Support Vector Machines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (546, 9) (546,)\n",
      "Test set: (137, 9) (137,)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       1.00      0.94      0.97        90\n",
      "           4       0.90      1.00      0.95        47\n",
      "\n",
      "    accuracy                           0.96       137\n",
      "   macro avg       0.95      0.97      0.96       137\n",
      "weighted avg       0.97      0.96      0.96       137\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[85  5]\n",
      " [ 0 47]]\n",
      "Avg F1-score: 0.9639\n",
      "Jaccard score: 0.9635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ashit\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  FutureWarning)\n",
      "C:\\Users\\ashit\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:664: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "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": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "#Load Data From CSV File\n",
    "cell_df = pd.read_csv(\"cell_samples.csv\")\n",
    "cell_df.head()\n",
    "\n",
    "ax = cell_df[cell_df['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\n",
    "cell_df[cell_df['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\n",
    "plt.show()\n",
    "\n",
    "#Data pre-processing and selection\n",
    "cell_df.dtypes\n",
    "\n",
    "#dropping columns that are not numerical\n",
    "cell_df = cell_df[pd.to_numeric(cell_df['BareNuc'], errors='coerce').notnull()]\n",
    "cell_df['BareNuc'] = cell_df['BareNuc'].astype('int')\n",
    "cell_df.dtypes\n",
    "\n",
    "cell_df['Class'] = cell_df['Class'].astype('int')\n",
    "y = np.asarray(cell_df['Class'])\n",
    "y [0:5]\n",
    "\n",
    "feature_df = cell_df[['Clump', 'UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
    "X = np.asarray(feature_df)\n",
    "X[0:5]\n",
    "\n",
    "#Train/Test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)\n",
    "\n",
    "#Modeling (SVM with Scikit-learn)\n",
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) \n",
    "\n",
    "#after fitting we can use the model to predict values\n",
    "yhat = clf.predict(X_test)\n",
    "yhat [0:5]\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, yhat, average='weighted') \n",
    "\n",
    "#finding accuracy using jacard\n",
    "from sklearn.metrics import jaccard_similarity_score\n",
    "jaccard_similarity_score(y_test, yhat)\n",
    "\n",
    "#using kernel as linear option\n",
    "clf2 = svm.SVC(kernel='linear')\n",
    "clf2.fit(X_train, y_train) \n",
    "yhat2 = clf2.predict(X_test)\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}