RF_BALANCED.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in c:\\users\\asus\\appdata\\roaming\\python\\python37\\site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in c:\\users\\asus\\appdata\\roaming\\python\\python37\\site-packages (from imblearn) (0.8.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in d:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.3.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in d:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.5)\n",
      "Requirement already satisfied: scikit-learn>=0.24 in d:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
      "Requirement already satisfied: joblib>=0.11 in d:\\users\\asus\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\users\\asus\\anaconda3\\lib\\site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score,f1_score,roc_curve,precision_score,recall_score,roc_auc_score,make_scorer\n",
    "from matplotlib.colors import Normalize\n",
    "import pandas as pd\n",
    "from pandas.core.frame import DataFrame \n",
    "import numpy as np \n",
    "from matplotlib import pyplot as plt \n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.over_sampling import SMOTE, ADASYN,RandomOverSampler,BorderlineSMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metric(y_test,y_pred):\n",
    "    cm=confusion_matrix(y_test, y_pred)\n",
    "    tp=cm[1][1] #vrai positifs \n",
    "    tn=cm[0][0] #vrai negatifs\n",
    "    fp=cm[0][1] #faux positifs\n",
    "    fn=cm[1][0] #faux negatifs\n",
    "    score=(2*((tp+tn)/y_test.size) + 5*(tp/(fn+tp)))/7 #(2*accuracy + 5*rappel)/7\n",
    "#     print(f\"score : {score} soit {score* 100:.2f}%\")\n",
    "    return float(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_forest_classifier(X_tr,y_tr,X_ts,y_ts):\n",
    "    #50 arbres de décision \n",
    "    rf_classifier = RandomForestClassifier(n_estimators=50)\n",
    "    rf_classifier.fit(X_tr, y_tr)\n",
    "    y_pred = rf_classifier.predict(X_test)\n",
    "    #performances\n",
    "    print('Precision : {0:0.5f}'.format(precision_score(y_ts , y_pred)))\n",
    "    print('Recall : {0:0.5f}'.format(recall_score(y_ts , y_pred)))\n",
    "    print('F1 : {0:0.5f}'.format(f1_score(y_ts , y_pred)))\n",
    "    print('roc_auc_score : {0:0.5f}'.format(roc_auc_score(y_ts , y_pred)))\n",
    "    print('metrique personnalisée (MP): {}'.format(metric(y_ts,y_pred)))\n",
    "    print(f\"\\\\\\\\Matrice de confusion ///:- \\n {confusion_matrix(y_ts, y_pred)}\")\n",
    "    sns.heatmap(pd.DataFrame(confusion_matrix(y_ts, y_pred)), annot=True, annot_kws={\"size\": 25}, cmap=\"winter\" ,fmt='g')\n",
    "    plt.title('Matrice de confusion', y=1.1, fontsize = 22)\n",
    "    plt.xlabel('Prédit',fontsize = 18)\n",
    "    plt.ylabel('Réel',fontsize = 18)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** random_forest_classifier APRES RUS **********\n",
      "Precision : 0.06085\n",
      "Recall : 0.91089\n",
      "F1 : 0.11407\n",
      "roc_auc_score : 0.94296\n",
      "metrique personnalisée (MP): 0.9291830922096158\n",
      "\\\\Matrice de confusion ///:- \n",
      " [[55441  1420]\n",
      " [    9    92]]\n"
     ]
    },
    {
     "data": {
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K/SadTm8Z0EmlUp0JB4Qa4BvpdHplKfXe3u2loi4l3EbiMcJtp8uhI2E20SGEqaFnoIvEdmoVGUA2s7cJd+zclJPeGZjl7vsU2G4cMA6A2044hHGH7uiqlqzT/NUMG/k7AJZeehArzkze2On567nsfnmIjfMfGkX9fk3T6ftO+Ct9b53FmmMG8e7PP73NtnWp+wD48Lz9+fAb/7xN/vZun7e+D73D7pe9AMDb6TOaKd1Kai+vdA0S+3u6hqFDjfHXNHLV9/P/H+77MXjlLzVs3AjDD2xkUc5VAHf9whg7uoZ585xhqWQTlmpq4JeTjdO+WMOmTc7pX27kNw/nL3ve142fTQgdCD37bWb16vzlvnmeceMNodwufTazdm2iqrQar++w3RMcjKsSHyydK6tuQkWlBpAbCX2RufeRGUCR+5a4+0RgIpT2xbSmTUN60NC7lo7L6+m8cE3zG2TZcGC/LeudFq7ZEgw6zV9Nn9vfpLFbR96/6EBsbd6ZqbECjVvyvXunsmwvlXPzjTXU1hpXXt3I8uXQvfvW+R3jf7BZU159PTQ05N9fTQ3cOykEgoYG5z/HesFAAPDuu03/ZgMHwltv5S83MI4srFzpbS4QSDKVCgbnA0+Y2RyabtmwJ2Fg87yCW+2kOi5ZhzU41tDAsOOKT9/ue9ub9L3tTQDmzjiFxp6dt3t7qZxhQ8PjddfWcN21hcsNGWKsWRbu8HH+RY3c+LNtz5WyWwSZQPDAg8XPqd54s2l9/+GFg8H++4UT4TdnF91ddSvl9LPq2gUVCgbu/kczqyNcRDSQ8NEtAmZ4/vvcV41OC1bTcXmYrblpUGmTKrq8+uGW9VK3FSkmXyD41a+bP7q9/TbMn+8MGWKMGmk8+NC223TrBp+KN+j405/bZIO9PBpLOMKX/EsRlVex6ww8/EjJC5V6/RZxD+3xIvn9fvRKWK0x1owYuFVesW1rVtTT57ZwIfGm3btRP7z3lrz1h/dvtk++WJ//9m4vldPcOECSMYOaGrjvbuNLp4YxgiQtgmx3/9K54rvGaV80rvmBMz+nc/fcc4wePYyGBueXU9pxMPAqPN0vga4zKEHHf6xlz1P+SK8pc+i0cE04wAM0Ol1e/YCBX5lOj2lhhG/ll/Zm015Nv13T45F5DDjvGXaZuoAOH27Ykm4bGuj+54Xs+aU/bRljeP+Sg6Cm7f3h2cbN1Czb0LSsaxp72Cp92QZobMcHhRbadVfo27dpqYn/fd26bZ2eOy6wPWpq4J67mgLBGaMbSwoEANdPcBYvdrp3N37/2xoOPiikd+oEXxtnXBPHSife4cwpeslklfMSliqkK5BL1OX1ZXR5fRkAjZ1raOzeiZq1m6jZ2HRWtvLf92Lp5YdstZ01Oj2mLdoSLBq7dcQ7d6Bm9UZss2/Z3/uXHsya43NvR9829Hhs/pbZQ7n2/vjWv8L5zhP/RoO6urbyyoth9lCu73y7hu98u+n5pLsbOfPs8hxRjvwEnHFaiDru8LMJNfws3xUw0be+vW2rYdUqOOHzjUx9rIb9hhsvvdCBVaucLl2gc+fwfqZOcy64qEqPgkm185aBgkEJNvfrwtIrDqHLKx9Q+9YKOizbQIdVG/HOHagftAsbDurHyi98lA2HbPvLi+sO788HFxxAl1ffp/PfVtFhRT01azbSuEsnNu25C+uO2J0VX9qbhsE6gEr51GS1/Tt3NnbfvXBZgK5d8x/QX34F9juokUsuMk443hg8CNauhRf/4ky+17lzktPub3PWzt9f1d6orq1OLZUKq6LrDKT1lOU6gw3XJr/OoMt/V10zQi0DEZEk2vkvNysYiIgkoTEDERFRMBARkXY/gKxgICKShFoGIiJS0u0oqpCCgYhIEuomEhERdROJiIhaBiIiQrtvGeiupSIiSZTxrqVmNs/MXjezV81sZkzrY2bTzGxOfOwd083MbjKzuWb2mpkdnLWfMbH8HDMbk5V+SNz/3Lhts5FMwUBEJIlGS74k8xl3P9DdMz/mfinwRPwN+Cfic4DjgH3iMg64BULwAK4EDif8UNiVmQASy4zL2m5Uc5VRMBARScIt+dIyJwGT4/pk4OSs9Ls9eAHY1cwGAMcC09x9mbsvB6YBo2JeT3d/3sOdSO/O2ldBCgYiIkmU0E1kZuPMbGbWMi7P3v5kZi9l5fV398UA8XG3mD6Qpt+Kh/ATwQObSV+UJ70oDSCLiCRRwhm/u08EJhYpcqS7v2tmuwHTzOytImXzvbC3IL0otQxERJIo4wCyu78bH5cCDxP6/N+LXTzEx6Wx+CJgcNbmg4B3m0kflCe9KAUDEZEkyjSAbGbdzaxHZh0YCbwBPApkZgSNAR6J648Co+OsoiOAlbEbaSow0sx6x4HjkcDUmLfazI6Is4hGZ+2rIHUTiYgkUb7rDPoDD8fZnh2B+9z9j2Y2A3jAzM4CFgCnxvKPA8cDc4F1wJkA7r7MzK4BZsRyV7v7srh+DjAJ6Ar8IS5F6WcvpX3Rz15KHmX52cv5P03+s5dDzq+6K9TUMhARSaKdX4GsYCAikkQ774tQMBARSUItAxER0Y/biIiIuolERAR1E4mICGoZiIgICgYiIoK6iUREBM0mEhER1DIQERE0ZiAiIqhlICIiqGUgIiJoAFlERFA3kYiIoG4iERFBLQMREUEtAxERQS0DERFBs4lERAR1E4mICOomEhER1DIQERHUMhAREdQyEBERNJtIRERQN5GIiKBuIhERod23DGoqXQERkargJSwJmFkHM3vFzB6Lz4eZ2YtmNsfMfmVmnWN6bXw+N+YPzdrHZTE9bWbHZqWPimlzzezSJPVRMBARSaLRki/JfAuYnfX8h8AEd98HWA6cFdPPApa7+97AhFgOMxsOnAbsB4wCfh4DTAfgZuA4YDhweixblIKBiEgSbsmXZpjZIOBfgdvjcwOOBh6MRSYDJ8f1k+JzYv4xsfxJwBR3r3f3vwNzgcPiMtfd33H3jcCUWLYoBQMRkSRK6CYys3FmNjNrGZezt58C3wEa4/O+wAp3b4jPFwED4/pAYCFAzF8Zy29Jz9mmUHpRGkAWEUmihNlE7j4RmJgvz8xOAJa6+0tmNiKTXOQVC+UVSs93kt9s7RUMRESSKN9soiOBfzOz44EuQE9CS2FXM+sYz/4HAe/G8ouAwcAiM+sI9AKWZaVnZG9TKL0gdROJiCRRptlE7n6Zuw9y96GEAeAn3f0/gKeAU2KxMcAjcf3R+JyY/6S7e0w/Lc42GgbsA/wFmAHsE2cndY6v8Whzb08tAxGRJBp3+LnzJcAUM/s+8ApwR0y/A7jHzOYSWgSnAbj7LDN7AHgTaADOdffNAGZ2HjAV6ADc6e6zmntxCwGm+hhXVWfFZceqvbzSNZA2yOs7bHcfj/3u/sTHHD/x9Kq7Qk0tAxGRJNr5FcgKBiIiSbTzvggFAxGRJNQyEBERtQxEREQ/biMiIqibSEREUDeRiIigloGIiKCWgYiIoAFkERFB3UQiIoK6iUREBLUMREQEtQxERAS1DEREBM0mEhER1E0kIiKom0hERFDLQEREUMtARETQALKIiKBuIhERQcFARETQmIGIiKCWgYiIoJaBiIig2URmtmdLduzuC1qynYhIm6SWAfNoWW9ZhxZsIyLSNmnMgKtp9x+DiEgzdvaWgbuPb4V6iIi0bWU6JTazLsAzQC3hGPygu19pZsOAKUAf4GXgy+6+0cxqgbuBQ4APgS+5+7y4r8uAs4DNwDfdfWpMHwXcSOihud3dr2uuXjXleXsiIu2cW/KluHrgaHc/ADgQGGVmRwA/BCa4+z7AcsJBnvi43N33BibEcpjZcOA0YD9gFPBzM+tgZh2Am4HjgOHA6bFsUS0KBvEFR5vZvWY2zcwOium9Y/rAluxXRKTNarTkSxEerIlPO8XFgaOBB2P6ZODkuH5SfE7MP8bMLKZPcfd6d/87MBc4LC5z3f0dd99IaG2c1NzbKzkYmFk34GlgUnyBo4HeMXsVcB1wTqn7FRFp0zz5YmbjzGxm1jIue1fxhPpVYCkwDfgbsMLdG2KRRUDmpHogsBAg5q8E+man52xTKL2olrQMxgOHAp8H9gK2hEF33ww8BBzbgv2KiLRdJXQTuftEdz80a5m41a7cN7v7gcAgwpn8vvleMT7ma2p4C9KLakkwOBWY6O6PAI158ucCQ1uwXxGRtquElkHiXbqvAKYDRwC7mllmUs8g4N24vggYDBDzewHLstNztimUXlRLgsEewF+L5K8DerRgvyIibVeZBpDN7CNmtmtc7wp8FpgNPAWcEouNAR6J64/G58T8J93dY/ppZlYbZyLtA/wFmAHsY2bDzKwzYZD50ebeXktuR/Ehxfuf9iNBFBIRqSrlux3FAGBynPVTAzzg7o+Z2ZvAFDP7PvAKcEcsfwdwj5nNJbQITgNw91lm9gDwJtAAnBu76jGz84CphKmld7r7rOYq1ZJg8ARwppldn5sRo9N/Afe0YL8iIm1Xma4zcPfXgIPypL9DGD/ITd9A6J7Pt69rgWvzpD8OPF5KvVrSTXQVYfbQDMKsISfMk/0B4UKJeuAHLdiviEjbVb7rDNqkkoOBu88FjiE0S64mjFxfBFxCmM50jLsvLLwHEZEqtAMGkNuSFt3C2t1fAg4ws/0JU6IMmOPur5SzciIibUaVnvEntV2/Z+DubwBvlKkupbErK/KyIrKTqtIz/qRaHAzM7NPASKA/cIO7v2VmuwAHA6/F+bMiIu1DO/9xm6JjBnHq0zZpZvYrwpzY7xJmD+0RsxuA3wJfL3M9RUQqaycfQD7fzI7OSbsE+AJwIU3jBcCWKVAPA8eXs5IiIhXXzgeQmwsGy4HHzOy4rLTRwN3ufiPwQZ5tZgMfLVP9RETahp25ZeDudwLnAb8ys2Ni8lDg+SKbraDpLqYiIu3DTt4yyASE44C9Y9Jqwi/xFLI38P72V01EpA3ZmVsGGe7+rLvfFp/+H/Cf8ccVtmJmvQkDyk+Vr4oiIm1AYwlLFWrJ7SiuJdwd70nghJh2gJl9lXA7iu6EH7gREWk/2nnLoOTrDNx9ppn9O+FOenfF5OsJs4qWAp939zfLV0URkTagSg/ySbX0dhSPm9lQ4HNk3Y4CmOru68pWOxGRtqJKB4aTavEVyO5eDzwWly3M7Ejganc/Ju+GIiLVSC2DJmbWl3ANwbJ499LsvCMIdzE9hqodQhERKWBnvh1FRrwFxa3Ae4RrDNJm9ryZ7WZmPc3sPuBZ4DPAfcA/7bAai4hUQju/ziBpy+AbwDjCDy2/QLiW4HDgZsKPLR9G+HWza9z9bzugniIilaVuIgC+DLwOfDwzQGxmNxN+6exD4JPuXuyqZBGR6lalZ/xJJb3OoI5wP6LsmUK3xMcfKhCISLun6wyAcCHZkpy0zPPXy1cdEZE2qp23DEqZTZT7UWSebypTXURE2q52PpuolGBwvJntnvW8GyEgnGpmB+aUdXefsN21ExFpK6q0+ycpc2++7WNmpV434O6+za+klZNZe2+0iUi5uLPdR3I7Z2biY47fcmjVRY6kLYPP7NBaiIi0de28ZZAoGLj70zu6IiIibVo774to8b2JRER2KmoZiIiIZhOJiIi6iUREhHbfTdSSn70UEdn5lOmupWY22MyeMrPZZjbLzL4V0/uY2TQzmxMfe8d0M7ObzGyumb1mZgdn7WtMLD/HzMZkpR9iZq/HbW7K95v1uRQMRESSKN+9iRqAb7v7vsARwLlmNhy4FHjC3fcBnojPAY4j/O78PoS7R98CIXgAVxLuIH0YcGUmgMQy47K2G9VcpRQMRESSaLTkSxHuvtjdX47rq4HZwEDgJGByLDYZODmun0S4Uai7+wvArmY2ADgWmObuy9x9OTANGBXzerr78x6uKr47a18FKRiIiCRRQjeRmY0zs5lZy7h8u4y/JX8Q8CLQ390XQwgYwG6x2EBgYdZmi2JasfRFedKL0gCyiEgSJQwgu/tEYGKxMma2C/Ab4Hx3X1WkWz9fhrcgvSi1DEREkijjz16aWSdCIPiluz8Uk9+LXTzEx6UxfREwOGvzQcC7zaQPypNelIKBiEgSZRpAjjN77gBmu/tPsrIeBTIzgsYAj2Slj46zimXs+dkAAAuZSURBVI4AVsZupKnASDPrHQeORwJTY95qMzsivtborH0VpG4iEZEkynfR2ZHEnxI2s1dj2neB64AHzOwsYAFwasx7HDgemAusA84EcPdlZnYNMCOWu9rdl8X1c4BJQFfgD3EpKtEtrNsi3cJaRJIqyy2svzQr+S2sf7Vf1V2hppaBiEgS7fwKZAUDEZEk2nlfhIKBiEgSahmIiIhaBiIiot8zEBER1E0kIiKom0hERFDLQEREUMtARERQy0BERNBsIhERQd1EIiKCuolERAS1DEREBLUMREQEtQxERATNJhIREdRNJCIiqJtIRERQy0BERFDLQERE0ACyiIigbiIREUHdRCIigloGIiKCWgYiIoKCgYiIAI01la7BDqVgICKShFoGIiLS3geQ23e7R0SkXLyEpRlmdqeZLTWzN7LS+pjZNDObEx97x3Qzs5vMbK6ZvWZmB2dtMyaWn2NmY7LSDzGz1+M2N5lZs5FMwUBEJAm35EvzJgGjctIuBZ5w932AJ+JzgOOAfeIyDrgFQvAArgQOBw4DrswEkFhmXNZ2ua+1DQUDEZEkytgycPdngGU5yScBk+P6ZODkrPS7PXgB2NXMBgDHAtPcfZm7LwemAaNiXk93f97dHbg7a18FacxARCSJHX9vov7uvhjA3Reb2W4xfSCwMKvcophWLH1RnvSi1DIQEUmihG4iMxtnZjOzlnHb8cr5opC3IL0otQxERJIoYWqpu08EJpb4Cu+Z2YDYKhgALI3pi4DBWeUGAe/G9BE56dNj+qA85YtSy0BEJInyDiDn8yiQmRE0BngkK310nFV0BLAydidNBUaaWe84cDwSmBrzVpvZEXEW0eisfRWkloGISBJlvOjMzO4nnNX3M7NFhFlB1wEPmNlZwALg1Fj8ceB4YC6wDjgTwN2Xmdk1wIxY7mp3zwxKn0OYsdQV+ENcitcpDDZXH7P2fj2giJSLe95+9JLYvh8kPub47H5Vd4WaWgYiIkm08yuQFQxERJJo530RCgYiIkmoZSAiImoZiIiIWgYiIkJr3I6iohQMRESSUDeRiIiom0hERNQyEBER1DIQERHUMhARETSbSEREUDeRiIigbiIREUEtAxERQS0DERFBA8giIoK6iUREBHUTiYgIahmIiAhqGYiICGoZiIgImk0kIiK0+26imkpXQFrmE5+A+++HBQtgwwZYuhSeeAJGj650zWRHasn33qcPjB0L99wDs2bBmjVh24UL4eGH4eSTW6361c0t+VKFzL06w51Ze4/ThV1zDVx+edPz5cuhWzeorQ3P//CH8A++cWNl6ic7Rku/940boVOnpufr18PmzbDLLk1pjz8Op5wS8tojd7b7CG3dNyU+5vjaTtUXEdy9Khdw3xmXs87yLe67z33gwJDeqZP7GWe4r1wZ8m67rfJ11dI2vnd39xdecP/a19yHDWtKHzLE/Re/aNrv3XdX/n3uqKUsx5yumzzpUunjY4veX6Ur0OKK76A/mra81NS4v/tu+MedOTN/mTFjQn5Dg/v++1e+zloq/72PGFF8/7fc4lsMGlT597sjlrIcc7ps8qRLpY+PLVk0ZlBFDj0UBgwI6zfckL/M5MmwZAl06ABjxrRe3WTH2d7vffr04vu/446tX0sKaLTkSxVSMKgiQ4Y0rb/5ZuFy6XR4HDVqx9ZHWseO/t43bGha79ChtG13Ku18AFnBoEoV+6fN5KVSWw8cSvXbEd/7iBFN66+/3qJq7Ry8hKUKKRhUkXnzmtb33z9/mQ4dwsEAwgHhIx/Z4dWSHWxHfu+9esFll4X1Z56Bt99ucTXbP7UMpK146SVYvDisX3JJ/rPEr3516wNBz56tUzfZcXbU924Wrj3YY4/QVfSNb5Snvu2WWgaty8zOrHQd2qrGRhg/PqwPHw6//z0cfHA4E9xtNzj//DDAmD3PvLGxIlWVMtpR3/uNN8KJJ4b1r38dXnut7FVvX9r5AHKbu+jMzBa4+54F8sYB4+LTie4+sfVq1qb8ALi0QN57wK3AlfF5f2Bpa1RKdriSvnczO7nI/8j1wLfj+gXAT8tWS6lKFQkGZlboHMSAOnevbc36VKnDgbOBwxYvXpwaMGDA68BUYEJM/x9gOdCXqm24Sh5bvndgV0Kgz/u9m9kMd883WfRHwMVx/WJCYJCdXKVuVNcfOJbwR5vNgOdavzpV6cW4sMcee8zM+af/dHx8HgWC9mbL955Hku/9x8BFcf07KBBIVKlg8Biwi7u/mpthZtNbvzrtyp7A5+L65EpWRFpVku89u2voO4TAIAJUKBi4+1lF8s5ozbq0E5l+4U5xvQPwBvBwxWokranQ9549XpAdCC4CClzLLDurNjebSJq1F/B94GCgC4C73wEcBTxJ6H5bA4wGNlWojlJ+23zvhIN/we89a/D4hzQFggtRIJA82txsImnWgcArcd2BFUAPmlp57wKnEPqNpf1o6fe+JzA/rjcC7zfzOtejcYSdkn7prPrMA64GRgB7A/2AlcDbwG+BnxPOEKV9mUfLvveanPX+zbzOLs3kSzullkGVM7NRwI2ELoPb3f26CldJKszM7gROAJa6e4EbWIhsTWMGVczMOgA3A8cBw4HTzWx4ZWslbcAkQPeslZIoGFS3w4C57v6Ou28EpgAnVbhOUmHu/gywrNL1kOqiYFDdBgILs54vimkiIiVRMKhu+e6IpUEgESmZgkF1WwQMzno+iDDFUESkJAoG1W0GsI+ZDTOzzsBpwKMVrpOIVCEFgyrm7g3AeYS7Vs4GHnD3WZWtlVSamd1PuPgsZWaLzKzg7V9EMnSdgYiIqGUgIiIKBiIigoKBiIigYCAiIigYSDtgwflm9vVK10WkWikYSHtwNfA94NmkG5jZWDNzMxtRLE1kZ6FgIK3GzEbEg232ssbMXjKzb8W7sJa6zy8C3wCOdfe/7oA6H2hm481saLn3LdKWKBhIJdwPfJnwE43XAN2AnwK3lLKTGDyGACPdfUYZ6nUP0BV4JivtQOBKYGgZ9i/SZumXzqQSXnb3ezNPzOwWwhXUXzGzK9z9vdwNzKwT0MHdN2TS3H0z8ONyVSrub3O59idSTdQykIpz91WE2ycYsFfslnEz28/MfmJmi4ANwBGZbczss2b2JzNbYWYbzOw1M/tavv2b2VfM7C0zqzezuWb2LfLc8TV3zMDMxgN3xeynsrq2JpXx7Yu0CWoZSMWZmRF+1xfgg6ysXwLrgRsIt+ZeHMuPA24FXgCuBdYCnwNuMbOPuvvFWfs+H5gA/BX4LqFL6mJgaYKqPQQMAMYB/0NovQD8reQ3KdLGKRhIJXQzs36Es/MBhAHgA4AX3H1OiA0ArAA+G2/IB4CZDQBuAqa4+xlZ+/y5md0IXGhmt7r738xsV0KwmA18wt3XxX3cBbzVXCXd/TUze54QDKa5+/TtetcibZi6iaQSrgLeJ5yd/xX4L8Ktt0/OKffT7EAQnQLUAneYWb/sBfgd4W/6mFh2JKElcHMmEAC4+yJCq0NEIrUMpBImAr8mdP2sBd5293y/2ft2nrR94+Ofi+y/f3zcKz7mawW8maCeIjsNBQOphDnuXuxgnrEuT1qmD2k0cQwhj3dyyua7T3u+nwwV2WkpGEi1mRMfP0gQUDIDvfsCT+bk7Usy+sEP2SlozECqzQNAPXCVmXXNzTSzXmZWG59OI8xGOtfMumWVGQSckbttAWviY5+WV1mk7VPLQKqKuy8ys3OA24HZZnYPMB/4CPBPhEHo4cA8d19uZlcA1wPPmdndhAHlrxFaGAcleMkZQCPw32bWmzDG8Xd3f7HMb02kohQMpOq4+11m9jZwEfBVYFfC9Qlp4ApgSVbZG8xsDXAh8ANgISE4rATuTPBaC8zsv4BLCLfL6ARMBhQMpF3RbyCLiIjGDERERMFARERQMBARERQMREQEBQMREUHBQEREUDAQEREUDEREBAUDEREB/j8V21Wu0Tc9nAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** random_forest_classifier APRES ROS **********\n",
      "Precision : 0.93182\n",
      "Recall : 0.81188\n",
      "F1 : 0.86772\n",
      "roc_auc_score : 0.90589\n",
      "metrique personnalisée (MP): 0.8655040232037369\n",
      "\\\\Matrice de confusion ///:- \n",
      " [[56855     6]\n",
      " [   19    82]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** random_forest_classifier APRES SMOTE **********\n",
      "Precision : 0.88298\n",
      "Recall : 0.82178\n",
      "F1 : 0.85128\n",
      "roc_auc_score : 0.91079\n",
      "metrique personnalisée (MP): 0.8725560954877633\n",
      "\\\\Matrice de confusion ///:- \n",
      " [[56850    11]\n",
      " [   18    83]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** random_forest_classifier APRES ADASYN SMOTE **********\n",
      "Precision : 0.84536\n",
      "Recall : 0.81188\n",
      "F1 : 0.82828\n",
      "roc_auc_score : 0.90581\n",
      "metrique personnalisée (MP): 0.8654588803265304\n",
      "\\\\Matrice de confusion ///:- \n",
      " [[56846    15]\n",
      " [   19    82]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** random_forest_classifier APRES BORDERLINE SMOTE **********\n",
      "Precision : 0.90323\n",
      "Recall : 0.83168\n",
      "F1 : 0.86598\n",
      "roc_auc_score : 0.91576\n",
      "metrique personnalisée (MP): 0.8796432788985058\n",
      "\\\\Matrice de confusion ///:- \n",
      " [[56852     9]\n",
      " [   17    84]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('creditcard.csv').astype(np.float32)\n",
    "Sc=StandardScaler()\n",
    "Amount=df[\"Amount\"].values\n",
    "df[\"Amount\"]=Sc.fit_transform(Amount.reshape(-1,1))\n",
    "Time=df[\"Time\"].values\n",
    "df[\"Time\"]=Sc.fit_transform(Time.reshape(-1,1))\n",
    "x = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)\n",
    "#ré-echantillonnage des données d'apprentissage \n",
    "rus=RandomUnderSampler() #SOUS-ECHANTILLONNAGE ALEATOIRE\n",
    "X_train_rus, y_train_rus = rus.fit_resample(X_train, Y_train)\n",
    "\n",
    "ros=RandomOverSampler() #SUR-ECHANTILLONNAGE ALEATOIRE\n",
    "X_train_ros, y_train_ros = ros.fit_resample(X_train, Y_train)\n",
    "\n",
    "smt=SMOTE() #SMOTE\n",
    "X_train_smt, y_train_smt = smt.fit_resample(X_train, Y_train)\n",
    "\n",
    "ada=ADASYN() #ADASYN-SMOTE\n",
    "X_train_ada, y_train_ada = ada.fit_resample(X_train, Y_train)\n",
    "\n",
    "Bl=BorderlineSMOTE() #BORDERLINE-SMOTE\n",
    "X_train_Bl, y_train_Bl = Bl.fit_resample(X_train, Y_train)\n",
    "\n",
    "print(\"*\"*10,\"random_forest_classifier APRES RUS\",\"*\"*10)\n",
    "random_forest_classifier(X_train_rus,y_train_rus,X_test,Y_test)\n",
    "print(\"*\"*10,\"random_forest_classifier APRES ROS\",\"*\"*10)\n",
    "random_forest_classifier(X_train_ros,y_train_ros,X_test,Y_test)\n",
    "print(\"*\"*10,\"random_forest_classifier APRES SMOTE\",\"*\"*10)\n",
    "random_forest_classifier(X_train_smt,y_train_smt,X_test,Y_test)\n",
    "print(\"*\"*10,\"random_forest_classifier APRES ADASYN SMOTE\",\"*\"*10)\n",
    "random_forest_classifier(X_train_ada,y_train_ada,X_test,Y_test)\n",
    "print(\"*\"*10,\"random_forest_classifier APRES BORDERLINE SMOTE\",\"*\"*10)\n",
    "random_forest_classifier(X_train_Bl,y_train_Bl,X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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