examprep_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "4893            6.2              0.21         0.29             1.6      0.039   \n",
       "4894            6.6              0.32         0.36             8.0      0.047   \n",
       "4895            6.5              0.24         0.19             1.2      0.041   \n",
       "4896            5.5              0.29         0.30             1.1      0.022   \n",
       "4897            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  \n",
       "4893     11.2      6.0  \n",
       "4894      9.6      5.0  \n",
       "4895      9.4      6.0  \n",
       "4896     12.8      7.0  \n",
       "4897     11.8      6.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Prepare data frame\n",
    "http_request = requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv')\n",
    "http_text = http_request.text.split(\"\\n\")\n",
    "data_array = []\n",
    "for lines in http_text:\n",
    "    data_array.append(lines.split(\";\"))\n",
    "col_names = []\n",
    "for col in data_array[0]:\n",
    "    col_names.append(col.strip('\"'))\n",
    "    \n",
    "fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)\n",
    "##Change to numeric\n",
    "data_frame = pd.DataFrame(data=data_array[1:]).apply(pd.to_numeric)\n",
    "## Remove last column as all are Nan\n",
    "data_frame = data_frame.ix[0:data_frame.shape[0]-2,:]\n",
    "##Remove nan with mean values\n",
    "data_frame = pd.DataFrame(fill_NaN.fit_transform(data_frame),columns=col_names)\n",
    "data_frame.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feature_cols = col_names[:-1]\n",
    "features = data_frame[feature_cols]\n",
    "labels = data_frame[\"quality\"]\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42)\n",
    "actuals = labels_test[:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected EOF while parsing (<ipython-input-27-70f8f320fdda>, line 15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-27-70f8f320fdda>\"\u001b[1;36m, line \u001b[1;32m15\u001b[0m\n\u001b[1;33m    #print(\"Predicted value \" +  str(predictions[i]) + \"and actual value is \" + str(actuals[i]))\u001b[0m\n\u001b[1;37m                                                                                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n"
     ]
    }
   ],
   "source": [
    "##SVM\n",
    "from sklearn import svm\n",
    "scaler_model = MinMaxScaler().fit_transform(features_train.values)\n",
    "bins = np.linspace(0, 1, 10)\n",
    "digitized = np.digitize(scaler_model, bins)\n",
    "print(digitized)\n",
    "clf = svm.SVC()\n",
    "clf.fit(X=digitized,y=labels_train.values)  \n",
    "score_knn = cross_val_score(clf, digitized, labels_train.values, cv=4)\n",
    "print(\"Cross Validation score : \" + str(score_knn))\n",
    "print(\"Cross Validation Mean score : \" + str(score_knn.mean()))\n",
    "\n",
    "predictions = clf.predict(features_test)\n",
    "for i in range (labels_test.size-1) :\n",
    "    if predictions[i] != actuals[i]:\n",
    "        print(\"Predicted value \" +  str(predictions[i]) + \"and actual value is \" + str(actuals[i]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('fixed acidity', -0.12352578913237661)]\n",
      "Mean squared error: 0.88\n",
      "Variance score: 0.01\n",
      "[[ 6.]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e8b0df9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(features_train, labels_train)\n",
    "\n",
    "coefs = list(zip(feature_cols, linreg.coef_))\n",
    "print(coefs)\n",
    "\n",
    "labels_predicted = linreg.predict(features_test)\n",
    "\n",
    "# ist(zip\n",
    "print(\"Mean squared error: %.2f\"\n",
    "    11  % np.sqrt(mean_squared_error(labels_test, labels_predicted)))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(labels_test, labels_predicted))\n",
    "print(features_test[:1].values)\n",
    "# Plot outputs\n",
    "#for i in range(len(feature_cols)):\n",
    "plt.scatter(features_test, labels_test,  color='black')\n",
    "plt.plot(features_test, labels_predicted, color='blue', linewidth=3)\n",
    "plt.xticks(range(5,10))\n",
    "plt.yticks(range(10))\n",
    "plt.xlabel('feature')\n",
    "plt.ylabel('quality')\n",
    "plt.title('Linear regression graph')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercepts are [-13.06609569  -5.95430694  -1.9894609    0.22400334   0.06808869\n",
      "  -1.77119271 -11.08037296]\n",
      "[('fixed acidity', array([ 1.01509739]))]\n",
      "[(array([ 6.]), 6.0), (array([ 5.4]), 6.0), (array([ 7.1]), 6.0), (array([ 7.3]), 6.0), (array([ 6.5]), 6.0), (array([ 6.3]), 6.0), (array([ 7.]), 6.0), (array([ 7.6]), 6.0), (array([ 8.3]), 6.0), (array([ 8.7]), 6.0), (array([ 6.4]), 6.0), (array([ 6.7]), 6.0), (array([ 6.3]), 6.0), (array([ 7.6]), 6.0), (array([ 5.9]), 6.0), (array([ 6.4]), 6.0), (array([ 5.2]), 6.0), (array([ 5.5]), 6.0), (array([ 7.7]), 6.0), (array([ 6.7]), 6.0), (array([ 5.9]), 6.0), (array([ 7.4]), 6.0), (array([ 6.9]), 6.0), (array([ 7.6]), 6.0), (array([ 5.9]), 6.0), (array([ 7.6]), 6.0), (array([ 6.8]), 6.0), (array([ 5.1]), 6.0), (array([ 8.7]), 6.0), (array([ 8.1]), 6.0), (array([ 6.8]), 6.0), (array([ 7.4]), 6.0), (array([ 7.1]), 6.0), (array([ 7.2]), 6.0), (array([ 7.4]), 6.0), (array([ 6.1]), 6.0), (array([ 7.1]), 6.0), (array([ 6.6]), 6.0), (array([ 7.6]), 6.0), (array([ 6.]), 6.0), (array([ 6.]), 6.0), (array([ 6.9]), 6.0), (array([ 6.9]), 6.0), (array([ 5.2]), 6.0), (array([ 6.3]), 6.0), 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     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Dimensions of C (1225, 2) are incompatible with X (1) and/or Y (1225); see help(pcolormesh)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-4ab62e4d517b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPaired\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'k'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPaired\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;31m#plt.plot(features_test, labels_predicted, color='blue', linewidth=3)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   3091\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3092\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3093\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3094\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3095\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1809\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1810\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1811\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1812\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1813\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   5393\u001b[0m         \u001b[0mallmatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mshading\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gouraud'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5395\u001b[1;33m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pcolorargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pcolormesh'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallmatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mallmatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5396\u001b[0m         \u001b[0mNy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36m_pcolorargs\u001b[1;34m(funcname, *args, **kw)\u001b[0m\n\u001b[0;32m   5019\u001b[0m                 raise TypeError('Dimensions of C %s are incompatible with'\n\u001b[0;32m   5020\u001b[0m                                 ' X (%d) and/or Y (%d); see help(%s)' % (\n\u001b[1;32m-> 5021\u001b[1;33m                                     C.shape, Nx, Ny, funcname))\n\u001b[0m\u001b[0;32m   5022\u001b[0m             \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mNy\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[0mNx\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5023\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Dimensions of C (1225, 2) are incompatible with X (1) and/or Y (1225); see help(pcolormesh)"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQkAAADKCAYAAAC2Y7XKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkpBUsiQklSwJSaX/BTYbdQrDQlCtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c40c05cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "feature_cols = col_names[:1]\n",
    "features = data_frame[feature_cols]\n",
    "labels = data_frame[\"quality\"]\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42)\n",
    "actuals = labels_test[:].values\n",
    "\n",
    "logreg = LogisticRegression(C=1e42) # Set Large C value for low regularization\n",
    "logreg.fit(features_train, labels_train)\n",
    "\n",
    "print(\"Intercepts are \"+str(logreg.intercept_))\n",
    "\n",
    "coefs = list(zip(feature_cols, logreg.coef_))\n",
    "print(coefs)\n",
    "\n",
    "labels_predicted = logreg.predict(features_test) # Categorial / Digital\n",
    "predicted_proba = logreg.predict_proba(features_test) # Analog (e.g. > 0.5 = true else false)\n",
    "\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "C = list(zip(features_test.values, labels_predicted))\n",
    "print(C)\n",
    "\n",
    "plt.pcolormesh(features_test, labels_test, C, cmap=plt.cm.Paired)\n",
    "plt.scatter(features_test, labels_test,c=C, edgecolors='k', cmap=plt.cm.Paired)\n",
    "#plt.plot(features_test, labels_predicted, color='blue', linewidth=3)\n",
    "plt.xticks(range(5,10))\n",
    "plt.yticks(range(10))\n",
    "plt.xlabel(feature_cols)\n",
    "plt.ylabel('quality')\n",
    "plt.title('Logistic regression graph')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "#for i in range (labels_test.size-1) :\n",
    " #   if predictions[i] != actuals[i]:\n",
    "  #      print(\"Predicted value \" +  str(predictions[i]) + \"and actual value is \" + str(actuals[i]))  \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.07202589  0.11345824  0.07007856  0.07960145  0.08561998  0.0964986\n",
      "  0.08236588  0.10712978  0.07353311  0.06761671  0.15207182]\n",
      "0.867138578818\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "multiclass format is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-69df49db28e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mpredicted_proba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m# Get success rate of the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicted_proba\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# [:,1] for only 2 classes (e.g. Churn, Not Churn)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\ranking.py\u001b[0m in \u001b[0;36mroc_auc_score\u001b[1;34m(y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[0;32m    258\u001b[0m     return _average_binary_score(\n\u001b[0;32m    259\u001b[0m         \u001b[0m_binary_roc_auc_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m         sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m    261\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\base.py\u001b[0m in \u001b[0;36m_average_binary_score\u001b[1;34m(binary_metric, y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[0;32m     79\u001b[0m     \u001b[0my_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"binary\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"multilabel-indicator\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{0} format is not supported\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"binary\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: multiclass format is not supported"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "random_forest = RandomForestClassifier(criterion=\"entropy\", max_depth = 10, min_samples_split=2, n_estimators = 100, random_state = 1)\n",
    "#random_forest = RandomForestClassifier(criterion=\"gini\", max_depth = 10, min_samples_split=2, n_estimators = 100, random_state = 1)\n",
    "random_forest = random_forest.fit(features_train, labels_train)\n",
    "\n",
    "print(random_forest.feature_importances_)\n",
    "\n",
    "print(random_forest.score(features_train, labels_train))\n",
    "\n",
    "predicted_proba = random_forest.predict_proba(features_test)\n",
    "# Get success rate of the model\n",
    "print(roc_auc_score(labels_test, predicted_proba)) # [:,1] for only 2 classes (e.g. Churn, Not Churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "multiclass format is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-c99eb3e2d0de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mpredicted_proba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mknn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m# Get success rate of the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicted_proba\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# [:,1] for only 2 classes (e.g. Legitimate or Malicious site)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\ranking.py\u001b[0m in \u001b[0;36mroc_auc_score\u001b[1;34m(y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[0;32m    258\u001b[0m     return _average_binary_score(\n\u001b[0;32m    259\u001b[0m         \u001b[0m_binary_roc_auc_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m         sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m    261\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\base.py\u001b[0m in \u001b[0;36m_average_binary_score\u001b[1;34m(binary_metric, y_true, y_score, average, sample_weight)\u001b[0m\n\u001b[0;32m     79\u001b[0m     \u001b[0my_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"binary\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"multilabel-indicator\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{0} format is not supported\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"binary\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: multiclass format is not supported"
     ]
    }
   ],
   "source": [
    "# kNN : k - Near Neighbors (Classification)\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=10, weights='distance')\n",
    "knn = knn.fit(features_train, labels_train)\n",
    "\n",
    "print(knn.score(features_train, labels_train))\n",
    "\n",
    "predicted_proba = knn.predict_proba(features_test)\n",
    "# Get success rate of the model\n",
    "print(roc_auc_score(labels_test, predicted_proba[:])) # [:,1] for only 2 classes (e.g. Legitimate or Malicious site)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "LookupError",
     "evalue": "\n**********************************************************************\n  Resource 'corpora/stopwords' not found.  Please use the NLTK\n  Downloader to obtain the resource:  >>> nltk.download()\n  Searched in:\n    - 'C:\\\\Users\\\\User/nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\Anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\Anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\AppData\\\\Roaming\\\\nltk_data'\n**********************************************************************",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'corpora/%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mzip_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\nltk\\data.py\u001b[0m in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    640\u001b[0m     \u001b[0mresource_not_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'\\n%s\\n%s\\n%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 641\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresource_not_found\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource 'corpora/stopwords.zip/stopwords/' not found.  Please\n  use the NLTK Downloader to obtain the resource:  >>>\n  nltk.download()\n  Searched in:\n    - 'C:\\\\Users\\\\User/nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\Anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\Anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\AppData\\\\Roaming\\\\nltk_data'\n**********************************************************************",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-8c7c1da29962>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m#TFIDF Vectorizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mstopset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstopwords\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'english'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mvectorizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTfidfVectorizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muse_idf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlowercase\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstrip_accents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ascii'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop_words\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstopset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvectorizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# transform sentence text from 1 column to features\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m     97\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"LazyCorpusLoader object has no attribute '__bases__'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    100\u001b[0m         \u001b[1;31m# This looks circular, but its not, since __load() changes our\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m         \u001b[1;31m# __class__ to something new:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'corpora/%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mzip_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m                 \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[1;31m# Load the corpus.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py\u001b[0m in \u001b[0;36m__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     59\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                 \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'corpora/%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mroot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'corpora/%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mzip_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\nltk\\data.py\u001b[0m in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    639\u001b[0m     \u001b[0msep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'*'\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m70\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    640\u001b[0m     \u001b[0mresource_not_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'\\n%s\\n%s\\n%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 641\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresource_not_found\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    642\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    643\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource 'corpora/stopwords' not found.  Please use the NLTK\n  Downloader to obtain the resource:  >>> nltk.download()\n  Searched in:\n    - 'C:\\\\Users\\\\User/nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\Anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\Anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\User\\\\AppData\\\\Roaming\\\\nltk_data'\n**********************************************************************"
     ]
    }
   ],
   "source": [
    "# Naive Bayes (Classification, strong independence between features)\n",
    "\n",
    "# Feature Extraction - http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "#TFIDF Vectorizer\n",
    "stopset = set(stopwords.words('english'))\n",
    "vectorizer = TfidfVectorizer(use_idf=True, lowercase=True, strip_accents='ascii', stop_words=stopset)\n",
    "features = vectorizer.fit_transform(dataframe.text) # transform sentence text from 1 column to features\n",
    "\n",
    "\n",
    "from sklearn import naive_bayes\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "#clf = naive_bayes.BernoulliNB() #discrete data, for binary/boolean features\n",
    "#clf = naive_bayes.GaussianNB() # ?\n",
    "clf = naive_bayes.MultinomialNB() #discrete data, for occurrence count\n",
    "clf.fit(features_train, labels_train)\n",
    "\n",
    "predicted_proba = clf.predict_proba(features_test)\n",
    "# Get success rate of the model\n",
    "print(roc_auc_score(labels_test, predicted_proba[:,1])) # [:,1] for only 2 classes (e.g. Spam or Ham)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   2,    0,    6,   12,    0,    0,    0],\n",
       "       [   0,   11,   89,   61,    2,    0,    0],\n",
       "       [   0,    0,  869,  582,    6,    0,    0],\n",
       "       [   0,    1,  316, 1785,   95,    0,    0],\n",
       "       [   0,    0,   36,  579,  264,    0,    0],\n",
       "       [   0,    0,    7,  120,   48,    0,    0],\n",
       "       [   0,    0,    0,    2,    3,    0,    0]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(label.values, clf.predict(X=digitized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'itertools' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-319c9c7c31f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mdf_confusion\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;31m#df_confusion\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_confusion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-45-319c9c7c31f2>\u001b[0m in \u001b[0;36mplot_confusion_matrix\u001b[1;34m(df_confusion, title, cmap)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mthresh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_confusion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mitertools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_confusion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_confusion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m         plt.text(j, i, df_confusion[i, j],\n\u001b[0;32m     18\u001b[0m                  \u001b[0mhorizontalalignment\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"center\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'itertools' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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aqJI4t0Jdx8ijbiKO1kF3S+yaL4isDrp5P+2fCXGOCfXIMAdtbCllJUc/2ujH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\nsEpEqyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e8b5376f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "def plot_confusion_matrix(df_confusion, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(df_confusion, cmap=cmap)\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.colorbar()\n",
    "    plt.yticks(np.arange(2), ('False', 'True'))\n",
    "    plt.xticks(np.arange(2), ('False', 'True'))\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    \n",
    "    thresh = df_confusion.max() / 2\n",
    "    for i, j in itertools.product(range(df_confusion.shape[0]), range(df_confusion.shape[1])):\n",
    "        plt.text(j, i, df_confusion[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if df_confusion[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "labels_pred = clf.predict(features)\n",
    "df_confusion = metrics.confusion_matrix(labels, labels_pred)\n",
    "#df_confusion\n",
    "plot_confusion_matrix(df_confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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