.71875 45.5625 \r\n",
       "Q 45.3125 48.734375 45.3125 54.390625 \r\n",
       "Q 45.3125 60.0625 41.71875 63.234375 \r\n",
       "Q 38.140625 66.40625 31.78125 66.40625 \r\n",
       "Q 25.390625 66.40625 21.84375 63.234375 \r\n",
       "Q 18.3125 60.0625 18.3125 54.390625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-56\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_6\">\r\n",
       "     <g id=\"line2d_11\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 491.043304 257.7066 \r\n",
       "L 491.043304 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_12\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"491.043304\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_6\">\r\n",
       "      <!-- 1000 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(478.318304 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 12.40625 8.296875 \r\n",
       "L 28.515625 8.296875 \r\n",
       "L 28.515625 63.921875 \r\n",
       "L 10.984375 60.40625 \r\n",
       "L 10.984375 69.390625 \r\n",
       "L 28.421875 72.90625 \r\n",
       "L 38.28125 72.90625 \r\n",
       "L 38.28125 8.296875 \r\n",
       "L 54.390625 8.296875 \r\n",
       "L 54.390625 0 \r\n",
       "L 12.40625 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-49\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_7\">\r\n",
       "     <g id=\"line2d_13\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 533.557589 257.7066 \r\n",
       "L 533.557589 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_14\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"533.557589\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_7\">\r\n",
       "      <!-- 1200 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(520.832589 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"xtick_8\">\r\n",
       "     <g id=\"line2d_15\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 576.071875 257.7066 \r\n",
       "L 576.071875 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_16\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"576.071875\" xlink:href=\"#mb4a43bffc2\" y=\"257.7066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_8\">\r\n",
       "      <!-- 1400 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(563.346875 272.305037)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_9\">\r\n",
       "     <!-- Number of spectra -->\r\n",
       "     <g style=\"fill:#555555;\" transform=\"translate(389.750312 287.50285)scale(0.12 -0.12)\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 9.8125 72.90625 \r\n",
       "L 23.09375 72.90625 \r\n",
       "L 55.421875 11.921875 \r\n",
       "L 55.421875 72.90625 \r\n",
       "L 64.984375 72.90625 \r\n",
       "L 64.984375 0 \r\n",
       "L 51.703125 0 \r\n",
       "L 19.390625 60.984375 \r\n",
       "L 19.390625 0 \r\n",
       "L 9.8125 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-78\"/>\r\n",
       "       <path d=\"M 8.5 21.578125 \r\n",
       "L 8.5 54.6875 \r\n",
       "L 17.484375 54.6875 \r\n",
       "L 17.484375 21.921875 \r\n",
       "Q 17.484375 14.15625 20.5 10.265625 \r\n",
       "Q 23.53125 6.390625 29.59375 6.390625 \r\n",
       "Q 36.859375 6.390625 41.078125 11.03125 \r\n",
       "Q 45.3125 15.671875 45.3125 23.6875 \r\n",
       "L 45.3125 54.6875 \r\n",
       "L 54.296875 54.6875 \r\n",
       "L 54.296875 0 \r\n",
       "L 45.3125 0 \r\n",
       "L 45.3125 8.40625 \r\n",
       "Q 42.046875 3.421875 37.71875 1 \r\n",
       "Q 33.40625 -1.421875 27.6875 -1.421875 \r\n",
       "Q 18.265625 -1.421875 13.375 4.4375 \r\n",
       "Q 8.5 10.296875 8.5 21.578125 \r\n",
       "z\r\n",
       "M 31.109375 56 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-117\"/>\r\n",
       "       <path d=\"M 52 44.1875 \r\n",
       "Q 55.375 50.25 60.0625 53.125 \r\n",
       "Q 64.75 56 71.09375 56 \r\n",
       "Q 79.640625 56 84.28125 50.015625 \r\n",
       "Q 88.921875 44.046875 88.921875 33.015625 \r\n",
       "L 88.921875 0 \r\n",
       "L 79.890625 0 \r\n",
       "L 79.890625 32.71875 \r\n",
       "Q 79.890625 40.578125 77.09375 44.375 \r\n",
       "Q 74.3125 48.1875 68.609375 48.1875 \r\n",
       "Q 61.625 48.1875 57.5625 43.546875 \r\n",
       "Q 53.515625 38.921875 53.515625 30.90625 \r\n",
       "L 53.515625 0 \r\n",
       "L 44.484375 0 \r\n",
       "L 44.484375 32.71875 \r\n",
       "Q 44.484375 40.625 41.703125 44.40625 \r\n",
       "Q 38.921875 48.1875 33.109375 48.1875 \r\n",
       "Q 26.21875 48.1875 22.15625 43.53125 \r\n",
       "Q 18.109375 38.875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.1875 51.21875 25.484375 53.609375 \r\n",
       "Q 29.78125 56 35.6875 56 \r\n",
       "Q 41.65625 56 45.828125 52.96875 \r\n",
       "Q 50 49.953125 52 44.1875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-109\"/>\r\n",
       "       <path d=\"M 48.6875 27.296875 \r\n",
       "Q 48.6875 37.203125 44.609375 42.84375 \r\n",
       "Q 40.53125 48.484375 33.40625 48.484375 \r\n",
       "Q 26.265625 48.484375 22.1875 42.84375 \r\n",
       "Q 18.109375 37.203125 18.109375 27.296875 \r\n",
       "Q 18.109375 17.390625 22.1875 11.75 \r\n",
       "Q 26.265625 6.109375 33.40625 6.109375 \r\n",
       "Q 40.53125 6.109375 44.609375 11.75 \r\n",
       "Q 48.6875 17.390625 48.6875 27.296875 \r\n",
       "z\r\n",
       "M 18.109375 46.390625 \r\n",
       "Q 20.953125 51.265625 25.265625 53.625 \r\n",
       "Q 29.59375 56 35.59375 56 \r\n",
       "Q 45.5625 56 51.78125 48.09375 \r\n",
       "Q 58.015625 40.1875 58.015625 27.296875 \r\n",
       "Q 58.015625 14.40625 51.78125 6.484375 \r\n",
       "Q 45.5625 -1.421875 35.59375 -1.421875 \r\n",
       "Q 29.59375 -1.421875 25.265625 0.953125 \r\n",
       "Q 20.953125 3.328125 18.109375 8.203125 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 75.984375 \r\n",
       "L 18.109375 75.984375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-98\"/>\r\n",
       "       <path d=\"M 56.203125 29.59375 \r\n",
       "L 56.203125 25.203125 \r\n",
       "L 14.890625 25.203125 \r\n",
       "Q 15.484375 15.921875 20.484375 11.0625 \r\n",
       "Q 25.484375 6.203125 34.421875 6.203125 \r\n",
       "Q 39.59375 6.203125 44.453125 7.46875 \r\n",
       "Q 49.3125 8.734375 54.109375 11.28125 \r\n",
       "L 54.109375 2.78125 \r\n",
       "Q 49.265625 0.734375 44.1875 -0.34375 \r\n",
       "Q 39.109375 -1.421875 33.890625 -1.421875 \r\n",
       "Q 20.796875 -1.421875 13.15625 6.1875 \r\n",
       "Q 5.515625 13.8125 5.515625 26.8125 \r\n",
       "Q 5.515625 40.234375 12.765625 48.109375 \r\n",
       "Q 20.015625 56 32.328125 56 \r\n",
       "Q 43.359375 56 49.78125 48.890625 \r\n",
       "Q 56.203125 41.796875 56.203125 29.59375 \r\n",
       "z\r\n",
       "M 47.21875 32.234375 \r\n",
       "Q 47.125 39.59375 43.09375 43.984375 \r\n",
       "Q 39.0625 48.390625 32.421875 48.390625 \r\n",
       "Q 24.90625 48.390625 20.390625 44.140625 \r\n",
       "Q 15.875 39.890625 15.1875 32.171875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-101\"/>\r\n",
       "       <path d=\"M 41.109375 46.296875 \r\n",
       "Q 39.59375 47.171875 37.8125 47.578125 \r\n",
       "Q 36.03125 48 33.890625 48 \r\n",
       "Q 26.265625 48 22.1875 43.046875 \r\n",
       "Q 18.109375 38.09375 18.109375 28.8125 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 20.953125 51.171875 25.484375 53.578125 \r\n",
       "Q 30.03125 56 36.53125 56 \r\n",
       "Q 37.453125 56 38.578125 55.875 \r\n",
       "Q 39.703125 55.765625 41.0625 55.515625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-114\"/>\r\n",
       "       <path id=\"DejaVuSans-32\"/>\r\n",
       "       <path d=\"M 30.609375 48.390625 \r\n",
       "Q 23.390625 48.390625 19.1875 42.75 \r\n",
       "Q 14.984375 37.109375 14.984375 27.296875 \r\n",
       "Q 14.984375 17.484375 19.15625 11.84375 \r\n",
       "Q 23.34375 6.203125 30.609375 6.203125 \r\n",
       "Q 37.796875 6.203125 41.984375 11.859375 \r\n",
       "Q 46.1875 17.53125 46.1875 27.296875 \r\n",
       "Q 46.1875 37.015625 41.984375 42.703125 \r\n",
       "Q 37.796875 48.390625 30.609375 48.390625 \r\n",
       "z\r\n",
       "M 30.609375 56 \r\n",
       "Q 42.328125 56 49.015625 48.375 \r\n",
       "Q 55.71875 40.765625 55.71875 27.296875 \r\n",
       "Q 55.71875 13.875 49.015625 6.21875 \r\n",
       "Q 42.328125 -1.421875 30.609375 -1.421875 \r\n",
       "Q 18.84375 -1.421875 12.171875 6.21875 \r\n",
       "Q 5.515625 13.875 5.515625 27.296875 \r\n",
       "Q 5.515625 40.765625 12.171875 48.375 \r\n",
       "Q 18.84375 56 30.609375 56 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-111\"/>\r\n",
       "       <path d=\"M 37.109375 75.984375 \r\n",
       "L 37.109375 68.5 \r\n",
       "L 28.515625 68.5 \r\n",
       "Q 23.6875 68.5 21.796875 66.546875 \r\n",
       "Q 19.921875 64.59375 19.921875 59.515625 \r\n",
       "L 19.921875 54.6875 \r\n",
       "L 34.71875 54.6875 \r\n",
       "L 34.71875 47.703125 \r\n",
       "L 19.921875 47.703125 \r\n",
       "L 19.921875 0 \r\n",
       "L 10.890625 0 \r\n",
       "L 10.890625 47.703125 \r\n",
       "L 2.296875 47.703125 \r\n",
       "L 2.296875 54.6875 \r\n",
       "L 10.890625 54.6875 \r\n",
       "L 10.890625 58.5 \r\n",
       "Q 10.890625 67.625 15.140625 71.796875 \r\n",
       "Q 19.390625 75.984375 28.609375 75.984375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-102\"/>\r\n",
       "       <path d=\"M 44.28125 53.078125 \r\n",
       "L 44.28125 44.578125 \r\n",
       "Q 40.484375 46.53125 36.375 47.5 \r\n",
       "Q 32.28125 48.484375 27.875 48.484375 \r\n",
       "Q 21.1875 48.484375 17.84375 46.4375 \r\n",
       "Q 14.5 44.390625 14.5 40.28125 \r\n",
       "Q 14.5 37.15625 16.890625 35.375 \r\n",
       "Q 19.28125 33.59375 26.515625 31.984375 \r\n",
       "L 29.59375 31.296875 \r\n",
       "Q 39.15625 29.25 43.1875 25.515625 \r\n",
       "Q 47.21875 21.78125 47.21875 15.09375 \r\n",
       "Q 47.21875 7.46875 41.1875 3.015625 \r\n",
       "Q 35.15625 -1.421875 24.609375 -1.421875 \r\n",
       "Q 20.21875 -1.421875 15.453125 -0.5625 \r\n",
       "Q 10.6875 0.296875 5.421875 2 \r\n",
       "L 5.421875 11.28125 \r\n",
       "Q 10.40625 8.6875 15.234375 7.390625 \r\n",
       "Q 20.0625 6.109375 24.8125 6.109375 \r\n",
       "Q 31.15625 6.109375 34.5625 8.28125 \r\n",
       "Q 37.984375 10.453125 37.984375 14.40625 \r\n",
       "Q 37.984375 18.0625 35.515625 20.015625 \r\n",
       "Q 33.0625 21.96875 24.703125 23.78125 \r\n",
       "L 21.578125 24.515625 \r\n",
       "Q 13.234375 26.265625 9.515625 29.90625 \r\n",
       "Q 5.8125 33.546875 5.8125 39.890625 \r\n",
       "Q 5.8125 47.609375 11.28125 51.796875 \r\n",
       "Q 16.75 56 26.8125 56 \r\n",
       "Q 31.78125 56 36.171875 55.265625 \r\n",
       "Q 40.578125 54.546875 44.28125 53.078125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-115\"/>\r\n",
       "       <path d=\"M 18.109375 8.203125 \r\n",
       "L 18.109375 -20.796875 \r\n",
       "L 9.078125 -20.796875 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.390625 \r\n",
       "Q 20.953125 51.265625 25.265625 53.625 \r\n",
       "Q 29.59375 56 35.59375 56 \r\n",
       "Q 45.5625 56 51.78125 48.09375 \r\n",
       "Q 58.015625 40.1875 58.015625 27.296875 \r\n",
       "Q 58.015625 14.40625 51.78125 6.484375 \r\n",
       "Q 45.5625 -1.421875 35.59375 -1.421875 \r\n",
       "Q 29.59375 -1.421875 25.265625 0.953125 \r\n",
       "Q 20.953125 3.328125 18.109375 8.203125 \r\n",
       "z\r\n",
       "M 48.6875 27.296875 \r\n",
       "Q 48.6875 37.203125 44.609375 42.84375 \r\n",
       "Q 40.53125 48.484375 33.40625 48.484375 \r\n",
       "Q 26.265625 48.484375 22.1875 42.84375 \r\n",
       "Q 18.109375 37.203125 18.109375 27.296875 \r\n",
       "Q 18.109375 17.390625 22.1875 11.75 \r\n",
       "Q 26.265625 6.109375 33.40625 6.109375 \r\n",
       "Q 40.53125 6.109375 44.609375 11.75 \r\n",
       "Q 48.6875 17.390625 48.6875 27.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-112\"/>\r\n",
       "       <path d=\"M 48.78125 52.59375 \r\n",
       "L 48.78125 44.1875 \r\n",
       "Q 44.96875 46.296875 41.140625 47.34375 \r\n",
       "Q 37.3125 48.390625 33.40625 48.390625 \r\n",
       "Q 24.65625 48.390625 19.8125 42.84375 \r\n",
       "Q 14.984375 37.3125 14.984375 27.296875 \r\n",
       "Q 14.984375 17.28125 19.8125 11.734375 \r\n",
       "Q 24.65625 6.203125 33.40625 6.203125 \r\n",
       "Q 37.3125 6.203125 41.140625 7.25 \r\n",
       "Q 44.96875 8.296875 48.78125 10.40625 \r\n",
       "L 48.78125 2.09375 \r\n",
       "Q 45.015625 0.34375 40.984375 -0.53125 \r\n",
       "Q 36.96875 -1.421875 32.421875 -1.421875 \r\n",
       "Q 20.0625 -1.421875 12.78125 6.34375 \r\n",
       "Q 5.515625 14.109375 5.515625 27.296875 \r\n",
       "Q 5.515625 40.671875 12.859375 48.328125 \r\n",
       "Q 20.21875 56 33.015625 56 \r\n",
       "Q 37.15625 56 41.109375 55.140625 \r\n",
       "Q 45.0625 54.296875 48.78125 52.59375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-99\"/>\r\n",
       "       <path d=\"M 18.3125 70.21875 \r\n",
       "L 18.3125 54.6875 \r\n",
       "L 36.8125 54.6875 \r\n",
       "L 36.8125 47.703125 \r\n",
       "L 18.3125 47.703125 \r\n",
       "L 18.3125 18.015625 \r\n",
       "Q 18.3125 11.328125 20.140625 9.421875 \r\n",
       "Q 21.96875 7.515625 27.59375 7.515625 \r\n",
       "L 36.8125 7.515625 \r\n",
       "L 36.8125 0 \r\n",
       "L 27.59375 0 \r\n",
       "Q 17.1875 0 13.234375 3.875 \r\n",
       "Q 9.28125 7.765625 9.28125 18.015625 \r\n",
       "L 9.28125 47.703125 \r\n",
       "L 2.6875 47.703125 \r\n",
       "L 2.6875 54.6875 \r\n",
       "L 9.28125 54.6875 \r\n",
       "L 9.28125 70.21875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-116\"/>\r\n",
       "       <path d=\"M 34.28125 27.484375 \r\n",
       "Q 23.390625 27.484375 19.1875 25 \r\n",
       "Q 14.984375 22.515625 14.984375 16.5 \r\n",
       "Q 14.984375 11.71875 18.140625 8.90625 \r\n",
       "Q 21.296875 6.109375 26.703125 6.109375 \r\n",
       "Q 34.1875 6.109375 38.703125 11.40625 \r\n",
       "Q 43.21875 16.703125 43.21875 25.484375 \r\n",
       "L 43.21875 27.484375 \r\n",
       "z\r\n",
       "M 52.203125 31.203125 \r\n",
       "L 52.203125 0 \r\n",
       "L 43.21875 0 \r\n",
       "L 43.21875 8.296875 \r\n",
       "Q 40.140625 3.328125 35.546875 0.953125 \r\n",
       "Q 30.953125 -1.421875 24.3125 -1.421875 \r\n",
       "Q 15.921875 -1.421875 10.953125 3.296875 \r\n",
       "Q 6 8.015625 6 15.921875 \r\n",
       "Q 6 25.140625 12.171875 29.828125 \r\n",
       "Q 18.359375 34.515625 30.609375 34.515625 \r\n",
       "L 43.21875 34.515625 \r\n",
       "L 43.21875 35.40625 \r\n",
       "Q 43.21875 41.609375 39.140625 45 \r\n",
       "Q 35.0625 48.390625 27.6875 48.390625 \r\n",
       "Q 23 48.390625 18.546875 47.265625 \r\n",
       "Q 14.109375 46.140625 10.015625 43.890625 \r\n",
       "L 10.015625 52.203125 \r\n",
       "Q 14.9375 54.109375 19.578125 55.046875 \r\n",
       "Q 24.21875 56 28.609375 56 \r\n",
       "Q 40.484375 56 46.34375 49.84375 \r\n",
       "Q 52.203125 43.703125 52.203125 31.203125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-97\"/>\r\n",
       "      </defs>\r\n",
       "      <use xlink:href=\"#DejaVuSans-78\"/>\r\n",
       "      <use x=\"74.804688\" xlink:href=\"#DejaVuSans-117\"/>\r\n",
       "      <use x=\"138.183594\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "      <use x=\"235.595703\" xlink:href=\"#DejaVuSans-98\"/>\r\n",
       "      <use x=\"299.072266\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"360.595703\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "      <use x=\"401.708984\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "      <use x=\"433.496094\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "      <use x=\"494.677734\" xlink:href=\"#DejaVuSans-102\"/>\r\n",
       "      <use x=\"529.882812\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "      <use x=\"561.669922\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "      <use x=\"613.769531\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "      <use x=\"677.246094\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"738.769531\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "      <use x=\"793.75\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "      <use x=\"832.958984\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "      <use x=\"874.072266\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"matplotlib.axis_2\">\r\n",
       "    <g id=\"ytick_1\">\r\n",
       "     <g id=\"line2d_17\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 239.5866 \r\n",
       "L 613.271875 239.5866 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_18\">\r\n",
       "      <defs>\r\n",
       "       <path d=\"M 0 0 \r\n",
       "L -3.5 0 \r\n",
       "\" id=\"md9e099bf16\" style=\"stroke:#555555;stroke-width:0.8;\"/>\r\n",
       "      </defs>\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#md9e099bf16\" y=\"239.5866\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_10\">\r\n",
       "      <!-- ideal case Spec2Vec &gt; 0.4 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(138.873437 243.385819)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 9.421875 54.6875 \r\n",
       "L 18.40625 54.6875 \r\n",
       "L 18.40625 0 \r\n",
       "L 9.421875 0 \r\n",
       "z\r\n",
       "M 9.421875 75.984375 \r\n",
       "L 18.40625 75.984375 \r\n",
       "L 18.40625 64.59375 \r\n",
       "L 9.421875 64.59375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-105\"/>\r\n",
       "        <path d=\"M 45.40625 46.390625 \r\n",
       "L 45.40625 75.984375 \r\n",
       "L 54.390625 75.984375 \r\n",
       "L 54.390625 0 \r\n",
       "L 45.40625 0 \r\n",
       "L 45.40625 8.203125 \r\n",
       "Q 42.578125 3.328125 38.25 0.953125 \r\n",
       "Q 33.9375 -1.421875 27.875 -1.421875 \r\n",
       "Q 17.96875 -1.421875 11.734375 6.484375 \r\n",
       "Q 5.515625 14.40625 5.515625 27.296875 \r\n",
       "Q 5.515625 40.1875 11.734375 48.09375 \r\n",
       "Q 17.96875 56 27.875 56 \r\n",
       "Q 33.9375 56 38.25 53.625 \r\n",
       "Q 42.578125 51.265625 45.40625 46.390625 \r\n",
       "z\r\n",
       "M 14.796875 27.296875 \r\n",
       "Q 14.796875 17.390625 18.875 11.75 \r\n",
       "Q 22.953125 6.109375 30.078125 6.109375 \r\n",
       "Q 37.203125 6.109375 41.296875 11.75 \r\n",
       "Q 45.40625 17.390625 45.40625 27.296875 \r\n",
       "Q 45.40625 37.203125 41.296875 42.84375 \r\n",
       "Q 37.203125 48.484375 30.078125 48.484375 \r\n",
       "Q 22.953125 48.484375 18.875 42.84375 \r\n",
       "Q 14.796875 37.203125 14.796875 27.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-100\"/>\r\n",
       "        <path d=\"M 9.421875 75.984375 \r\n",
       "L 18.40625 75.984375 \r\n",
       "L 18.40625 0 \r\n",
       "L 9.421875 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-108\"/>\r\n",
       "        <path d=\"M 53.515625 70.515625 \r\n",
       "L 53.515625 60.890625 \r\n",
       "Q 47.90625 63.578125 42.921875 64.890625 \r\n",
       "Q 37.9375 66.21875 33.296875 66.21875 \r\n",
       "Q 25.25 66.21875 20.875 63.09375 \r\n",
       "Q 16.5 59.96875 16.5 54.203125 \r\n",
       "Q 16.5 49.359375 19.40625 46.890625 \r\n",
       "Q 22.3125 44.4375 30.421875 42.921875 \r\n",
       "L 36.375 41.703125 \r\n",
       "Q 47.40625 39.59375 52.65625 34.296875 \r\n",
       "Q 57.90625 29 57.90625 20.125 \r\n",
       "Q 57.90625 9.515625 50.796875 4.046875 \r\n",
       "Q 43.703125 -1.421875 29.984375 -1.421875 \r\n",
       "Q 24.8125 -1.421875 18.96875 -0.25 \r\n",
       "Q 13.140625 0.921875 6.890625 3.21875 \r\n",
       "L 6.890625 13.375 \r\n",
       "Q 12.890625 10.015625 18.65625 8.296875 \r\n",
       "Q 24.421875 6.59375 29.984375 6.59375 \r\n",
       "Q 38.421875 6.59375 43.015625 9.90625 \r\n",
       "Q 47.609375 13.234375 47.609375 19.390625 \r\n",
       "Q 47.609375 24.75 44.3125 27.78125 \r\n",
       "Q 41.015625 30.8125 33.5 32.328125 \r\n",
       "L 27.484375 33.5 \r\n",
       "Q 16.453125 35.6875 11.515625 40.375 \r\n",
       "Q 6.59375 45.0625 6.59375 53.421875 \r\n",
       "Q 6.59375 63.09375 13.40625 68.65625 \r\n",
       "Q 20.21875 74.21875 32.171875 74.21875 \r\n",
       "Q 37.3125 74.21875 42.625 73.28125 \r\n",
       "Q 47.953125 72.359375 53.515625 70.515625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-83\"/>\r\n",
       "        <path d=\"M 28.609375 0 \r\n",
       "L 0.78125 72.90625 \r\n",
       "L 11.078125 72.90625 \r\n",
       "L 34.1875 11.53125 \r\n",
       "L 57.328125 72.90625 \r\n",
       "L 67.578125 72.90625 \r\n",
       "L 39.796875 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-86\"/>\r\n",
       "        <path d=\"M 10.59375 49.21875 \r\n",
       "L 10.59375 58.109375 \r\n",
       "L 73.1875 35.40625 \r\n",
       "L 73.1875 27.296875 \r\n",
       "L 10.59375 4.59375 \r\n",
       "L 10.59375 13.484375 \r\n",
       "L 60.890625 31.296875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-62\"/>\r\n",
       "        <path d=\"M 10.6875 12.40625 \r\n",
       "L 21 12.40625 \r\n",
       "L 21 0 \r\n",
       "L 10.6875 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-46\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"27.783203\" xlink:href=\"#DejaVuSans-100\"/>\r\n",
       "       <use x=\"91.259766\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"152.783203\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"214.0625\" xlink:href=\"#DejaVuSans-108\"/>\r\n",
       "       <use x=\"241.845703\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"273.632812\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"328.613281\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"389.892578\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "       <use x=\"441.992188\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"503.515625\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"535.302734\" xlink:href=\"#DejaVuSans-83\"/>\r\n",
       "       <use x=\"598.779297\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "       <use x=\"662.255859\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"723.779297\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"778.759766\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "       <use x=\"842.382812\" xlink:href=\"#DejaVuSans-86\"/>\r\n",
       "       <use x=\"903.041016\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"964.564453\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"1019.544922\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1051.332031\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"1135.121094\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1166.908203\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"1230.53125\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"1262.318359\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_2\">\r\n",
       "     <g id=\"line2d_19\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 203.3466 \r\n",
       "L 613.271875 203.3466 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_20\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#md9e099bf16\" y=\"203.3466\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_11\">\r\n",
       "      <!-- RF tanimoto &gt; 0.6 + NN tanimoto &gt; 0.6 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(71.314063 207.145819)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 44.390625 34.1875 \r\n",
       "Q 47.5625 33.109375 50.5625 29.59375 \r\n",
       "Q 53.5625 26.078125 56.59375 19.921875 \r\n",
       "L 66.609375 0 \r\n",
       "L 56 0 \r\n",
       "L 46.6875 18.703125 \r\n",
       "Q 43.0625 26.03125 39.671875 28.421875 \r\n",
       "Q 36.28125 30.8125 30.421875 30.8125 \r\n",
       "L 19.671875 30.8125 \r\n",
       "L 19.671875 0 \r\n",
       "L 9.8125 0 \r\n",
       "L 9.8125 72.90625 \r\n",
       "L 32.078125 72.90625 \r\n",
       "Q 44.578125 72.90625 50.734375 67.671875 \r\n",
       "Q 56.890625 62.453125 56.890625 51.90625 \r\n",
       "Q 56.890625 45.015625 53.6875 40.46875 \r\n",
       "Q 50.484375 35.9375 44.390625 34.1875 \r\n",
       "z\r\n",
       "M 19.671875 64.796875 \r\n",
       "L 19.671875 38.921875 \r\n",
       "L 32.078125 38.921875 \r\n",
       "Q 39.203125 38.921875 42.84375 42.21875 \r\n",
       "Q 46.484375 45.515625 46.484375 51.90625 \r\n",
       "Q 46.484375 58.296875 42.84375 61.546875 \r\n",
       "Q 39.203125 64.796875 32.078125 64.796875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-82\"/>\r\n",
       "        <path d=\"M 9.8125 72.90625 \r\n",
       "L 51.703125 72.90625 \r\n",
       "L 51.703125 64.59375 \r\n",
       "L 19.671875 64.59375 \r\n",
       "L 19.671875 43.109375 \r\n",
       "L 48.578125 43.109375 \r\n",
       "L 48.578125 34.8125 \r\n",
       "L 19.671875 34.8125 \r\n",
       "L 19.671875 0 \r\n",
       "L 9.8125 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-70\"/>\r\n",
       "        <path d=\"M 54.890625 33.015625 \r\n",
       "L 54.890625 0 \r\n",
       "L 45.90625 0 \r\n",
       "L 45.90625 32.71875 \r\n",
       "Q 45.90625 40.484375 42.875 44.328125 \r\n",
       "Q 39.84375 48.1875 33.796875 48.1875 \r\n",
       "Q 26.515625 48.1875 22.3125 43.546875 \r\n",
       "Q 18.109375 38.921875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 54.6875 \r\n",
       "L 18.109375 54.6875 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.34375 51.125 25.703125 53.5625 \r\n",
       "Q 30.078125 56 35.796875 56 \r\n",
       "Q 45.21875 56 50.046875 50.171875 \r\n",
       "Q 54.890625 44.34375 54.890625 33.015625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-110\"/>\r\n",
       "        <path d=\"M 46 62.703125 \r\n",
       "L 46 35.5 \r\n",
       "L 73.1875 35.5 \r\n",
       "L 73.1875 27.203125 \r\n",
       "L 46 27.203125 \r\n",
       "L 46 0 \r\n",
       "L 37.796875 0 \r\n",
       "L 37.796875 27.203125 \r\n",
       "L 10.59375 27.203125 \r\n",
       "L 10.59375 35.5 \r\n",
       "L 37.796875 35.5 \r\n",
       "L 37.796875 62.703125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-43\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-82\"/>\r\n",
       "       <use x=\"69.482422\" xlink:href=\"#DejaVuSans-70\"/>\r\n",
       "       <use x=\"127.001953\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"158.789062\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"197.998047\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"259.277344\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "       <use x=\"322.65625\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"350.439453\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"447.851562\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"509.033203\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"548.242188\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"609.423828\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"641.210938\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"725\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"756.787109\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"820.410156\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"852.197266\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "       <use x=\"915.820312\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"947.607422\" xlink:href=\"#DejaVuSans-43\"/>\r\n",
       "       <use x=\"1031.396484\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1063.183594\" xlink:href=\"#DejaVuSans-78\"/>\r\n",
       "       <use x=\"1137.988281\" xlink:href=\"#DejaVuSans-78\"/>\r\n",
       "       <use x=\"1212.792969\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1244.580078\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"1283.789062\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"1345.068359\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "       <use x=\"1408.447266\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"1436.230469\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"1533.642578\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"1594.824219\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"1634.033203\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"1695.214844\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1727.001953\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"1810.791016\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1842.578125\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"1906.201172\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"1937.988281\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_3\">\r\n",
       "     <g id=\"line2d_21\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 167.1066 \r\n",
       "L 613.271875 167.1066 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_22\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#md9e099bf16\" y=\"167.1066\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_12\">\r\n",
       "      <!-- NN tanimoto &gt; 0.6 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(177.629687 170.905819)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-78\"/>\r\n",
       "       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-78\"/>\r\n",
       "       <use x=\"149.609375\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"181.396484\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"220.605469\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"281.884766\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "       <use x=\"345.263672\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"373.046875\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"470.458984\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"531.640625\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"570.849609\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"632.03125\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"663.818359\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"747.607422\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"779.394531\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"843.017578\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"874.804688\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_4\">\r\n",
       "     <g id=\"line2d_23\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 130.8666 \r\n",
       "L 613.271875 130.8666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_24\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#md9e099bf16\" y=\"130.8666\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_13\">\r\n",
       "      <!-- RF tanimoto &gt; 0.6 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(179.892188 134.665819)scale(0.1 -0.1)\">\r\n",
       "       <use xlink:href=\"#DejaVuSans-82\"/>\r\n",
       "       <use x=\"69.482422\" xlink:href=\"#DejaVuSans-70\"/>\r\n",
       "       <use x=\"127.001953\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"158.789062\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"197.998047\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"259.277344\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "       <use x=\"322.65625\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"350.439453\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"447.851562\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"509.033203\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"548.242188\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"609.423828\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"641.210938\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"725\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"756.787109\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"820.410156\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"852.197266\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_5\">\r\n",
       "     <g id=\"line2d_25\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 94.6266 \r\n",
       "L 613.271875 94.6266 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_26\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#md9e099bf16\" y=\"94.6266\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_14\">\r\n",
       "      <!-- mass &lt; 100.0Da + Spec2Vec &gt; 0.4 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(94.189063 98.425819)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 73.1875 49.21875 \r\n",
       "L 22.796875 31.296875 \r\n",
       "L 73.1875 13.484375 \r\n",
       "L 73.1875 4.59375 \r\n",
       "L 10.59375 27.296875 \r\n",
       "L 10.59375 35.40625 \r\n",
       "L 73.1875 58.109375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-60\"/>\r\n",
       "        <path d=\"M 19.671875 64.796875 \r\n",
       "L 19.671875 8.109375 \r\n",
       "L 31.59375 8.109375 \r\n",
       "Q 46.6875 8.109375 53.6875 14.9375 \r\n",
       "Q 60.6875 21.78125 60.6875 36.53125 \r\n",
       "Q 60.6875 51.171875 53.6875 57.984375 \r\n",
       "Q 46.6875 64.796875 31.59375 64.796875 \r\n",
       "z\r\n",
       "M 9.8125 72.90625 \r\n",
       "L 30.078125 72.90625 \r\n",
       "Q 51.265625 72.90625 61.171875 64.09375 \r\n",
       "Q 71.09375 55.28125 71.09375 36.53125 \r\n",
       "Q 71.09375 17.671875 61.125 8.828125 \r\n",
       "Q 51.171875 0 30.078125 0 \r\n",
       "L 9.8125 0 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-68\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"97.412109\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"158.691406\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "       <use x=\"210.791016\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "       <use x=\"262.890625\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"294.677734\" xlink:href=\"#DejaVuSans-60\"/>\r\n",
       "       <use x=\"378.466797\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"410.253906\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"473.876953\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"537.5\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"601.123047\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"632.910156\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"696.533203\" xlink:href=\"#DejaVuSans-68\"/>\r\n",
       "       <use x=\"773.535156\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"834.814453\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"866.601562\" xlink:href=\"#DejaVuSans-43\"/>\r\n",
       "       <use x=\"950.390625\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"982.177734\" xlink:href=\"#DejaVuSans-83\"/>\r\n",
       "       <use x=\"1045.654297\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "       <use x=\"1109.130859\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"1170.654297\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"1225.634766\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "       <use x=\"1289.257812\" xlink:href=\"#DejaVuSans-86\"/>\r\n",
       "       <use x=\"1349.916016\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"1411.439453\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"1466.419922\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1498.207031\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"1581.996094\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1613.783203\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"1677.40625\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"1709.193359\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"ytick_6\">\r\n",
       "     <g id=\"line2d_27\">\r\n",
       "      <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 58.3866 \r\n",
       "L 613.271875 58.3866 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-width:0.8;\"/>\r\n",
       "     </g>\r\n",
       "     <g id=\"line2d_28\">\r\n",
       "      <g>\r\n",
       "       <use style=\"fill:#555555;stroke:#555555;stroke-width:0.8;\" x=\"278.471875\" xlink:href=\"#md9e099bf16\" y=\"58.3866\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "     <g id=\"text_15\">\r\n",
       "      <!-- mass &lt; 100.0Da + mod cosine &gt; 0.4 + min match 6 -->\r\n",
       "      <g style=\"fill:#555555;\" transform=\"translate(7.2 62.185819)scale(0.1 -0.1)\">\r\n",
       "       <defs>\r\n",
       "        <path d=\"M 54.890625 33.015625 \r\n",
       "L 54.890625 0 \r\n",
       "L 45.90625 0 \r\n",
       "L 45.90625 32.71875 \r\n",
       "Q 45.90625 40.484375 42.875 44.328125 \r\n",
       "Q 39.84375 48.1875 33.796875 48.1875 \r\n",
       "Q 26.515625 48.1875 22.3125 43.546875 \r\n",
       "Q 18.109375 38.921875 18.109375 30.90625 \r\n",
       "L 18.109375 0 \r\n",
       "L 9.078125 0 \r\n",
       "L 9.078125 75.984375 \r\n",
       "L 18.109375 75.984375 \r\n",
       "L 18.109375 46.1875 \r\n",
       "Q 21.34375 51.125 25.703125 53.5625 \r\n",
       "Q 30.078125 56 35.796875 56 \r\n",
       "Q 45.21875 56 50.046875 50.171875 \r\n",
       "Q 54.890625 44.34375 54.890625 33.015625 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-104\"/>\r\n",
       "       </defs>\r\n",
       "       <use xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"97.412109\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"158.691406\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "       <use x=\"210.791016\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "       <use x=\"262.890625\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"294.677734\" xlink:href=\"#DejaVuSans-60\"/>\r\n",
       "       <use x=\"378.466797\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"410.253906\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "       <use x=\"473.876953\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"537.5\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"601.123047\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"632.910156\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"696.533203\" xlink:href=\"#DejaVuSans-68\"/>\r\n",
       "       <use x=\"773.535156\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"834.814453\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"866.601562\" xlink:href=\"#DejaVuSans-43\"/>\r\n",
       "       <use x=\"950.390625\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"982.177734\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"1079.589844\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"1140.771484\" xlink:href=\"#DejaVuSans-100\"/>\r\n",
       "       <use x=\"1204.248047\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1236.035156\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"1291.015625\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "       <use x=\"1352.197266\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "       <use x=\"1404.296875\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"1432.080078\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "       <use x=\"1495.458984\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "       <use x=\"1556.982422\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1588.769531\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "       <use x=\"1672.558594\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1704.345703\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "       <use x=\"1767.96875\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "       <use x=\"1799.755859\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "       <use x=\"1863.378906\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"1895.166016\" xlink:href=\"#DejaVuSans-43\"/>\r\n",
       "       <use x=\"1978.955078\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"2010.742188\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"2108.154297\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "       <use x=\"2135.9375\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "       <use x=\"2199.316406\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"2231.103516\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "       <use x=\"2328.515625\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "       <use x=\"2389.794922\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "       <use x=\"2429.003906\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "       <use x=\"2483.984375\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "       <use x=\"2547.363281\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "       <use x=\"2579.150391\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "      </g>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_3\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 248.6466 \r\n",
       "L 530.581589 248.6466 \r\n",
       "L 530.581589 230.5266 \r\n",
       "L 278.471875 230.5266 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_4\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 212.4066 \r\n",
       "L 483.603304 212.4066 \r\n",
       "L 483.603304 194.2866 \r\n",
       "L 278.471875 194.2866 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_5\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 176.1666 \r\n",
       "L 493.381589 176.1666 \r\n",
       "L 493.381589 158.0466 \r\n",
       "L 278.471875 158.0466 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_6\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 139.9266 \r\n",
       "L 494.231875 139.9266 \r\n",
       "L 494.231875 121.8066 \r\n",
       "L 278.471875 121.8066 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_7\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 103.6866 \r\n",
       "L 495.294732 103.6866 \r\n",
       "L 495.294732 85.5666 \r\n",
       "L 278.471875 85.5666 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_8\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 67.4466 \r\n",
       "L 455.331304 67.4466 \r\n",
       "L 455.331304 49.3266 \r\n",
       "L 278.471875 49.3266 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_9\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 278.471875 248.6466 \r\n",
       "L 278.471875 248.6466 \r\n",
       "L 278.471875 230.5266 \r\n",
       "L 278.471875 230.5266 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_10\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 483.603304 212.4066 \r\n",
       "L 510.812446 212.4066 \r\n",
       "L 510.812446 194.2866 \r\n",
       "L 483.603304 194.2866 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_11\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 493.381589 176.1666 \r\n",
       "L 530.794161 176.1666 \r\n",
       "L 530.794161 158.0466 \r\n",
       "L 493.381589 158.0466 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_12\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 494.231875 139.9266 \r\n",
       "L 540.572446 139.9266 \r\n",
       "L 540.572446 121.8066 \r\n",
       "L 494.231875 121.8066 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_13\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 495.294732 103.6866 \r\n",
       "L 552.263875 103.6866 \r\n",
       "L 552.263875 85.5666 \r\n",
       "L 495.294732 85.5666 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_14\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 455.331304 67.4466 \r\n",
       "L 515.701589 67.4466 \r\n",
       "L 515.701589 49.3266 \r\n",
       "L 455.331304 49.3266 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_15\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 530.581589 248.6466 \r\n",
       "L 597.329018 248.6466 \r\n",
       "L 597.329018 230.5266 \r\n",
       "L 530.581589 230.5266 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_16\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 510.812446 212.4066 \r\n",
       "L 597.329018 212.4066 \r\n",
       "L 597.329018 194.2866 \r\n",
       "L 510.812446 194.2866 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_17\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 530.794161 176.1666 \r\n",
       "L 597.329018 176.1666 \r\n",
       "L 597.329018 158.0466 \r\n",
       "L 530.794161 158.0466 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_18\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 540.572446 139.9266 \r\n",
       "L 597.329018 139.9266 \r\n",
       "L 597.329018 121.8066 \r\n",
       "L 540.572446 121.8066 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_19\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 552.263875 103.6866 \r\n",
       "L 597.329018 103.6866 \r\n",
       "L 597.329018 85.5666 \r\n",
       "L 552.263875 85.5666 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_20\">\r\n",
       "    <path clip-path=\"url(#p67fc2f8f97)\" d=\"M 515.701589 67.4466 \r\n",
       "L 597.329018 67.4466 \r\n",
       "L 597.329018 49.3266 \r\n",
       "L 515.701589 49.3266 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_21\">\r\n",
       "    <path d=\"M 278.471875 257.7066 \r\n",
       "L 278.471875 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_22\">\r\n",
       "    <path d=\"M 613.271875 257.7066 \r\n",
       "L 613.271875 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_23\">\r\n",
       "    <path d=\"M 278.471875 257.7066 \r\n",
       "L 613.271875 257.7066 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_24\">\r\n",
       "    <path d=\"M 278.471875 40.2666 \r\n",
       "L 613.271875 40.2666 \r\n",
       "\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_16\">\r\n",
       "    <!-- 1186 -->\r\n",
       "    <g transform=\"translate(391.801732 242.345975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "     <use x=\"190.869141\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_17\">\r\n",
       "    <!-- 965 -->\r\n",
       "    <g transform=\"translate(371.493839 206.105975)scale(0.1 -0.1)\">\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 10.984375 1.515625 \r\n",
       "L 10.984375 10.5 \r\n",
       "Q 14.703125 8.734375 18.5 7.8125 \r\n",
       "Q 22.3125 6.890625 25.984375 6.890625 \r\n",
       "Q 35.75 6.890625 40.890625 13.453125 \r\n",
       "Q 46.046875 20.015625 46.78125 33.40625 \r\n",
       "Q 43.953125 29.203125 39.59375 26.953125 \r\n",
       "Q 35.25 24.703125 29.984375 24.703125 \r\n",
       "Q 19.046875 24.703125 12.671875 31.3125 \r\n",
       "Q 6.296875 37.9375 6.296875 49.421875 \r\n",
       "Q 6.296875 60.640625 12.9375 67.421875 \r\n",
       "Q 19.578125 74.21875 30.609375 74.21875 \r\n",
       "Q 43.265625 74.21875 49.921875 64.515625 \r\n",
       "Q 56.59375 54.828125 56.59375 36.375 \r\n",
       "Q 56.59375 19.140625 48.40625 8.859375 \r\n",
       "Q 40.234375 -1.421875 26.421875 -1.421875 \r\n",
       "Q 22.703125 -1.421875 18.890625 -0.6875 \r\n",
       "Q 15.09375 0.046875 10.984375 1.515625 \r\n",
       "z\r\n",
       "M 30.609375 32.421875 \r\n",
       "Q 37.25 32.421875 41.125 36.953125 \r\n",
       "Q 45.015625 41.5 45.015625 49.421875 \r\n",
       "Q 45.015625 57.28125 41.125 61.84375 \r\n",
       "Q 37.25 66.40625 30.609375 66.40625 \r\n",
       "Q 23.96875 66.40625 20.09375 61.84375 \r\n",
       "Q 16.21875 57.28125 16.21875 49.421875 \r\n",
       "Q 16.21875 41.5 20.09375 36.953125 \r\n",
       "Q 23.96875 32.421875 30.609375 32.421875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-57\"/>\r\n",
       "      <path d=\"M 10.796875 72.90625 \r\n",
       "L 49.515625 72.90625 \r\n",
       "L 49.515625 64.59375 \r\n",
       "L 19.828125 64.59375 \r\n",
       "L 19.828125 46.734375 \r\n",
       "Q 21.96875 47.46875 24.109375 47.828125 \r\n",
       "Q 26.265625 48.1875 28.421875 48.1875 \r\n",
       "Q 40.625 48.1875 47.75 41.5 \r\n",
       "Q 54.890625 34.8125 54.890625 23.390625 \r\n",
       "Q 54.890625 11.625 47.5625 5.09375 \r\n",
       "Q 40.234375 -1.421875 26.90625 -1.421875 \r\n",
       "Q 22.3125 -1.421875 17.546875 -0.640625 \r\n",
       "Q 12.796875 0.140625 7.71875 1.703125 \r\n",
       "L 7.71875 11.625 \r\n",
       "Q 12.109375 9.234375 16.796875 8.0625 \r\n",
       "Q 21.484375 6.890625 26.703125 6.890625 \r\n",
       "Q 35.15625 6.890625 40.078125 11.328125 \r\n",
       "Q 45.015625 15.765625 45.015625 23.390625 \r\n",
       "Q 45.015625 31 40.078125 35.4375 \r\n",
       "Q 35.15625 39.890625 26.703125 39.890625 \r\n",
       "Q 22.75 39.890625 18.8125 39.015625 \r\n",
       "Q 14.890625 38.140625 10.796875 36.28125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-53\"/>\r\n",
       "     </defs>\r\n",
       "     <use xlink:href=\"#DejaVuSans-57\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_18\">\r\n",
       "    <!-- 1011 -->\r\n",
       "    <g transform=\"translate(373.201732 169.865975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"190.869141\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_19\">\r\n",
       "    <!-- 1015 -->\r\n",
       "    <g transform=\"translate(373.626875 133.625975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"190.869141\" xlink:href=\"#DejaVuSans-53\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_20\">\r\n",
       "    <!-- 1020 -->\r\n",
       "    <g transform=\"translate(374.158304 97.385975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_21\">\r\n",
       "    <!-- 832 -->\r\n",
       "    <g transform=\"translate(357.357839 61.145975)scale(0.1 -0.1)\">\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 40.578125 39.3125 \r\n",
       "Q 47.65625 37.796875 51.625 33 \r\n",
       "Q 55.609375 28.21875 55.609375 21.1875 \r\n",
       "Q 55.609375 10.40625 48.1875 4.484375 \r\n",
       "Q 40.765625 -1.421875 27.09375 -1.421875 \r\n",
       "Q 22.515625 -1.421875 17.65625 -0.515625 \r\n",
       "Q 12.796875 0.390625 7.625 2.203125 \r\n",
       "L 7.625 11.71875 \r\n",
       "Q 11.71875 9.328125 16.59375 8.109375 \r\n",
       "Q 21.484375 6.890625 26.8125 6.890625 \r\n",
       "Q 36.078125 6.890625 40.9375 10.546875 \r\n",
       "Q 45.796875 14.203125 45.796875 21.1875 \r\n",
       "Q 45.796875 27.640625 41.28125 31.265625 \r\n",
       "Q 36.765625 34.90625 28.71875 34.90625 \r\n",
       "L 20.21875 34.90625 \r\n",
       "L 20.21875 43.015625 \r\n",
       "L 29.109375 43.015625 \r\n",
       "Q 36.375 43.015625 40.234375 45.921875 \r\n",
       "Q 44.09375 48.828125 44.09375 54.296875 \r\n",
       "Q 44.09375 59.90625 40.109375 62.90625 \r\n",
       "Q 36.140625 65.921875 28.71875 65.921875 \r\n",
       "Q 24.65625 65.921875 20.015625 65.03125 \r\n",
       "Q 15.375 64.15625 9.8125 62.3125 \r\n",
       "L 9.8125 71.09375 \r\n",
       "Q 15.4375 72.65625 20.34375 73.4375 \r\n",
       "Q 25.25 74.21875 29.59375 74.21875 \r\n",
       "Q 40.828125 74.21875 47.359375 69.109375 \r\n",
       "Q 53.90625 64.015625 53.90625 55.328125 \r\n",
       "Q 53.90625 49.265625 50.4375 45.09375 \r\n",
       "Q 46.96875 40.921875 40.578125 39.3125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-51\"/>\r\n",
       "     </defs>\r\n",
       "     <use xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_22\">\r\n",
       "    <!-- 128 -->\r\n",
       "    <g transform=\"translate(487.664125 206.105975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_23\">\r\n",
       "    <!-- 176 -->\r\n",
       "    <g transform=\"translate(502.544125 169.865975)scale(0.1 -0.1)\">\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 8.203125 72.90625 \r\n",
       "L 55.078125 72.90625 \r\n",
       "L 55.078125 68.703125 \r\n",
       "L 28.609375 0 \r\n",
       "L 18.3125 0 \r\n",
       "L 43.21875 64.59375 \r\n",
       "L 8.203125 64.59375 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-55\"/>\r\n",
       "     </defs>\r\n",
       "     <use xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_24\">\r\n",
       "    <!-- 218 -->\r\n",
       "    <g transform=\"translate(507.858411 133.625975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_25\">\r\n",
       "    <!-- 268 -->\r\n",
       "    <g transform=\"translate(514.235554 97.385975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_26\">\r\n",
       "    <!-- 284 -->\r\n",
       "    <g transform=\"translate(475.972696 61.145975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_27\">\r\n",
       "    <!-- 314 -->\r\n",
       "    <g transform=\"translate(554.411554 242.345975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-51\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_28\">\r\n",
       "    <!-- 407 -->\r\n",
       "    <g transform=\"translate(544.526982 206.105975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_29\">\r\n",
       "    <!-- 313 -->\r\n",
       "    <g transform=\"translate(554.517839 169.865975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-51\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-51\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_30\">\r\n",
       "    <!-- 267 -->\r\n",
       "    <g transform=\"translate(559.406982 133.625975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-55\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_31\">\r\n",
       "    <!-- 212 -->\r\n",
       "    <g transform=\"translate(565.252696 97.385975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-49\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_32\">\r\n",
       "    <!-- 384 -->\r\n",
       "    <g transform=\"translate(546.971554 61.145975)scale(0.1 -0.1)\">\r\n",
       "     <use xlink:href=\"#DejaVuSans-51\"/>\r\n",
       "     <use x=\"63.623047\" xlink:href=\"#DejaVuSans-56\"/>\r\n",
       "     <use x=\"127.246094\" xlink:href=\"#DejaVuSans-52\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"text_33\">\r\n",
       "    <!-- Compare if top hit is &gt;= 0.6 tanimoto similarity -->\r\n",
       "    <g transform=\"translate(274.25425 18.14175)scale(0.144 -0.144)\">\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 64.40625 67.28125 \r\n",
       "L 64.40625 56.890625 \r\n",
       "Q 59.421875 61.53125 53.78125 63.8125 \r\n",
       "Q 48.140625 66.109375 41.796875 66.109375 \r\n",
       "Q 29.296875 66.109375 22.65625 58.46875 \r\n",
       "Q 16.015625 50.828125 16.015625 36.375 \r\n",
       "Q 16.015625 21.96875 22.65625 14.328125 \r\n",
       "Q 29.296875 6.6875 41.796875 6.6875 \r\n",
       "Q 48.140625 6.6875 53.78125 8.984375 \r\n",
       "Q 59.421875 11.28125 64.40625 15.921875 \r\n",
       "L 64.40625 5.609375 \r\n",
       "Q 59.234375 2.09375 53.4375 0.328125 \r\n",
       "Q 47.65625 -1.421875 41.21875 -1.421875 \r\n",
       "Q 24.65625 -1.421875 15.125 8.703125 \r\n",
       "Q 5.609375 18.84375 5.609375 36.375 \r\n",
       "Q 5.609375 53.953125 15.125 64.078125 \r\n",
       "Q 24.65625 74.21875 41.21875 74.21875 \r\n",
       "Q 47.75 74.21875 53.53125 72.484375 \r\n",
       "Q 59.328125 70.75 64.40625 67.28125 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-67\"/>\r\n",
       "      <path d=\"M 10.59375 45.40625 \r\n",
       "L 73.1875 45.40625 \r\n",
       "L 73.1875 37.203125 \r\n",
       "L 10.59375 37.203125 \r\n",
       "z\r\n",
       "M 10.59375 25.484375 \r\n",
       "L 73.1875 25.484375 \r\n",
       "L 73.1875 17.1875 \r\n",
       "L 10.59375 17.1875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-61\"/>\r\n",
       "      <path d=\"M 32.171875 -5.078125 \r\n",
       "Q 28.375 -14.84375 24.75 -17.8125 \r\n",
       "Q 21.140625 -20.796875 15.09375 -20.796875 \r\n",
       "L 7.90625 -20.796875 \r\n",
       "L 7.90625 -13.28125 \r\n",
       "L 13.1875 -13.28125 \r\n",
       "Q 16.890625 -13.28125 18.9375 -11.515625 \r\n",
       "Q 21 -9.765625 23.484375 -3.21875 \r\n",
       "L 25.09375 0.875 \r\n",
       "L 2.984375 54.6875 \r\n",
       "L 12.5 54.6875 \r\n",
       "L 29.59375 11.921875 \r\n",
       "L 46.6875 54.6875 \r\n",
       "L 56.203125 54.6875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-121\"/>\r\n",
       "     </defs>\r\n",
       "     <use xlink:href=\"#DejaVuSans-67\"/>\r\n",
       "     <use x=\"69.824219\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "     <use x=\"131.005859\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "     <use x=\"228.417969\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "     <use x=\"291.894531\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     <use x=\"353.173828\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "     <use x=\"392.037109\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "     <use x=\"453.560547\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"485.347656\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"513.130859\" xlink:href=\"#DejaVuSans-102\"/>\r\n",
       "     <use x=\"548.335938\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"580.123047\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"619.332031\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "     <use x=\"680.513672\" xlink:href=\"#DejaVuSans-112\"/>\r\n",
       "     <use x=\"743.990234\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"775.777344\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "     <use x=\"839.15625\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"866.939453\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"906.148438\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"937.935547\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"965.71875\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     <use x=\"1017.818359\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"1049.605469\" xlink:href=\"#DejaVuSans-62\"/>\r\n",
       "     <use x=\"1133.394531\" xlink:href=\"#DejaVuSans-61\"/>\r\n",
       "     <use x=\"1217.183594\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"1248.970703\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"1312.59375\" xlink:href=\"#DejaVuSans-46\"/>\r\n",
       "     <use x=\"1344.380859\" xlink:href=\"#DejaVuSans-54\"/>\r\n",
       "     <use x=\"1408.003906\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"1439.791016\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"1479\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     <use x=\"1540.279297\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "     <use x=\"1603.658203\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"1631.441406\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "     <use x=\"1728.853516\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "     <use x=\"1790.035156\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"1829.244141\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "     <use x=\"1890.425781\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"1922.212891\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     <use x=\"1974.3125\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"2002.095703\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "     <use x=\"2099.507812\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"2127.291016\" xlink:href=\"#DejaVuSans-108\"/>\r\n",
       "     <use x=\"2155.074219\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     <use x=\"2216.353516\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "     <use x=\"2257.466797\" xlink:href=\"#DejaVuSans-105\"/>\r\n",
       "     <use x=\"2285.25\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"2324.458984\" xlink:href=\"#DejaVuSans-121\"/>\r\n",
       "    </g>\r\n",
       "    <!-- (2000 query dataset) -->\r\n",
       "    <g transform=\"translate(369.346 34.2666)scale(0.144 -0.144)\">\r\n",
       "     <defs>\r\n",
       "      <path d=\"M 31 75.875 \r\n",
       "Q 24.46875 64.65625 21.28125 53.65625 \r\n",
       "Q 18.109375 42.671875 18.109375 31.390625 \r\n",
       "Q 18.109375 20.125 21.3125 9.0625 \r\n",
       "Q 24.515625 -2 31 -13.1875 \r\n",
       "L 23.1875 -13.1875 \r\n",
       "Q 15.875 -1.703125 12.234375 9.375 \r\n",
       "Q 8.59375 20.453125 8.59375 31.390625 \r\n",
       "Q 8.59375 42.28125 12.203125 53.3125 \r\n",
       "Q 15.828125 64.359375 23.1875 75.875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-40\"/>\r\n",
       "      <path d=\"M 14.796875 27.296875 \r\n",
       "Q 14.796875 17.390625 18.875 11.75 \r\n",
       "Q 22.953125 6.109375 30.078125 6.109375 \r\n",
       "Q 37.203125 6.109375 41.296875 11.75 \r\n",
       "Q 45.40625 17.390625 45.40625 27.296875 \r\n",
       "Q 45.40625 37.203125 41.296875 42.84375 \r\n",
       "Q 37.203125 48.484375 30.078125 48.484375 \r\n",
       "Q 22.953125 48.484375 18.875 42.84375 \r\n",
       "Q 14.796875 37.203125 14.796875 27.296875 \r\n",
       "z\r\n",
       "M 45.40625 8.203125 \r\n",
       "Q 42.578125 3.328125 38.25 0.953125 \r\n",
       "Q 33.9375 -1.421875 27.875 -1.421875 \r\n",
       "Q 17.96875 -1.421875 11.734375 6.484375 \r\n",
       "Q 5.515625 14.40625 5.515625 27.296875 \r\n",
       "Q 5.515625 40.1875 11.734375 48.09375 \r\n",
       "Q 17.96875 56 27.875 56 \r\n",
       "Q 33.9375 56 38.25 53.625 \r\n",
       "Q 42.578125 51.265625 45.40625 46.390625 \r\n",
       "L 45.40625 54.6875 \r\n",
       "L 54.390625 54.6875 \r\n",
       "L 54.390625 -20.796875 \r\n",
       "L 45.40625 -20.796875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-113\"/>\r\n",
       "      <path d=\"M 8.015625 75.875 \r\n",
       "L 15.828125 75.875 \r\n",
       "Q 23.140625 64.359375 26.78125 53.3125 \r\n",
       "Q 30.421875 42.28125 30.421875 31.390625 \r\n",
       "Q 30.421875 20.453125 26.78125 9.375 \r\n",
       "Q 23.140625 -1.703125 15.828125 -13.1875 \r\n",
       "L 8.015625 -13.1875 \r\n",
       "Q 14.5 -2 17.703125 9.0625 \r\n",
       "Q 20.90625 20.125 20.90625 31.390625 \r\n",
       "Q 20.90625 42.671875 17.703125 53.65625 \r\n",
       "Q 14.5 64.65625 8.015625 75.875 \r\n",
       "z\r\n",
       "\" id=\"DejaVuSans-41\"/>\r\n",
       "     </defs>\r\n",
       "     <use xlink:href=\"#DejaVuSans-40\"/>\r\n",
       "     <use x=\"39.013672\" xlink:href=\"#DejaVuSans-50\"/>\r\n",
       "     <use x=\"102.636719\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"166.259766\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"229.882812\" xlink:href=\"#DejaVuSans-48\"/>\r\n",
       "     <use x=\"293.505859\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"325.292969\" xlink:href=\"#DejaVuSans-113\"/>\r\n",
       "     <use x=\"388.769531\" xlink:href=\"#DejaVuSans-117\"/>\r\n",
       "     <use x=\"452.148438\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "     <use x=\"513.671875\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "     <use x=\"554.785156\" xlink:href=\"#DejaVuSans-121\"/>\r\n",
       "     <use x=\"613.964844\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "     <use x=\"645.751953\" xlink:href=\"#DejaVuSans-100\"/>\r\n",
       "     <use x=\"709.228516\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     <use x=\"770.507812\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"809.716797\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "     <use x=\"870.996094\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     <use x=\"923.095703\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "     <use x=\"984.619141\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "     <use x=\"1023.828125\" xlink:href=\"#DejaVuSans-41\"/>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "   <g id=\"legend_1\">\r\n",
       "    <g id=\"patch_25\">\r\n",
       "     <path d=\"M 630.315875 92.300975 \r\n",
       "L 752.778375 92.300975 \r\n",
       "Q 754.778375 92.300975 754.778375 90.300975 \r\n",
       "L 754.778375 47.2666 \r\n",
       "Q 754.778375 45.2666 752.778375 45.2666 \r\n",
       "L 630.315875 45.2666 \r\n",
       "Q 628.315875 45.2666 628.315875 47.2666 \r\n",
       "L 628.315875 90.300975 \r\n",
       "Q 628.315875 92.300975 630.315875 92.300975 \r\n",
       "z\r\n",
       "\" style=\"fill:#e5e5e5;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;stroke-width:0.5;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"patch_26\">\r\n",
       "     <path d=\"M 632.315875 56.865037 \r\n",
       "L 652.315875 56.865037 \r\n",
       "L 652.315875 49.865037 \r\n",
       "L 632.315875 49.865037 \r\n",
       "z\r\n",
       "\" style=\"fill:#e24a33;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_34\">\r\n",
       "     <!-- true matches -->\r\n",
       "     <g transform=\"translate(660.315875 56.865037)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "      <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\r\n",
       "      <use x=\"80.322266\" xlink:href=\"#DejaVuSans-117\"/>\r\n",
       "      <use x=\"143.701172\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"205.224609\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "      <use x=\"237.011719\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "      <use x=\"334.423828\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "      <use x=\"395.703125\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "      <use x=\"434.912109\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "      <use x=\"489.892578\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "      <use x=\"553.271484\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"614.794922\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"patch_27\">\r\n",
       "     <path d=\"M 632.315875 71.543162 \r\n",
       "L 652.315875 71.543162 \r\n",
       "L 652.315875 64.543162 \r\n",
       "L 632.315875 64.543162 \r\n",
       "z\r\n",
       "\" style=\"fill:#348abd;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_35\">\r\n",
       "     <!-- false matches -->\r\n",
       "     <g transform=\"translate(660.315875 71.543162)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-102\"/>\r\n",
       "      <use x=\"35.205078\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "      <use x=\"96.484375\" xlink:href=\"#DejaVuSans-108\"/>\r\n",
       "      <use x=\"124.267578\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "      <use x=\"176.367188\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"237.890625\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "      <use x=\"269.677734\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "      <use x=\"367.089844\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "      <use x=\"428.369141\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "      <use x=\"467.578125\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "      <use x=\"522.558594\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "      <use x=\"585.9375\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"647.460938\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "    <g id=\"patch_28\">\r\n",
       "     <path d=\"M 632.315875 86.221288 \r\n",
       "L 652.315875 86.221288 \r\n",
       "L 652.315875 79.221288 \r\n",
       "L 632.315875 79.221288 \r\n",
       "z\r\n",
       "\" style=\"fill:#988ed5;\"/>\r\n",
       "    </g>\r\n",
       "    <g id=\"text_36\">\r\n",
       "     <!-- no matches found -->\r\n",
       "     <g transform=\"translate(660.315875 86.221288)scale(0.1 -0.1)\">\r\n",
       "      <use xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "      <use x=\"63.378906\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "      <use x=\"124.560547\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "      <use x=\"156.347656\" xlink:href=\"#DejaVuSans-109\"/>\r\n",
       "      <use x=\"253.759766\" xlink:href=\"#DejaVuSans-97\"/>\r\n",
       "      <use x=\"315.039062\" xlink:href=\"#DejaVuSans-116\"/>\r\n",
       "      <use x=\"354.248047\" xlink:href=\"#DejaVuSans-99\"/>\r\n",
       "      <use x=\"409.228516\" xlink:href=\"#DejaVuSans-104\"/>\r\n",
       "      <use x=\"472.607422\" xlink:href=\"#DejaVuSans-101\"/>\r\n",
       "      <use x=\"534.130859\" xlink:href=\"#DejaVuSans-115\"/>\r\n",
       "      <use x=\"586.230469\" xlink:href=\"#DejaVuSans-32\"/>\r\n",
       "      <use x=\"618.017578\" xlink:href=\"#DejaVuSans-102\"/>\r\n",
       "      <use x=\"653.222656\" xlink:href=\"#DejaVuSans-111\"/>\r\n",
       "      <use x=\"714.404297\" xlink:href=\"#DejaVuSans-117\"/>\r\n",
       "      <use x=\"777.783203\" xlink:href=\"#DejaVuSans-110\"/>\r\n",
       "      <use x=\"841.162109\" xlink:href=\"#DejaVuSans-100\"/>\r\n",
       "     </g>\r\n",
       "    </g>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"p67fc2f8f97\">\r\n",
       "   <rect height=\"217.44\" width=\"334.8\" x=\"278.471875\" y=\"40.2666\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import log\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('svg')\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "da_diff = log(mass_sim_thresh, 0.93)\n",
    "\n",
    "if restr_nn:\n",
    "    ylabels = ['mass < {:.1f}Da + mod cosine > {} + min match 6'.format(da_diff, threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {}'.format(da_diff, threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {} + NN tanimoto > {}'.format(da_diff, threshold, pred_thresh),\n",
    "               'ideal case Spec2Vec > {}'.format(threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {} + RF tanimoto > {}'.format(da_diff, threshold, pred_thresh),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {} + RF tanimoto > {} + NN tanimoto > {}'.format(da_diff, threshold, pred_thresh, pred_thresh),]\n",
    "else:\n",
    "    ylabels = ['mass < {:.1f}Da + mod cosine > {} + min match 6'.format(da_diff, threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {}'.format(da_diff, threshold),\n",
    "               'NN tanimoto > {}'.format(pred_thresh),\n",
    "               'ideal case Spec2Vec > {}'.format(threshold),\n",
    "               'RF tanimoto > {}'.format(pred_thresh),\n",
    "               'RF tanimoto > {} + NN tanimoto > {}'.format(pred_thresh, pred_thresh),]\n",
    "xlabels = ['true matches', 'false matches', 'no matches found']\n",
    "          #'mass + mod.cosine \\n + as backup: Spec2Vec + mod.cosine']\n",
    "data = np.array([[np.sum(test_sim_mcos_arr == 1), np.sum(test_sim_mcos_arr == 0), np.sum(test_sim_mcos_arr == -1)],\n",
    "                 [np.sum(test_sim_s2v_arr == 1), np.sum(test_sim_s2v_arr == 0), np.sum(test_sim_s2v_arr == -1)],\n",
    "                 [np.sum(test_tan_nn_top20_arr == 1), np.sum(test_tan_nn_top20_arr == 0), np.sum(test_tan_nn_top20_arr == -1)],\n",
    "                 [np.sum(ideal_case_top20_arr == 1), np.sum(ideal_case_top20_arr == 0), np.sum(ideal_case_top20_arr == -1)],\n",
    "                 [np.sum(test_tan_rf_top20_arr == 1), np.sum(test_tan_rf_top20_arr == 0), np.sum(test_tan_rf_top20_arr == -1)],\n",
    "                 [np.sum(test_nn_rf_top20_arr == 1), np.sum(test_nn_rf_top20_arr == 0), np.sum(test_nn_rf_top20_arr == -1)]])\n",
    "\n",
    "dataframe = pd.DataFrame(data, columns=xlabels, index=ylabels)\n",
    "\n",
    "ax = dataframe.sort_values(by=['false matches'], ascending=True).plot.barh(stacked=True, edgecolor='none', figsize=(6, 4))\n",
    "\n",
    "for p in ax.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy()\n",
    "    if width != 0:\n",
    "        ax.text(x+width/2, \n",
    "                y+height/2, \n",
    "                '{:.0f}'.format(width), \n",
    "                horizontalalignment='center', \n",
    "                verticalalignment='center', size=10)\n",
    "\n",
    "horiz_offset = 1.03\n",
    "vert_offset = 1.\n",
    "ax.legend(bbox_to_anchor=(horiz_offset, vert_offset))\n",
    "plt.xlabel('Number of spectra')\n",
    "plt.title('Compare if top hit is >= {} tanimoto similarity\\n(2000 query dataset)'.format(sim_threshold))\n",
    "#plt.figure(figsize=(5,5))\n",
    "# plt.savefig(os.path.join(path_data, \"Plots\", \"Performance_plot_ML_2000_queries.svg\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}