3. RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae22b002-6f70-4bab-9bc4-c9a196a5e4b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "### General analysis package\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm, pearsonr, spearmanr\n",
    "from scipy.spatial import distance\n",
    "### Drawing package\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.font_manager import FontProperties\n",
    "#import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42 # Output editable PDF text\n",
    "### else\n",
    "#import cloudpickle as pickle\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # Error alert\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all' #last_expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9023676-fe65-40f0-91d0-f325b4dccd03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using torch 1.10.1\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.preprocessing import StandardScaler, Normalizer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, recall_score, precision_score, roc_auc_score\n",
    "\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as TorchData\n",
    "from torchmetrics import Accuracy\n",
    "from pytorch_lightning import LightningModule, Trainer\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "SEED = 42\n",
    "def seed_everything(seed=0):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "seed_everything(SEED)\n",
    "\n",
    "print(\"Using torch\", torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73a57914-ba31-4cd4-bf3f-a702faa09172",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.read_csv('20220610_Results/1. X_train.csv', index_col=0)\n",
    "X_test = pd.read_csv('20220610_Results/1. X_test.csv', index_col=0)\n",
    "y_train = pd.read_csv('20220610_Results/1. y_train.csv', index_col=0)\n",
    "y_test = pd.read_csv('20220610_Results/1. y_test.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da5b54c3-83b2-46dc-9726-9280c15d532b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler = Normalizer() # StandardScaler()\n",
    "#X_train = pd.DataFrame(scaler.fit_transform(X_train), index=X_train.index, columns=X_train.columns)\n",
    "#X_test = pd.DataFrame(scaler.transform(X_test), index=X_test.index, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24444b8d-45cd-44d1-bb5b-26aeb562b5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "microRNA = pd.read_csv('20220610_Results/1. diff_microRNA(FC2_FDR0.05).csv', index_col=0)['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d81246d3-3ba9-47ba-a72e-9b7e11b34576",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(clf, x, y):\n",
    "    prob = clf.predict_proba(x)[:, 1]\n",
    "    pred = clf.predict(x)\n",
    "    auc = roc_auc_score(y, pred)\n",
    "    acc = accuracy_score(y, pred)\n",
    "    percision = precision_score(y, pred)\n",
    "    recall = recall_score(y, pred)\n",
    "    return auc, acc, percision, recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79490852-0a0b-4f00-a0d0-9c73b24383ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=0.1, n_estimators=101)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0, 1.0, 1.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=101, max_features=0.1)\n",
    "clf.fit(X_train[microRNA], y_train)\n",
    "\n",
    "evaluate(clf, X_train[microRNA], y_train)\n",
    "#evaluate(clf, X_test[microRNA], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b11c105-4ade-4822-98eb-cdf4af96db51",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFECV\n",
    "estimator = RandomForestClassifier(n_estimators=11)\n",
    "selector = RFECV(estimator, step=1, cv=5, scoring='roc_auc', n_jobs=5)\n",
    "selector = selector.fit(X_train[microRNA], y_train)\n",
    "selector.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ccc42f2-e5e9-478b-ad20-2030fa9d437e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_microRNA = microRNA[selector.support_]\n",
    "len(select_microRNA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cdce0229-3ca6-4c9c-a769-3956790fdfc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({'RFE':select_microRNA}).to_csv('20220610_Results/3. RF_RFE_features(108).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72d00910-5c42-4b69-b6ca-04843a432918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'class_weight': ['balanced'], 'criterion': ['gini'],\n",
       "                         'max_depth': [None, 2, 4, 6],\n",
       "                         'max_features': [None, 0.1, 0.3, 0.5],\n",
       "                         'max_samples': [None, 0.9, 0.7, 0.5],\n",
       "                         'n_estimators': [101], 'n_jobs': [3],\n",
       "                         'random_state': [42]},\n",
       "             return_train_score=True, scoring='roc_auc')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': None, 'max_features': 0.1, 'max_samples': None, 'n_estimators': 101, 'n_jobs': 3, 'random_state': 42}\n",
      "Training score mean: 100.00\n",
      "Valid score mean: 99.08\n",
      "Valid score 3*std: +/- 0.86\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'n_estimators':[101],\n",
    "              'criterion': ['gini'], \n",
    "              'max_depth': [None, 2, 4, 6], \n",
    "              'max_features': [None, 0.1, 0.3, 0.5], \n",
    "              'max_samples': [None, 0.9, 0.7, 0.5],\n",
    "              'class_weight': ['balanced'],\n",
    "              'n_jobs': [3],\n",
    "              'random_state': [SEED]\n",
    "             }\n",
    "\n",
    "tune_model = model_selection.GridSearchCV(RandomForestClassifier(), param_grid=param_grid, \n",
    "                                          scoring = 'roc_auc', cv = 5, return_train_score=True)\n",
    "tune_model.fit(X_train[select_microRNA], y_train)\n",
    "print('Parameters: ', tune_model.best_params_)\n",
    "print(\"Training score mean: {:.2f}\". format(tune_model.cv_results_['mean_train_score'][tune_model.best_index_]*100)) \n",
    "print(\"Valid score mean: {:.2f}\". format(tune_model.cv_results_['mean_test_score'][tune_model.best_index_]*100))\n",
    "print(\"Valid score 3*std: +/- {:.2f}\". format(tune_model.cv_results_['std_test_score'][tune_model.best_index_]*100*3))\n",
    "print('-'*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "876aa6e2-b32d-472f-92f6-31d28ec6bc37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', max_features=0.1,\n",
       "                       n_estimators=101, n_jobs=3, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', max_features=0.1,\n",
       "                       n_estimators=101, n_jobs=3, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0, 1.0, 1.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.9768794091710759, 0.979951397326853, 0.9668615984405458, 0.96875)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(**tune_model.best_params_)\n",
    "model.fit(X_train[select_microRNA], y_train)\n",
    "evaluate(model, X_train[select_microRNA], y_train)\n",
    "evaluate(model, X_test[select_microRNA], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "971fecde-3a34-4484-8522-b41777cf919b",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2400x1800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "probs = model.predict_proba(X_test[select_microRNA])[:, 1]\n",
    "pred = model.predict(X_test[select_microRNA])\n",
    "auc = roc_auc_score(y_test, pred)\n",
    "\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, probs, pos_label=1)\n",
    "fig = plt.figure(figsize=(8, 6), dpi=300)\n",
    "_ = plt.plot(fpr, tpr, color=\"darkorange\", lw=2, label=\"ROC curve (area = %0.3f)\" % auc)\n",
    "_ = plt.plot([0, 1], [0, 1], color=\"navy\", lw=2, linestyle=\"--\")\n",
    "_ = plt.xlim([-0.01, 1.01])\n",
    "_ = plt.ylim([-0.01, 1.01])\n",
    "_ = plt.xlabel(\"False Positive Rate\")\n",
    "_ = plt.ylabel(\"True Positive Rate\")\n",
    "_ = plt.legend(loc=\"lower right\")\n",
    "_ = plt.show()\n",
    "fig.savefig('20220610_Results/3. ROC.pdf', dpi=300)\n",
    "\n",
    "precision, recall, _ = metrics.precision_recall_curve(y_test, pred)\n",
    "disp = metrics.PrecisionRecallDisplay(precision=precision, recall=recall)\n",
    "fig = plt.figure(figsize=(8, 6), dpi=300)\n",
    "_ = disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "21e1f739-3eb2-457e-bcbb-3acab6df88e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "fig = plt.figure(1, (6, 6), dpi=80)\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "_ = plot_confusion_matrix(model, X_test[select_microRNA], y_test, ax=ax, cmap='Blues')\n",
    "fig.savefig('20220610_Results/3. plot_confusion_matrix.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "216c7827-a36b-4a65-ad19-e2491a96e07d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da1bb572-fff8-4ddf-be4a-57ba04b1150c",
   "metadata": {},
   "source": [
    "# External data validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cad07446-7e2b-4731-8b2b-70eb2babc14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(108, 73)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>hsa-miR-9-5p</th>\n",
       "      <th>hsa-miR-126-3p</th>\n",
       "      <th>hsa-miR-140-5p</th>\n",
       "      <th>hsa-miR-105-5p</th>\n",
       "      <th>hsa-miR-297</th>\n",
       "      <th>hsa-let-7i-5p</th>\n",
       "      <th>hsa-miR-130a-3p</th>\n",
       "      <th>hsa-miR-146b-5p</th>\n",
       "      <th>hsa_negative_control_6</th>\n",
       "      <th>hsa_negative_control_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hsa-miR-205-3p</th>\n",
       "      <th>hsa-miR-1260a</th>\n",
       "      <th>hsa-miR-525-5p</th>\n",
       "      <th>hsa-miR-187-5p</th>\n",
       "      <th>hsa-miRPlus-A1086</th>\n",
       "      <th>hsa-miR-551b-5p</th>\n",
       "      <th>hsa-miR-300</th>\n",
       "      <th>hsa-miR-193b-5p</th>\n",
       "      <th>Label</th>\n",
       "      <th>Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.877643</td>\n",
       "      <td>8.080215</td>\n",
       "      <td>6.245265</td>\n",
       "      <td>6.304782</td>\n",
       "      <td>6.686064</td>\n",
       "      <td>7.805950</td>\n",
       "      <td>6.814781</td>\n",
       "      <td>7.146955</td>\n",
       "      <td>6.170680</td>\n",
       "      <td>6.129586</td>\n",
       "      <td>...</td>\n",
       "      <td>7.176684</td>\n",
       "      <td>7.724408</td>\n",
       "      <td>6.035476</td>\n",
       "      <td>6.255048</td>\n",
       "      <td>6.572019</td>\n",
       "      <td>6.251777</td>\n",
       "      <td>5.873303</td>\n",
       "      <td>6.362097</td>\n",
       "      <td>1</td>\n",
       "      <td>primary breast cancer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.544287</td>\n",
       "      <td>6.882722</td>\n",
       "      <td>5.874236</td>\n",
       "      <td>6.324987</td>\n",
       "      <td>6.638048</td>\n",
       "      <td>6.946567</td>\n",
       "      <td>6.488574</td>\n",
       "      <td>6.690773</td>\n",
       "      <td>6.160272</td>\n",
       "      <td>6.104929</td>\n",
       "      <td>...</td>\n",
       "      <td>7.249094</td>\n",
       "      <td>7.308890</td>\n",
       "      <td>6.021796</td>\n",
       "      <td>6.902473</td>\n",
       "      <td>6.347183</td>\n",
       "      <td>6.625569</td>\n",
       "      <td>6.004899</td>\n",
       "      <td>6.560329</td>\n",
       "      <td>1</td>\n",
       "      <td>primary breast cancer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 1928 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hsa-miR-9-5p  hsa-miR-126-3p  hsa-miR-140-5p  hsa-miR-105-5p  hsa-miR-297  \\\n",
       "0      6.877643        8.080215        6.245265        6.304782     6.686064   \n",
       "1      6.544287        6.882722        5.874236        6.324987     6.638048   \n",
       "\n",
       "   hsa-let-7i-5p  hsa-miR-130a-3p  hsa-miR-146b-5p  hsa_negative_control_6  \\\n",
       "0       7.805950         6.814781         7.146955                6.170680   \n",
       "1       6.946567         6.488574         6.690773                6.160272   \n",
       "\n",
       "   hsa_negative_control_7  ...  hsa-miR-205-3p  hsa-miR-1260a  hsa-miR-525-5p  \\\n",
       "0                6.129586  ...        7.176684       7.724408        6.035476   \n",
       "1                6.104929  ...        7.249094       7.308890        6.021796   \n",
       "\n",
       "   hsa-miR-187-5p  hsa-miRPlus-A1086  hsa-miR-551b-5p  hsa-miR-300  \\\n",
       "0        6.255048           6.572019         6.251777     5.873303   \n",
       "1        6.902473           6.347183         6.625569     6.004899   \n",
       "\n",
       "   hsa-miR-193b-5p  Label                Disease  \n",
       "0         6.362097      1  primary breast cancer  \n",
       "1         6.560329      1  primary breast cancer  \n",
       "\n",
       "[2 rows x 1928 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extenal = pd.read_csv('20220610_Results/1. GSE58606.csv', index_col=0)\n",
    "len(select_microRNA), len(set(select_microRNA)&set(extenal.columns))\n",
    "extenal.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ade394b6-0286-4332-9c91-1f813dac897b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hsa-miR-30b-5p</th>\n",
       "      <th>hsa-miR-663a</th>\n",
       "      <th>hsa-miR-1203</th>\n",
       "      <th>hsa-miR-15b-5p</th>\n",
       "      <th>hsa-miR-4428</th>\n",
       "      <th>hsa-miR-199a-5p</th>\n",
       "      <th>hsa-miR-3137</th>\n",
       "      <th>hsa-miR-3945</th>\n",
       "      <th>hsa-miR-19b-3p</th>\n",
       "      <th>hsa-miR-1469</th>\n",
       "      <th>...</th>\n",
       "      <th>hsa-miR-6861-5p</th>\n",
       "      <th>hsa-miR-517a-3p;hsa-miR-517b-3p</th>\n",
       "      <th>hsa-miR-6080</th>\n",
       "      <th>hsa-miR-8077</th>\n",
       "      <th>hsa-miR-8063</th>\n",
       "      <th>hsa-miR-6784-5p</th>\n",
       "      <th>hsa-miR-1266-3p</th>\n",
       "      <th>hsa-miR-6825-5p</th>\n",
       "      <th>hsa-miR-1233-5p</th>\n",
       "      <th>hsa-miR-6817-3p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.909195</td>\n",
       "      <td>7.757628</td>\n",
       "      <td>6.445467</td>\n",
       "      <td>7.053920</td>\n",
       "      <td>6.020254</td>\n",
       "      <td>7.794381</td>\n",
       "      <td>6.565553</td>\n",
       "      <td>6.675869</td>\n",
       "      <td>6.725680</td>\n",
       "      <td>9.052207</td>\n",
       "      <td>...</td>\n",
       "      <td>7.184513</td>\n",
       "      <td>2.864974</td>\n",
       "      <td>2.782163</td>\n",
       "      <td>3.340092</td>\n",
       "      <td>7.80029</td>\n",
       "      <td>12.337571</td>\n",
       "      <td>3.226616</td>\n",
       "      <td>7.169881</td>\n",
       "      <td>11.290753</td>\n",
       "      <td>2.964473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.699708</td>\n",
       "      <td>7.473577</td>\n",
       "      <td>6.347183</td>\n",
       "      <td>6.514951</td>\n",
       "      <td>6.347964</td>\n",
       "      <td>6.749673</td>\n",
       "      <td>6.532515</td>\n",
       "      <td>6.762352</td>\n",
       "      <td>6.677894</td>\n",
       "      <td>8.863357</td>\n",
       "      <td>...</td>\n",
       "      <td>7.184513</td>\n",
       "      <td>2.864974</td>\n",
       "      <td>2.782163</td>\n",
       "      <td>3.340092</td>\n",
       "      <td>7.80029</td>\n",
       "      <td>12.337571</td>\n",
       "      <td>3.226616</td>\n",
       "      <td>7.169881</td>\n",
       "      <td>11.290753</td>\n",
       "      <td>2.964473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hsa-miR-30b-5p  hsa-miR-663a  hsa-miR-1203  hsa-miR-15b-5p  hsa-miR-4428  \\\n",
       "0        7.909195      7.757628      6.445467        7.053920      6.020254   \n",
       "1        6.699708      7.473577      6.347183        6.514951      6.347964   \n",
       "\n",
       "   hsa-miR-199a-5p  hsa-miR-3137  hsa-miR-3945  hsa-miR-19b-3p  hsa-miR-1469  \\\n",
       "0         7.794381      6.565553      6.675869        6.725680      9.052207   \n",
       "1         6.749673      6.532515      6.762352        6.677894      8.863357   \n",
       "\n",
       "   ...  hsa-miR-6861-5p  hsa-miR-517a-3p;hsa-miR-517b-3p  hsa-miR-6080  \\\n",
       "0  ...         7.184513                         2.864974      2.782163   \n",
       "1  ...         7.184513                         2.864974      2.782163   \n",
       "\n",
       "   hsa-miR-8077  hsa-miR-8063  hsa-miR-6784-5p  hsa-miR-1266-3p  \\\n",
       "0      3.340092       7.80029        12.337571         3.226616   \n",
       "1      3.340092       7.80029        12.337571         3.226616   \n",
       "\n",
       "   hsa-miR-6825-5p  hsa-miR-1233-5p  hsa-miR-6817-3p  \n",
       "0         7.169881        11.290753         2.964473  \n",
       "1         7.169881        11.290753         2.964473  \n",
       "\n",
       "[2 rows x 108 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = extenal[set(select_microRNA)&set(extenal.columns)]\n",
    "for i in set(select_microRNA)-set(extenal.columns):\n",
    "    temp[i] = X_train[i].mean()\n",
    "temp.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cc073607-5539-4bfb-952a-ac32f7c02891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 0.08270676691729323, 0.0, 0.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(model, temp[select_microRNA], extenal['Label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "77f56b98-aa01-483c-93be-b7e916fd7efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 0.97222222, 1.        , 0.86458333])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "extent_model = RandomForestClassifier(**tune_model.best_params_)\n",
    "\n",
    "cross_val_score(extent_model, extenal[set(select_microRNA)&set(extenal.columns)], extenal['Label'], \n",
    "                cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2822b85-b130-426e-95b1-0e988bef50e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b07f46e1-60b7-487d-b556-69601be6334f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e530a00b-f6a5-4b78-90cd-26d8eaa75e2d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
  },
  "language_info": {
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 },
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