SVM_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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": 25,
   "metadata": {},
   "outputs": [],
   "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tools are studied seperately to make it easier to review the graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "    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.show()\n",
    "    plt.savefig(\"SVM_Results/\"+ tool +\".png\")"
   ]
  }
 ],
 "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
}