09 SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('00 df.csv')\n",
    "train = df[df['flag']=='train']\n",
    "test = df[df['flag']=='test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat_feats = ['age_bin','capital_gl_bin','education_bin','hours_per_week_bin','msr_bin','occupation_bin','race_sex_bin']\n",
    "\n",
    "y_train = train['y']\n",
    "x_train = train[['age_bin','capital_gl_bin','education_bin','hours_per_week_bin','msr_bin','occupation_bin','race_sex_bin']]\n",
    "x_train = pd.get_dummies(x_train,columns=cat_feats,drop_first=True)\n",
    "\n",
    "y_test = test['y']\n",
    "x_test = test[['age_bin','capital_gl_bin','education_bin','hours_per_week_bin','msr_bin','occupation_bin','race_sex_bin']]\n",
    "x_test = pd.get_dummies(x_test,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm =  SVC(kernel=\"rbf\", C=0.025,random_state=101)\n",
    "svm.fit(x_train, y_train)\n",
    "y_pred=svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11943   492]\n",
      " [ 2254  1592]]\n",
      "accuracy: 0.831337141453\n",
      "precision: 0.763915547025\n",
      "recall: 0.413936557462\n",
      "f1 score:  0.536930860034\n"
     ]
    }
   ],
   "source": [
    "test_calc = pd.concat([pd.DataFrame(y_test).reset_index(drop=True),pd.DataFrame(y_pred).reset_index(drop=True)],axis=1)\n",
    "test_calc.rename(columns={0: 'predicted'}, inplace=True)\n",
    "\n",
    "test_calc['predicted'] = test_calc['predicted'].apply(lambda x: 1 if x > 0.5 else 0)\n",
    "df_table = confusion_matrix(test_calc['y'],test_calc['predicted'])\n",
    "print (df_table)\n",
    "\n",
    "print('accuracy:', (df_table[0,0] + df_table[1,1]) / (df_table[0,0] + df_table[0,1] + df_table[1,0] + df_table[1,1]))\n",
    "print ('precision:', df_table[1,1] / (df_table[1,1] + df_table[0,1]))\n",
    "print('recall:', df_table[1,1] / (df_table[1,1] + df_table[1,0]))\n",
    "\n",
    "p = df_table[1,1] / (df_table[1,1] + df_table[0,1])\n",
    "r = df_table[1,1] / (df_table[1,1] + df_table[1,0])\n",
    "print('f1 score: ', (2*p*r)/(p+r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm =  SVC(kernel=\"linear\", C=0.025,random_state=101)\n",
    "svm.fit(x_train, y_train)\n",
    "y_pred=svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11625   810]\n",
      " [ 1781  2065]]\n",
      "accuracy: 0.840857441189\n",
      "precision: 0.718260869565\n",
      "recall: 0.536921476859\n",
      "f1 score:  0.614491891088\n"
     ]
    }
   ],
   "source": [
    "test_calc = pd.concat([pd.DataFrame(y_test).reset_index(drop=True),pd.DataFrame(y_pred).reset_index(drop=True)],axis=1)\n",
    "test_calc.rename(columns={0: 'predicted'}, inplace=True)\n",
    "\n",
    "test_calc['predicted'] = test_calc['predicted'].apply(lambda x: 1 if x > 0.5 else 0)\n",
    "df_table = confusion_matrix(test_calc['y'],test_calc['predicted'])\n",
    "print (df_table)\n",
    "\n",
    "print('accuracy:', (df_table[0,0] + df_table[1,1]) / (df_table[0,0] + df_table[0,1] + df_table[1,0] + df_table[1,1]))\n",
    "print ('precision:', df_table[1,1] / (df_table[1,1] + df_table[0,1]))\n",
    "print('recall:', df_table[1,1] / (df_table[1,1] + df_table[1,0]))\n",
    "\n",
    "p = df_table[1,1] / (df_table[1,1] + df_table[0,1])\n",
    "r = df_table[1,1] / (df_table[1,1] + df_table[1,0])\n",
    "print('f1 score: ', (2*p*r)/(p+r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm =  SVC(kernel=\"poly\", C=0.025,random_state=101)\n",
    "svm.fit(x_train, y_train)\n",
    "y_pred=svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12435     0]\n",
      " [ 3846     0]]\n",
      "accuracy: 0.763773723973\n",
      "precision: nan\n",
      "recall: 0.0\n",
      "f1 score:  nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in longlong_scalars\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in longlong_scalars\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "test_calc = pd.concat([pd.DataFrame(y_test).reset_index(drop=True),pd.DataFrame(y_pred).reset_index(drop=True)],axis=1)\n",
    "test_calc.rename(columns={0: 'predicted'}, inplace=True)\n",
    "\n",
    "test_calc['predicted'] = test_calc['predicted'].apply(lambda x: 1 if x > 0.5 else 0)\n",
    "df_table = confusion_matrix(test_calc['y'],test_calc['predicted'])\n",
    "print (df_table)\n",
    "\n",
    "print('accuracy:', (df_table[0,0] + df_table[1,1]) / (df_table[0,0] + df_table[0,1] + df_table[1,0] + df_table[1,1]))\n",
    "print ('precision:', df_table[1,1] / (df_table[1,1] + df_table[0,1]))\n",
    "print('recall:', df_table[1,1] / (df_table[1,1] + df_table[1,0]))\n",
    "\n",
    "p = df_table[1,1] / (df_table[1,1] + df_table[0,1])\n",
    "r = df_table[1,1] / (df_table[1,1] + df_table[1,0])\n",
    "print('f1 score: ', (2*p*r)/(p+r))"
   ]
  }
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