SVM_linear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e114934f-f677-4180-b944-ab1f512771d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "from sklearnex import patch_sklearn\n",
    "patch_sklearn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7508edc4-aa35-475c-9da0-73c8beab298d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas is used for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import cross_val_score,train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_fscore_support, f1_score, roc_curve, roc_auc_score, RocCurveDisplay, auc\n",
    "from collections import Counter\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn import svm\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "def convert(data):\n",
    "    number = preprocessing.LabelEncoder()\n",
    "    data['operator'] = number.fit_transform(data.operator)\n",
    "    data['methodReturn'] = number.fit_transform(data.methodReturn)\n",
    "    data['isKilled'] = number.fit_transform(data.isKilled)\n",
    "    return data\n",
    "\n",
    "features = pd.read_csv('py_files/training_data.csv',encoding= 'unicode_escape')\n",
    "features=convert(features)\n",
    "# Labels are the values we want to predict\n",
    "labels = np.array(features['isKilled'])\n",
    "features= features.drop('isKilled', axis = 1)\n",
    "# Saving feature names for later use\n",
    "feature_list = list(features.columns)\n",
    "# Convert to numpy array\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "408d1ae7-0912-4435-ada1-afdbbe3ce063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147953, 14) (147953,)\n",
      "(443860, 14) (443860,)\n"
     ]
    }
   ],
   "source": [
    "##Validation set create training and testing vars\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, train_size=0.25)\n",
    "print (X_train.shape, y_train.shape)\n",
    "print (X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22806daa-8728-40b9-bcda-f1701c104ceb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/padmin/.local/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "#Create a svm Classifier\n",
    "clf = svm.LinearSVC(dual=True) # Linear Kernel\n",
    "\n",
    "#Train the model using the training sets\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dcc3d512-2d40-40ca-8951-70db5c07b256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7981593295183166\n"
     ]
    }
   ],
   "source": [
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e87ce44-b90d-4364-b36c-64bd33bd9d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5399482800420224\n",
      "Confusion_matrix:\n",
      " [[41850 51132]\n",
      " [17182 38328]]\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('py_files/testing_data.csv',encoding= 'unicode_escape')\n",
    "\n",
    "#Convert string to float\n",
    "test=convert(test)\n",
    "\n",
    "# ## Labels are the values we want to predict\n",
    "np_test_labels = np.array(test['isKilled'])\n",
    "\n",
    "# # Remove the labels from the features\n",
    "# # axis 1 refers to the columns\n",
    "test= test.drop('isKilled', axis = 1)\n",
    "\n",
    "# # Saving feature names for later use\n",
    "feature_list = list(test.columns)\n",
    "\n",
    "# # Convert to numpy array\n",
    "np_test = np.array(test)\n",
    "y_pred_test = clf.predict(np_test)\n",
    "#print(\"Predicted labels::\", y_pred_test)\n",
    "print(accuracy_score(np_test_labels, y_pred_test))\n",
    "confusion = confusion_matrix(np_test_labels, y_pred_test)\n",
    "print(\"Confusion_matrix:\\n\",confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a488818-9ff6-4887-bb0d-5993cb4b78ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.798021898796918\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, train_size=0.25)\n",
    "\n",
    "#Create a svm Classifier\n",
    "clf = svm.LinearSVC(dual=False) # Linear Kernel\n",
    "\n",
    "#Train the model using the training sets\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912d57ce-0f8d-4189-9a74-e62fda1d7370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147953, 14) (147953,)\n",
      "(443860, 14) (443860,)\n"
     ]
    }
   ],
   "source": [
    "##Validation set create training and testing vars\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, train_size=0.25)\n",
    "print (X_train.shape, y_train.shape)\n",
    "print (X_test.shape, y_test.shape)\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "#Create a svm Classifier\n",
    "clf = svm.LinearSVC(C=100,max_iter=) # Linear Kernel\n",
    "\n",
    "#Train the model using the training sets\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "265eee1d-5369-413e-a256-2c4ce9d1d498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For sample: 1  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7974363566931502\n",
      "***Test accuracy***\n",
      "0.6383172157422622\n",
      "For sample: 1  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7974363566931502\n",
      "***Test accuracy***\n",
      "0.6383172157422622\n",
      "For sample: 1  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.797564775419304\n",
      "***Test accuracy***\n",
      "0.6387953559787732\n",
      "For sample: 1  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7974363566931502\n",
      "***Test accuracy***\n",
      "0.6383172157422622\n",
      "For sample: 1  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7974363566931502\n",
      "***Test accuracy***\n",
      "0.6383172157422622\n",
      "For sample: 2  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7970815154761463\n",
      "***Test accuracy***\n",
      "0.6384855749804703\n",
      "For sample: 2  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7970815154761463\n",
      "***Test accuracy***\n",
      "0.6384855749804703\n",
      "For sample: 2  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7970815154761463\n",
      "***Test accuracy***\n",
      "0.6384855749804703\n",
      "For sample: 2  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7970815154761463\n",
      "***Test accuracy***\n",
      "0.6384855749804703\n",
      "For sample: 2  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7970815154761463\n",
      "***Test accuracy***\n",
      "0.6384855749804703\n",
      "For sample: 3  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7973417323686158\n",
      "***Test accuracy***\n",
      "0.63880882471783\n",
      "For sample: 3  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7973417323686158\n",
      "***Test accuracy***\n",
      "0.63880882471783\n",
      "For sample: 3  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7973417323686158\n",
      "***Test accuracy***\n",
      "0.63880882471783\n",
      "For sample: 3  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7973417323686158\n",
      "***Test accuracy***\n",
      "0.63880882471783\n",
      "For sample: 3  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7973417323686158\n",
      "***Test accuracy***\n",
      "0.63880882471783\n",
      "For sample: 4  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7980074820805185\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 4  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7980074820805185\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 4  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7980074820805185\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 4  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7980074820805185\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 4  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7980074820805185\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 5  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.798274457853312\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 5  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.798274457853312\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 5  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.798274457853312\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 5  for C: 10\n",
      "***Validation accuracy***\n",
      "0.798274457853312\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 5  for C: 100\n",
      "***Validation accuracy***\n",
      "0.798274457853312\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 6  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.797743885747887\n",
      "***Test accuracy***\n",
      "0.6386808716967918\n",
      "For sample: 6  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.797743885747887\n",
      "***Test accuracy***\n",
      "0.6386808716967918\n",
      "For sample: 6  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.797743885747887\n",
      "***Test accuracy***\n",
      "0.6386808716967918\n",
      "For sample: 6  for C: 10\n",
      "***Validation accuracy***\n",
      "0.797743885747887\n",
      "***Test accuracy***\n",
      "0.6386808716967918\n",
      "For sample: 6  for C: 100\n",
      "***Validation accuracy***\n",
      "0.797743885747887\n",
      "***Test accuracy***\n",
      "0.6386808716967918\n",
      "For sample: 7  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7983116316950933\n",
      "***Test accuracy***\n",
      "0.6386404654796218\n",
      "For sample: 7  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7983116316950933\n",
      "***Test accuracy***\n",
      "0.6386404654796218\n",
      "For sample: 7  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7983116316950933\n",
      "***Test accuracy***\n",
      "0.6386404654796218\n",
      "For sample: 7  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7983116316950933\n",
      "***Test accuracy***\n",
      "0.6386404654796218\n",
      "For sample: 7  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7983116316950933\n",
      "***Test accuracy***\n",
      "0.6386404654796218\n",
      "For sample: 8  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7975005660562271\n",
      "***Test accuracy***\n",
      "0.638748215392075\n",
      "For sample: 8  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7975005660562271\n",
      "***Test accuracy***\n",
      "0.638748215392075\n",
      "For sample: 8  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7975005660562271\n",
      "***Test accuracy***\n",
      "0.638748215392075\n",
      "For sample: 8  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7975005660562271\n",
      "***Test accuracy***\n",
      "0.638748215392075\n",
      "For sample: 8  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7975005660562271\n",
      "***Test accuracy***\n",
      "0.638748215392075\n",
      "For sample: 9  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7971186893179276\n",
      "***Test accuracy***\n",
      "0.6386202623710369\n",
      "For sample: 9  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7971186893179276\n",
      "***Test accuracy***\n",
      "0.6386202623710369\n",
      "For sample: 9  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7971186893179276\n",
      "***Test accuracy***\n",
      "0.6386202623710369\n",
      "For sample: 9  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7971186893179276\n",
      "***Test accuracy***\n",
      "0.6386202623710369\n",
      "For sample: 9  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7971186893179276\n",
      "***Test accuracy***\n",
      "0.6386202623710369\n",
      "For sample: 10  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.798511018664648\n",
      "***Test accuracy***\n",
      "0.638418231285187\n",
      "For sample: 10  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.798511018664648\n",
      "***Test accuracy***\n",
      "0.638418231285187\n",
      "For sample: 10  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7987104056342026\n",
      "***Test accuracy***\n",
      "0.6388963715216981\n",
      "For sample: 10  for C: 10\n",
      "***Validation accuracy***\n",
      "0.798511018664648\n",
      "***Test accuracy***\n",
      "0.638418231285187\n",
      "For sample: 10  for C: 100\n",
      "***Validation accuracy***\n",
      "0.798511018664648\n",
      "***Test accuracy***\n",
      "0.638418231285187\n",
      "For sample: 11  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7988928954029475\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 11  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7988928954029475\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 11  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7988928954029475\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 11  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7988928954029475\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 11  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7988928954029475\n",
      "***Test accuracy***\n",
      "0.6388155590873582\n",
      "For sample: 12  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7974160800521786\n",
      "***Test accuracy***\n",
      "0.6386067936319801\n",
      "For sample: 12  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7974532538939599\n",
      "***Test accuracy***\n",
      "0.6387010748053767\n",
      "For sample: 12  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7974532538939599\n",
      "***Test accuracy***\n",
      "0.6387010748053767\n",
      "For sample: 12  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7974532538939599\n",
      "***Test accuracy***\n",
      "0.6387010748053767\n",
      "For sample: 12  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7974532538939599\n",
      "***Test accuracy***\n",
      "0.6387010748053767\n",
      "For sample: 13  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7979432727174416\n",
      "***Test accuracy***\n",
      "0.6386269967405651\n",
      "For sample: 13  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7979432727174416\n",
      "***Test accuracy***\n",
      "0.6386269967405651\n",
      "For sample: 13  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7979432727174416\n",
      "***Test accuracy***\n",
      "0.6386269967405651\n",
      "For sample: 13  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7979432727174416\n",
      "***Test accuracy***\n",
      "0.6386269967405651\n",
      "For sample: 13  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7979432727174416\n",
      "***Test accuracy***\n",
      "0.6386269967405651\n",
      "For sample: 14  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7982676989729881\n",
      "***Test accuracy***\n",
      "0.6386539342186784\n",
      "For sample: 14  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7982676989729881\n",
      "***Test accuracy***\n",
      "0.6386539342186784\n",
      "For sample: 14  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7982676989729881\n",
      "***Test accuracy***\n",
      "0.6386539342186784\n",
      "For sample: 14  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7982676989729881\n",
      "***Test accuracy***\n",
      "0.6386539342186784\n",
      "For sample: 14  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7982676989729881\n",
      "***Test accuracy***\n",
      "0.6386539342186784\n",
      "For sample: 15  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7980514148026238\n",
      "***Test accuracy***\n",
      "0.6387616841311317\n",
      "For sample: 15  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7980683120034335\n",
      "***Test accuracy***\n",
      "0.6387616841311317\n",
      "For sample: 15  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.79792299607647\n",
      "***Test accuracy***\n",
      "0.6386269967405651\n",
      "For sample: 15  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7979432727174416\n",
      "***Test accuracy***\n",
      "0.6386135280015085\n",
      "For sample: 15  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7979365138371177\n",
      "***Test accuracy***\n",
      "0.6386337311100935\n",
      "For sample: 16  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7981359008066724\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 16  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7981359008066724\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 16  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7981359008066724\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 16  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7981359008066724\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 16  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7981359008066724\n",
      "***Test accuracy***\n",
      "0.6388290278264149\n",
      "For sample: 17  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7974295978128263\n",
      "***Test accuracy***\n",
      "0.6388357621959432\n",
      "For sample: 17  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7974295978128263\n",
      "***Test accuracy***\n",
      "0.6388357621959432\n",
      "For sample: 17  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7974295978128263\n",
      "***Test accuracy***\n",
      "0.6388357621959432\n",
      "For sample: 17  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7974295978128263\n",
      "***Test accuracy***\n",
      "0.6388357621959432\n",
      "For sample: 17  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7974295978128263\n",
      "***Test accuracy***\n",
      "0.6388357621959432\n",
      "For sample: 18  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7974701510947696\n",
      "***Test accuracy***\n",
      "0.6391792150418878\n",
      "For sample: 18  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7974701510947696\n",
      "***Test accuracy***\n",
      "0.6391792150418878\n",
      "For sample: 18  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7974701510947696\n",
      "***Test accuracy***\n",
      "0.6391792150418878\n",
      "For sample: 18  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7974701510947696\n",
      "***Test accuracy***\n",
      "0.6391792150418878\n",
      "For sample: 18  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7974701510947696\n",
      "***Test accuracy***\n",
      "0.6391792150418878\n",
      "For sample: 19  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7979196166363081\n",
      "***Test accuracy***\n",
      "0.6386741373272634\n",
      "For sample: 19  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7979196166363081\n",
      "***Test accuracy***\n",
      "0.6386741373272634\n",
      "For sample: 19  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.798041276482138\n",
      "***Test accuracy***\n",
      "0.6391590119333028\n",
      "For sample: 19  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7979196166363081\n",
      "***Test accuracy***\n",
      "0.6386741373272634\n",
      "For sample: 19  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7979196166363081\n",
      "***Test accuracy***\n",
      "0.6386741373272634\n",
      "For sample: 20  for C: 0.01\n",
      "***Validation accuracy***\n",
      "0.7979973437600327\n",
      "***Test accuracy***\n",
      "0.6387818872397166\n",
      "For sample: 20  for C: 0.1\n",
      "***Validation accuracy***\n",
      "0.7979973437600327\n",
      "***Test accuracy***\n",
      "0.6387818872397166\n",
      "For sample: 20  for C: 1.0\n",
      "***Validation accuracy***\n",
      "0.7979973437600327\n",
      "***Test accuracy***\n",
      "0.6387818872397166\n",
      "For sample: 20  for C: 10\n",
      "***Validation accuracy***\n",
      "0.7979973437600327\n",
      "***Test accuracy***\n",
      "0.6387818872397166\n",
      "For sample: 20  for C: 100\n",
      "***Validation accuracy***\n",
      "0.7979973437600327\n",
      "***Test accuracy***\n",
      "0.6387818872397166\n"
     ]
    }
   ],
   "source": [
    "##Validation set create training and testing vars\n",
    "size = [0.5]\n",
    "for i in size:\n",
    "    validation_accuracy  = 0.0\n",
    "    test_accuracy = 0.0\n",
    "    for j in range(1,21):\n",
    "        X_train, X_test, y_train, y_test = train_test_split(features, labels, train_size=i)\n",
    "        C = [0.01,0.1,1.0,10,100]\n",
    "        for l in C:\n",
    "            clf_grid = svm.LinearSVC(C=l,dual=False)\n",
    "            clf_grid.fit(X_train, y_train)\n",
    "            y_pred = clf_grid.predict(X_test)\n",
    "            y_pred_test = clf_grid.predict(np_test)\n",
    "            print(\"For sample:\",j, \" for C:\",l)\n",
    "            print(\"***Validation accuracy***\")\n",
    "            print(accuracy_score(y_test, y_pred))\n",
    "            print(\"***Test accuracy***\")\n",
    "            print(accuracy_score(np_test_labels, y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f721702-5431-436e-aac3-5b3cb7a9aff3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_587814/1425707104.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mpipe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'poly'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# apply scaling on training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m Pipeline(steps=[('standardscaler', StandardScaler()),\n\u001b[0m\u001b[1;32m     10\u001b[0m                 ('logisticregression', LogisticRegression())])\n\u001b[1;32m     11\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# apply scaling on testing data, without leaking training data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, train_size=0.25, random_state=42)\n",
    "pipe = make_pipeline(StandardScaler(), svm.SVC(kernel='poly'))\n",
    "pipe.fit(X_train, y_train)  # apply scaling on training data\n",
    "pipe.score(X_test, y_test)  # apply scaling on testing data, without leaking training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9372951b-59cb-45ed-bd68-e56955ca47ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def convert(data):\n",
    "    number = preprocessing.LabelEncoder()\n",
    "    data['operator'] = number.fit_transform(data.operator)\n",
    "    data['methodReturn'] = number.fit_transform(data.methodReturn)\n",
    "    return data\n",
    "\n",
    "features = pd.read_csv('py_files/training_data.csv',encoding= 'unicode_escape')\n",
    "features=convert(features)\n",
    "\n",
    "corr = features.corr()\n",
    "a4_dims = (11.7, 8.27)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "sn.heatmap(ax=ax, data=corr,annot=True)\n",
    "plt.savefig('corr_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d81a727f-bd6c-4e10-9081-464dbf8f8f32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas is used for data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import cross_val_score,train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_fscore_support, f1_score, roc_curve, roc_auc_score, RocCurveDisplay, auc\n",
    "from collections import Counter\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "def convert(data):\n",
    "    number = preprocessing.LabelEncoder()\n",
    "    data['operator'] = number.fit_transform(data.operator)\n",
    "    data['methodReturn'] = number.fit_transform(data.methodReturn)\n",
    "    return data\n",
    "\n",
    "features = pd.read_csv('py_files/training_data.csv',encoding= 'unicode_escape')\n",
    "features=convert(features)\n",
    "np_labels = np.array(features['isKilled'])\n",
    "# Remove the labels from the features\n",
    "# axis 1 refers to the columns\n",
    "features= features.drop('isKilled', axis = 1)\n",
    "# Saving feature names for later use\n",
    "feature_list = list(features.columns)\n",
    "# Convert to numpy array\n",
    "np_features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "29e7bbc5-3cec-4f29-96b5-7fa7eb3792be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(295906, 14) (295906,)\n",
      "(295907, 14) (295907,)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(np_features)\n",
    "np_features = scaler.fit_transform(np_features)\n",
    "\n",
    "##Validation set create training and testing vars\n",
    "X_train, X_test, y_train, y_test = train_test_split(np_features, np_labels, test_size=0.5)\n",
    "print (X_train.shape, y_train.shape)\n",
    "print (X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a16355d4-12b2-40a1-ba10-d565508f0673",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(295906, 14) (295906,)\n",
      "(295907, 14) (295907,)\n"
     ]
    }
   ],
   "source": [
    "##Validation set create training and testing vars\n",
    "X_train, X_test, y_train, y_test = train_test_split(np_features, np_labels, test_size=0.5)\n",
    "print (X_train.shape, y_train.shape)\n",
    "print (X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "073e2f93-1870-4b42-8993-6fb4846ac370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.798041276482138\n",
      "[[233374   1703]\n",
      " [ 58058   2772]]\n"
     ]
    }
   ],
   "source": [
    "clf = svm.LinearSVC(dual=False)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred_train = clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_train))\n",
    "print(confusion_matrix(y_test, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c3cd247b-7e7e-4910-a5e0-55aca5352274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7975276015775227\n",
      "[[233953    944]\n",
      " [ 58969   2041]]\n"
     ]
    }
   ],
   "source": [
    "#Standardization reduces accuracy\n",
    "clf = svm.SVC(kernel='rbf',gamma=5e-06,C=10)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred_train = clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_train))\n",
    "print(confusion_matrix(y_test, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f221e8e-0c70-47d4-b41b-e579652583dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of our features is: (591813, 10)\n",
      "0.7977743007093445\n",
      "[[234440    687]\n",
      " [ 59153   1627]]\n"
     ]
    }
   ],
   "source": [
    "new_features= features.drop('DepthTree', axis = 1)\n",
    "new_features= new_features.drop('methodReturn', axis = 1)\n",
    "new_features= new_features.drop('DepthNested', axis = 1)\n",
    "new_features= new_features.drop('mutantAssert', axis = 1)\n",
    "print('The shape of our features is:', new_features.shape)\n",
    "# Convert to numpy array\n",
    "np_features = np.array(new_features)\n",
    "X_train, X_test, y_train, y_test = train_test_split(np_features, np_labels, test_size=0.5)\n",
    "clf = svm.LinearSVC(dual=False)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred_train = clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_train))\n",
    "print(confusion_matrix(y_test, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1017596e-2f73-479c-b1c3-bb368c9d670d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.638458637502357\n",
      "Confusion_matrix:\n",
      " [[92491   491]\n",
      " [53195  2315]]\n",
      "(0.638458637502357, 0.638458637502357, 0.638458637502357, None)\n",
      "0.638458637502357\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('py_files/testing_data.csv',encoding= 'unicode_escape')\n",
    "\n",
    "#Convert string to float\n",
    "test=convert(test)\n",
    "\n",
    "# ## Labels are the values we want to predict\n",
    "np_test_labels = np.array(test['isKilled'])\n",
    "\n",
    "# # Remove the labels from the features\n",
    "# # axis 1 refers to the columns\n",
    "test= test.drop('isKilled', axis = 1)\n",
    "\n",
    "# # Saving feature names for later use\n",
    "feature_list = list(test.columns)\n",
    "# # Convert to numpy array\n",
    "np_test = np.array(test)\n",
    "\n",
    "y_pred_test = clf.predict(np_test)\n",
    "#print(\"Predicted labels::\", y_pred_test)\n",
    "print(accuracy_score(np_test_labels, y_pred_test))\n",
    "confusion = confusion_matrix(np_test_labels, y_pred_test)\n",
    "print(\"Confusion_matrix:\\n\",confusion)\n",
    "print(precision_recall_fscore_support(np_test_labels, y_pred_test, average='micro'))\n",
    "print(f1_score(np_test_labels, y_pred_test, average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41b3564c-21ea-4ea6-9be0-d0abfe790e11",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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