TabPy/HD ML Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Python and Machine Learning Algorithms within Tableau: Heart Disease\n",
    "\n",
    "UCI Machine Learning Repository link:\n",
    "\n",
    "https://archive.ics.uci.edu/ml/datasets/Heart+Disease\n",
    "    \n",
    "Used reference code to deploy functions to Tableau via TabPy:\n",
    "\n",
    "https://www.tableau.com/about/blog/2017/1/building-advanced-analytics-applications-tabpy-64916"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Orysya\\Anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "C:\\Users\\Orysya\\Anaconda\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.cross_validation import cross_val_score, cross_val_predict, StratifiedKFold \n",
    "from sklearn import preprocessing, metrics, svm, ensemble\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import tabpy_client\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data pre-processing: drop nulls, examine class attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the file, before nulls dropped, is:  (303, 14)\n",
      "The size of the file, after nulls dropped, is:  (297, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest_pain</th>\n",
       "      <th>resting_bp</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest_pain  resting_bp  chol  fbs  restecg  thalach  exang  \\\n",
       "0   63    1           1         145   233    1        2      150      0   \n",
       "1   67    1           4         160   286    0        2      108      1   \n",
       "2   67    1           4         120   229    0        2      129      1   \n",
       "3   37    1           3         130   250    0        0      187      0   \n",
       "4   41    0           2         130   204    0        2      172      0   \n",
       "\n",
       "   oldpeak  slope ca thal diagnosis  \n",
       "0      2.3      3  0    6   healthy  \n",
       "1      1.5      2  3    3      risk  \n",
       "2      2.6      2  2    7      risk  \n",
       "3      3.5      3  0    3   healthy  \n",
       "4      1.4      1  0    3   healthy  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd = pd.read_csv('./processed.cleveland.data.csv', names= [\"age\", \"sex\", \"chest_pain\", \"resting_bp\", \"chol\", \"fbs\", \"restecg\", \"thalach\", \"exang\", \"oldpeak\", \"slope\", \"ca\", \"thal\", \"diagnosis\"])\n",
    "print 'The size of the file, before nulls dropped, is: ', hd.shape\n",
    "\n",
    "hd = hd.replace('?', np.nan)\n",
    "hd = hd.dropna()\n",
    "# Define diagnosis of 1-4 as 'risk' and 0 as 'healthy'\n",
    "def diagnosis(row):\n",
    "    if row['diagnosis'] > 0:\n",
    "        return 'risk'\n",
    "    else:\n",
    "        return 'healthy'\n",
    "hd['diagnosis'] = hd.apply(diagnosis, axis=1)\n",
    "print 'The size of the file, after nulls dropped, is: ', hd.shape\n",
    "#hd.to_csv('./cleveland_data_for tableau.csv', index=False)\n",
    "hd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th>age</th>\n",
       "      <th>chest_pain</th>\n",
       "      <th>chol</th>\n",
       "      <th>exang</th>\n",
       "      <th>fbs</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>restecg</th>\n",
       "      <th>resting_bp</th>\n",
       "      <th>sex</th>\n",
       "      <th>slope</th>\n",
       "      <th>thalach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">healthy</th>\n",
       "      <th>count</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.643750</td>\n",
       "      <td>2.793750</td>\n",
       "      <td>243.493750</td>\n",
       "      <td>0.143750</td>\n",
       "      <td>0.143750</td>\n",
       "      <td>0.598750</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>129.175000</td>\n",
       "      <td>0.556250</td>\n",
       "      <td>1.412500</td>\n",
       "      <td>158.581250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.551151</td>\n",
       "      <td>0.925508</td>\n",
       "      <td>53.757550</td>\n",
       "      <td>0.351938</td>\n",
       "      <td>0.351938</td>\n",
       "      <td>0.787160</td>\n",
       "      <td>0.987640</td>\n",
       "      <td>16.373990</td>\n",
       "      <td>0.498386</td>\n",
       "      <td>0.597558</td>\n",
       "      <td>19.043304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>208.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>235.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>161.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>268.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>172.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">risk</th>\n",
       "      <th>count</th>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.759124</td>\n",
       "      <td>3.583942</td>\n",
       "      <td>251.854015</td>\n",
       "      <td>0.540146</td>\n",
       "      <td>0.145985</td>\n",
       "      <td>1.589051</td>\n",
       "      <td>1.175182</td>\n",
       "      <td>134.635036</td>\n",
       "      <td>0.817518</td>\n",
       "      <td>1.824818</td>\n",
       "      <td>139.109489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.899670</td>\n",
       "      <td>0.828201</td>\n",
       "      <td>49.679937</td>\n",
       "      <td>0.500215</td>\n",
       "      <td>0.354387</td>\n",
       "      <td>1.305006</td>\n",
       "      <td>0.976924</td>\n",
       "      <td>18.896730</td>\n",
       "      <td>0.387658</td>\n",
       "      <td>0.567474</td>\n",
       "      <td>22.710673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>157.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        age  chest_pain        chol       exang         fbs  \\\n",
       "diagnosis                                                                     \n",
       "healthy   count  160.000000  160.000000  160.000000  160.000000  160.000000   \n",
       "          mean    52.643750    2.793750  243.493750    0.143750    0.143750   \n",
       "          std      9.551151    0.925508   53.757550    0.351938    0.351938   \n",
       "          min     29.000000    1.000000  126.000000    0.000000    0.000000   \n",
       "          25%     44.750000    2.000000  208.750000    0.000000    0.000000   \n",
       "          50%     52.000000    3.000000  235.500000    0.000000    0.000000   \n",
       "          75%     59.000000    3.000000  268.250000    0.000000    0.000000   \n",
       "          max     76.000000    4.000000  564.000000    1.000000    1.000000   \n",
       "risk      count  137.000000  137.000000  137.000000  137.000000  137.000000   \n",
       "          mean    56.759124    3.583942  251.854015    0.540146    0.145985   \n",
       "          std      7.899670    0.828201   49.679937    0.500215    0.354387   \n",
       "          min     35.000000    1.000000  131.000000    0.000000    0.000000   \n",
       "          25%     53.000000    4.000000  218.000000    0.000000    0.000000   \n",
       "          50%     58.000000    4.000000  253.000000    1.000000    0.000000   \n",
       "          75%     62.000000    4.000000  284.000000    1.000000    0.000000   \n",
       "          max     77.000000    4.000000  409.000000    1.000000    1.000000   \n",
       "\n",
       "                    oldpeak     restecg  resting_bp         sex       slope  \\\n",
       "diagnosis                                                                     \n",
       "healthy   count  160.000000  160.000000  160.000000  160.000000  160.000000   \n",
       "          mean     0.598750    0.843750  129.175000    0.556250    1.412500   \n",
       "          std      0.787160    0.987640   16.373990    0.498386    0.597558   \n",
       "          min      0.000000    0.000000   94.000000    0.000000    1.000000   \n",
       "          25%      0.000000    0.000000  120.000000    0.000000    1.000000   \n",
       "          50%      0.200000    0.000000  130.000000    1.000000    1.000000   \n",
       "          75%      1.100000    2.000000  140.000000    1.000000    2.000000   \n",
       "          max      4.200000    2.000000  180.000000    1.000000    3.000000   \n",
       "risk      count  137.000000  137.000000  137.000000  137.000000  137.000000   \n",
       "          mean     1.589051    1.175182  134.635036    0.817518    1.824818   \n",
       "          std      1.305006    0.976924   18.896730    0.387658    0.567474   \n",
       "          min      0.000000    0.000000  100.000000    0.000000    1.000000   \n",
       "          25%      0.600000    0.000000  120.000000    1.000000    1.000000   \n",
       "          50%      1.400000    2.000000  130.000000    1.000000    2.000000   \n",
       "          75%      2.500000    2.000000  145.000000    1.000000    2.000000   \n",
       "          max      6.200000    2.000000  200.000000    1.000000    3.000000   \n",
       "\n",
       "                    thalach  \n",
       "diagnosis                    \n",
       "healthy   count  160.000000  \n",
       "          mean   158.581250  \n",
       "          std     19.043304  \n",
       "          min     96.000000  \n",
       "          25%    149.000000  \n",
       "          50%    161.000000  \n",
       "          75%    172.000000  \n",
       "          max    202.000000  \n",
       "risk      count  137.000000  \n",
       "          mean   139.109489  \n",
       "          std     22.710673  \n",
       "          min     71.000000  \n",
       "          25%    125.000000  \n",
       "          50%    142.000000  \n",
       "          75%    157.000000  \n",
       "          max    195.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd.groupby('diagnosis').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest_pain</th>\n",
       "      <th>resting_bp</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest_pain  resting_bp  chol  fbs  restecg  thalach  exang  \\\n",
       "0   63    1           1         145   233    1        2      150      0   \n",
       "1   67    1           4         160   286    0        2      108      1   \n",
       "2   67    1           4         120   229    0        2      129      1   \n",
       "3   37    1           3         130   250    0        0      187      0   \n",
       "4   41    0           2         130   204    0        2      172      0   \n",
       "\n",
       "   oldpeak  slope ca thal  diagnosis  \n",
       "0      2.3      3  0    6          0  \n",
       "1      1.5      2  3    3          1  \n",
       "2      2.6      2  2    7          1  \n",
       "3      3.5      3  0    3          0  \n",
       "4      1.4      1  0    3          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since class attribute is vales 0-4, there is no need to convert text to numeric using encoder, no transformation needed\n",
    "encoder = preprocessing.LabelEncoder()\n",
    "hd['diagnosis'] = encoder.fit_transform(hd['diagnosis'])\n",
    "hd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Split data into X, y\n",
    "X = np.array(hd.drop(['diagnosis'], 1))\n",
    "y = np.array(hd['diagnosis'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machine reference:\n",
    "\n",
    "http://scikit-learn.org/stable/modules/svm.html\n",
    "\n",
    "To determine which model evaluations work best, via 'scoring':\n",
    "\n",
    "http://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Orysya\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# Scale the data (Assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.)\n",
    "# Note in order for StandardScaler to work, need to remove any nulls in data set prior to running\n",
    "scalar = preprocessing.StandardScaler().fit(X)\n",
    "X = scalar.transform(X)\n",
    "\n",
    "# 10 fold stratified cross validation\n",
    "kf = StratifiedKFold(y, n_folds=10, random_state=None, shuffle=True)\n",
    "\n",
    "# Define the parameter grid to use for tuning the Support Vector Machine\n",
    "parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]},\n",
    "              {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n",
    "\n",
    "# Choose performance measures for modeling\n",
    "scoringmethods = ['f1','accuracy','precision', 'recall','roc_auc']\n",
    "# scoringmethods = ['f1_weighted', 'accuracy', 'precision_weighted', 'recall_weighted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~ Hyper-parameter tuning for best f1 ~~~\n",
      "Scores for different parameter combinations in the grid:\n",
      "  0.806 (+/-0.040) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n",
      "  0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}\n",
      "  0.821 (+/-0.053) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}\n",
      "  0.806 (+/-0.040) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}\n",
      "  0.807 (+/-0.045) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}\n",
      "  0.817 (+/-0.053) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}\n",
      "  0.814 (+/-0.044) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}\n",
      "  0.803 (+/-0.044) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n",
      "  0.803 (+/-0.044) for {'kernel': 'linear', 'C': 1}\n",
      "  0.807 (+/-0.045) for {'kernel': 'linear', 'C': 10}\n",
      "  0.803 (+/-0.044) for {'kernel': 'linear', 'C': 100}\n",
      "  0.803 (+/-0.044) for {'kernel': 'linear', 'C': 1000}\n",
      "\n",
      "~~~ Hyper-parameter tuning for best accuracy ~~~\n",
      "Scores for different parameter combinations in the grid:\n",
      "  0.842 (+/-0.030) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n",
      "  0.539 (+/-0.004) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}\n",
      "  0.845 (+/-0.043) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}\n",
      "  0.842 (+/-0.030) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}\n",
      "  0.832 (+/-0.035) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}\n",
      "  0.842 (+/-0.043) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}\n",
      "  0.835 (+/-0.037) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}\n",
      "  0.828 (+/-0.033) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n",
      "  0.828 (+/-0.033) for {'kernel': 'linear', 'C': 1}\n",
      "  0.832 (+/-0.035) for {'kernel': 'linear', 'C': 10}\n",
      "  0.828 (+/-0.033) for {'kernel': 'linear', 'C': 100}\n",
      "  0.828 (+/-0.033) for {'kernel': 'linear', 'C': 1000}\n",
      "\n",
      "~~~ Hyper-parameter tuning for best precision ~~~\n",
      "Scores for different parameter combinations in the grid:\n",
      "  0.918 (+/-0.045) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n",
      "  0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}\n",
      "  0.858 (+/-0.044) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}\n",
      "  0.918 (+/-0.045) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}\n",
      "  0.836 (+/-0.032) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}\n",
      "  0.856 (+/-0.044) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}\n",
      "  0.838 (+/-0.040) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}\n",
      "  0.835 (+/-0.032) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n",
      "  0.835 (+/-0.032) for {'kernel': 'linear', 'C': 1}\n",
      "  0.836 (+/-0.032) for {'kernel': 'linear', 'C': 10}\n",
      "  0.835 (+/-0.032) for {'kernel': 'linear', 'C': 100}\n",
      "  0.835 (+/-0.032) for {'kernel': 'linear', 'C': 1000}\n",
      "\n",
      "~~~ Hyper-parameter tuning for best recall ~~~\n",
      "Scores for different parameter combinations in the grid:\n",
      "  0.729 (+/-0.055) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n",
      "  0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}\n",
      "  0.795 (+/-0.068) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}\n",
      "  0.729 (+/-0.055) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}\n",
      "  0.788 (+/-0.063) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}\n",
      "  0.788 (+/-0.068) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}\n",
      "  0.795 (+/-0.054) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}\n",
      "  0.781 (+/-0.060) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n",
      "  0.781 (+/-0.060) for {'kernel': 'linear', 'C': 1}\n",
      "  0.788 (+/-0.063) for {'kernel': 'linear', 'C': 10}\n",
      "  0.781 (+/-0.060) for {'kernel': 'linear', 'C': 100}\n",
      "  0.781 (+/-0.060) for {'kernel': 'linear', 'C': 1000}\n",
      "\n",
      "~~~ Hyper-parameter tuning for best roc_auc ~~~\n",
      "Scores for different parameter combinations in the grid:\n",
      "  0.905 (+/-0.028) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n",
      "  0.904 (+/-0.026) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}\n",
      "  0.904 (+/-0.029) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}\n",
      "  0.904 (+/-0.027) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}\n",
      "  0.911 (+/-0.026) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}\n",
      "  0.905 (+/-0.029) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}\n",
      "  0.902 (+/-0.026) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}\n",
      "  0.907 (+/-0.027) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n",
      "  0.904 (+/-0.028) for {'kernel': 'linear', 'C': 1}\n",
      "  0.903 (+/-0.028) for {'kernel': 'linear', 'C': 10}\n",
      "  0.903 (+/-0.027) for {'kernel': 'linear', 'C': 100}\n",
      "  0.903 (+/-0.027) for {'kernel': 'linear', 'C': 1000}\n",
      "\n",
      "Classification report:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.89      0.86       160\n",
      "          1       0.86      0.80      0.83       137\n",
      "\n",
      "avg / total       0.85      0.85      0.85       297\n",
      "\n",
      "Best model:\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Accuracy: 0.848\n",
      "Aucroc: 0.845\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Iterate through different metrics looking for best parameter set\n",
    "for score in scoringmethods:\n",
    "    print(\"~~~ Hyper-parameter tuning for best %s ~~~\" % score)\n",
    "    \n",
    "    # Setup for grid search with cross-validation for Support Vector Machine\n",
    "    # n_jobs=-1 for parallel execution using all available cores\n",
    "    svmclf = GridSearchCV(svm.SVC(C=1), parameters, cv=kf, scoring=score,n_jobs=-1)\n",
    "    svmclf.fit(X, y)\n",
    "    \n",
    "    # Show each result from grid search\n",
    "    print(\"Scores for different parameter combinations in the grid:\")\n",
    "    for params, mean_score, scores in svmclf.grid_scores_:\n",
    "        print(\"  %0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean_score, scores.std() / 2, params)) \n",
    "    print(\"\")\n",
    "    \n",
    "# Show classification report for the best model (set of parameters) run over the full dataset\n",
    "print(\"Classification report:\")\n",
    "y_pred = svmclf.predict(X)\n",
    "print(classification_report(y, y_pred))\n",
    "    \n",
    "# Show the definition of the best model\n",
    "print(\"Best model:\")\n",
    "print(svmclf.best_estimator_)\n",
    "    \n",
    "# Show accuracy\n",
    "print(\"Accuracy: %0.3f\" % accuracy_score(y, y_pred, normalize=True))\n",
    "print(\"Aucroc: %0.3f\" % metrics.roc_auc_score(y, y_pred))\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.88      0.86       160\n",
      "          1       0.85      0.80      0.83       137\n",
      "\n",
      "avg / total       0.85      0.85      0.84       297\n",
      "\n",
      "Accuracy: 0.845\n",
      "Aucroc: 0.842\n"
     ]
    }
   ],
   "source": [
    "# Logistic regression with 10 fold stratified cross-validation using model specific cross-validation in scikit-learn\n",
    "lgclf = LogisticRegressionCV(Cs=list(np.power(10.0, np.arange(-10, 10))),penalty='l2',scoring='roc_auc',cv=kf)\n",
    "lgclf.fit(X, y)\n",
    "y_pred = lgclf.predict(X)\n",
    "\n",
    "# Show classification report for the best model (set of parameters) run over the full dataset\n",
    "print(\"Classification report:\")\n",
    "print(classification_report(y, y_pred))\n",
    "\n",
    "# Show accuracy and area under ROC curve\n",
    "print(\"Accuracy: %0.3f\" % accuracy_score(y, y_pred, normalize=True))\n",
    "print(\"Aucroc: %0.3f\" % metrics.roc_auc_score(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.841\n",
      "Aucroc: 0.838\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes with 10 fold stratified cross-validation\n",
    "nbclf = GaussianNB()\n",
    "scores = cross_val_score(nbclf, X, y, cv=kf, scoring= 'accuracy')\n",
    "\n",
    "print(\"Accuracy: %0.3f\" % (scores.mean()))\n",
    "print(\"Aucroc: %0.3f\" % metrics.roc_auc_score(y, cross_val_predict(nbclf, X, y, cv=kf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model:\n",
      "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
      "              learning_rate=0.01, loss='deviance', max_depth=4,\n",
      "              max_features=None, max_leaf_nodes=None,\n",
      "              min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "              min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "              n_estimators=1500, presort='auto', random_state=None,\n",
      "              subsample=1.0, verbose=0, warm_start=False)\n",
      "\n",
      "Classification report:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00       160\n",
      "          1       1.00      1.00      1.00       137\n",
      "\n",
      "avg / total       1.00      1.00      1.00       297\n",
      "\n",
      "Accuracy: 1.000\n",
      "Aucroc: 1.000\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid to use for tuning the Gradient Boosting Classifier\n",
    "gridparams = dict(learning_rate=[0.01, 0.1],loss=['deviance','exponential'])\n",
    "\n",
    "# Parameters we're not tuning for this classifier\n",
    "params = {'n_estimators': 1500, 'max_depth': 4}\n",
    "\n",
    "# Setup for grid search with cross-validation for Gradient Boosting Classifier\n",
    "# n_jobs=-1 for parallel execution using all available cores\n",
    "gbclf = GridSearchCV(ensemble.GradientBoostingClassifier(**params), gridparams, cv=kf, scoring='roc_auc',n_jobs=-1)\n",
    "gbclf.fit(X,y)\n",
    "\n",
    "# Show the definition of the best model\n",
    "print(\"Best model:\")\n",
    "print(gbclf.best_estimator_)\n",
    "print(\"\")\n",
    "\n",
    "# Show classification report for the best model (set of parameters) run over the full dataset\n",
    "print(\"Classification report:\")    \n",
    "y_pred = gbclf.predict(X)\n",
    "print(classification_report(y, y_pred))\n",
    "\n",
    "# Show accuracy and area under ROC curve\n",
    "print(\"Accuracy: %0.3f\" % accuracy_score(y, y_pred, normalize=True))\n",
    "print(\"Aucroc: %0.3f\" % metrics.roc_auc_score(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Connect to TabPy server using the client library\n",
    "connection = tabpy_client.Client('http://localhost:9004/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The scoring function that will use the Gradient Boosting Classifier to classify new data points\n",
    "def HDDiagnosis(age, sex, chest_pain, resting_bp, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):\n",
    "    X = np.column_stack([age, sex, chest_pain, resting_bp, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal])\n",
    "    X = scalar.transform(X)\n",
    "    return encoder.inverse_transform(gbclf.predict(X)).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "connection.deploy('HDDiagnosis',\n",
    "                  HDDiagnosis,\n",
    "                  'Returns diagnosis suggestion (healthy or risk) based on ensemble model trained using Cleveland Heart Disease dataset',\n",
    "                  override=True)"
   ]
  }
 ],
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