Heart_Disease_Analysis_Complete.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Heart Disease Analysis and Probability Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Reading, Loading and Storing in a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "List of available features:\n",
    "\n",
    " 1. age: continuous\n",
    " 2. sex: categorical, 2 values {0: female, 1: male}\n",
    " 3. cp (chest pain type): categorical, 4 values\n",
    "    {1: typical angina, 2: atypical angina, 3: non-angina, 4: asymptomatic angina}\n",
    " 4. restbp (resting blood pressure on admission to hospital): continuous (mmHg)\n",
    " 5. chol (serum cholesterol level): continuous (mg/dl)\n",
    " 6. fbs (fasting blood sugar): categorical, 2 values {0: <= 120 mg/dl, 1: > 120 mg/dl}\n",
    " 7. restecg (resting electrocardiography): categorical, 3 values\n",
    "    {0: normal, 1: ST-T wave abnormality, 2: left ventricular hypertrophy}\n",
    " 8. thalach (maximum heart rate achieved): continuous\n",
    " 9. exang (exercise induced angina): categorical, 2 values {0: no, 1: yes}\n",
    "10. oldpeak (ST depression induced by exercise relative to rest): continuous\n",
    "11. slope (slope of peak exercise ST segment): categorical, 3 values \n",
    "    {1: upsloping, 2: flat, 3: downsloping}\n",
    "12. ca (number of major vessels colored by fluoroscopy): discrete (0,1,2,3)\n",
    "13. thal: categorical, 3 values {3: normal, 6: fixed defect, 7: reversible defect}\n",
    "14. num (diagnosis of heart disease): categorical, 5 values \n",
    "    {0: less than 50% narrowing in any major vessel, \n",
    "    1-4: more than 50% narrowing in 1-4 vessels}\n",
    "    \n",
    "The actual number of feature variables (after converting categorical variables\n",
    "to dummy ones) is: \n",
    "1 (age) + 1 (sex) + 3 (cp) + 1 (restbp) + 1 (chol) + 1 (fbs) + 2 (restecg) + \n",
    "1 (thalach) + 1 (exang) + 1 (oldpeak) + 2 (slope) + 1 (ca) + 2 (thal) = 18\n",
    "\n",
    "The response variable (num) is categorical with 5 values, but we don't have\n",
    "enough data to predict all the categories. Therefore we'll replace num with:\n",
    "14. hd (heart disease): categorical, 2 values {0: no, 1: yes}\n",
    "'''\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "# Given two histograms x and y (with the same range and binning), the following function\n",
    "# calculates the intrinsic discrepancy (a symmetrized Kullback-Leibler distance) between them.\n",
    "def intrinsic_discrepancy(x,y):\n",
    "    assert len(x)==len(y)\n",
    "    sumx = sum(xval for xval in x)\n",
    "    sumy = sum(yval for yval in y)\n",
    "    id1  = 0.0\n",
    "    id2  = 0.0\n",
    "    for (xval,yval) in zip(x,y):\n",
    "        if (xval>0) and (yval>0):\n",
    "            id1 += (float(xval)/sumx) * np.log((float(xval)/sumx)/(float(yval)/sumy))\n",
    "            id2 += (float(yval)/sumy) * np.log((float(yval)/sumy)/(float(xval)/sumx))\n",
    "    return min(id1,id2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Reading the data fron CSV file using pandas and creating Pandas dataframe.\n",
    "columns = [\"age\", \"sex\", \"cp\", \"restbp\", \"chol\", \"fbs\", \"restecg\", \n",
    "           \"thalach\", \"exang\", \"oldpeak\", \"slope\", \"ca\", \"thal\", \"num\"]\n",
    "df0 = pd.read_table(\"original_datasets/heart_disease_all14.csv\", sep=',', header=None, names=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>restbp</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>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex   cp  restbp   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0   145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1  67.0  1.0  4.0   160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0   120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0   130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4  41.0  0.0  2.0   130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "\n",
       "   slope   ca  thal  num  \n",
       "0    3.0  0.0   6.0  0.0  \n",
       "1    2.0  3.0   3.0  2.0  \n",
       "2    2.0  2.0   7.0  1.0  \n",
       "3    3.0  0.0   3.0  0.0  \n",
       "4    1.0  0.0   3.0  0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing first few rows of dataset\n",
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>cp</th>\n",
       "      <th>restbp</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>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.521739</td>\n",
       "      <td>0.67893</td>\n",
       "      <td>3.163880</td>\n",
       "      <td>131.715719</td>\n",
       "      <td>246.785953</td>\n",
       "      <td>0.143813</td>\n",
       "      <td>0.989967</td>\n",
       "      <td>149.327759</td>\n",
       "      <td>0.331104</td>\n",
       "      <td>1.058528</td>\n",
       "      <td>1.605351</td>\n",
       "      <td>0.672241</td>\n",
       "      <td>4.745819</td>\n",
       "      <td>0.946488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.030264</td>\n",
       "      <td>0.46767</td>\n",
       "      <td>0.964069</td>\n",
       "      <td>17.747751</td>\n",
       "      <td>52.532582</td>\n",
       "      <td>0.351488</td>\n",
       "      <td>0.994903</td>\n",
       "      <td>23.121062</td>\n",
       "      <td>0.471399</td>\n",
       "      <td>1.162769</td>\n",
       "      <td>0.616962</td>\n",
       "      <td>0.937438</td>\n",
       "      <td>1.940977</td>\n",
       "      <td>1.230409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>165.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        sex          cp      restbp        chol         fbs  \\\n",
       "count  299.000000  299.00000  299.000000  299.000000  299.000000  299.000000   \n",
       "mean    54.521739    0.67893    3.163880  131.715719  246.785953    0.143813   \n",
       "std      9.030264    0.46767    0.964069   17.747751   52.532582    0.351488   \n",
       "min     29.000000    0.00000    1.000000   94.000000  100.000000    0.000000   \n",
       "25%     48.000000    0.00000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.00000    3.000000  130.000000  242.000000    0.000000   \n",
       "75%     61.000000    1.00000    4.000000  140.000000  275.500000    0.000000   \n",
       "max     77.000000    1.00000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  299.000000  299.000000  299.000000  299.000000  299.000000  299.000000   \n",
       "mean     0.989967  149.327759    0.331104    1.058528    1.605351    0.672241   \n",
       "std      0.994903   23.121062    0.471399    1.162769    0.616962    0.937438   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  132.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  152.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  165.500000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal         num  \n",
       "count  299.000000  299.000000  \n",
       "mean     4.745819    0.946488  \n",
       "std      1.940977    1.230409  \n",
       "min      3.000000    0.000000  \n",
       "25%      3.000000    0.000000  \n",
       "50%      3.000000    0.000000  \n",
       "75%      7.000000    2.000000  \n",
       "max      7.000000    4.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Computing mean,count,etc. characteristics\n",
    "df0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of rows and columns in the dataset\n",
    "df0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 14 columns):\n",
      "age        299 non-null float64\n",
      "sex        299 non-null float64\n",
      "cp         299 non-null float64\n",
      "restbp     299 non-null float64\n",
      "chol       299 non-null float64\n",
      "fbs        299 non-null float64\n",
      "restecg    299 non-null float64\n",
      "thalach    299 non-null float64\n",
      "exang      299 non-null float64\n",
      "oldpeak    299 non-null float64\n",
      "slope      299 non-null float64\n",
      "ca         299 non-null float64\n",
      "thal       299 non-null float64\n",
      "num        299 non-null float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 32.8 KB\n"
     ]
    }
   ],
   "source": [
    "#Checking feature information\n",
    "df0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Generating histograms for viewing distribution of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yeyFJ0iaZgyQNoznLTUn+JMllSS5NcnKSByTZNslZSa5qn7eZrdeTNB56PTP4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JkjRSFoOS1L4j2UAXUUmSpPHGYlCS2pBkc+BNwFd6HYskSVInTLh7BiWpG6rqEeB5vY5DkiSp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Zuk6WN7cs146yZ5apIvJ/le8+9T2tofSdqSreU8SdqSVs4MJpkBnAP8FrAOuDrJpVW1ZkSz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\nIPorTbFXwLlNm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3oU0pTYlvzUrfxi3mrH9+e8iL3XX9Hads1N7TA3dTeHmJ+mJ/NT9011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\nMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c403cb1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots( nrows=10, ncols=3, figsize=(15,40) )\n",
    "plt.subplots_adjust( wspace=0.20, hspace=0.20, top=0.97 )\n",
    "plt.suptitle(\"Heart Disease Dataset\", fontsize=20)\n",
    "axes[0,0].hist(df0.age)\n",
    "axes[0,0].set_xlabel(\"Age (years)\")\n",
    "axes[0,0].set_ylabel(\"Number of Patients\")\n",
    "axes[0,1].hist(df0.sex)\n",
    "axes[0,1].set_xlabel(\"Sex (0=female,1=male)\")\n",
    "axes[0,1].set_ylabel(\"Number of Patients\")\n",
    "axes[0,2].hist(df0.cp,bins=4,range=(0.5,4.5),rwidth=0.80)\n",
    "axes[0,2].set_xlim(0.0,5.0)\n",
    "axes[0,2].set_xlabel(\"Type of Chest Pain [cp]\")\n",
    "axes[0,2].set_ylabel(\"Number of Patients\")\n",
    "axes[1,0].hist(df0.restbp)\n",
    "axes[1,0].set_xlabel(\"Resting Blood Pressure [restbp]\")\n",
    "axes[1,0].set_ylabel(\"Number of Patients\")\n",
    "axes[1,1].hist(df0.chol)\n",
    "axes[1,1].set_xlabel(\"Serum Cholesterol [chol]\")\n",
    "axes[1,1].set_ylabel(\"Number of Patients\")\n",
    "axes[1,2].hist(df0.fbs)\n",
    "axes[1,2].set_xlabel(\"Fasting Blood Sugar [fbs]\")\n",
    "axes[1,2].set_ylabel(\"Number of Patients\")\n",
    "axes[2,0].hist(df0.restecg)\n",
    "axes[2,0].set_xlabel(\"Resting Electrocardiography [restecg]\")\n",
    "axes[2,0].set_ylabel(\"Number of Patients\")\n",
    "axes[2,1].hist(df0.thalach)\n",
    "axes[2,1].set_xlabel(\"Maximum Heart Rate Achieved [thalach]\")\n",
    "axes[2,1].set_ylabel(\"Number of Patients\")\n",
    "axes[2,2].hist(df0.exang)\n",
    "axes[2,2].set_xlabel(\"Exercise Induced Angina [exang]\")\n",
    "axes[2,2].set_ylabel(\"Number of Patients\")\n",
    "axes[3,0].hist(df0.oldpeak)\n",
    "axes[3,0].set_xlabel(\"Exercise Induced ST Depression [oldpeak]\")\n",
    "axes[3,0].set_ylabel(\"Number of Patients\")\n",
    "axes[3,1].hist(df0.slope)\n",
    "axes[3,1].set_xlabel(\"Slope of Peak Exercise ST Segment [slope]\")\n",
    "axes[3,1].set_ylabel(\"Number of Patients\")\n",
    "axes[3,2].hist(df0.ca,bins=4,range=(-0.5,3.5),rwidth=0.8)\n",
    "axes[3,2].set_xlim(-0.7,3.7)\n",
    "axes[3,2].set_xlabel(\"Major Vessels colored by Fluoroscopy [ca]\")\n",
    "axes[3,2].set_ylabel(\"Number of Patients\")\n",
    "axes[4,0].hist(df0.thal)\n",
    "axes[4,0].set_xlabel(\"Thal\")\n",
    "axes[4,0].set_ylabel(\"Number of Patients\")\n",
    "axes[4,1].hist(df0.num,bins=5,range=(-0.5,4.5),rwidth=0.8)\n",
    "axes[4,1].set_xlim(-0.7,4.7)\n",
    "axes[4,1].set_xlabel(\"Heart Disease [num]\")\n",
    "axes[4,1].set_ylabel(\"Number of Patients\")\n",
    "axes[4,2].axis(\"off\")\n",
    "\n",
    "#Marginal Feature Distribution comapared for disease and not-disease\n",
    "bins = np.linspace(20, 80, 15)\n",
    "axes[5,0].hist(df0[df0.num>0].age.tolist(),bins,color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[5,0].hist(df0[df0.num==0].age,bins,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[5,0].set_xlabel(\"Age (years)\")\n",
    "axes[5,0].set_ylim(0.0,0.070)\n",
    "axes[5,0].legend(prop={'size': 10},loc=\"upper left\")\n",
    "axes[5,1].hist(df0[df0.num>0].sex.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[5,1].hist(df0[df0.num==0].sex,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[5,1].set_xlabel(\"Sex (0=female,1=male)\")\n",
    "axes[5,1].legend(prop={'size': 10},loc=\"upper left\")\n",
    "axes[5,2].hist(df0[df0.num>0].cp.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[5,2].hist(df0[df0.num==0].cp,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[5,2].set_xlabel(\"Type of Chest Pain [cp]\")\n",
    "axes[5,2].legend(prop={'size': 10},loc=\"upper left\")\n",
    "bins = np.linspace(80, 200, 15)\n",
    "axes[6,0].hist(df0[df0.num>0].restbp.tolist(),bins,color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[6,0].hist(df0[df0.num==0].restbp,bins,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[6,0].set_xlabel(\"Resting Blood Pressure [restbp]\")\n",
    "axes[6,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[6,1].hist(df0[df0.num>0].chol.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[6,1].hist(df0[df0.num==0].chol,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[6,1].set_xlabel(\"Serum Cholesterol [chol]\")\n",
    "axes[6,1].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[6,2].hist(df0[df0.num>0].fbs.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[6,2].hist(df0[df0.num==0].fbs,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[6,2].set_xlabel(\"Fasting blood sugar [fbs]\")\n",
    "axes[6,2].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[7,0].hist(df0[df0.num>0].restecg.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[7,0].hist(df0[df0.num==0].restecg,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[7,0].set_xlabel(\"Rest ECG\")\n",
    "axes[7,0].set_ylim(0.0,4.0)\n",
    "axes[7,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[7,1].hist(df0[df0.num>0].thalach.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[7,1].hist(df0[df0.num==0].thalach,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[7,1].set_xlabel(\"thalach\")\n",
    "axes[7,1].legend(prop={'size': 10},loc=\"upper left\")\n",
    "axes[7,2].hist(df0[df0.num>0].exang.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[7,2].hist(df0[df0.num==0].exang,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[7,2].set_xlabel(\"exang\")\n",
    "axes[7,2].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[8,0].hist(df0[df0.num>0].oldpeak.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[8,0].hist(df0[df0.num==0].oldpeak,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[8,0].set_xlabel(\"oldpeak\")\n",
    "axes[8,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[8,1].hist(df0[df0.num>0].slope.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[8,1].hist(df0[df0.num==0].slope,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[8,1].set_xlabel(\"slope\")\n",
    "axes[8,1].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[8,2].hist(df0[df0.num>0].ca.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[8,2].hist(df0[df0.num==0].ca,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[8,2].set_xlabel(\"ca\")\n",
    "axes[8,2].legend(prop={'size': 10},loc=\"upper right\")\n",
    "axes[9,0].hist(df0[df0.num>0].thal.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[9,0].hist(df0[df0.num==0].thal,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[9,0].set_xlabel(\"thal\")\n",
    "axes[9,0].set_ylim(0.0,2.5)\n",
    "axes[9,0].legend(prop={'size': 10},loc=\"upper right\")\n",
    "\n",
    "axes[9,1].axis(\"off\")\n",
    "axes[9,2].axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intrinsic discrepancies between disease and no-disease, in decreasing order: \n",
      "   0.609044  (thal)\n",
      "   0.584354  (cp)\n",
      "   0.478634  (ca)\n",
      "   0.456496  (thalach)\n",
      "   0.420033  (oldpeak)\n",
      "   0.352659  (exang)\n",
      "   0.312049  (slope)\n",
      "   0.198552  (age)\n",
      "   0.156002  (sex)\n",
      "   0.058716  (restecg)\n",
      "   0.033252  (restbp)\n",
      "   0.030527  (chol)\n",
      "   0.000000  (fbs)\n"
     ]
    }
   ],
   "source": [
    "# Compute intrinsic discrepancies between disease and no-disease feature distributions\n",
    "int_discr = {}\n",
    "hist,bin_edges   = np.histogram(df0.age,density=False)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].age,bins=bin_edges,density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].age,bins=bin_edges,density=False)\n",
    "int_discr[\"age\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].sex,bins=(-0.5,0.5,1.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].sex,bins=(-0.5,0.5,1.5),density=False)\n",
    "int_discr[\"sex\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].cp,bins=(0.5,1.5,2.5,3.5,4.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].cp,bins=(0.5,1.5,2.5,3.5,4.5),density=False)\n",
    "int_discr[\"cp\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist,bin_edges   = np.histogram(df0.restbp,density=False)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].restbp,bins=bin_edges,density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].restbp,bins=bin_edges,density=False)\n",
    "int_discr[\"restbp\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist,bin_edges   = np.histogram(df0.chol,density=False)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].chol,bins=bin_edges,density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].chol,bins=bin_edges,density=False)\n",
    "int_discr[\"chol\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].fbs,bins=(-0.5,0.5,1.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].fbs,bins=(-0.5,0.5,1.5),density=False)\n",
    "int_discr[\"fbs\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].restecg,bins=(-0.5,0.5,1.5,2.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].restecg,bins=(-0.5,0.5,1.5,2.5),density=False)\n",
    "int_discr[\"restecg\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist,bin_edges   = np.histogram(df0.thalach,density=False)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].thalach,bins=bin_edges,density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].thalach,bins=bin_edges,density=False)\n",
    "int_discr[\"thalach\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].exang,bins=(-0.5,0.5,1.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].exang,bins=(-0.5,0.5,1.5),density=False)\n",
    "int_discr[\"exang\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist,bin_edges   = np.histogram(df0.oldpeak,density=False)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].oldpeak,bins=bin_edges,density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].oldpeak,bins=bin_edges,density=False)\n",
    "int_discr[\"oldpeak\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].slope,bins=(0.5,1.5,2.5,3.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].slope,bins=(0.5,1.5,2.5,3.5),density=False)\n",
    "int_discr[\"slope\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].ca,bins=(-0.5,0.5,1.5,2.5,3.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].ca,bins=(-0.5,0.5,1.5,2.5,3.5),density=False)\n",
    "int_discr[\"ca\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "hist1,bin_edges1 = np.histogram(df0[df0.num>0].thal,bins=(2.5,3.5,6.5,7.5),density=False)\n",
    "hist2,bin_edges2 = np.histogram(df0[df0.num==0].thal,bins=(2.5,3.5,6.5,7.5),density=False)\n",
    "int_discr[\"thal\"] = intrinsic_discrepancy(hist1,hist2)\n",
    "id_list = Counter(int_discr).most_common()\n",
    "print ('Intrinsic discrepancies between disease and no-disease, in decreasing order: ')\n",
    "for item in id_list:\n",
    "    print ('   %f  (%s)' % (item[1],item[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of patients in dataframe: 299, with disease: 139, without disease: 160\n",
      "\n",
      "    age  sex  restbp   chol  fbs  thalach  exang  oldpeak   ca   hd  cp_1  \\\n",
      "0  63.0  1.0   145.0  233.0  1.0    150.0    0.0      2.3  0.0  0.0     1   \n",
      "1  67.0  1.0   160.0  286.0  0.0    108.0    1.0      1.5  3.0  1.0     0   \n",
      "2  67.0  1.0   120.0  229.0  0.0    129.0    1.0      2.6  2.0  1.0     0   \n",
      "3  37.0  1.0   130.0  250.0  0.0    187.0    0.0      3.5  0.0  0.0     0   \n",
      "4  41.0  0.0   130.0  204.0  0.0    172.0    0.0      1.4  0.0  0.0     0   \n",
      "\n",
      "   cp_2  cp_3  recg_1  recg_2  slope_1  slope_3  thal_6  thal_7  \n",
      "0     0     0       0       1        0        1       1       0  \n",
      "1     0     0       0       1        0        0       0       0  \n",
      "2     0     0       0       1        0        0       0       1  \n",
      "3     0     1       0       0        0        1       0       0  \n",
      "4     1     0       0       1        1        0       0       0  \n",
      "              age        sex      restbp        chol         fbs     thalach  \\\n",
      "count  299.000000  299.00000  299.000000  299.000000  299.000000  299.000000   \n",
      "mean    54.521739    0.67893  131.715719  246.785953    0.143813  149.327759   \n",
      "std      9.030264    0.46767   17.747751   52.532582    0.351488   23.121062   \n",
      "min     29.000000    0.00000   94.000000  100.000000    0.000000   71.000000   \n",
      "25%     48.000000    0.00000  120.000000  211.000000    0.000000  132.500000   \n",
      "50%     56.000000    1.00000  130.000000  242.000000    0.000000  152.000000   \n",
      "75%     61.000000    1.00000  140.000000  275.500000    0.000000  165.500000   \n",
      "max     77.000000    1.00000  200.000000  564.000000    1.000000  202.000000   \n",
      "\n",
      "            exang     oldpeak          ca          hd        cp_1        cp_2  \\\n",
      "count  299.000000  299.000000  299.000000  299.000000  299.000000  299.000000   \n",
      "mean     0.331104    1.058528    0.672241    0.464883    0.076923    0.163880   \n",
      "std      0.471399    1.162769    0.937438    0.499601    0.266916    0.370787   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "50%      0.000000    0.800000    0.000000    0.000000    0.000000    0.000000   \n",
      "75%      1.000000    1.600000    1.000000    1.000000    0.000000    0.000000   \n",
      "max      1.000000    6.200000    3.000000    1.000000    1.000000    1.000000   \n",
      "\n",
      "             cp_3      recg_1      recg_2     slope_1     slope_3      thal_6  \\\n",
      "count  299.000000  299.000000  299.000000  299.000000  299.000000  299.000000   \n",
      "mean     0.277592    0.013378    0.488294    0.464883    0.070234    0.060201   \n",
      "std      0.448562    0.115079    0.500701    0.499601    0.255970    0.238257   \n",
      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "50%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
      "75%      1.000000    0.000000    1.000000    1.000000    0.000000    0.000000   \n",
      "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
      "\n",
      "           thal_7  \n",
      "count  299.000000  \n",
      "mean     0.391304  \n",
      "std      0.488860  \n",
      "min      0.000000  \n",
      "25%      0.000000  \n",
      "50%      0.000000  \n",
      "75%      1.000000  \n",
      "max      1.000000  \n"
     ]
    }
   ],
   "source": [
    "#Converting categorial values into discrete values\n",
    "#Note that feature ca is discrete but not categorical, so we don't convert it.\n",
    "df = df0.copy()\n",
    "dummies = pd.get_dummies(df[\"cp\"],prefix=\"cp\")\n",
    "df = df.join(dummies)\n",
    "del df[\"cp\"]\n",
    "del df[\"cp_4.0\"]\n",
    "df = df.rename(columns = {\"cp_1.0\":\"cp_1\",\"cp_2.0\":\"cp_2\",\"cp_3.0\":\"cp_3\"})\n",
    "\n",
    "dummies = pd.get_dummies(df[\"restecg\"],prefix=\"recg\")\n",
    "df = df.join(dummies)\n",
    "del df[\"restecg\"]\n",
    "del df[\"recg_0.0\"]\n",
    "df = df.rename(columns = {\"recg_1.0\":\"recg_1\",\"recg_2.0\":\"recg_2\"})\n",
    "\n",
    "dummies = pd.get_dummies(df[\"slope\"],prefix=\"slope\")\n",
    "df = df.join(dummies)\n",
    "del df[\"slope\"]\n",
    "del df[\"slope_2.0\"]\n",
    "df = df.rename(columns = {\"slope_1.0\":\"slope_1\",\"slope_3.0\":\"slope_3\"})\n",
    "\n",
    "dummies = pd.get_dummies(df[\"thal\"],prefix=\"thal\")\n",
    "df = df.join(dummies)\n",
    "del df[\"thal\"]\n",
    "del df[\"thal_3.0\"]\n",
    "df = df.rename(columns = {\"thal_6.0\":\"thal_6\",\"thal_7.0\":\"thal_7\"})\n",
    "\n",
    "#Replace response variable values and renaming it hd\n",
    "#The num variable values of 1,2,3,4 are replaced with 1 in order to signify that the patient has heart disease\n",
    "df[\"num\"].replace(to_replace=[1,2,3,4],value=1,inplace=True)\n",
    "df = df.rename(columns = {\"num\":\"hd\"})\n",
    "\n",
    "new_columns_1 = [\"age\", \"sex\", \"restbp\", \"chol\", \"fbs\", \"thalach\", \n",
    "                 \"exang\", \"oldpeak\", \"ca\", \"hd\", \"cp_1\", \"cp_2\",\n",
    "                 \"cp_3\", \"recg_1\", \"recg_2\", \"slope_1\", \"slope_3\",\n",
    "                 \"thal_6\", \"thal_7\"]\n",
    "\n",
    "print ('\\nNumber of patients in dataframe: %i, with disease: %i, without disease: %i\\n' \\\n",
    "      % (len(df.index),len(df[df.hd==1].index),len(df[df.hd==0].index)))\n",
    "print (df.head())\n",
    "print (df.describe())\n",
    "\n",
    "# Standardize the dataframe\n",
    "stdcols = [\"age\",\"restbp\",\"chol\",\"thalach\",\"oldpeak\"]\n",
    "nrmcols = [\"ca\"]\n",
    "stddf = df.copy()\n",
    "stddf[stdcols] = stddf[stdcols].apply(lambda x: (x-x.mean())/x.std())\n",
    "stddf[nrmcols] = stddf[nrmcols].apply(lambda x: (x-x.mean())/(x.max()-x.min()))\n",
    "\n",
    "new_columns_2 = new_columns_1[:9] + new_columns_1[10:]\n",
    "new_columns_2.insert(0,new_columns_1[9])\n",
    "stddf = stddf.reindex(columns=new_columns_2)\n",
    "\n",
    "# Convert dataframe into lists for use by classifiers\n",
    "yall = stddf[\"hd\"]\n",
    "Xall = stddf[new_columns_2[1:]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a641VuX18FHYHlmTmcxHRJyI2AoiIPsCBwMwqYpEkSWqXMnMlMBG4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\nkjRq/h9MSoEV7j4xhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c40a4ae5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Generate plots for blog\n",
    "'''\n",
    "fig, axes = plt.subplots( nrows=4, ncols=4, figsize=(20,20) )\n",
    "plt.subplots_adjust( wspace=0.20, hspace=0.60, top=0.955)\n",
    "plt.suptitle(\"Heart Disease Data\", fontsize=20)\n",
    "\n",
    "bins = np.linspace(20, 80, 15)\n",
    "axes[0,0].hist(df0[df0.num>0].age.tolist(),bins,color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[0,0].hist(df0[df0.num==0].age,bins,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[0,0].set_xlabel(\"Age (years)\",fontsize=15)\n",
    "axes[0,0].set_ylim(0.0,0.080)\n",
    "axes[0,0].legend(prop={'size': 15},loc=\"upper left\")\n",
    "\n",
    "bins = np.arange(2)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"sex\"].groupby(df0[\"sex\"]).count()\n",
    "heights2 = df0[df0.num==0][\"sex\"].groupby(df0[\"sex\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[0,1].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[0,1].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[0,1].set_xlabel(\"Sex\",fontsize=15)\n",
    "axes[0,1].set_xticks(bins)\n",
    "axes[0,1].set_xticklabels([\"female\",\"male\"],ha=\"center\")\n",
    "\n",
    "bins = np.arange(4)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"cp\"].groupby(df0[\"cp\"]).count()\n",
    "heights2 = df0[df0.num==0][\"cp\"].groupby(df0[\"cp\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[0,2].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[0,2].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[0,2].set_xlabel(\"Type of Chest Pain\",fontsize=15)\n",
    "axes[0,2].set_xticks(bins)\n",
    "axes[0,2].set_xticklabels([\"typical angina\", \"atypical angina\", \"non-angina\", \"asymptomatic angina\"],\n",
    "                          ha=\"right\",rotation=45.)\n",
    "\n",
    "bins = np.linspace(80, 200, 15)\n",
    "axes[0,3].hist(df0[df0.num>0].restbp.tolist(),bins,color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[0,3].hist(df0[df0.num==0].restbp,bins,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[0,3].set_xlabel(\"Resting Blood Pressure (mm Hg)\",fontsize=15)\n",
    "\n",
    "axes[1,0].hist(df0[df0.num>0].chol.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[1,0].hist(df0[df0.num==0].chol,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[1,0].set_xlabel(\"Serum Cholesterol (mg/dl)\",fontsize=15)\n",
    "\n",
    "bins = np.arange(2)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"fbs\"].groupby(df0[\"fbs\"]).count()\n",
    "heights2 = df0[df0.num==0][\"fbs\"].groupby(df0[\"fbs\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[1,1].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=(0.917,0.083,0,0.75),color=[\"none\"],label=\"disease\")\n",
    "axes[1,1].bar(bins,heights2,width,align=\"center\",edgecolor=(0.467,0.533,0,0.75),color=[\"none\"],label=\"no disease\")\n",
    "axes[1,1].set_xlabel(\"Fasting Blood Sugar\",fontsize=15)\n",
    "axes[1,1].set_xticks(bins)\n",
    "axes[1,1].set_xticklabels([\"< 120 mg/dl\",\"> 120 mg/dl\"],ha=\"center\")\n",
    "\n",
    "bins = np.arange(3)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"restecg\"].groupby(df0[\"restecg\"]).count()\n",
    "heights2 = df0[df0.num==0][\"restecg\"].groupby(df0[\"restecg\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[1,2].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[1,2].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[1,2].set_xlabel(\"Rest ECG\",fontsize=15)\n",
    "axes[1,2].set_xticks(bins)\n",
    "axes[1,2].set_xticklabels([\"Normal\",\"ST-T wave abnorm.\",\"left ventr. hypertrophy\"],ha=\"right\",rotation=45.)\n",
    "\n",
    "axes[1,3].hist(df0[df0.num>0].thalach.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[1,3].hist(df0[df0.num==0].thalach,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[1,3].set_xlabel(\"Thalium Test: Max. Heart Rate\",fontsize=15)\n",
    "\n",
    "bins = np.arange(2)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"exang\"].groupby(df0[\"exang\"]).count()\n",
    "heights2 = df0[df0.num==0][\"exang\"].groupby(df0[\"exang\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[2,0].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[2,0].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[2,0].set_xlabel(\"Exercise Induced Angina\",fontsize=15)\n",
    "axes[2,0].set_xticks(bins)\n",
    "axes[2,0].set_xticklabels([\"No\",\"Yes\"],ha=\"center\")\n",
    "\n",
    "axes[2,1].hist(df0[df0.num>0].oldpeak.tolist(),color=[\"crimson\"],histtype=\"step\",label=\"disease\",normed=True)\n",
    "axes[2,1].hist(df0[df0.num==0].oldpeak,color=[\"chartreuse\"],histtype=\"step\",label=\"no disease\",normed=True)\n",
    "axes[2,1].set_xlabel(\"ST Depression Induced by Exercise\", fontsize=15)\n",
    "\n",
    "bins = np.arange(3)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"slope\"].groupby(df0[\"slope\"]).count()\n",
    "heights2 = df0[df0.num==0][\"slope\"].groupby(df0[\"slope\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[2,2].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[2,2].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[2,2].set_xlabel(\"Slope of Peak Exercise ST Segment\",fontsize=15)\n",
    "axes[2,2].set_xticks(bins)\n",
    "axes[2,2].set_xticklabels([\"Upsloping\",\"Flat\",\"Downsloping\"],ha=\"right\",rotation=45.)\n",
    "\n",
    "bins = np.arange(4)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"ca\"].groupby(df0[\"ca\"]).count()\n",
    "heights2 = df0[df0.num==0][\"ca\"].groupby(df0[\"ca\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[2,3].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[2,3].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[2,3].set_xlabel(\"Major Vessels Colored by Fluoroscopy\",fontsize=15)\n",
    "axes[2,3].set_xticks(bins)\n",
    "axes[2,3].set_xticklabels([\"0\",\"1\",\"2\",\"3\"],ha=\"center\")\n",
    "\n",
    "bins = np.arange(3)\n",
    "width = 0.5\n",
    "heights1 = df0[df0.num>0][\"thal\"].groupby(df0[\"thal\"]).count()\n",
    "heights2 = df0[df0.num==0][\"thal\"].groupby(df0[\"thal\"]).count()\n",
    "heights1 = heights1/sum(heights1)\n",
    "heights2 = heights2/sum(heights2)\n",
    "axes[3,0].bar(bins+0.025,heights1,width,align=\"center\",edgecolor=[\"crimson\"],color=[\"none\"],label=\"disease\")\n",
    "axes[3,0].bar(bins,heights2,width,align=\"center\",edgecolor=[\"chartreuse\"],color=[\"none\"],label=\"no disease\")\n",
    "axes[3,0].set_xlabel(\"Thalium Stress Test Result\",fontsize=15)\n",
    "axes[3,0].set_xticks(bins)\n",
    "axes[3,0].set_xticklabels([\"Normal\",\"Fixed Defect\",\"Reversible Defect\"],ha=\"right\",rotation=45.)\n",
    "axes[3,0].set_ylim(0.0,1.0)\n",
    "\n",
    "axes[3,1].axis(\"off\")\n",
    "axes[3,2].axis(\"off\")\n",
    "axes[3,3].axis(\"off\")\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"plots.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1000.0, class_weight=None, dual=False,\n",
      "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
      "          multi_class='ovr', n_jobs=1, penalty='l1', random_state=None,\n",
      "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)\n",
      "\n",
      "LogisticRegression score on full data set: 0.872910\n",
      "\n",
      "\n",
      "Classification report on full data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.86      0.91      0.88       160\n",
      "        1.0       0.89      0.83      0.86       139\n",
      "\n",
      "avg / total       0.87      0.87      0.87       299\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[146  14]\n",
      " [ 24 115]]\n",
      "\n",
      "LogisticRegression coefficients:\n",
      "      age : -0.12369\n",
      "      sex :  1.54885\n",
      "   restbp :  0.43320\n",
      "     chol :  0.22079\n",
      "      fbs : -0.59484\n",
      "  thalach : -0.41726\n",
      "    exang :  0.71859\n",
      "  oldpeak :  0.41976\n",
      "       ca :  3.92875\n",
      "     cp_1 : -2.10034\n",
      "     cp_2 : -0.84540\n",
      "     cp_3 : -1.84976\n",
      "   recg_1 :  0.77573\n",
      "   recg_2 :  0.46907\n",
      "  slope_1 : -1.16387\n",
      "  slope_3 : -0.64683\n",
      "   thal_6 : -0.03073\n",
      "   thal_7 :  1.40152\n",
      "Intercept : -0.606571\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Note about logistic regression with scikit-learn:\n",
    "-> In scikit-learn we can specify penalty=\"l1\" or penalty=\"l2\", with\n",
    "  an associated C=c, which is the *inverse* of the regularization strength.\n",
    "  Thus, for zero regularization specify a high value of c.  Scikit-learn\n",
    "  does not calculate uncertainties on the fit coefficients.\n",
    "'''\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import metrics\n",
    "\n",
    "lasso = False\n",
    "\n",
    "nfeatures = len(stddf.columns)\n",
    "if lasso:           # lasso regularization\n",
    "    penalty = \"l1\"\n",
    "    cval    = 1.0\n",
    "    alpha   = [1.0]*nfeatures\n",
    "else:               # no regularization\n",
    "    penalty = \"l1\"\n",
    "    cval    = 1000.0\n",
    "    alpha   = 0.0\n",
    "\n",
    "model = LogisticRegression(fit_intercept=True,penalty=penalty,dual=False,C=cval)\n",
    "print(model)\n",
    "lrfit = model.fit(Xall,yall)\n",
    "print('\\nLogisticRegression score on full data set: %f\\n' % lrfit.score(Xall,yall))\n",
    "ypred = model.predict(Xall)\n",
    "print ('\\nClassification report on full data set:')\n",
    "print(metrics.classification_report(yall,ypred))\n",
    "print ('\\nConfusion matrix:')\n",
    "print(metrics.confusion_matrix(yall,ypred))\n",
    "print ('\\nLogisticRegression coefficients:')\n",
    "coeff = model.coef_.tolist()[0]\n",
    "for index in range(len(coeff)):\n",
    "    print ('%s : %8.5f' % (new_columns_2[index+1].rjust(9),coeff[index]))\n",
    "print( 'Intercept : %f' %model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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