8(f) Final Classification - XGBoost classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae5be26a",
   "metadata": {},
   "source": [
    "# Final classification with XGBoost classifier\n",
    "### This file contains the training and testing of XGBoost classifier with and without fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eca6fceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a3d154e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"cnn.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fdc701a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([       1,        2,        3,        4,        5,        6,        7,\n",
       "              8,        9,       10,       11,       12,       13,       14,\n",
       "             15,       16,       17,       18,       19,       20,       21,\n",
       "             22,       23,       24,       25,       26,       27,       28,\n",
       "             29,       30,       31,       32,       33,       34,       35,\n",
       "             36,       37,       38,       39,       40,       41,       42,\n",
       "             43,       44,       45,       46,       47,       48,       49,\n",
       "             50, 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = df.columns\n",
    "\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3d502ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis=1)\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfc11646",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb5624bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((438, 50), (189, 50))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "276cd54f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c220061",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e704301",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79b59941",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46def344",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "\n",
    "def evaluate(model, X_train, X_test, y_train, y_test):\n",
    "    \n",
    "    y_test_pred = model.predict(X_test)\n",
    "    y_train_pred = model.predict(X_train)\n",
    "\n",
    "    print(\"Training:\\n\")\n",
    "    clf_report = pd.DataFrame(classification_report(y_train, y_train_pred, output_dict=True))\n",
    "    print(f\"Accuracy:\\n{accuracy_score(y_train, y_train_pred):.4f}\")\n",
    "\n",
    "    print(\"Testing: \\n\")\n",
    "    clf_report = pd.DataFrame(classification_report(y_test, y_test_pred, output_dict=True))\n",
    "    print(f\"Accuracy:\\n{accuracy_score(y_test, y_test_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c692e709",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "xgb_cl = xgb.XGBClassifier()\n",
    "\n",
    "xgb_cl.fit(X_train, y_train)\n",
    "\n",
    "preds = xgb_cl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cdbe50c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_cl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2bad75a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.0000\n",
      "Test set score: 0.9301\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(xgb_cl.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(xgb_cl.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "820424fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[90  7]\n",
      " [ 6 83]]\n",
      "\n",
      "True Positives(TP) =  90\n",
      "\n",
      "True Negatives(TN) =  83\n",
      "\n",
      "False Positives(FP) =  7\n",
      "\n",
      "False Negatives(FN) =  6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8d1edef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93        97\n",
      "           1       0.92      0.93      0.93        89\n",
      "\n",
      "    accuracy                           0.93       186\n",
      "   macro avg       0.93      0.93      0.93       186\n",
      "weighted avg       0.93      0.93      0.93       186\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5115b6",
   "metadata": {},
   "source": [
    "## With Fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1facd4d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"cnn_abcd.xlsx\",sheet_name=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48554f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([       1,        2,        3,        4,        5,        6,        7,\n",
       "              8,        9,       10,       11,       12,       13,       14,\n",
       "             15,       16,       17,       18,       19,       20,       21,\n",
       "             22,       23,       24,       25,       26,       27,       28,\n",
       "             29,       30,       31,       32,       33,       34,       35,\n",
       "             36,       37,       38,       39,       40,       41,       42,\n",
       "             43,       44,       45,       46,       47,       48,       49,\n",
       "             50,       51,       52,       53,       54, 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = df.columns\n",
    "\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ea6cff85",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis=1)\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b6e54bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "acc7a145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((432, 54), (186, 54))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ff96a188",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1cbc5514",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2efb3162",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "xgb_cl = xgb.XGBClassifier()\n",
    "\n",
    "xgb_cl.fit(X_train, y_train)\n",
    "\n",
    "preds = xgb_cl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a8ae8228",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_cl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0c9343a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.0000\n",
      "Test set score: 0.9301\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(xgb_cl.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(xgb_cl.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "822ba32a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[90  7]\n",
      " [ 6 83]]\n",
      "\n",
      "True Positives(TP) =  90\n",
      "\n",
      "True Negatives(TN) =  83\n",
      "\n",
      "False Positives(FP) =  7\n",
      "\n",
      "False Negatives(FN) =  6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print('Confusion matrix\\n\\n', cm)\n",
    "\n",
    "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
    "\n",
    "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
    "\n",
    "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
    "\n",
    "print('\\nFalse Negatives(FN) = ', cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bf6827a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.93      0.93        97\n",
      "           1       0.92      0.93      0.93        89\n",
      "\n",
      "    accuracy                           0.93       186\n",
      "   macro avg       0.93      0.93      0.93       186\n",
      "weighted avg       0.93      0.93      0.93       186\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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