DiabetesClassification-Graph-Latest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ruruki.graphs import Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Attribute Information:\n",
    "#1. Number of times pregnant \n",
    "#2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test \n",
    "#3. Diastolic blood pressure (mm Hg) \n",
    "#4. Triceps skin fold thickness (mm) \n",
    "#5. 2-Hour serum insulin (mu U/ml) \n",
    "#6. Body mass index (weight in kg/(height in m)^2) \n",
    "#7. Diabetes pedigree function \n",
    "#8. Age (years) \n",
    "#9. Class variable (0 or 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
       "0     6   148    72    35     0  33.6  0.627   50      1\n",
       "1     1    85    66    29     0  26.6  0.351   31      0\n",
       "2     8   183    64     0     0  23.3  0.672   32      1\n",
       "3     1    89    66    23    94  28.1  0.167   21      0\n",
       "4     0   137    40    35   168  43.1  2.288   33      1"
      ]
     },
     "execution_count": 606,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "diabetes = pd.read_csv(\"pima-indians-diabetes.data\", names=names)\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'], dtype='object')"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "0    500\n",
       "1    268\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.groupby(['class'])['class'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "preg     768 non-null int64\n",
      "plas     768 non-null int64\n",
      "pres     768 non-null int64\n",
      "skin     768 non-null int64\n",
      "test     768 non-null int64\n",
      "mass     768 non-null float64\n",
      "pedi     768 non-null float64\n",
      "age      768 non-null int64\n",
      "class    768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "\n",
    "X = diabetes[['plas','mass']]\n",
    "X = diabetes[['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi','age']].apply(zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.848324</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.204013</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.123396</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>0.530902</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.684422</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>1.943724</td>\n",
       "      <td>-0.263941</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-1.103255</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.998208</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>0.123302</td>\n",
       "      <td>-0.494043</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504055</td>\n",
       "      <td>-1.504687</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>0.765836</td>\n",
       "      <td>1.409746</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>-0.153185</td>\n",
       "      <td>0.253036</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.811341</td>\n",
       "      <td>-0.818079</td>\n",
       "      <td>-0.275760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>-1.342476</td>\n",
       "      <td>-0.987710</td>\n",
       "      <td>0.719086</td>\n",
       "      <td>0.071204</td>\n",
       "      <td>-0.125977</td>\n",
       "      <td>-0.676133</td>\n",
       "      <td>-0.616111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>-0.184482</td>\n",
       "      <td>-3.572597</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.419775</td>\n",
       "      <td>-1.020427</td>\n",
       "      <td>-0.360847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.547919</td>\n",
       "      <td>2.381884</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>1.534551</td>\n",
       "      <td>4.021922</td>\n",
       "      <td>-0.189437</td>\n",
       "      <td>-0.947944</td>\n",
       "      <td>1.681259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>0.128489</td>\n",
       "      <td>1.390387</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-4.060474</td>\n",
       "      <td>-0.724455</td>\n",
       "      <td>1.766346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.046014</td>\n",
       "      <td>-0.340968</td>\n",
       "      <td>1.183596</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.711690</td>\n",
       "      <td>-0.848280</td>\n",
       "      <td>-0.275760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>1.474267</td>\n",
       "      <td>0.253036</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.762457</td>\n",
       "      <td>0.196681</td>\n",
       "      <td>0.064591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>0.566649</td>\n",
       "      <td>0.563223</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.620962</td>\n",
       "      <td>2.926869</td>\n",
       "      <td>2.021610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>2.131507</td>\n",
       "      <td>-0.470732</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>6.652839</td>\n",
       "      <td>-0.240205</td>\n",
       "      <td>-0.223115</td>\n",
       "      <td>2.191785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>1.411672</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>-0.096379</td>\n",
       "      <td>0.826616</td>\n",
       "      <td>-0.785957</td>\n",
       "      <td>0.347687</td>\n",
       "      <td>1.511083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>-0.653939</td>\n",
       "      <td>-3.572597</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.252897</td>\n",
       "      <td>0.036615</td>\n",
       "      <td>-0.105584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>-0.090591</td>\n",
       "      <td>0.770014</td>\n",
       "      <td>1.660007</td>\n",
       "      <td>1.304175</td>\n",
       "      <td>1.752428</td>\n",
       "      <td>0.238963</td>\n",
       "      <td>-0.190672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>-0.434859</td>\n",
       "      <td>0.253036</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.303664</td>\n",
       "      <td>-0.658012</td>\n",
       "      <td>-0.190672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.560048</td>\n",
       "      <td>-2.021665</td>\n",
       "      <td>1.095454</td>\n",
       "      <td>0.027790</td>\n",
       "      <td>1.435129</td>\n",
       "      <td>-0.872441</td>\n",
       "      <td>-0.020496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.184482</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.593630</td>\n",
       "      <td>0.140667</td>\n",
       "      <td>0.330932</td>\n",
       "      <td>0.172520</td>\n",
       "      <td>-0.105584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>0.159787</td>\n",
       "      <td>0.976805</td>\n",
       "      <td>1.283638</td>\n",
       "      <td>1.347590</td>\n",
       "      <td>0.927452</td>\n",
       "      <td>0.701041</td>\n",
       "      <td>-0.531023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>-0.685236</td>\n",
       "      <td>0.770014</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.432467</td>\n",
       "      <td>-0.253316</td>\n",
       "      <td>1.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>2.350587</td>\n",
       "      <td>1.080200</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.990912</td>\n",
       "      <td>-0.063049</td>\n",
       "      <td>0.660206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>-0.059293</td>\n",
       "      <td>0.563223</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.379816</td>\n",
       "      <td>-0.630831</td>\n",
       "      <td>-0.360847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.124780</td>\n",
       "      <td>0.691838</td>\n",
       "      <td>1.286991</td>\n",
       "      <td>0.781814</td>\n",
       "      <td>0.574812</td>\n",
       "      <td>0.584771</td>\n",
       "      <td>-0.658012</td>\n",
       "      <td>1.511083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>0.128489</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.342717</td>\n",
       "      <td>0.305642</td>\n",
       "      <td>-0.113285</td>\n",
       "      <td>-0.805998</td>\n",
       "      <td>0.660206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>0.817027</td>\n",
       "      <td>0.356432</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.940144</td>\n",
       "      <td>-0.648952</td>\n",
       "      <td>0.830381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.747831</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>-0.347291</td>\n",
       "      <td>0.522715</td>\n",
       "      <td>-1.115947</td>\n",
       "      <td>0.045675</td>\n",
       "      <td>-0.956462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.718712</td>\n",
       "      <td>0.754432</td>\n",
       "      <td>0.666618</td>\n",
       "      <td>-0.096379</td>\n",
       "      <td>0.262228</td>\n",
       "      <td>-1.242867</td>\n",
       "      <td>-0.685193</td>\n",
       "      <td>2.021610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>-0.121888</td>\n",
       "      <td>1.183596</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.267472</td>\n",
       "      <td>-0.407342</td>\n",
       "      <td>0.404942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>-0.547919</td>\n",
       "      <td>-0.685236</td>\n",
       "      <td>-0.470732</td>\n",
       "      <td>-0.221835</td>\n",
       "      <td>0.696373</td>\n",
       "      <td>0.584771</td>\n",
       "      <td>-0.057009</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.591345</td>\n",
       "      <td>0.253036</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.952836</td>\n",
       "      <td>-0.540228</td>\n",
       "      <td>0.745293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2.124780</td>\n",
       "      <td>-0.027996</td>\n",
       "      <td>0.563223</td>\n",
       "      <td>1.032726</td>\n",
       "      <td>0.609544</td>\n",
       "      <td>1.308210</td>\n",
       "      <td>0.945671</td>\n",
       "      <td>1.255820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>-0.591345</td>\n",
       "      <td>-1.297896</td>\n",
       "      <td>-0.033651</td>\n",
       "      <td>0.123302</td>\n",
       "      <td>-0.151361</td>\n",
       "      <td>-0.217075</td>\n",
       "      <td>-0.616111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.372265</td>\n",
       "      <td>-0.574128</td>\n",
       "      <td>-0.159107</td>\n",
       "      <td>0.314325</td>\n",
       "      <td>-0.443275</td>\n",
       "      <td>-0.763716</td>\n",
       "      <td>-0.956462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>0.597947</td>\n",
       "      <td>1.286991</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.089785</td>\n",
       "      <td>0.791645</td>\n",
       "      <td>1.000557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2.718712</td>\n",
       "      <td>1.004810</td>\n",
       "      <td>0.976805</td>\n",
       "      <td>1.032726</td>\n",
       "      <td>0.522715</td>\n",
       "      <td>1.092447</td>\n",
       "      <td>2.120497</td>\n",
       "      <td>0.490030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>2.421746</td>\n",
       "      <td>-0.653939</td>\n",
       "      <td>0.770014</td>\n",
       "      <td>0.781814</td>\n",
       "      <td>0.218813</td>\n",
       "      <td>-0.252897</td>\n",
       "      <td>0.048695</td>\n",
       "      <td>1.085644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0.817027</td>\n",
       "      <td>1.286991</td>\n",
       "      <td>1.283638</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>2.196645</td>\n",
       "      <td>-0.343920</td>\n",
       "      <td>-0.531023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.248585</td>\n",
       "      <td>0.253036</td>\n",
       "      <td>1.283638</td>\n",
       "      <td>-0.197966</td>\n",
       "      <td>1.815887</td>\n",
       "      <td>1.884928</td>\n",
       "      <td>-0.105584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>2.068912</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.091805</td>\n",
       "      <td>1.043689</td>\n",
       "      <td>0.559387</td>\n",
       "      <td>-0.192914</td>\n",
       "      <td>0.234767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>1.286484</td>\n",
       "      <td>-0.367337</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.976336</td>\n",
       "      <td>-0.887541</td>\n",
       "      <td>1.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.046014</td>\n",
       "      <td>0.472758</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.100593</td>\n",
       "      <td>2.144658</td>\n",
       "      <td>-0.956462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>0.459827</td>\n",
       "      <td>1.158182</td>\n",
       "      <td>-0.050356</td>\n",
       "      <td>0.889377</td>\n",
       "      <td>-0.636871</td>\n",
       "      <td>-0.445935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>-0.250952</td>\n",
       "      <td>-0.403562</td>\n",
       "      <td>-0.367337</td>\n",
       "      <td>0.217261</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.760573</td>\n",
       "      <td>-0.751636</td>\n",
       "      <td>-0.701198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>1.881130</td>\n",
       "      <td>0.976805</td>\n",
       "      <td>1.471822</td>\n",
       "      <td>3.735386</td>\n",
       "      <td>1.435129</td>\n",
       "      <td>-0.754656</td>\n",
       "      <td>-0.616111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>1.036107</td>\n",
       "      <td>0.459827</td>\n",
       "      <td>0.719086</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.051710</td>\n",
       "      <td>-0.087210</td>\n",
       "      <td>1.000557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0.222381</td>\n",
       "      <td>0.976805</td>\n",
       "      <td>1.158182</td>\n",
       "      <td>0.262228</td>\n",
       "      <td>0.572079</td>\n",
       "      <td>1.767143</td>\n",
       "      <td>0.319855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>0.504055</td>\n",
       "      <td>1.080200</td>\n",
       "      <td>1.283638</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>-0.244256</td>\n",
       "      <td>0.490030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.065895</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.546695</td>\n",
       "      <td>-0.645932</td>\n",
       "      <td>1.596171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.466156</td>\n",
       "      <td>0.356432</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.698998</td>\n",
       "      <td>-0.830159</td>\n",
       "      <td>-0.616111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>2.162804</td>\n",
       "      <td>1.183596</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.445159</td>\n",
       "      <td>-0.585529</td>\n",
       "      <td>2.787399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>-0.547919</td>\n",
       "      <td>-1.029505</td>\n",
       "      <td>-0.574128</td>\n",
       "      <td>0.342717</td>\n",
       "      <td>-0.553964</td>\n",
       "      <td>-0.455967</td>\n",
       "      <td>0.888288</td>\n",
       "      <td>-0.956462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>1.536861</td>\n",
       "      <td>0.253036</td>\n",
       "      <td>0.656358</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>1.523973</td>\n",
       "      <td>-0.208015</td>\n",
       "      <td>0.830381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>-0.998208</td>\n",
       "      <td>-0.367337</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-1.204791</td>\n",
       "      <td>-0.996266</td>\n",
       "      <td>-0.020496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>-0.622642</td>\n",
       "      <td>0.356432</td>\n",
       "      <td>1.722735</td>\n",
       "      <td>0.870031</td>\n",
       "      <td>0.115169</td>\n",
       "      <td>-0.908682</td>\n",
       "      <td>2.532136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>-0.547919</td>\n",
       "      <td>0.034598</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.405445</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.610154</td>\n",
       "      <td>-0.398282</td>\n",
       "      <td>-0.531023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>0.279594</td>\n",
       "      <td>-0.735190</td>\n",
       "      <td>-0.685193</td>\n",
       "      <td>-0.275760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0.159787</td>\n",
       "      <td>-0.470732</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.240205</td>\n",
       "      <td>-0.371101</td>\n",
       "      <td>1.170732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.873019</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.656358</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.202129</td>\n",
       "      <td>-0.473785</td>\n",
       "      <td>-0.871374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         preg      plas      pres      skin      test      mass      pedi  \\\n",
       "0    0.639947  0.848324  0.149641  0.907270 -0.692891  0.204013  0.468492   \n",
       "1   -0.844885 -1.123396 -0.160546  0.530902 -0.692891 -0.684422 -0.365061   \n",
       "2    1.233880  1.943724 -0.263941 -1.288212 -0.692891 -1.103255  0.604397   \n",
       "3   -0.844885 -0.998208 -0.160546  0.154533  0.123302 -0.494043 -0.920763   \n",
       "4   -1.141852  0.504055 -1.504687  0.907270  0.765836  1.409746  5.484909   \n",
       "5    0.342981 -0.153185  0.253036 -1.288212 -0.692891 -0.811341 -0.818079   \n",
       "6   -0.250952 -1.342476 -0.987710  0.719086  0.071204 -0.125977 -0.676133   \n",
       "7    1.827813 -0.184482 -3.572597 -1.288212 -0.692891  0.419775 -1.020427   \n",
       "8   -0.547919  2.381884  0.046245  1.534551  4.021922 -0.189437 -0.947944   \n",
       "9    1.233880  0.128489  1.390387 -1.288212 -0.692891 -4.060474 -0.724455   \n",
       "10   0.046014 -0.340968  1.183596 -1.288212 -0.692891  0.711690 -0.848280   \n",
       "11   1.827813  1.474267  0.253036 -1.288212 -0.692891  0.762457  0.196681   \n",
       "12   1.827813  0.566649  0.563223 -1.288212 -0.692891 -0.620962  2.926869   \n",
       "13  -0.844885  2.131507 -0.470732  0.154533  6.652839 -0.240205 -0.223115   \n",
       "14   0.342981  1.411672  0.149641 -0.096379  0.826616 -0.785957  0.347687   \n",
       "15   0.936914 -0.653939 -3.572597 -1.288212 -0.692891 -0.252897  0.036615   \n",
       "16  -1.141852 -0.090591  0.770014  1.660007  1.304175  1.752428  0.238963   \n",
       "17   0.936914 -0.434859  0.253036 -1.288212 -0.692891 -0.303664 -0.658012   \n",
       "18  -0.844885 -0.560048 -2.021665  1.095454  0.027790  1.435129 -0.872441   \n",
       "19  -0.844885 -0.184482  0.046245  0.593630  0.140667  0.330932  0.172520   \n",
       "20  -0.250952  0.159787  0.976805  1.283638  1.347590  0.927452  0.701041   \n",
       "21   1.233880 -0.685236  0.770014 -1.288212 -0.692891  0.432467 -0.253316   \n",
       "22   0.936914  2.350587  1.080200 -1.288212 -0.692891  0.990912 -0.063049   \n",
       "23   1.530847 -0.059293  0.563223  0.907270 -0.692891 -0.379816 -0.630831   \n",
       "24   2.124780  0.691838  1.286991  0.781814  0.574812  0.584771 -0.658012   \n",
       "25   1.827813  0.128489  0.046245  0.342717  0.305642 -0.113285 -0.805998   \n",
       "26   0.936914  0.817027  0.356432 -1.288212 -0.692891  0.940144 -0.648952   \n",
       "27  -0.844885 -0.747831 -0.160546 -0.347291  0.522715 -1.115947  0.045675   \n",
       "28   2.718712  0.754432  0.666618 -0.096379  0.262228 -1.242867 -0.685193   \n",
       "29   0.342981 -0.121888  1.183596 -1.288212 -0.692891  0.267472 -0.407342   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "738 -0.547919 -0.685236 -0.470732 -0.221835  0.696373  0.584771 -0.057009   \n",
       "739 -0.844885 -0.591345  0.253036 -1.288212 -0.692891  0.952836 -0.540228   \n",
       "740  2.124780 -0.027996  0.563223  1.032726  0.609544  1.308210  0.945671   \n",
       "741 -0.250952 -0.591345 -1.297896 -0.033651  0.123302 -0.151361 -0.217075   \n",
       "742 -0.844885 -0.372265 -0.574128 -0.159107  0.314325 -0.443275 -0.763716   \n",
       "743  1.530847  0.597947  1.286991 -1.288212 -0.692891  0.089785  0.791645   \n",
       "744  2.718712  1.004810  0.976805  1.032726  0.522715  1.092447  2.120497   \n",
       "745  2.421746 -0.653939  0.770014  0.781814  0.218813 -0.252897  0.048695   \n",
       "746 -0.844885  0.817027  1.286991  1.283638 -0.692891  2.196645 -0.343920   \n",
       "747 -0.844885 -1.248585  0.253036  1.283638 -0.197966  1.815887  1.884928   \n",
       "748 -0.250952  2.068912  0.046245  0.091805  1.043689  0.559387 -0.192914   \n",
       "749  0.639947  1.286484 -0.367337 -1.288212 -0.692891 -0.976336 -0.887541   \n",
       "750  0.046014  0.472758  0.046245 -1.288212 -0.692891 -0.100593  2.144658   \n",
       "751 -0.844885  0.003301  0.459827  1.158182 -0.050356  0.889377 -0.636871   \n",
       "752 -0.250952 -0.403562 -0.367337  0.217261 -0.692891 -0.760573 -0.751636   \n",
       "753 -1.141852  1.881130  0.976805  1.471822  3.735386  1.435129 -0.754656   \n",
       "754  1.233880  1.036107  0.459827  0.719086 -0.692891  0.051710 -0.087210   \n",
       "755 -0.844885  0.222381  0.976805  1.158182  0.262228  0.572079  1.767143   \n",
       "756  0.936914  0.504055  1.080200  1.283638 -0.692891  0.000942 -0.244256   \n",
       "757 -1.141852  0.065895  0.149641 -1.288212 -0.692891  0.546695 -0.645932   \n",
       "758 -0.844885 -0.466156  0.356432 -1.288212 -0.692891  0.698998 -0.830159   \n",
       "759  0.639947  2.162804  1.183596 -1.288212 -0.692891  0.445159 -0.585529   \n",
       "760 -0.547919 -1.029505 -0.574128  0.342717 -0.553964 -0.455967  0.888288   \n",
       "761  1.530847  1.536861  0.253036  0.656358 -0.692891  1.523973 -0.208015   \n",
       "762  1.530847 -0.998208 -0.367337 -1.288212 -0.692891 -1.204791 -0.996266   \n",
       "763  1.827813 -0.622642  0.356432  1.722735  0.870031  0.115169 -0.908682   \n",
       "764 -0.547919  0.034598  0.046245  0.405445 -0.692891  0.610154 -0.398282   \n",
       "765  0.342981  0.003301  0.149641  0.154533  0.279594 -0.735190 -0.685193   \n",
       "766 -0.844885  0.159787 -0.470732 -1.288212 -0.692891 -0.240205 -0.371101   \n",
       "767 -0.844885 -0.873019  0.046245  0.656358 -0.692891 -0.202129 -0.473785   \n",
       "\n",
       "          age  \n",
       "0    1.425995  \n",
       "1   -0.190672  \n",
       "2   -0.105584  \n",
       "3   -1.041549  \n",
       "4   -0.020496  \n",
       "5   -0.275760  \n",
       "6   -0.616111  \n",
       "7   -0.360847  \n",
       "8    1.681259  \n",
       "9    1.766346  \n",
       "10  -0.275760  \n",
       "11   0.064591  \n",
       "12   2.021610  \n",
       "13   2.191785  \n",
       "14   1.511083  \n",
       "15  -0.105584  \n",
       "16  -0.190672  \n",
       "17  -0.190672  \n",
       "18  -0.020496  \n",
       "19  -0.105584  \n",
       "20  -0.531023  \n",
       "21   1.425995  \n",
       "22   0.660206  \n",
       "23  -0.360847  \n",
       "24   1.511083  \n",
       "25   0.660206  \n",
       "26   0.830381  \n",
       "27  -0.956462  \n",
       "28   2.021610  \n",
       "29   0.404942  \n",
       "..        ...  \n",
       "738 -1.041549  \n",
       "739  0.745293  \n",
       "740  1.255820  \n",
       "741 -0.616111  \n",
       "742 -0.956462  \n",
       "743  1.000557  \n",
       "744  0.490030  \n",
       "745  1.085644  \n",
       "746 -0.531023  \n",
       "747 -0.105584  \n",
       "748  0.234767  \n",
       "749  1.425995  \n",
       "750 -0.956462  \n",
       "751 -0.445935  \n",
       "752 -0.701198  \n",
       "753 -0.616111  \n",
       "754  1.000557  \n",
       "755  0.319855  \n",
       "756  0.490030  \n",
       "757  1.596171  \n",
       "758 -0.616111  \n",
       "759  2.787399  \n",
       "760 -0.956462  \n",
       "761  0.830381  \n",
       "762 -0.020496  \n",
       "763  2.532136  \n",
       "764 -0.531023  \n",
       "765 -0.275760  \n",
       "766  1.170732  \n",
       "767 -0.871374  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = diabetes['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "5      0\n",
       "6      1\n",
       "7      0\n",
       "8      1\n",
       "9      1\n",
       "10     0\n",
       "11     1\n",
       "12     0\n",
       "13     1\n",
       "14     1\n",
       "15     1\n",
       "16     1\n",
       "17     1\n",
       "18     0\n",
       "19     1\n",
       "20     0\n",
       "21     0\n",
       "22     1\n",
       "23     1\n",
       "24     1\n",
       "25     1\n",
       "26     1\n",
       "27     0\n",
       "28     0\n",
       "29     0\n",
       "      ..\n",
       "738    0\n",
       "739    1\n",
       "740    1\n",
       "741    0\n",
       "742    0\n",
       "743    1\n",
       "744    0\n",
       "745    0\n",
       "746    1\n",
       "747    0\n",
       "748    1\n",
       "749    1\n",
       "750    1\n",
       "751    0\n",
       "752    0\n",
       "753    1\n",
       "754    1\n",
       "755    1\n",
       "756    0\n",
       "757    1\n",
       "758    0\n",
       "759    1\n",
       "760    0\n",
       "761    1\n",
       "762    0\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: class, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X_train = np.array(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array(y_train)\n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [],
   "source": [
    "min = X_train.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1418515161634994"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [],
   "source": [
    "max = X_train.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.906578350084603"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = X_train.shape[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [],
   "source": [
    "## this a python function to create the buckets given \n",
    "## min, max and num of buckets\n",
    "\n",
    "def get_buckets(min, max, num_buckets):\n",
    "    buckets = []\n",
    "    bucket_size= (max-min)/num_buckets\n",
    "    for i in range(1,num_buckets):\n",
    "        buckets.append((min+(i-1)*bucket_size))\n",
    "    buckets.append(max)    \n",
    "    return buckets  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = []\n",
    "for i in range (0,cols):\n",
    "    bins.append(get_buckets(X_train.min(axis=0)[i],X_train.max(axis=0)[i],1000))\n",
    "#bins   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1418515161634994"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_bin(bins,value):\n",
    "    first_bin = bins[0]\n",
    "    for i in range (1,len(bins)):\n",
    "        if (value >= first_bin and value <= bins[i]):\n",
    "            return i\n",
    "        else:\n",
    "            first_bin = bins[i]\n",
    "    return -1       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_bin(bins[0],0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1418515161634994"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc= graph.get_or_create_vertex(\"mdata\", wcol=-1, cbin=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(cc.as_dict().get(\"count\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 635,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "response_classes = np.array(list(set(y_train)))\n",
    "print(len(response_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for i in range (0,rows):\n",
    "    for j in range (0,cols):\n",
    "        wbin=find_bin(bins[j],X_train[i][j])\n",
    "        v = graph.get_or_create_vertex(\"data\",col=j,cbin=wbin)\n",
    "        out = graph.get_or_create_vertex(\"outc\",out=y_train[i])\n",
    "        #print(out)\n",
    "        edge = graph.get_or_create_edge(v, \"outcome\", out)\n",
    "        if(edge.as_dict().get('properties').get(\"count\") == None):\n",
    "            edge.set_property(count=1)\n",
    "        else:\n",
    "            cc = int(str(edge.as_dict().get('properties').get(\"count\")))+1\n",
    "            edge.set_property(count=cc)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EntitySet> items: 1"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_vertices(label=\"outc\",out=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EntitySet> items: 952"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_vertices(label=\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EntitySet> items: 1344"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Vertex> ident: 955, label: outc, properties: {'row': 0, 'col': 0, 'cbin': 1, 'out': 'yes'}"
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_or_create_vertex(\"outc\",row=0,col=0,cbin=1,out=\"yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Vertex> ident: 955, label: outc, properties: {'row': 0, 'col': 0, 'cbin': 1, 'out': 'yes'}"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_or_create_vertex(\"outc\",row=0,col=0,cbin=1,out=\"yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [],
   "source": [
    "#graph.get_or_create_vertex(\"outc\",col=0,cbin=1,out=\"yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = graph.get_vertices(\"data\",col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(result.all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EntitySet> items: 17"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = graph.get_edges(label=\"outcome\",count__gt=1)\n",
    "#print(result.all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EntitySet> items: 1344"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = graph.get_edges(label=\"outcome\")\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_index(y, response_classes):\n",
    "    for i in range (0,len(response_classes)):\n",
    "        if(y == response_classes[i]):\n",
    "            return i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_index(0,response_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_index(1,response_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "[288. 138.]\n",
      "0\n",
      "[129.  47.]\n",
      "0\n",
      "[318. 189.]\n",
      "0\n",
      "[58. 34.]\n",
      "0\n",
      "[322. 197.]\n",
      "0\n",
      "[129.  42.]\n",
      "0\n",
      "[36. 39.]\n",
      "1\n",
      "[58. 43.]\n",
      "0\n",
      "[99. 30.]\n",
      "0\n",
      "[65. 35.]\n",
      "0\n",
      "[81. 53.]\n",
      "0\n",
      "[58. 38.]\n",
      "0\n",
      "[312. 183.]\n",
      "0\n",
      "[95. 30.]\n",
      "0\n",
      "[211. 131.]\n",
      "0\n",
      "[250. 151.]\n",
      "0\n",
      "[42. 42.]\n",
      "0\n",
      "[265. 128.]\n",
      "0\n",
      "[206. 125.]\n",
      "0\n",
      "[62. 29.]\n",
      "0\n",
      "[220. 138.]\n",
      "0\n",
      "[372. 179.]\n",
      "0\n",
      "[272. 134.]\n",
      "0\n",
      "[224. 136.]\n",
      "0\n",
      "[143.  37.]\n",
      "0\n",
      "[287. 147.]\n",
      "0\n",
      "[336. 206.]\n",
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    }
   ],
   "source": [
    "import random \n",
    "\n",
    "print(response_classes)\n",
    "    \n",
    "y_pred =[]\n",
    "for i in range (0,X_test.shape[0]):\n",
    "    count= np.zeros(len(response_classes))\n",
    "    totv = []\n",
    "    for j in range (0,X_test.shape[-1]):\n",
    "        wbin=find_bin(bins[j],X_test[i][j])\n",
    "        for v in graph.get_vertices(\"data\",col=j,cbin=wbin):\n",
    "            totv.append(v)\n",
    "    for r in totv:\n",
    "        mv = r\n",
    "        #print(mv.as_dict().get('properties'))\n",
    "        for m in mv.get_out_edges():\n",
    "                #print(m.as_dict().get('properties'))\n",
    "                #print(m.get_out_vertex().as_dict())\n",
    "                indx = int(str(m.get_out_vertex().as_dict().get('properties').get(\"out\")))\n",
    "                count[indx] = count[indx]+int(str(m.as_dict().get('properties').get(\"count\")))\n",
    "                #print(\"index\"+str(indx)+\" --\"+str(count[indx]))\n",
    "    print(count)\n",
    "    for ii in range (0,len(count)):\n",
    "        if(count[ii] == count.max()):\n",
    "                print(response_classes[ii])\n",
    "                y_pred.append(response_classes[ii])\n",
    "                break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7012987012987013"
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)\n",
    "\n",
    "### doing better than baseline accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.69      1.00      0.82       157\n",
      "          1       1.00      0.07      0.13        74\n",
      "\n",
      "avg / total       0.79      0.70      0.60       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# calculate accuracy measures and confusion matrix\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7792207792207793"
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "lr = LogisticRegression();\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.90      0.85       157\n",
      "          1       0.71      0.53      0.60        74\n",
      "\n",
      "avg / total       0.77      0.78      0.77       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# calculate accuracy measures and confusion matrix\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7619047619047619"
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb = GaussianNB();\n",
    "nb.fit(X_train,y_train)\n",
    "y_pred = nb.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7705627705627706"
      ]
     },
     "execution_count": 659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier();\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ruruki_eye.server as s "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [],
   "source": [
    " #s.run(\"localhost\",5000,True,graph)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model = DecisionTreeClassifier(criterion = 'entropy' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7229437229437229"
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(X_test , y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<EntitySet> items: 1344"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.get_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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