SVM_Classification_PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#In this notebook we are going to use SVM to predict which repair tools could have generated a specific commit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necesseray imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(75, 70)\n"
     ]
    }
   ],
   "source": [
    "# Read the data\n",
    "# This is DRR features manually selected to reduce the computation complexityn.\n",
    "data=pd.read_csv(\"/Users/macbook/PycharmProjects/ML/megadiff-correct.csv\")\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The data has 70 features so it may be useful to reduce the number of features using PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dir=\"/Users/macbook/Documents/university/internship/MachineLearningOnComingData/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tools_list=data['tool'].unique()\n",
    "dummy=pd.get_dummies(data['tool'])\n",
    "\n",
    "\n",
    "X = data.drop('tool', axis=1,inplace=True)\n",
    "X = data.drop('file_name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dimension Reduction : PCA\n",
    "from sklearn.decomposition import PCA\n",
    "# Make an instance of the Model\n",
    "pca = PCA(.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA is scale effective so you have to standaralize the data before appling PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ACS' 'arja' 'CapGen' 'DeepRepair' 'elixir' 'genprog' 'jaid' 'nopol'\n",
      " 'SequenceR' 'SimFix' 'SketchFix' 'ssFix']\n"
     ]
    }
   ],
   "source": [
    "print(tools_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tools are studied seperately to make it easier to review the graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"ACS\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    # PCA is scale effective so you have to standaralize the data before appling PCA\n",
    "    # Apply transform to both the training set and the test set.\n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir +\"/SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"arja\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"CapGen\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"DeepRepair\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"elixir\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"genprog\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"jaid\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"nopol\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"SequenceR\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"SimFix\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"SketchFix\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "    tool=\"ssFix\"\n",
    "    y = dummy[tool]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    \n",
    "    scaler.fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    X_train = pca.transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    svclassifier = SVC(kernel='linear')\n",
    "    svclassifier.fit(X_train, y_train)\n",
    "    y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "#     print(confusion_matrix(y_test, y_pred))\n",
    "#     print(classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the '+tool +\" classifier\")\n",
    "    fig.colorbar(cax)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.savefig(Dir + \"SVM_Results_PCA/\"+ tool +\".png\")\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}