8(a) Final Classification - SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd67c46b",
   "metadata": {},
   "source": [
    "# Final classification with SVM\n",
    "### This file contains the training and testing of SVM with and without fusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6dba26",
   "metadata": {},
   "source": [
    "## Without fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01e1a814",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b9d0eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"cnn.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b8aeb5c",
   "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": "5b8950af",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis=1)\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f2fef93",
   "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": "cdae3a52",
   "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": "fcddec61",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2c7e42f",
   "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": "1f83e47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de22ccfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20b78f05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      "0.9047619047619048\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "svc=SVC(kernel='linear',C=0.5)\n",
    "\n",
    "svc.fit(X_train,y_train)\n",
    "y_pred=svc.predict(X_test)\n",
    "\n",
    "print('Accuracy Score:')\n",
    "print(metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87c545b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.9064\n",
      "Test set score: 0.9048\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(svc.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(svc.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73ab27bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[84  9]\n",
      " [ 9 87]]\n",
      "\n",
      "True Positives(TP) =  84\n",
      "\n",
      "True Negatives(TN) =  87\n",
      "\n",
      "False Positives(FP) =  9\n",
      "\n",
      "False Negatives(FN) =  9\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": 15,
   "id": "7fd5d869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90        93\n",
      "           1       0.91      0.91      0.91        96\n",
      "\n",
      "    accuracy                           0.90       189\n",
      "   macro avg       0.90      0.90      0.90       189\n",
      "weighted avg       0.90      0.90      0.90       189\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4202b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy : 0.9048\n",
      "Precision : 0.9032\n",
      "Recall or Sensitivity : 0.9032\n",
      "Specificity : 0.9062\n"
     ]
    }
   ],
   "source": [
    "TP = cm[0,0]\n",
    "TN = cm[1,1]\n",
    "FP = cm[0,1]\n",
    "FN = cm[1,0]\n",
    "\n",
    "classification_accuracy = (TP + TN) / float(TP + TN + FP + FN)\n",
    "\n",
    "print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))\n",
    "\n",
    "precision = TP / float(TP + FP)\n",
    "\n",
    "print('Precision : {0:0.4f}'.format(precision))\n",
    "\n",
    "recall = TP / float(TP + FN)\n",
    "\n",
    "print('Recall or Sensitivity : {0:0.4f}'.format(recall))\n",
    "\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "print('Specificity : {0:0.4f}'.format(specificity))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b649f91",
   "metadata": {},
   "source": [
    "## With fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd695393",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2fcfa9ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"cnn_abcd.xlsx\",sheet_name=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8442ecb",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = df.columns\n",
    "\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "685e9e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis=1)\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "849ab9c7",
   "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": 22,
   "id": "bbbada67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((432, 54), (186, 54))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2ff40262",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "19d1862e",
   "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": 25,
   "id": "da9f26f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f8879e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f6077527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      "0.9301075268817204\n"
     ]
    }
   ],
   "source": [
    "svc=SVC(kernel='linear',C=0.5)\n",
    "\n",
    "svc.fit(X_train,y_train)\n",
    "y_pred=svc.predict(X_test)\n",
    "\n",
    "print('Accuracy Score:')\n",
    "print(metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10d787a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.9190\n",
      "Test set score: 0.9301\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(svc.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(svc.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27dc35bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[89  8]\n",
      " [ 5 84]]\n",
      "\n",
      "True Positives(TP) =  89\n",
      "\n",
      "True Negatives(TN) =  84\n",
      "\n",
      "False Positives(FP) =  8\n",
      "\n",
      "False Negatives(FN) =  5\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": 30,
   "id": "ae3187bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.92      0.93        97\n",
      "           1       0.91      0.94      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": "code",
   "execution_count": 31,
   "id": "bbba5c3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy : 0.9301\n",
      "Precision : 0.9175\n",
      "Recall or Sensitivity : 0.9468\n",
      "Specificity : 0.9130\n"
     ]
    }
   ],
   "source": [
    "TP = cm[0,0]\n",
    "TN = cm[1,1]\n",
    "FP = cm[0,1]\n",
    "FN = cm[1,0]\n",
    "\n",
    "classification_accuracy = (TP + TN) / float(TP + TN + FP + FN)\n",
    "\n",
    "print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))\n",
    "\n",
    "precision = TP / float(TP + FP)\n",
    "\n",
    "print('Precision : {0:0.4f}'.format(precision))\n",
    "\n",
    "recall = TP / float(TP + FN)\n",
    "\n",
    "print('Recall or Sensitivity : {0:0.4f}'.format(recall))\n",
    "\n",
    "specificity = TN / (TN + FP)\n",
    "\n",
    "print('Specificity : {0:0.4f}'.format(specificity))"
   ]
  }
 ],
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