8(b) Final Classification - Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acf563de",
   "metadata": {},
   "source": [
    "# Final classification with Logistic Regression\n",
    "### This file contains the training and testing of Logistic Regression with and without fusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3112d66d",
   "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\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "234c8c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"cnn.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63621599",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = df.columns\n",
    "\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "356bcfd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis=1)\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d45e0ce",
   "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": 7,
   "id": "a9b0403d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((438, 50), (189, 50))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18f68f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12e5214c",
   "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": 10,
   "id": "ca4fa083",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f33dda39",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d7b6c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', random_state=0)\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "y_pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38e240ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.9087\n",
      "Test set score: 0.8995\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(logreg.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "054858c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix\n",
      "\n",
      " [[84  9]\n",
      " [10 86]]\n",
      "\n",
      "True Positives(TP) =  84\n",
      "\n",
      "True Negatives(TN) =  86\n",
      "\n",
      "False Positives(FP) =  9\n",
      "\n",
      "False Negatives(FN) =  10\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": 21,
   "id": "0bf77604",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.90      0.90        93\n",
      "           1       0.91      0.90      0.90        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": 22,
   "id": "318795e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy : 0.8995\n",
      "Precision : 0.9032\n",
      "Recall or Sensitivity : 0.8936\n",
      "Specificity : 0.9053\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": "43e79b34",
   "metadata": {},
   "source": [
    "## With fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d97ce8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e34b9d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"cnn_abcd.xlsx\",sheet_name=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad3c7632",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = df.columns\n",
    "\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7f72428",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['target'], axis=1)\n",
    "\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b7a2b9ba",
   "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": 29,
   "id": "bf40f6e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((432, 54), (186, 54))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7bc1f3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fa472d37",
   "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": 32,
   "id": "9ae34e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "24115117",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91f03607",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', random_state=0)\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "y_pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f5e2492b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.9097\n",
      "Test set score: 0.9301\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(logreg.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a6fb2000",
   "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": 38,
   "id": "7c9a99d3",
   "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": "code",
   "execution_count": 39,
   "id": "f31d0677",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy : 0.9301\n",
      "Precision : 0.9278\n",
      "Recall or Sensitivity : 0.9375\n",
      "Specificity : 0.9222\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))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}