3. Benchmark.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "0a971c7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:37.368058Z",
     "start_time": "2022-03-23T09:38:34.673226Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3-2021-11\\envs\\pytorch\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "# 基础包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle # 打包数据\n",
    "\n",
    "# BOW处理\n",
    "from tox21_project import data_preprocessing as dp\n",
    "\n",
    "# sklearn数据预处理\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# xgboost 和 RandomForest 模型\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "\n",
    "# 模型的测试\n",
    "from tox21_project import testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4d8e9c",
   "metadata": {},
   "source": [
    "## 1. 数据准备"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0119c9e9",
   "metadata": {},
   "source": [
    "### 1.1 数据的导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c00e6d52",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.141521Z",
     "start_time": "2022-03-23T09:38:37.369815Z"
    }
   },
   "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>Formula</th>\n",
       "      <th>FW</th>\n",
       "      <th>DSSTox_CID</th>\n",
       "      <th>SR-HSE</th>\n",
       "      <th>ID</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Molecule</th>\n",
       "      <th>NR-AR</th>\n",
       "      <th>SR-ARE</th>\n",
       "      <th>NR-Aromatase</th>\n",
       "      <th>NR-ER-LBD</th>\n",
       "      <th>NR-AhR</th>\n",
       "      <th>SR-MMP</th>\n",
       "      <th>NR-ER</th>\n",
       "      <th>NR-PPAR-gamma</th>\n",
       "      <th>SR-p53</th>\n",
       "      <th>SR-ATAD5</th>\n",
       "      <th>NR-AR-LBD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C27H25ClN6</td>\n",
       "      <td>468.9806</td>\n",
       "      <td>25848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NCGC00178831-03</td>\n",
       "      <td>C[n+]1c2cc(N)ccc2cc2ccc(N)cc21.Nc1ccc2cc3ccc(N...</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C20H6Br4Na2O5</td>\n",
       "      <td>691.8542</td>\n",
       "      <td>5234</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NCGC00166114-03</td>\n",
       "      <td>O=C([O-])c1ccccc1-c1c2cc(Br)c(=O)c(Br)c-2oc2c(...</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C47H83NO17</td>\n",
       "      <td>934.1584</td>\n",
       "      <td>28909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NCGC00263563-01</td>\n",
       "      <td>CO[C@H]1CC(O[C@H]2C[C@H]([C@H]3O[C@](C)(O)[C@H...</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C52H54N4O12</td>\n",
       "      <td>927.0048</td>\n",
       "      <td>5513</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NCGC00013058-02</td>\n",
       "      <td>CN(C)c1ccc(C(=C2C=CC(=[N+](C)C)C=C2)c2ccccc2)c...</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C66H87N17O14</td>\n",
       "      <td>1342.5025</td>\n",
       "      <td>26683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NCGC00167516-01</td>\n",
       "      <td>CC(=O)O.CCNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=...</td>\n",
       "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Formula         FW  DSSTox_CID  SR-HSE               ID  \\\n",
       "0     C27H25ClN6   468.9806       25848     0.0  NCGC00178831-03   \n",
       "1  C20H6Br4Na2O5   691.8542        5234     0.0  NCGC00166114-03   \n",
       "2     C47H83NO17   934.1584       28909     0.0  NCGC00263563-01   \n",
       "3    C52H54N4O12   927.0048        5513     1.0  NCGC00013058-02   \n",
       "4   C66H87N17O14  1342.5025       26683     NaN  NCGC00167516-01   \n",
       "\n",
       "                                              SMILES  \\\n",
       "0  C[n+]1c2cc(N)ccc2cc2ccc(N)cc21.Nc1ccc2cc3ccc(N...   \n",
       "1  O=C([O-])c1ccccc1-c1c2cc(Br)c(=O)c(Br)c-2oc2c(...   \n",
       "2  CO[C@H]1CC(O[C@H]2C[C@H]([C@H]3O[C@](C)(O)[C@H...   \n",
       "3  CN(C)c1ccc(C(=C2C=CC(=[N+](C)C)C=C2)c2ccccc2)c...   \n",
       "4  CC(=O)O.CCNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=...   \n",
       "\n",
       "                                            Molecule  NR-AR  SR-ARE  \\\n",
       "0  <img data-content=\"rdkit/molecule\" src=\"data:i...    0.0     NaN   \n",
       "1  <img data-content=\"rdkit/molecule\" src=\"data:i...    0.0     NaN   \n",
       "2  <img data-content=\"rdkit/molecule\" src=\"data:i...    0.0     NaN   \n",
       "3  <img data-content=\"rdkit/molecule\" src=\"data:i...    0.0     NaN   \n",
       "4  <img data-content=\"rdkit/molecule\" src=\"data:i...    0.0     NaN   \n",
       "\n",
       "   NR-Aromatase  NR-ER-LBD  NR-AhR  SR-MMP  NR-ER  NR-PPAR-gamma  SR-p53  \\\n",
       "0           1.0        NaN     1.0     1.0    NaN            NaN     1.0   \n",
       "1           0.0        0.0     0.0     NaN    0.0            NaN     0.0   \n",
       "2           0.0        0.0     0.0     NaN    NaN            0.0     1.0   \n",
       "3           NaN        0.0     1.0     1.0    NaN            NaN     NaN   \n",
       "4           NaN        NaN     NaN     NaN    NaN            NaN     NaN   \n",
       "\n",
       "   SR-ATAD5  NR-AR-LBD  \n",
       "0       NaN        NaN  \n",
       "1       0.0        NaN  \n",
       "2       0.0        0.0  \n",
       "3       0.0        NaN  \n",
       "4       NaN        NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = pd.read_csv('data/data_dups_removed.csv', index_col=0)\n",
    "source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d7dc3e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.156449Z",
     "start_time": "2022-03-23T09:38:38.143485Z"
    }
   },
   "outputs": [],
   "source": [
    "features = ['FW', 'SMILES']\n",
    "targets = ['SR-HSE',\n",
    "          'NR-AR', 'SR-ARE', 'NR-Aromatase', 'NR-ER-LBD', 'NR-AhR', 'SR-MMP',\n",
    "          'NR-ER', 'NR-PPAR-gamma', 'SR-p53', 'SR-ATAD5', 'NR-AR-LBD']\n",
    "raw_X, raw_y = source[features], source[targets]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401d6ece",
   "metadata": {},
   "source": [
    "### 1.2 NULL值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e9bebaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.172441Z",
     "start_time": "2022-03-23T09:38:38.159441Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, ..., 1, 0, 0],\n",
       "       [1, 1, 0, ..., 1, 1, 0],\n",
       "       [1, 1, 0, ..., 1, 1, 1],\n",
       "       ...,\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1],\n",
       "       [1, 1, 1, ..., 1, 1, 1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NULL的mask为0\n",
    "null_mask = np.array(np.logical_not(raw_y.isnull().values), int)\n",
    "null_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b8422e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.188521Z",
     "start_time": "2022-03-23T09:38:38.175402Z"
    }
   },
   "outputs": [],
   "source": [
    "raw_y = raw_y.fillna(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f351b08f",
   "metadata": {},
   "source": [
    "给raw_Y后面插上null_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb957464",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.204559Z",
     "start_time": "2022-03-23T09:38:38.191363Z"
    }
   },
   "outputs": [],
   "source": [
    "mask_df = pd.DataFrame(null_mask, columns=[i + '_mask' for i in raw_y.columns], index=raw_y.index)\n",
    "raw_y = pd.concat([raw_y, mask_df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e688613a",
   "metadata": {},
   "source": [
    "### 1.3 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec3e473b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.220313Z",
     "start_time": "2022-03-23T09:38:38.206317Z"
    }
   },
   "outputs": [],
   "source": [
    "test_size = 0.33\n",
    "X_train, X_test, y_train, y_test = train_test_split(raw_X, raw_y, test_size=test_size, random_state=42)\n",
    "y_train, mask_train = y_train[targets], y_train[mask_df.columns]\n",
    "y_test, mask_test = y_test[targets], y_test[mask_df.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87ca755",
   "metadata": {},
   "source": [
    "### 1.4 数据转化为BOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86c1a304",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.332144Z",
     "start_time": "2022-03-23T09:38:38.221309Z"
    }
   },
   "outputs": [],
   "source": [
    "smiles = X_train['SMILES']\n",
    "bow = dp.BagOfWords(smiles)\n",
    "bow_train = bow.fit()\n",
    "bow_test = bow.transform(X_test['SMILES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "897f01db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.347937Z",
     "start_time": "2022-03-23T09:38:38.332979Z"
    }
   },
   "outputs": [],
   "source": [
    "bow_train = np.insert(bow_train, 0, X_train['FW'], axis=1)\n",
    "bow_test = np.insert(bow_test, 0, X_test['FW'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df800abc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.364060Z",
     "start_time": "2022-03-23T09:38:38.351927Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5385, 56)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b107ae",
   "metadata": {},
   "source": [
    "### 1.5 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e10c95c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:38.379852Z",
     "start_time": "2022-03-23T09:38:38.365890Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler() # 针对列来做归一化\n",
    "bow_train = scaler.fit_transform(bow_train)\n",
    "bow_test = scaler.transform(bow_test)\n",
    "X_train, X_test = bow_train, bow_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7a8fb3",
   "metadata": {},
   "source": [
    "## 2. 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13e9639",
   "metadata": {},
   "source": [
    "### 2.1 随机森林 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc1ce1b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:39.955568Z",
     "start_time": "2022-03-23T09:38:38.380850Z"
    }
   },
   "outputs": [],
   "source": [
    "rf_model = RandomForestClassifier()\n",
    "rf_model.fit(X_train, y_train)\n",
    "pickle.dump(rf_model, open('models/rf_benchmark.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d382da4",
   "metadata": {},
   "source": [
    "### 2.2 XGBoost 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da1f632f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:45.241597Z",
     "start_time": "2022-03-23T09:38:39.958320Z"
    }
   },
   "outputs": [],
   "source": [
    "# MultiOutputClassifier 用于扩展本身不支持多目标分类的分类器\n",
    "# use_label_encoder 是否使用xgb训练时候的编码标签\n",
    "xgb_model = MultiOutputClassifier(xgb.XGBClassifier(eval_metric=['logloss','auc','error'], use_label_encoder=False))\n",
    "xgb_model.fit(X_train, y_train)\n",
    "pickle.dump(xgb_model, open('models/xgb_benchmark.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a202ec2",
   "metadata": {},
   "source": [
    "## 3. 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd182023",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:45.257385Z",
     "start_time": "2022-03-23T09:38:45.242423Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_pred_and_scores(model, X):\n",
    "    pred = model.predict(X)\n",
    "    scores = np.array([model.predict_proba(X)[i][:, 1] for i in range(12)]).T # 返回一个三维的list，2，3维为0，1的可能性\n",
    "    return pred, scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff58be3",
   "metadata": {},
   "source": [
    "### 3.1 XGBoost 测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab4267a",
   "metadata": {},
   "source": [
    "训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58181dc3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:46.999537Z",
     "start_time": "2022-03-23T09:38:45.259380Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36649970e87740ffafd93b12b83bd8fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>AUPRC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SR-HSE</th>\n",
       "      <td>0.99435</td>\n",
       "      <td>0.67433</td>\n",
       "      <td>0.803653</td>\n",
       "      <td>0.957037</td>\n",
       "      <td>0.980565</td>\n",
       "      <td>0.837045</td>\n",
       "      <td>0.995227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR</th>\n",
       "      <td>0.988571</td>\n",
       "      <td>0.816038</td>\n",
       "      <td>0.894057</td>\n",
       "      <td>0.986112</td>\n",
       "      <td>0.991813</td>\n",
       "      <td>0.90781</td>\n",
       "      <td>0.999258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ARE</th>\n",
       "      <td>0.9825</td>\n",
       "      <td>0.609302</td>\n",
       "      <td>0.752153</td>\n",
       "      <td>0.945199</td>\n",
       "      <td>0.935201</td>\n",
       "      <td>0.803607</td>\n",
       "      <td>0.982493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-Aromatase</th>\n",
       "      <td>0.994083</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.900804</td>\n",
       "      <td>0.994826</td>\n",
       "      <td>0.990757</td>\n",
       "      <td>0.911633</td>\n",
       "      <td>0.9997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER-LBD</th>\n",
       "      <td>0.994286</td>\n",
       "      <td>0.707317</td>\n",
       "      <td>0.826603</td>\n",
       "      <td>0.966188</td>\n",
       "      <td>0.984709</td>\n",
       "      <td>0.853548</td>\n",
       "      <td>0.997613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AhR</th>\n",
       "      <td>0.995181</td>\n",
       "      <td>0.789675</td>\n",
       "      <td>0.880597</td>\n",
       "      <td>0.983596</td>\n",
       "      <td>0.975061</td>\n",
       "      <td>0.894585</td>\n",
       "      <td>0.997209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-MMP</th>\n",
       "      <td>0.989899</td>\n",
       "      <td>0.76087</td>\n",
       "      <td>0.860404</td>\n",
       "      <td>0.979341</td>\n",
       "      <td>0.96015</td>\n",
       "      <td>0.879688</td>\n",
       "      <td>0.995211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER</th>\n",
       "      <td>0.975779</td>\n",
       "      <td>0.523191</td>\n",
       "      <td>0.681159</td>\n",
       "      <td>0.917353</td>\n",
       "      <td>0.937677</td>\n",
       "      <td>0.760649</td>\n",
       "      <td>0.983259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-PPAR-gamma</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.782946</td>\n",
       "      <td>0.878261</td>\n",
       "      <td>0.984732</td>\n",
       "      <td>0.993682</td>\n",
       "      <td>0.891473</td>\n",
       "      <td>0.999284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-p53</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.762069</td>\n",
       "      <td>0.864971</td>\n",
       "      <td>0.989588</td>\n",
       "      <td>0.985177</td>\n",
       "      <td>0.881034</td>\n",
       "      <td>0.999104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ATAD5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.870787</td>\n",
       "      <td>0.930931</td>\n",
       "      <td>0.991864</td>\n",
       "      <td>0.995278</td>\n",
       "      <td>0.935393</td>\n",
       "      <td>0.999638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR-LBD</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92638</td>\n",
       "      <td>0.961783</td>\n",
       "      <td>0.998376</td>\n",
       "      <td>0.997412</td>\n",
       "      <td>0.96319</td>\n",
       "      <td>0.999942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Precision    Recall        F1     AUPRC  Accuracy  \\\n",
       "SR-HSE          0.99435   0.67433  0.803653  0.957037  0.980565   \n",
       "NR-AR          0.988571  0.816038  0.894057  0.986112  0.991813   \n",
       "SR-ARE           0.9825  0.609302  0.752153  0.945199  0.935201   \n",
       "NR-Aromatase   0.994083  0.823529  0.900804  0.994826  0.990757   \n",
       "NR-ER-LBD      0.994286  0.707317  0.826603  0.966188  0.984709   \n",
       "NR-AhR         0.995181  0.789675  0.880597  0.983596  0.975061   \n",
       "SR-MMP         0.989899   0.76087  0.860404  0.979341   0.96015   \n",
       "NR-ER          0.975779  0.523191  0.681159  0.917353  0.937677   \n",
       "NR-PPAR-gamma       1.0  0.782946  0.878261  0.984732  0.993682   \n",
       "SR-p53              1.0  0.762069  0.864971  0.989588  0.985177   \n",
       "SR-ATAD5            1.0  0.870787  0.930931  0.991864  0.995278   \n",
       "NR-AR-LBD           1.0   0.92638  0.961783  0.998376  0.997412   \n",
       "\n",
       "              Balanced Accuracy   ROC_AUC  \n",
       "SR-HSE                 0.837045  0.995227  \n",
       "NR-AR                   0.90781  0.999258  \n",
       "SR-ARE                 0.803607  0.982493  \n",
       "NR-Aromatase           0.911633    0.9997  \n",
       "NR-ER-LBD              0.853548  0.997613  \n",
       "NR-AhR                 0.894585  0.997209  \n",
       "SR-MMP                 0.879688  0.995211  \n",
       "NR-ER                  0.760649  0.983259  \n",
       "NR-PPAR-gamma          0.891473  0.999284  \n",
       "SR-p53                 0.881034  0.999104  \n",
       "SR-ATAD5               0.935393  0.999638  \n",
       "NR-AR-LBD               0.96319  0.999942  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pred和scores\n",
    "y_pred, y_scores = get_pred_and_scores(xgb_model, X_train)\n",
    "# 计算指标\n",
    "xgb_train_panel = testing.panel_of_test(targets, y_train.values, mask_train.values)\n",
    "xgb_train_panel.compute_basic_metrics(y_pred, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90eeccc2",
   "metadata": {},
   "source": [
    "测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72ed7966",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:48.320335Z",
     "start_time": "2022-03-23T09:38:47.000399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8764b71c365245a88bfa32c0c3b97606",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>AUPRC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SR-HSE</th>\n",
       "      <td>0.52381</td>\n",
       "      <td>0.09322</td>\n",
       "      <td>0.158273</td>\n",
       "      <td>0.206151</td>\n",
       "      <td>0.946527</td>\n",
       "      <td>0.544195</td>\n",
       "      <td>0.723278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR</th>\n",
       "      <td>0.792453</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.556291</td>\n",
       "      <td>0.548508</td>\n",
       "      <td>0.972709</td>\n",
       "      <td>0.711952</td>\n",
       "      <td>0.806395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ARE</th>\n",
       "      <td>0.671053</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.255639</td>\n",
       "      <td>0.429698</td>\n",
       "      <td>0.848392</td>\n",
       "      <td>0.571307</td>\n",
       "      <td>0.753342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-Aromatase</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.11215</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.265358</td>\n",
       "      <td>0.948417</td>\n",
       "      <td>0.554454</td>\n",
       "      <td>0.761685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER-LBD</th>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.350649</td>\n",
       "      <td>0.384426</td>\n",
       "      <td>0.957609</td>\n",
       "      <td>0.618063</td>\n",
       "      <td>0.763576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AhR</th>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.273764</td>\n",
       "      <td>0.394521</td>\n",
       "      <td>0.513226</td>\n",
       "      <td>0.90045</td>\n",
       "      <td>0.629217</td>\n",
       "      <td>0.845283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-MMP</th>\n",
       "      <td>0.702899</td>\n",
       "      <td>0.326599</td>\n",
       "      <td>0.445977</td>\n",
       "      <td>0.580313</td>\n",
       "      <td>0.87622</td>\n",
       "      <td>0.650875</td>\n",
       "      <td>0.863433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER</th>\n",
       "      <td>0.635135</td>\n",
       "      <td>0.180769</td>\n",
       "      <td>0.281437</td>\n",
       "      <td>0.358892</td>\n",
       "      <td>0.885387</td>\n",
       "      <td>0.583024</td>\n",
       "      <td>0.69302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-PPAR-gamma</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.12432</td>\n",
       "      <td>0.97235</td>\n",
       "      <td>0.532385</td>\n",
       "      <td>0.723231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-p53</th>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.242256</td>\n",
       "      <td>0.938353</td>\n",
       "      <td>0.539971</td>\n",
       "      <td>0.778932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ATAD5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.123596</td>\n",
       "      <td>0.198198</td>\n",
       "      <td>0.256508</td>\n",
       "      <td>0.962605</td>\n",
       "      <td>0.559397</td>\n",
       "      <td>0.814908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR-LBD</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>0.525053</td>\n",
       "      <td>0.975055</td>\n",
       "      <td>0.697285</td>\n",
       "      <td>0.885762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Precision    Recall        F1     AUPRC  Accuracy  \\\n",
       "SR-HSE          0.52381   0.09322  0.158273  0.206151  0.946527   \n",
       "NR-AR          0.792453  0.428571  0.556291  0.548508  0.972709   \n",
       "SR-ARE         0.671053  0.157895  0.255639  0.429698  0.848392   \n",
       "NR-Aromatase   0.666667   0.11215     0.192  0.265358  0.948417   \n",
       "NR-ER-LBD      0.627907  0.243243  0.350649  0.384426  0.957609   \n",
       "NR-AhR         0.705882  0.273764  0.394521  0.513226   0.90045   \n",
       "SR-MMP         0.702899  0.326599  0.445977  0.580313   0.87622   \n",
       "NR-ER          0.635135  0.180769  0.281437  0.358892  0.885387   \n",
       "NR-PPAR-gamma       0.5  0.066667  0.117647   0.12432   0.97235   \n",
       "SR-p53         0.521739  0.085106  0.146341  0.242256  0.938353   \n",
       "SR-ATAD5            0.5  0.123596  0.198198  0.256508  0.962605   \n",
       "NR-AR-LBD      0.714286       0.4  0.512821  0.525053  0.975055   \n",
       "\n",
       "              Balanced Accuracy   ROC_AUC  \n",
       "SR-HSE                 0.544195  0.723278  \n",
       "NR-AR                  0.711952  0.806395  \n",
       "SR-ARE                 0.571307  0.753342  \n",
       "NR-Aromatase           0.554454  0.761685  \n",
       "NR-ER-LBD              0.618063  0.763576  \n",
       "NR-AhR                 0.629217  0.845283  \n",
       "SR-MMP                 0.650875  0.863433  \n",
       "NR-ER                  0.583024   0.69302  \n",
       "NR-PPAR-gamma          0.532385  0.723231  \n",
       "SR-p53                 0.539971  0.778932  \n",
       "SR-ATAD5               0.559397  0.814908  \n",
       "NR-AR-LBD              0.697285  0.885762  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pred和scores\n",
    "y_pred, y_scores = get_pred_and_scores(xgb_model, X_test)\n",
    "# 计算指标\n",
    "xgb_test_panel = testing.panel_of_test(targets, y_test.values, mask_test.values)\n",
    "xgb_test_metrics = xgb_test_panel.compute_basic_metrics(y_pred, y_scores)\n",
    "xgb_test_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "256ab2b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:48.335935Z",
     "start_time": "2022-03-23T09:38:48.323159Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|               |   Precision |    Recall |       F1 |    AUPRC |   Accuracy |   Balanced Accuracy |   ROC_AUC |\n",
      "|:--------------|------------:|----------:|---------:|---------:|-----------:|--------------------:|----------:|\n",
      "| SR-HSE        |    0.52381  | 0.0932203 | 0.158273 | 0.206151 |   0.946527 |            0.544195 |  0.723278 |\n",
      "| NR-AR         |    0.792453 | 0.428571  | 0.556291 | 0.548508 |   0.972709 |            0.711952 |  0.806395 |\n",
      "| SR-ARE        |    0.671053 | 0.157895  | 0.255639 | 0.429698 |   0.848392 |            0.571307 |  0.753342 |\n",
      "| NR-Aromatase  |    0.666667 | 0.11215   | 0.192    | 0.265358 |   0.948417 |            0.554454 |  0.761685 |\n",
      "| NR-ER-LBD     |    0.627907 | 0.243243  | 0.350649 | 0.384426 |   0.957609 |            0.618063 |  0.763576 |\n",
      "| NR-AhR        |    0.705882 | 0.273764  | 0.394521 | 0.513226 |   0.90045  |            0.629217 |  0.845283 |\n",
      "| SR-MMP        |    0.702899 | 0.326599  | 0.445977 | 0.580313 |   0.87622  |            0.650875 |  0.863433 |\n",
      "| NR-ER         |    0.635135 | 0.180769  | 0.281437 | 0.358892 |   0.885387 |            0.583024 |  0.69302  |\n",
      "| NR-PPAR-gamma |    0.5      | 0.0666667 | 0.117647 | 0.12432  |   0.97235  |            0.532385 |  0.723231 |\n",
      "| SR-p53        |    0.521739 | 0.0851064 | 0.146341 | 0.242256 |   0.938353 |            0.539971 |  0.778932 |\n",
      "| SR-ATAD5      |    0.5      | 0.123596  | 0.198198 | 0.256508 |   0.962605 |            0.559397 |  0.814908 |\n",
      "| NR-AR-LBD     |    0.714286 | 0.4       | 0.512821 | 0.525053 |   0.975055 |            0.697285 |  0.885762 |\n"
     ]
    }
   ],
   "source": [
    "print(xgb_test_metrics.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "59f841c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:48.351038Z",
     "start_time": "2022-03-23T09:38:48.338926Z"
    }
   },
   "outputs": [],
   "source": [
    "xgb_test_metrics.to_csv('data/xgb_metrics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed3bf755",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:49.363988Z",
     "start_time": "2022-03-23T09:38:48.351891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "xgb_test_panel.plot_roc(y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c31aa9b",
   "metadata": {},
   "source": [
    "### 3.2 随机森林 测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7457111e",
   "metadata": {},
   "source": [
    "训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "510b2865",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:54.177356Z",
     "start_time": "2022-03-23T09:38:49.365984Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9cb1ea7e8b624c66b5a3dcf37f4e516d",
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       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>AUPRC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SR-HSE</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.938697</td>\n",
       "      <td>0.968379</td>\n",
       "      <td>0.997911</td>\n",
       "      <td>0.996384</td>\n",
       "      <td>0.969349</td>\n",
       "      <td>0.999874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.957547</td>\n",
       "      <td>0.978313</td>\n",
       "      <td>0.998985</td>\n",
       "      <td>0.998203</td>\n",
       "      <td>0.978774</td>\n",
       "      <td>0.999959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ARE</th>\n",
       "      <td>0.99359</td>\n",
       "      <td>0.96124</td>\n",
       "      <td>0.977147</td>\n",
       "      <td>0.998161</td>\n",
       "      <td>0.992745</td>\n",
       "      <td>0.980023</td>\n",
       "      <td>0.999672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-Aromatase</th>\n",
       "      <td>0.995025</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.987654</td>\n",
       "      <td>0.999622</td>\n",
       "      <td>0.998751</td>\n",
       "      <td>0.990064</td>\n",
       "      <td>0.999983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER-LBD</th>\n",
       "      <td>0.995726</td>\n",
       "      <td>0.947154</td>\n",
       "      <td>0.970833</td>\n",
       "      <td>0.998583</td>\n",
       "      <td>0.997067</td>\n",
       "      <td>0.973467</td>\n",
       "      <td>0.999927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AhR</th>\n",
       "      <td>0.994094</td>\n",
       "      <td>0.965583</td>\n",
       "      <td>0.979631</td>\n",
       "      <td>0.999263</td>\n",
       "      <td>0.995324</td>\n",
       "      <td>0.982414</td>\n",
       "      <td>0.999907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-MMP</th>\n",
       "      <td>0.995231</td>\n",
       "      <td>0.97205</td>\n",
       "      <td>0.983504</td>\n",
       "      <td>0.99935</td>\n",
       "      <td>0.994737</td>\n",
       "      <td>0.985577</td>\n",
       "      <td>0.999877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER</th>\n",
       "      <td>0.993915</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.949612</td>\n",
       "      <td>0.99455</td>\n",
       "      <td>0.987724</td>\n",
       "      <td>0.95414</td>\n",
       "      <td>0.999192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-PPAR-gamma</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.97619</td>\n",
       "      <td>0.998711</td>\n",
       "      <td>0.998646</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.999967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-p53</th>\n",
       "      <td>0.996491</td>\n",
       "      <td>0.97931</td>\n",
       "      <td>0.987826</td>\n",
       "      <td>0.999566</td>\n",
       "      <td>0.998496</td>\n",
       "      <td>0.989541</td>\n",
       "      <td>0.999974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ATAD5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960674</td>\n",
       "      <td>0.979943</td>\n",
       "      <td>0.998885</td>\n",
       "      <td>0.998563</td>\n",
       "      <td>0.980337</td>\n",
       "      <td>0.999962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR-LBD</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.97546</td>\n",
       "      <td>0.987578</td>\n",
       "      <td>0.999522</td>\n",
       "      <td>0.999137</td>\n",
       "      <td>0.98773</td>\n",
       "      <td>0.999986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Precision    Recall        F1     AUPRC  Accuracy  \\\n",
       "SR-HSE              1.0  0.938697  0.968379  0.997911  0.996384   \n",
       "NR-AR               1.0  0.957547  0.978313  0.998985  0.998203   \n",
       "SR-ARE          0.99359   0.96124  0.977147  0.998161  0.992745   \n",
       "NR-Aromatase   0.995025  0.980392  0.987654  0.999622  0.998751   \n",
       "NR-ER-LBD      0.995726  0.947154  0.970833  0.998583  0.997067   \n",
       "NR-AhR         0.994094  0.965583  0.979631  0.999263  0.995324   \n",
       "SR-MMP         0.995231   0.97205  0.983504   0.99935  0.994737   \n",
       "NR-ER          0.993915  0.909091  0.949612   0.99455  0.987724   \n",
       "NR-PPAR-gamma       1.0  0.953488   0.97619  0.998711  0.998646   \n",
       "SR-p53         0.996491   0.97931  0.987826  0.999566  0.998496   \n",
       "SR-ATAD5            1.0  0.960674  0.979943  0.998885  0.998563   \n",
       "NR-AR-LBD           1.0   0.97546  0.987578  0.999522  0.999137   \n",
       "\n",
       "              Balanced Accuracy   ROC_AUC  \n",
       "SR-HSE                 0.969349  0.999874  \n",
       "NR-AR                  0.978774  0.999959  \n",
       "SR-ARE                 0.980023  0.999672  \n",
       "NR-Aromatase           0.990064  0.999983  \n",
       "NR-ER-LBD              0.973467  0.999927  \n",
       "NR-AhR                 0.982414  0.999907  \n",
       "SR-MMP                 0.985577  0.999877  \n",
       "NR-ER                   0.95414  0.999192  \n",
       "NR-PPAR-gamma          0.976744  0.999967  \n",
       "SR-p53                 0.989541  0.999974  \n",
       "SR-ATAD5               0.980337  0.999962  \n",
       "NR-AR-LBD               0.98773  0.999986  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pred和scores\n",
    "y_pred, y_scores = get_pred_and_scores(rf_model, X_train)\n",
    "# 计算指标\n",
    "rf_train_panel = testing.panel_of_test(targets, y_train.values, mask_train.values)\n",
    "rf_train_panel.compute_basic_metrics(y_pred, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e4f824",
   "metadata": {},
   "source": [
    "测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7fa42f4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:56.800182Z",
     "start_time": "2022-03-23T09:38:54.178352Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "model_id": "cb906c0db80246e68a5f7ac17e13b595",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>AUPRC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SR-HSE</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.190948</td>\n",
       "      <td>0.945155</td>\n",
       "      <td>0.5155</td>\n",
       "      <td>0.737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR</th>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.418367</td>\n",
       "      <td>0.550336</td>\n",
       "      <td>0.523565</td>\n",
       "      <td>0.972709</td>\n",
       "      <td>0.707062</td>\n",
       "      <td>0.805185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ARE</th>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.126935</td>\n",
       "      <td>0.21466</td>\n",
       "      <td>0.455964</td>\n",
       "      <td>0.846861</td>\n",
       "      <td>0.557966</td>\n",
       "      <td>0.782138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-Aromatase</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.056075</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.288739</td>\n",
       "      <td>0.946374</td>\n",
       "      <td>0.526957</td>\n",
       "      <td>0.784229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER-LBD</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.252252</td>\n",
       "      <td>0.370861</td>\n",
       "      <td>0.414161</td>\n",
       "      <td>0.959729</td>\n",
       "      <td>0.623457</td>\n",
       "      <td>0.790096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AhR</th>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.243346</td>\n",
       "      <td>0.371014</td>\n",
       "      <td>0.530833</td>\n",
       "      <td>0.902252</td>\n",
       "      <td>0.617074</td>\n",
       "      <td>0.855347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-MMP</th>\n",
       "      <td>0.732143</td>\n",
       "      <td>0.276094</td>\n",
       "      <td>0.400978</td>\n",
       "      <td>0.610641</td>\n",
       "      <td>0.874165</td>\n",
       "      <td>0.628956</td>\n",
       "      <td>0.879781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER</th>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.352279</td>\n",
       "      <td>0.880611</td>\n",
       "      <td>0.576996</td>\n",
       "      <td>0.675641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-PPAR-gamma</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.161326</td>\n",
       "      <td>0.97235</td>\n",
       "      <td>0.516193</td>\n",
       "      <td>0.794297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-p53</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.049645</td>\n",
       "      <td>0.091503</td>\n",
       "      <td>0.250953</td>\n",
       "      <td>0.938793</td>\n",
       "      <td>0.523649</td>\n",
       "      <td>0.771661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ATAD5</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>0.163265</td>\n",
       "      <td>0.301237</td>\n",
       "      <td>0.965546</td>\n",
       "      <td>0.544726</td>\n",
       "      <td>0.819717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR-LBD</th>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.564103</td>\n",
       "      <td>0.560506</td>\n",
       "      <td>0.977681</td>\n",
       "      <td>0.717964</td>\n",
       "      <td>0.875771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Precision    Recall        F1     AUPRC  Accuracy  \\\n",
       "SR-HSE              0.4  0.033898    0.0625  0.190948  0.945155   \n",
       "NR-AR          0.803922  0.418367  0.550336  0.523565  0.972709   \n",
       "SR-ARE         0.694915  0.126935   0.21466  0.455964  0.846861   \n",
       "NR-Aromatase        0.6  0.056075  0.102564  0.288739  0.946374   \n",
       "NR-ER-LBD           0.7  0.252252  0.370861  0.414161  0.959729   \n",
       "NR-AhR         0.780488  0.243346  0.371014  0.530833  0.902252   \n",
       "SR-MMP         0.732143  0.276094  0.400978  0.610641  0.874165   \n",
       "NR-ER            0.5625  0.173077  0.264706  0.352279  0.880611   \n",
       "NR-PPAR-gamma       0.5  0.033333    0.0625  0.161326   0.97235   \n",
       "SR-p53         0.583333  0.049645  0.091503  0.250953  0.938793   \n",
       "SR-ATAD5       0.888889  0.089888  0.163265  0.301237  0.965546   \n",
       "NR-AR-LBD      0.785714      0.44  0.564103  0.560506  0.977681   \n",
       "\n",
       "              Balanced Accuracy   ROC_AUC  \n",
       "SR-HSE                   0.5155     0.737  \n",
       "NR-AR                  0.707062  0.805185  \n",
       "SR-ARE                 0.557966  0.782138  \n",
       "NR-Aromatase           0.526957  0.784229  \n",
       "NR-ER-LBD              0.623457  0.790096  \n",
       "NR-AhR                 0.617074  0.855347  \n",
       "SR-MMP                 0.628956  0.879781  \n",
       "NR-ER                  0.576996  0.675641  \n",
       "NR-PPAR-gamma          0.516193  0.794297  \n",
       "SR-p53                 0.523649  0.771661  \n",
       "SR-ATAD5               0.544726  0.819717  \n",
       "NR-AR-LBD              0.717964  0.875771  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pred和scores\n",
    "y_pred, y_scores = get_pred_and_scores(rf_model, X_test)\n",
    "# 计算指标\n",
    "rf_test_panel = testing.panel_of_test(targets, y_test.values, mask_test.values)\n",
    "rf_test_metrics = rf_test_panel.compute_basic_metrics(y_pred, y_scores)\n",
    "rf_test_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8c07414",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:56.815228Z",
     "start_time": "2022-03-23T09:38:56.801179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|               |   Precision |    Recall |        F1 |    AUPRC |   Accuracy |   Balanced Accuracy |   ROC_AUC |\n",
      "|:--------------|------------:|----------:|----------:|---------:|-----------:|--------------------:|----------:|\n",
      "| SR-HSE        |    0.4      | 0.0338983 | 0.0625    | 0.190948 |   0.945155 |            0.5155   |  0.737    |\n",
      "| NR-AR         |    0.803922 | 0.418367  | 0.550336  | 0.523565 |   0.972709 |            0.707062 |  0.805185 |\n",
      "| SR-ARE        |    0.694915 | 0.126935  | 0.21466   | 0.455964 |   0.846861 |            0.557966 |  0.782138 |\n",
      "| NR-Aromatase  |    0.6      | 0.0560748 | 0.102564  | 0.288739 |   0.946374 |            0.526957 |  0.784229 |\n",
      "| NR-ER-LBD     |    0.7      | 0.252252  | 0.370861  | 0.414161 |   0.959729 |            0.623457 |  0.790096 |\n",
      "| NR-AhR        |    0.780488 | 0.243346  | 0.371014  | 0.530833 |   0.902252 |            0.617074 |  0.855347 |\n",
      "| SR-MMP        |    0.732143 | 0.276094  | 0.400978  | 0.610641 |   0.874165 |            0.628956 |  0.879781 |\n",
      "| NR-ER         |    0.5625   | 0.173077  | 0.264706  | 0.352279 |   0.880611 |            0.576996 |  0.675641 |\n",
      "| NR-PPAR-gamma |    0.5      | 0.0333333 | 0.0625    | 0.161326 |   0.97235  |            0.516193 |  0.794297 |\n",
      "| SR-p53        |    0.583333 | 0.0496454 | 0.0915033 | 0.250953 |   0.938793 |            0.523649 |  0.771661 |\n",
      "| SR-ATAD5      |    0.888889 | 0.0898876 | 0.163265  | 0.301237 |   0.965546 |            0.544726 |  0.819717 |\n",
      "| NR-AR-LBD     |    0.785714 | 0.44      | 0.564103  | 0.560506 |   0.977681 |            0.717964 |  0.875771 |\n"
     ]
    }
   ],
   "source": [
    "print(rf_test_metrics.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3eadcaa4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:56.830202Z",
     "start_time": "2022-03-23T09:38:56.817221Z"
    }
   },
   "outputs": [],
   "source": [
    "rf_test_metrics.to_csv('data/rf_metrics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fce98e9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:57.782184Z",
     "start_time": "2022-03-23T09:38:56.832183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "rf_test_panel.plot_roc(y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff303a8c",
   "metadata": {},
   "source": [
    "## 4. 总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "60e9c6fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:57.797322Z",
     "start_time": "2022-03-23T09:38:57.784180Z"
    }
   },
   "outputs": [],
   "source": [
    "res = pd.DataFrame(index = ['Precision', 'Recall', 'F1', 'AUPRC', 'Accuracy', 'Balanced Accuracy','ROC_AUC'])\n",
    "\n",
    "def f(x):\n",
    "    if x:\n",
    "        return 'RF'\n",
    "    else:\n",
    "        return 'XGB'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79892c81",
   "metadata": {},
   "source": [
    "applymap和apply的区别：[here](https://zhuanlan.zhihu.com/p/100064394)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "18d33063",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-23T09:38:57.827313Z",
     "start_time": "2022-03-23T09:38:57.799317Z"
    }
   },
   "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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>AUPRC</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SR-HSE</th>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR</th>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ARE</th>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-Aromatase</th>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER-LBD</th>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AhR</th>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-MMP</th>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-ER</th>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-PPAR-gamma</th>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-p53</th>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-ATAD5</th>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "      <td>RF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR-AR-LBD</th>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>RF</td>\n",
       "      <td>XGB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Precision Recall   F1 AUPRC Accuracy Balanced Accuracy ROC_AUC\n",
       "SR-HSE              XGB    XGB  XGB   XGB      XGB               XGB      RF\n",
       "NR-AR                RF    XGB  XGB   XGB      XGB               XGB     XGB\n",
       "SR-ARE               RF    XGB  XGB    RF      XGB               XGB      RF\n",
       "NR-Aromatase        XGB    XGB  XGB    RF      XGB               XGB      RF\n",
       "NR-ER-LBD            RF     RF   RF    RF       RF                RF      RF\n",
       "NR-AhR               RF    XGB  XGB    RF       RF               XGB      RF\n",
       "SR-MMP               RF    XGB  XGB    RF      XGB               XGB      RF\n",
       "NR-ER               XGB    XGB  XGB   XGB      XGB               XGB     XGB\n",
       "NR-PPAR-gamma       XGB    XGB  XGB    RF      XGB               XGB      RF\n",
       "SR-p53               RF    XGB  XGB    RF       RF               XGB     XGB\n",
       "SR-ATAD5             RF    XGB  XGB    RF       RF               XGB      RF\n",
       "NR-AR-LBD            RF     RF   RF    RF       RF                RF     XGB"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applymap 对 DataFrame 逐元素调用函数\n",
    "(rf_test_metrics > xgb_test_metrics).applymap(lambda x: f(x))"
   ]
  }
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