eva_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.layers import *\n",
    "from src.utils import process_edges, auprc_auroc_ap\n",
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy imporinterp\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "from umap import UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "go-basic.obo: fmt(1.2) rel(2021-05-01) 47,284 GO Terms\n",
      "  EXISTS: gene2go\n",
      "HMS:0:00:06.084110 335,858 annotations, 20,671 genes, 18,441 GOs, 1 taxids READ: gene2go \n",
      "\n",
      "Load BP Gene Ontology Analysis ...\n",
      "fisher module not installed.  Falling back on scipy.stats.fisher_exact\n",
      " 81% 16,936 of 20,913 population items found in association\n",
      "\n",
      "Load CC Gene Ontology Analysis ...\n",
      "fisher module not installed.  Falling back on scipy.stats.fisher_exact\n",
      " 85% 17,872 of 20,913 population items found in association\n",
      "\n",
      "Load MF Gene Ontology Analysis ...\n",
      "fisher module not installed.  Falling back on scipy.stats.fisher_exact\n",
      " 83% 17,408 of 20,913 population items found in association\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Model(\n",
       "  (pp): PP(\n",
       "    (conv_list): ModuleList(\n",
       "      (0): myGCN(64, 32)\n",
       "      (1): myGCN(32, 32)\n",
       "    )\n",
       "  )\n",
       "  (pd): PD(\n",
       "    (conv): myGCN(128, 128)\n",
       "  )\n",
       "  (mip): MultiInnerProductDecoder()\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "pose = Pose(data_dir, device='cpu')\n",
    "\n",
    "model = pose.model\n",
    "model.eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(mod: str, pos_score: torch.Tensor, neg_score: torch.Tensor) -> np.array:\n",
    "    assert mod in {'train', 'test'}, \"'idx' should in {'train', 'test'}\"\n",
    "    \n",
    "    record = np.zeros((3, gdata.n_et))\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    precision = dict()\n",
    "    recall = dict()\n",
    "    average_precision = dict()\n",
    "    \n",
    "    for i in range(getattr(gdata, f'{mod}_range').shape[0]):\n",
    "        [start, end] = getattr(gdata, f'{mod}_range')[i]\n",
    "        p_s = pos_score[start: end]\n",
    "        n_s = neg_score[start: end]\n",
    "\n",
    "        pos_target = torch.ones(p_s.shape[0])\n",
    "        neg_target = torch.zeros(n_s.shape[0])\n",
    "\n",
    "        score = torch.cat([p_s, n_s])\n",
    "        target = torch.cat([pos_target, neg_target])\n",
    "\n",
    "        # auprc_auroc_ap\n",
    "        record[0, i], record[1, i], record[2, i] = auprc_auroc_ap(target, score)\n",
    "        \n",
    "        # auroc\n",
    "        fpr[i], tpr[i], _ = roc_curve(target.detach().numpy(), score.detach().numpy())\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "        # auprc\n",
    "        precision[i], recall[i], _ = precision_recall_curve(target.detach().numpy(), score.detach().numpy())\n",
    "\n",
    "    return record, fpr, tpr, roc_auc, precision, recall, average_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_static_edge_weights = torch.ones((gdata.pp_index.shape[1]))\n",
    "pd_static_edge_weights = torch.ones((gdata.pd_index.shape[1]))\n",
    "test_neg_index = typed_negative_sampling(gdata.test_idx, gdata.n_drug, gdata.test_range)\n",
    "\n",
    "z = model.pp(gdata.p_feat, gdata.pp_index, pp_static_edge_weights)\n",
    "z = model.pd(z, gdata.pd_index, pd_static_edge_weights)\n",
    "\n",
    "pos_score = model.mip(z, gdata.test_idx, gdata.test_et)\n",
    "neg_score = model.mip(z, test_neg_index, gdata.test_et)\n",
    "\n",
    "record, fpr, tpr, roc_auc, precision, recall, average_precision = evaluate('test', pos_score, neg_score)\n",
    "y_score = torch.cat((pos_score, neg_score)).detach().numpy()\n",
    "y_test = torch.cat((torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0]))).detach().numpy()\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test, y_score)\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "# # Compute macro-average ROC curve and ROC area\n",
    "\n",
    "# # First aggregate all false positive rates\n",
    "# n_classes = record.shape[1]\n",
    "# all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "# # Then interpolate all ROC curves at this points\n",
    "# mean_tpr = np.zeros_like(all_fpr)\n",
    "# for i in range(n_classes):\n",
    "#     mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "# # Finally average it and compute AUC\n",
    "# mean_tpr /= n_classes\n",
    "\n",
    "# fpr[\"macro\"] = all_fpr\n",
    "# tpr[\"macro\"] = mean_tpr\n",
    "# roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_static_edge_weights = torch.ones((gdata.pp_index.shape[1]))\n",
    "pd_static_edge_weights = torch.ones((gdata.pd_index.shape[1]))\n",
    "train_neg_index = typed_negative_sampling(gdata.train_idx, gdata.n_drug, gdata.train_range)\n",
    "\n",
    "z = model.pp(gdata.p_feat, gdata.pp_index, pp_static_edge_weights)\n",
    "z = model.pd(z, gdata.pd_index, pd_static_edge_weights)\n",
    "\n",
    "pos_score_train = model.mip(z, gdata.train_idx, gdata.train_et)\n",
    "neg_score_train = model.mip(z, train_neg_index, gdata.train_et)\n",
    "\n",
    "y_score_train = torch.cat((pos_score_train, neg_score_train)).detach().numpy()\n",
    "y_test_train = torch.cat((torch.ones(pos_score_train.shape[0]), torch.zeros(neg_score_train.shape[0]))).detach().numpy()\n",
    "\n",
    "record_train, fpr_train, tpr_train, roc_auc_train, _, _, _ = evaluate('train', pos_score_train, neg_score_train)\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area_train\n",
    "fpr_train[\"micro\"], tpr_train[\"micro\"], _ = roc_curve(y_test_train, y_score_train)\n",
    "roc_auc_train[\"micro\"] = auc(fpr_train[\"micro\"], tpr_train[\"micro\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(fpr_train[\"micro\"], tpr_train[\"micro\"],\n",
    "         label='Train - Mi-ROC curve (area = {0:0.4f})'\n",
    "               ''.format(roc_auc_train[\"micro\"]),\n",
    "         color='darkblue', linestyle='-', linewidth=2, alpha=0.5)\n",
    "\n",
    "# plt.plot(fpr_train[\"macro\"], tpr_train[\"macro\"],\n",
    "#          label='Test - Ma-ROC curve for test (area = {0:0.2f})'\n",
    "#                ''.format(roc_auc_train[\"macro\"]),\n",
    "#          color='darkorange', linestyle='dashdot', linewidth=2)\n",
    "\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='Test - Mi-ROC curve (area = {0:0.4f})'\n",
    "               ''.format(roc_auc[\"micro\"]),\n",
    "         color='darkorange', linestyle='-', linewidth=2, alpha=0.5)\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "         label='Test - Ma-ROC curve for test (area = {0:0.4f})'\n",
    "               ''.format(roc_auc[\"macro\"]),\n",
    "         color='darkorange', linestyle='dashdot', linewidth=2, alpha=0.5)\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.85682845, 0.88356708, 0.85718549]),\n",
       " array([0.7946841 , 0.83275441, 0.79720226]))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_train.mean(axis=1), record.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "micro-prc - train - 0.8345149626588475\n",
      "micro-prc - test - 0.8347589294847769\n"
     ]
    }
   ],
   "source": [
    "y, xx, _ = precision_recall_curve(y_test_train, y_score_train)\n",
    "auprc = auc(xx, y)\n",
    "print(f\"micro-prc - train - {auprc}\")\n",
    "y, xx, _ = precision_recall_curve(y_test, y_score)\n",
    "auprc = auc(xx, y)\n",
    "print(f\"micro-prc - test - {auprc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### side effect embedding learned from pose-pred\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(861, 160)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "# get trined embedding of side effects\n",
    "side_effect_embedding = model.mip.weight.data.numpy()\n",
    "side_effect_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          side effect catagory                                catagory name\n",
       "0         atelectasis      C08                   respiratory tract diseases\n",
       "1           back ache      C23  pathological conditions, signs and symptoms\n",
       "2          lung edema      C08                   respiratory tract diseases\n",
       "3            acidosis      C18           nutritional and metabolic diseases\n",
       "4            peliosis      C06                    digestive system diseases\n",
       "..                ...      ...                                          ...\n",
       "838  corneal abrasion      C10                      nervous system diseases\n",
       "839  corneal abrasion      C11                                 eye diseases\n",
       "840  corneal abrasion      C26                          wounds and injuries\n",
       "841            bunion      C05                     musculoskeletal diseases\n",
       "842        asthenopia      C11                                 eye diseases\n",
       "\n",
       "[843 rows x 3 columns]"
      ],
      "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>side effect</th>\n      <th>catagory</th>\n      <th>catagory name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>atelectasis</td>\n      <td>C08</td>\n      <td>respiratory tract diseases</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>back ache</td>\n      <td>C23</td>\n      <td>pathological conditions, signs and symptoms</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>lung edema</td>\n      <td>C08</td>\n      <td>respiratory tract diseases</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>acidosis</td>\n      <td>C18</td>\n      <td>nutritional and metabolic diseases</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>peliosis</td>\n      <td>C06</td>\n      <td>digestive system diseases</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>838</th>\n      <td>corneal abrasion</td>\n      <td>C10</td>\n      <td>nervous system diseases</td>\n    </tr>\n    <tr>\n      <th>839</th>\n      <td>corneal abrasion</td>\n      <td>C11</td>\n      <td>eye diseases</td>\n    </tr>\n    <tr>\n      <th>840</th>\n      <td>corneal abrasion</td>\n      <td>C26</td>\n      <td>wounds and injuries</td>\n    </tr>\n    <tr>\n      <th>841</th>\n      <td>bunion</td>\n      <td>C05</td>\n      <td>musculoskeletal diseases</td>\n    </tr>\n    <tr>\n      <th>842</th>\n      <td>asthenopia</td>\n      <td>C11</td>\n      <td>eye diseases</td>\n    </tr>\n  </tbody>\n</table>\n<p>843 rows × 3 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# load side effect catagory\n",
    "se_c = pandas.read_csv('index-map/side_effect+catagory.tsv', sep='\\t')\n",
    "se_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# side effect mapping\n",
    "se_name2idx, se_idx2name = {}, {}\n",
    "for se, idx in gdata.side_effect_name_to_idx.items():\n",
    "    se = se.lower()\n",
    "    se_name2idx[se] = idx\n",
    "    se_idx2name[idx] = se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cata2se, cata2name = OrderedDict(), OrderedDict()\n",
    "for se, ca, na in se_c.values:\n",
    "    if ca == 'C23':\n",
    "        ca = 'S'\n",
    "    if cata2se.get(ca) is None:\n",
    "        cata2se[ca] = []\n",
    "    if cata2name.get(ca) is None:\n",
    "        cata2name[ca] = na\n",
    "    cata2se[ca].append(se_name2idx[se.lower()])\n",
    "    se_set = set()\n",
    "\n",
    "# update symptom cata\n",
    "for ca, ses in cata2se.items():\n",
    "    if ca == 'S':\n",
    "        continue\n",
    "    se_set.update(set(ses))\n",
    "se_all = set(range(gdata.n_et))\n",
    "se_tmp = (se_all - se_set) | set(cata2se['S'])\n",
    "cata2se['S'] = list(se_tmp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = list(cata2se.values())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "se_ca_embed = np.concatenate([side_effect_embedding[v].mean(axis=0) for v in list(cata2se.values())]).reshape((-1, side_effect_embedding.shape[1]))\n",
    "se_ca_embed = np.concatenate((se_ca_embed, side_effect_embedding[cata2se['F']]), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_embed = se_ca_embed[:22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           x          y category  n_se  \\\n",
       "8   4.737510   8.858061      C01    50   \n",
       "10  6.224280   8.271191      C04    32   \n",
       "17  6.210504   8.924448      C05    38   \n",
       "3   4.216232   9.131621      C06    55   \n",
       "18  6.037347   8.604804      C07    18   \n",
       "0   4.183841   9.743940      C08    39   \n",
       "16  4.940152  11.090220      C09    21   \n",
       "9   4.262570  11.085861      C10    96   \n",
       "11  4.915984  10.317763      C11    24   \n",
       "12  4.903810   9.444127      C12    30   \n",
       "13  5.278651   9.622085      C13    39   \n",
       "4   4.428204   8.473840      C14    68   \n",
       "7   4.164100   8.717392      C15    23   \n",
       "21  5.260113   8.542829      C16     6   \n",
       "15  6.699581   8.731540      C17    44   \n",
       "2   5.208558   8.968893      C18    32   \n",
       "19  5.700172   9.978020      C19    17   \n",
       "5   5.737392   7.886411      C20    19   \n",
       "6   6.476809   9.600780      C25     7   \n",
       "14  5.991090  11.384032      C26    16   \n",
       "20  6.708154  11.197752        F     9   \n",
       "1   5.752867   9.234724        S   485   \n",
       "\n",
       "                                              cat_all color  \n",
       "8                                      C01-infections     C  \n",
       "10                                      C04-neoplasms     C  \n",
       "17                       C05-musculoskeletal diseases     C  \n",
       "3                       C06-digestive system diseases     C  \n",
       "18                        C07-stomatognathic diseases     C  \n",
       "0                      C08-respiratory tract diseases     C  \n",
       "16                  C09-otorhinolaryngologic diseases     C  \n",
       "9                         C10-nervous system diseases     C  \n",
       "11                                   C11-eye diseases     C  \n",
       "12                       C12-male urogenital diseases     C  \n",
       "13  C13-female urogenital diseases and pregnancy c...     C  \n",
       "4                         C14-cardiovascular diseases     C  \n",
       "7                    C15-hemic and lymphatic diseases     C  \n",
       "21  C16-congenital, hereditary, and neonatal disea...     C  \n",
       "15            C17-skin and connective tissue diseases     C  \n",
       "2              C18-nutritional and metabolic diseases     C  \n",
       "19                      C19-endocrine system diseases     C  \n",
       "5                          C20-immune system diseases     C  \n",
       "6                    C25-chemically-induced disorders     C  \n",
       "14                            C26-wounds and injuries     C  \n",
       "20                                F-mental discorders     F  \n",
       "1       S-pathological conditions, signs and symptoms     S  "
      ],
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     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "umap = UMAP(n_components=2, init='random', random_state=8)\n",
    "se_umap_np = umap.fit_transform(se_ca_embed)\n",
    "se_umap = pandas.DataFrame(se_umap_np[:22, :], columns=['x', 'y'])\n",
    "se_umap['category'] = [f\"{i}\" for i in cata2se.keys()]\n",
    "se_umap['n_se'] = [len(v) for v in cata2se.values()]\n",
    "se_umap['cat_all'] = [f\"{i}-{cata2name[i]}\" for i in cata2se.keys()]\n",
    "se_umap['color'] = [f\"{i[0]}\" for i in cata2se.keys()]\n",
    "se_umap.sort_values(by='category')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 6)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "se_umap.to_csv('se_embed_2d_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot mental disorder\n",
    "se_umap_md = se_umap_np[22:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "col_dict = {'C': 'rgb(93, 164, 214)', 'S': 'rgb(44, 160, 101)', 'F': 'rgb(255, 65, 54)'}\n",
    "fig = go.Figure(data=[go.Scatter(\n",
    "    x = se_umap['x'],\n",
    "    y = se_umap['y'],\n",
    "    mode = 'markers+text',\n",
    "    text = se_umap['category'],\n",
    "    textposition='middle center',\n",
    "    textfont=dict(\n",
    "        family=\"sans serif\",\n",
    "        size=15,\n",
    "        # color=\"LightSeaGreen\"\n",
    "    ),\n",
    "    marker = dict(color=[col_dict[i] for i in se_umap['color']], opacity=0.3, size=np.log(se_umap['n_se'])*15))])\n",
    "# fig = px.scatter(se_umap, x='x', y='y', color=[col_dict[i] for i in se_umap['color']], text='category', size='n_se', size_max=100, opacity=0.3)\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x = se_umap_md[:, 0],\n",
    "    y = se_umap_md[:, 1],\n",
    "    mode='markers'\n",
    "))\n",
    "\n",
    "fig.update_xaxes(gridcolor='#e0e0e0', showline=True, linecolor='black', mirror=True)\n",
    "fig.update_yaxes(gridcolor='#e0e0e0', showline=True, linecolor='black', mirror=True)\n",
    "fig.update_layout(plot_bgcolor='#FFF')\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# average after umap\n",
    "umap = UMAP(n_components=2, init='random', random_state=8)\n",
    "se_umap_np = umap.fit_transform(side_effect_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "se_umap_np = (se_umap_np - se_umap_np.mean(axis=0))*10+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            x          y category  n_se  \\\n",
       "8    9.086743   7.471842      C01    50   \n",
       "10  13.716795  10.056763      C04    32   \n",
       "17  17.519848  13.967044      C05    38   \n",
       "3    8.308552   5.276290      C06    55   \n",
       "18  23.783897  12.842505      C07    18   \n",
       "0    9.170995   8.973595      C08    39   \n",
       "16  12.150425  16.570591      C09    21   \n",
       "9    5.474772  13.598949      C10    96   \n",
       "11   8.939240  16.282234      C11    24   \n",
       "12  10.514725   6.916021      C12    30   \n",
       "13  11.896503  10.410193      C13    39   \n",
       "4    7.222163   0.302137      C14    68   \n",
       "7    4.876615  -2.424417      C15    23   \n",
       "21  18.216923  13.266033      C16     6   \n",
       "15  13.920984   7.972413      C17    44   \n",
       "2    9.822960   8.535341      C18    32   \n",
       "19  10.411778  17.730890      C19    17   \n",
       "5   10.702909   7.753428      C20    19   \n",
       "6    4.185222  17.252052      C25     7   \n",
       "14  15.029292  22.785431      C26    16   \n",
       "20   7.902514  32.681877        F     9   \n",
       "1    9.607096   9.944313        S   485   \n",
       "\n",
       "                                              cat_all color  \n",
       "8                                    C01 - infections     C  \n",
       "10                                    C04 - neoplasms     C  \n",
       "17                     C05 - musculoskeletal diseases     C  \n",
       "3                     C06 - digestive system diseases     C  \n",
       "18                      C07 - stomatognathic diseases     C  \n",
       "0                    C08 - respiratory tract diseases     C  \n",
       "16                C09 - otorhinolaryngologic diseases     C  \n",
       "9                       C10 - nervous system diseases     C  \n",
       "11                                 C11 - eye diseases     C  \n",
       "12                     C12 - male urogenital diseases     C  \n",
       "13  C13 - female urogenital diseases and pregnancy...     C  \n",
       "4                       C14 - cardiovascular diseases     C  \n",
       "7                  C15 - hemic and lymphatic diseases     C  \n",
       "21  C16 - congenital, hereditary, and neonatal dis...     C  \n",
       "15          C17 - skin and connective tissue diseases     C  \n",
       "2            C18 - nutritional and metabolic diseases     C  \n",
       "19                    C19 - endocrine system diseases     C  \n",
       "5                        C20 - immune system diseases     C  \n",
       "6                  C25 - chemically-induced disorders     C  \n",
       "14                          C26 - wounds and injuries     C  \n",
       "20                              F - mental discorders     F  \n",
       "1     S - pathological conditions, signs and symptoms     S  "
      ],
      "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>x</th>\n      <th>y</th>\n      <th>category</th>\n      <th>n_se</th>\n      <th>cat_all</th>\n      <th>color</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>9.086743</td>\n      <td>7.471842</td>\n      <td>C01</td>\n      <td>50</td>\n      <td>C01 - infections</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>13.716795</td>\n      <td>10.056763</td>\n      <td>C04</td>\n      <td>32</td>\n      <td>C04 - neoplasms</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>17.519848</td>\n      <td>13.967044</td>\n      <td>C05</td>\n      <td>38</td>\n      <td>C05 - musculoskeletal diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.308552</td>\n      <td>5.276290</td>\n      <td>C06</td>\n      <td>55</td>\n      <td>C06 - digestive system diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>23.783897</td>\n      <td>12.842505</td>\n      <td>C07</td>\n      <td>18</td>\n      <td>C07 - stomatognathic diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>9.170995</td>\n      <td>8.973595</td>\n      <td>C08</td>\n      <td>39</td>\n      <td>C08 - respiratory tract diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>12.150425</td>\n      <td>16.570591</td>\n      <td>C09</td>\n      <td>21</td>\n      <td>C09 - otorhinolaryngologic diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>5.474772</td>\n      <td>13.598949</td>\n      <td>C10</td>\n      <td>96</td>\n      <td>C10 - nervous system diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>8.939240</td>\n      <td>16.282234</td>\n      <td>C11</td>\n      <td>24</td>\n      <td>C11 - eye diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>10.514725</td>\n      <td>6.916021</td>\n      <td>C12</td>\n      <td>30</td>\n      <td>C12 - male urogenital diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>11.896503</td>\n      <td>10.410193</td>\n      <td>C13</td>\n      <td>39</td>\n      <td>C13 - female urogenital diseases and pregnancy...</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>7.222163</td>\n      <td>0.302137</td>\n      <td>C14</td>\n      <td>68</td>\n      <td>C14 - cardiovascular diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>4.876615</td>\n      <td>-2.424417</td>\n      <td>C15</td>\n      <td>23</td>\n      <td>C15 - hemic and lymphatic diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>18.216923</td>\n      <td>13.266033</td>\n      <td>C16</td>\n      <td>6</td>\n      <td>C16 - congenital, hereditary, and neonatal dis...</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>13.920984</td>\n      <td>7.972413</td>\n      <td>C17</td>\n      <td>44</td>\n      <td>C17 - skin and connective tissue diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9.822960</td>\n      <td>8.535341</td>\n      <td>C18</td>\n      <td>32</td>\n      <td>C18 - nutritional and metabolic diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>10.411778</td>\n      <td>17.730890</td>\n      <td>C19</td>\n      <td>17</td>\n      <td>C19 - endocrine system diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>10.702909</td>\n      <td>7.753428</td>\n      <td>C20</td>\n      <td>19</td>\n      <td>C20 - immune system diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>4.185222</td>\n      <td>17.252052</td>\n      <td>C25</td>\n      <td>7</td>\n      <td>C25 - chemically-induced disorders</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15.029292</td>\n      <td>22.785431</td>\n      <td>C26</td>\n      <td>16</td>\n      <td>C26 - wounds and injuries</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>7.902514</td>\n      <td>32.681877</td>\n      <td>F</td>\n      <td>9</td>\n      <td>F - mental discorders</td>\n      <td>F</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9.607096</td>\n      <td>9.944313</td>\n      <td>S</td>\n      <td>485</td>\n      <td>S - pathological conditions, signs and symptoms</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "se_ca_embed = np.concatenate([se_umap_np[v].mean(axis=0) for v in list(cata2se.values())]).reshape((-1, 2))\n",
    "se_umap = pandas.DataFrame(se_ca_embed, columns=['x', 'y'])\n",
    "se_umap['category'] = [f\"{i}\" for i in cata2se.keys()]\n",
    "se_umap['n_se'] = [len(v) for v in cata2se.values()]\n",
    "se_umap['cat_all'] = [f\"{i} - {cata2name[i]}\" for i in cata2se.keys()]\n",
    "se_umap['color'] = [f\"{i[0]}\" for i in cata2se.keys()]\n",
    "se_umap = se_umap.sort_values(by='category')\n",
    "se_umap"
   ]
  },
  {
   "cell_type": "code",
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    "col_dict = {'C': 'rgb(93, 164, 214)', 'S': 'rgb(44, 160, 101)', 'F': 'rgb(255, 65, 54)'}\n",
    "fig = go.Figure(data=[go.Scatter(\n",
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    "    textposition='middle center',\n",
    "    textfont=dict(\n",
    "        family=\"sans serif\",\n",
    "        size=15,\n",
    "        # color=\"LightSeaGreen\"\n",
    "    ),\n",
    "    marker = dict(color=[col_dict[i] for i in se_umap['color']], opacity=0.3, size=np.log(se_umap['n_se'])*15))])\n",
    "# fig = px.scatter(se_umap, x='x', y='y', color=[col_dict[i] for i in se_umap['color']], text='category', size='n_se', size_max=100, opacity=0.3)\n",
    "\n",
    "# se_umap_md = se_umap_np[cata2se['F']]\n",
    "# fig.add_trace(go.Scatter(\n",
    "#     x = se_umap_md[:, 1],\n",
    "#     y = se_umap_md[:, 0],\n",
    "#     mode='markers+text',\n",
    "\n",
    "# ))\n",
    "\n",
    "# se_umap_md = se_umap_np[cata2se['C07']]\n",
    "# fig.add_trace(go.Scatter(\n",
    "#     x = se_umap_md[:, 1],\n",
    "#     y = se_umap_md[:, 0],\n",
    "#     mode='markers+text',\n",
    "\n",
    "# ))\n",
    "\n",
    "# se_umap_md = se_umap_np[cata2se['S']]\n",
    "# fig.add_trace(go.Scatter(\n",
    "#     x = se_umap_md[:, 1],\n",
    "#     y = se_umap_md[:, 0],\n",
    "#     mode='markers+text',\n",
    "\n",
    "# ))\n",
    "\n",
    "fig.update_xaxes(range=(0, 40), gridcolor='#e0e0e0', showline=True, linecolor='black', mirror=True)\n",
    "fig.update_yaxes(range=(2.5, 25), tickvals=[2.5, 5,7.5, 10,12.5,15, 17.5, 20,22.5, 25], gridcolor='#e0e0e0', showline=True, linecolor='black', mirror=True)\n",
    "fig.update_layout(plot_bgcolor='#FFF', width=800, height=1000)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C01 - infections\n",
      "C04 - neoplasms\n",
      "C05 - musculoskeletal diseases\n",
      "C06 - digestive system diseases\n",
      "C07 - stomatognathic diseases\n",
      "C08 - respiratory tract diseases\n",
      "C09 - otorhinolaryngologic diseases\n",
      "C10 - nervous system diseases\n",
      "C11 - eye diseases\n",
      "C12 - male urogenital diseases\n",
      "C13 - female urogenital diseases and pregnancy complications\n",
      "C14 - cardiovascular diseases\n",
      "C15 - hemic and lymphatic diseases\n",
      "C16 - congenital, hereditary, and neonatal diseases and abnormalities\n",
      "C17 - skin and connective tissue diseases\n",
      "C18 - nutritional and metabolic diseases\n",
      "C19 - endocrine system diseases\n",
      "C20 - immune system diseases\n",
      "C25 - chemically-induced disorders\n",
      "C26 - wounds and injuries\n",
      "F - mental discorders\n",
      "S - pathological conditions, signs and symptoms\n"
     ]
    }
   ],
   "source": [
    "for i in se_umap['cat_all'].values:\n",
    "    print(i)"
   ]
  },
  {
   "source": [
    "## overlapping between side effect categories\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            x          y category  n_se                          cat_all color\n",
       "8    9.086743   7.471842      C01    50                 C01 - infections     C\n",
       "10  13.716795  10.056763      C04    32                  C04 - neoplasms     C\n",
       "17  17.519848  13.967044      C05    38   C05 - musculoskeletal diseases     C\n",
       "3    8.308552   5.276290      C06    55  C06 - digestive system diseases     C\n",
       "18  23.783897  12.842505      C07    18    C07 - stomatognathic diseases     C"
      ],
      "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>x</th>\n      <th>y</th>\n      <th>category</th>\n      <th>n_se</th>\n      <th>cat_all</th>\n      <th>color</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>9.086743</td>\n      <td>7.471842</td>\n      <td>C01</td>\n      <td>50</td>\n      <td>C01 - infections</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>13.716795</td>\n      <td>10.056763</td>\n      <td>C04</td>\n      <td>32</td>\n      <td>C04 - neoplasms</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>17.519848</td>\n      <td>13.967044</td>\n      <td>C05</td>\n      <td>38</td>\n      <td>C05 - musculoskeletal diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.308552</td>\n      <td>5.276290</td>\n      <td>C06</td>\n      <td>55</td>\n      <td>C06 - digestive system diseases</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>23.783897</td>\n      <td>12.842505</td>\n      <td>C07</td>\n      <td>18</td>\n      <td>C07 - stomatognathic diseases</td>\n      <td>C</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "se_umap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "odict_keys(['C08', 'S', 'C18', 'C06', 'C14', 'C20', 'C25', 'C15', 'C01', 'C10', 'C04', 'C11', 'C12', 'C13', 'C26', 'C17', 'C09', 'C05', 'C07', 'C19', 'F', 'C16'])"
      ]
     },
     "metadata": {},
     "execution_count": 84
    }
   ],
   "source": [
    "cata2se.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = se_umap.shape[0]\n",
    "cate_pair_dis_js = []\n",
    "for i in range(n):\n",
    "    for j in range(i+1, n):\n",
    "        # get category and coordinates\n",
    "        x1, y1, c1, _, _, _ = se_umap.iloc[i, :]\n",
    "        x2, y2, c2, _, _, _ = se_umap.iloc[j, :]\n",
    "\n",
    "        # get side effets\n",
    "        s1 = set(cata2se[c1])\n",
    "        s2 = set(cata2se[c2])\n",
    "\n",
    "        # compute distance\n",
    "        dis = np.sqrt((x1-x2)**2 + (y1-y2)**2)\n",
    "        \n",
    "        # compute jaccard similarity coefficient score\n",
    "        js = len(s1&s2) / len(s1) / len(s2)\n",
    "\n",
    "        if js < 1e-10:\n",
    "            continue\n",
    "\n",
    "        cate_pair_dis_js.append([c1, c2, dis, js])\n",
    "cate_pair_dis_js = pandas.DataFrame(cate_pair_dis_js)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      0    1          2         3\n",
       "0   C01  C05  10.644478  0.001053\n",
       "1   C01  C06   2.329384  0.001818\n",
       "2   C01  C07  15.647695  0.003333\n",
       "3   C01  C08   1.504114  0.007179\n",
       "4   C01  C09   9.600697  0.003810\n",
       "..  ...  ...        ...       ...\n",
       "95  C17    S   4.743208  0.000281\n",
       "96  C18  C19   9.214382  0.003676\n",
       "97  C18    S   1.425412  0.000129\n",
       "98  C20  C25  11.519726  0.022556\n",
       "99  C20    S   2.449649  0.000109\n",
       "\n",
       "[100 rows x 4 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "cate_pair_dis_js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
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     "metadata": {}
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   ],
   "source": [
    "fig = go.Figure(data=[go.Scatter(\n",
    "    x = cate_pair_dis_js.iloc[:, 2],\n",
    "    y = cate_pair_dis_js.iloc[:, 3],\n",
    "    mode = 'markers+text',\n",
    "    text = [f'{i}-{j}' if js > 0.006 else '' for i, j, _, js in cate_pair_dis_js.values],\n",
    "    textposition='top center',\n",
    "    textfont=dict(\n",
    "        family=\"sans serif\",\n",
    "        size=12,\n",
    "        # color=\"LightSeaGreen\"\n",
    "    ),\n",
    "    marker = dict(opacity=0.3))])\n",
    "\n",
    "fig.update_xaxes(gridcolor='#e0e0e0', zerolinecolor='#e0e0e0', showline=True, linecolor='black', mirror=True, title='embedding distance')\n",
    "fig.update_yaxes(gridcolor='#e0e0e0', zerolinecolor='#e0e0e0', showline=True, linecolor='black', mirror=True, title='jaccard distance')\n",
    "fig.update_layout(plot_bgcolor='#FFF')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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