3 - hyperparameter-search-comparison-grid-vs-random.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2491bdb1-ee55-4545-b41a-c427e1dc7718",
    "_uuid": "b9b5f2a9ce59d7086e508751553e7d8392c390ad"
   },
   "source": [
    "# Data pre-processing steps\n",
    "\n",
    "1. Remove columns that contain \"Call\" data\n",
    "2. Transpose the dataframe so that each row is a patient and each column is a gene\n",
    "3. Remove gene description and set the gene accession numbers as the column headers\n",
    "4. Merge the data (expression values) with the class labels (patient numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "e96760a5-4ff1-4261-a2c8-1a9e2e1bd880",
    "_execution_state": "idle",
    "_kg_hide-input": false,
    "_uuid": "8a1b2cf36761d30c89f72c1c2be37034b2733c59",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "6a0cfafc-8afa-41c6-ba7a-fa61596bab1f",
    "_execution_state": "idle",
    "_kg_hide-input": false,
    "_uuid": "05345f350192b24c9ea7dac7e36e673f5f6a9db8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testfile='../input/data_set_ALL_AML_independent.csv'\n",
    "trainfile='../input/data_set_ALL_AML_train.csv'\n",
    "patient_cancer='../input/actual.csv'\n",
    "\n",
    "train = pd.read_csv(trainfile)\n",
    "test = pd.read_csv(testfile)\n",
    "patient_cancer = pd.read_csv(patient_cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "c52961c6-9e65-45b5-8967-b4caf1978391",
    "_execution_state": "idle",
    "_uuid": "689db3f563881b7c6f2441d1696cde299b200a40"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene Description</th>\n",
       "      <th>Gene Accession Number</th>\n",
       "      <th>1</th>\n",
       "      <th>call</th>\n",
       "      <th>2</th>\n",
       "      <th>call.1</th>\n",
       "      <th>3</th>\n",
       "      <th>call.2</th>\n",
       "      <th>4</th>\n",
       "      <th>call.3</th>\n",
       "      <th>...</th>\n",
       "      <th>29</th>\n",
       "      <th>call.33</th>\n",
       "      <th>30</th>\n",
       "      <th>call.34</th>\n",
       "      <th>31</th>\n",
       "      <th>call.35</th>\n",
       "      <th>32</th>\n",
       "      <th>call.36</th>\n",
       "      <th>33</th>\n",
       "      <th>call.37</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFFX-BioB-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-5_at</td>\n",
       "      <td>-214</td>\n",
       "      <td>A</td>\n",
       "      <td>-139</td>\n",
       "      <td>A</td>\n",
       "      <td>-76</td>\n",
       "      <td>A</td>\n",
       "      <td>-135</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>A</td>\n",
       "      <td>-318</td>\n",
       "      <td>A</td>\n",
       "      <td>-32</td>\n",
       "      <td>A</td>\n",
       "      <td>-124</td>\n",
       "      <td>A</td>\n",
       "      <td>-135</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFFX-BioB-M_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-M_at</td>\n",
       "      <td>-153</td>\n",
       "      <td>A</td>\n",
       "      <td>-73</td>\n",
       "      <td>A</td>\n",
       "      <td>-49</td>\n",
       "      <td>A</td>\n",
       "      <td>-114</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-114</td>\n",
       "      <td>A</td>\n",
       "      <td>-192</td>\n",
       "      <td>A</td>\n",
       "      <td>-49</td>\n",
       "      <td>A</td>\n",
       "      <td>-79</td>\n",
       "      <td>A</td>\n",
       "      <td>-186</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFFX-BioB-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-3_at</td>\n",
       "      <td>-58</td>\n",
       "      <td>A</td>\n",
       "      <td>-1</td>\n",
       "      <td>A</td>\n",
       "      <td>-307</td>\n",
       "      <td>A</td>\n",
       "      <td>265</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>-95</td>\n",
       "      <td>A</td>\n",
       "      <td>49</td>\n",
       "      <td>A</td>\n",
       "      <td>-37</td>\n",
       "      <td>A</td>\n",
       "      <td>-70</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFFX-BioC-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-5_at</td>\n",
       "      <td>88</td>\n",
       "      <td>A</td>\n",
       "      <td>283</td>\n",
       "      <td>A</td>\n",
       "      <td>309</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>193</td>\n",
       "      <td>A</td>\n",
       "      <td>312</td>\n",
       "      <td>A</td>\n",
       "      <td>230</td>\n",
       "      <td>P</td>\n",
       "      <td>330</td>\n",
       "      <td>A</td>\n",
       "      <td>337</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFFX-BioC-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-3_at</td>\n",
       "      <td>-295</td>\n",
       "      <td>A</td>\n",
       "      <td>-264</td>\n",
       "      <td>A</td>\n",
       "      <td>-376</td>\n",
       "      <td>A</td>\n",
       "      <td>-419</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-51</td>\n",
       "      <td>A</td>\n",
       "      <td>-139</td>\n",
       "      <td>A</td>\n",
       "      <td>-367</td>\n",
       "      <td>A</td>\n",
       "      <td>-188</td>\n",
       "      <td>A</td>\n",
       "      <td>-407</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gene Description Gene Accession Number    1 call    2  \\\n",
       "0  AFFX-BioB-5_at (endogenous control)        AFFX-BioB-5_at -214    A -139   \n",
       "1  AFFX-BioB-M_at (endogenous control)        AFFX-BioB-M_at -153    A  -73   \n",
       "2  AFFX-BioB-3_at (endogenous control)        AFFX-BioB-3_at  -58    A   -1   \n",
       "3  AFFX-BioC-5_at (endogenous control)        AFFX-BioC-5_at   88    A  283   \n",
       "4  AFFX-BioC-3_at (endogenous control)        AFFX-BioC-3_at -295    A -264   \n",
       "\n",
       "  call.1    3 call.2    4 call.3   ...     29 call.33   30 call.34   31  \\\n",
       "0      A  -76      A -135      A   ...     15       A -318       A  -32   \n",
       "1      A  -49      A -114      A   ...   -114       A -192       A  -49   \n",
       "2      A -307      A  265      A   ...      2       A  -95       A   49   \n",
       "3      A  309      A   12      A   ...    193       A  312       A  230   \n",
       "4      A -376      A -419      A   ...    -51       A -139       A -367   \n",
       "\n",
       "  call.35   32 call.36   33 call.37  \n",
       "0       A -124       A -135       A  \n",
       "1       A  -79       A -186       A  \n",
       "2       A  -37       A  -70       A  \n",
       "3       P  330       A  337       A  \n",
       "4       A -188       A -407       A  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "f07b2efe-d87c-42b9-8d7d-f774cd3cb78b",
    "_execution_state": "idle",
    "_kg_hide-input": false,
    "_uuid": "a0f8f43756a6947faa1d4aed16035ac944c1c397",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove \"call\" columns from training a test dataframes\n",
    "train_keepers = [col for col in train.columns if \"call\" not in col]\n",
    "test_keepers = [col for col in test.columns if \"call\" not in col]\n",
    "\n",
    "train = train[train_keepers]\n",
    "test = test[test_keepers]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "577a80e1-8ee1-464b-973a-698189ecc9e6",
    "_execution_state": "idle",
    "_uuid": "8493380de0d84b7b5dd2d7906122c7876466f020"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene Description</th>\n",
       "      <th>Gene Accession Number</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFFX-BioB-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-5_at</td>\n",
       "      <td>-214</td>\n",
       "      <td>-139</td>\n",
       "      <td>-76</td>\n",
       "      <td>-135</td>\n",
       "      <td>-106</td>\n",
       "      <td>-138</td>\n",
       "      <td>-72</td>\n",
       "      <td>-413</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>-213</td>\n",
       "      <td>-25</td>\n",
       "      <td>-72</td>\n",
       "      <td>-4</td>\n",
       "      <td>15</td>\n",
       "      <td>-318</td>\n",
       "      <td>-32</td>\n",
       "      <td>-124</td>\n",
       "      <td>-135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFFX-BioB-M_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-M_at</td>\n",
       "      <td>-153</td>\n",
       "      <td>-73</td>\n",
       "      <td>-49</td>\n",
       "      <td>-114</td>\n",
       "      <td>-125</td>\n",
       "      <td>-85</td>\n",
       "      <td>-144</td>\n",
       "      <td>-260</td>\n",
       "      <td>...</td>\n",
       "      <td>-100</td>\n",
       "      <td>-252</td>\n",
       "      <td>-20</td>\n",
       "      <td>-139</td>\n",
       "      <td>-116</td>\n",
       "      <td>-114</td>\n",
       "      <td>-192</td>\n",
       "      <td>-49</td>\n",
       "      <td>-79</td>\n",
       "      <td>-186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFFX-BioB-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-3_at</td>\n",
       "      <td>-58</td>\n",
       "      <td>-1</td>\n",
       "      <td>-307</td>\n",
       "      <td>265</td>\n",
       "      <td>-76</td>\n",
       "      <td>215</td>\n",
       "      <td>238</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>-57</td>\n",
       "      <td>136</td>\n",
       "      <td>124</td>\n",
       "      <td>-1</td>\n",
       "      <td>-125</td>\n",
       "      <td>2</td>\n",
       "      <td>-95</td>\n",
       "      <td>49</td>\n",
       "      <td>-37</td>\n",
       "      <td>-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFFX-BioC-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-5_at</td>\n",
       "      <td>88</td>\n",
       "      <td>283</td>\n",
       "      <td>309</td>\n",
       "      <td>12</td>\n",
       "      <td>168</td>\n",
       "      <td>71</td>\n",
       "      <td>55</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>318</td>\n",
       "      <td>325</td>\n",
       "      <td>392</td>\n",
       "      <td>241</td>\n",
       "      <td>193</td>\n",
       "      <td>312</td>\n",
       "      <td>230</td>\n",
       "      <td>330</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFFX-BioC-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-3_at</td>\n",
       "      <td>-295</td>\n",
       "      <td>-264</td>\n",
       "      <td>-376</td>\n",
       "      <td>-419</td>\n",
       "      <td>-230</td>\n",
       "      <td>-272</td>\n",
       "      <td>-399</td>\n",
       "      <td>-541</td>\n",
       "      <td>...</td>\n",
       "      <td>-377</td>\n",
       "      <td>-209</td>\n",
       "      <td>-396</td>\n",
       "      <td>-324</td>\n",
       "      <td>-191</td>\n",
       "      <td>-51</td>\n",
       "      <td>-139</td>\n",
       "      <td>-367</td>\n",
       "      <td>-188</td>\n",
       "      <td>-407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gene Description Gene Accession Number    1    2    3  \\\n",
       "0  AFFX-BioB-5_at (endogenous control)        AFFX-BioB-5_at -214 -139  -76   \n",
       "1  AFFX-BioB-M_at (endogenous control)        AFFX-BioB-M_at -153  -73  -49   \n",
       "2  AFFX-BioB-3_at (endogenous control)        AFFX-BioB-3_at  -58   -1 -307   \n",
       "3  AFFX-BioC-5_at (endogenous control)        AFFX-BioC-5_at   88  283  309   \n",
       "4  AFFX-BioC-3_at (endogenous control)        AFFX-BioC-3_at -295 -264 -376   \n",
       "\n",
       "     4    5    6    7    8 ...    35   36   37   38   28   29   30   31   32  \\\n",
       "0 -135 -106 -138  -72 -413 ...     7 -213  -25  -72   -4   15 -318  -32 -124   \n",
       "1 -114 -125  -85 -144 -260 ...  -100 -252  -20 -139 -116 -114 -192  -49  -79   \n",
       "2  265  -76  215  238    7 ...   -57  136  124   -1 -125    2  -95   49  -37   \n",
       "3   12  168   71   55   -2 ...   132  318  325  392  241  193  312  230  330   \n",
       "4 -419 -230 -272 -399 -541 ...  -377 -209 -396 -324 -191  -51 -139 -367 -188   \n",
       "\n",
       "    33  \n",
       "0 -135  \n",
       "1 -186  \n",
       "2  -70  \n",
       "3  337  \n",
       "4 -407  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "b123fe25-65c4-42b9-8da4-99d93f3b11fc",
    "_uuid": "f444169669f997a125418a3cb900ec421396dd94"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>7119</th>\n",
       "      <th>7120</th>\n",
       "      <th>7121</th>\n",
       "      <th>7122</th>\n",
       "      <th>7123</th>\n",
       "      <th>7124</th>\n",
       "      <th>7125</th>\n",
       "      <th>7126</th>\n",
       "      <th>7127</th>\n",
       "      <th>7128</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gene Description</th>\n",
       "      <td>AFFX-BioB-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-M_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioC-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioDn-5_at (endogenous control)</td>\n",
       "      <td>AFFX-BioDn-3_at (endogenous control)</td>\n",
       "      <td>AFFX-CreX-5_at (endogenous control)</td>\n",
       "      <td>AFFX-CreX-3_at (endogenous control)</td>\n",
       "      <td>AFFX-BioB-5_st (endogenous control)</td>\n",
       "      <td>...</td>\n",
       "      <td>Transcription factor Stat5b (stat5b) mRNA</td>\n",
       "      <td>Breast epithelial antigen BA46 mRNA</td>\n",
       "      <td>GB DEF = Calcium/calmodulin-dependent protein ...</td>\n",
       "      <td>TUBULIN ALPHA-4 CHAIN</td>\n",
       "      <td>CYP4B1 Cytochrome P450; subfamily IVB; polypep...</td>\n",
       "      <td>PTGER3 Prostaglandin E receptor 3 (subtype EP3...</td>\n",
       "      <td>HMG2 High-mobility group (nonhistone chromosom...</td>\n",
       "      <td>RB1 Retinoblastoma 1 (including osteosarcoma)</td>\n",
       "      <td>GB DEF = Glycophorin Sta (type A) exons 3 and ...</td>\n",
       "      <td>GB DEF = mRNA (clone 1A7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene Accession Number</th>\n",
       "      <td>AFFX-BioB-5_at</td>\n",
       "      <td>AFFX-BioB-M_at</td>\n",
       "      <td>AFFX-BioB-3_at</td>\n",
       "      <td>AFFX-BioC-5_at</td>\n",
       "      <td>AFFX-BioC-3_at</td>\n",
       "      <td>AFFX-BioDn-5_at</td>\n",
       "      <td>AFFX-BioDn-3_at</td>\n",
       "      <td>AFFX-CreX-5_at</td>\n",
       "      <td>AFFX-CreX-3_at</td>\n",
       "      <td>AFFX-BioB-5_st</td>\n",
       "      <td>...</td>\n",
       "      <td>U48730_at</td>\n",
       "      <td>U58516_at</td>\n",
       "      <td>U73738_at</td>\n",
       "      <td>X06956_at</td>\n",
       "      <td>X16699_at</td>\n",
       "      <td>X83863_at</td>\n",
       "      <td>Z17240_at</td>\n",
       "      <td>L49218_f_at</td>\n",
       "      <td>M71243_f_at</td>\n",
       "      <td>Z78285_f_at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-214</td>\n",
       "      <td>-153</td>\n",
       "      <td>-58</td>\n",
       "      <td>88</td>\n",
       "      <td>-295</td>\n",
       "      <td>-558</td>\n",
       "      <td>199</td>\n",
       "      <td>-176</td>\n",
       "      <td>252</td>\n",
       "      <td>206</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>511</td>\n",
       "      <td>-125</td>\n",
       "      <td>389</td>\n",
       "      <td>-37</td>\n",
       "      <td>793</td>\n",
       "      <td>329</td>\n",
       "      <td>36</td>\n",
       "      <td>191</td>\n",
       "      <td>-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-139</td>\n",
       "      <td>-73</td>\n",
       "      <td>-1</td>\n",
       "      <td>283</td>\n",
       "      <td>-264</td>\n",
       "      <td>-400</td>\n",
       "      <td>-330</td>\n",
       "      <td>-168</td>\n",
       "      <td>101</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>837</td>\n",
       "      <td>-36</td>\n",
       "      <td>442</td>\n",
       "      <td>-17</td>\n",
       "      <td>782</td>\n",
       "      <td>295</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-76</td>\n",
       "      <td>-49</td>\n",
       "      <td>-307</td>\n",
       "      <td>309</td>\n",
       "      <td>-376</td>\n",
       "      <td>-650</td>\n",
       "      <td>33</td>\n",
       "      <td>-367</td>\n",
       "      <td>206</td>\n",
       "      <td>-215</td>\n",
       "      <td>...</td>\n",
       "      <td>315</td>\n",
       "      <td>1199</td>\n",
       "      <td>33</td>\n",
       "      <td>168</td>\n",
       "      <td>52</td>\n",
       "      <td>1138</td>\n",
       "      <td>777</td>\n",
       "      <td>41</td>\n",
       "      <td>228</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0     \\\n",
       "Gene Description       AFFX-BioB-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-5_at   \n",
       "1                                                     -214   \n",
       "2                                                     -139   \n",
       "3                                                      -76   \n",
       "\n",
       "                                                      1     \\\n",
       "Gene Description       AFFX-BioB-M_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-M_at   \n",
       "1                                                     -153   \n",
       "2                                                      -73   \n",
       "3                                                      -49   \n",
       "\n",
       "                                                      2     \\\n",
       "Gene Description       AFFX-BioB-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-3_at   \n",
       "1                                                      -58   \n",
       "2                                                       -1   \n",
       "3                                                     -307   \n",
       "\n",
       "                                                      3     \\\n",
       "Gene Description       AFFX-BioC-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioC-5_at   \n",
       "1                                                       88   \n",
       "2                                                      283   \n",
       "3                                                      309   \n",
       "\n",
       "                                                      4     \\\n",
       "Gene Description       AFFX-BioC-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioC-3_at   \n",
       "1                                                     -295   \n",
       "2                                                     -264   \n",
       "3                                                     -376   \n",
       "\n",
       "                                                       5     \\\n",
       "Gene Description       AFFX-BioDn-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioDn-5_at   \n",
       "1                                                      -558   \n",
       "2                                                      -400   \n",
       "3                                                      -650   \n",
       "\n",
       "                                                       6     \\\n",
       "Gene Description       AFFX-BioDn-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioDn-3_at   \n",
       "1                                                       199   \n",
       "2                                                      -330   \n",
       "3                                                        33   \n",
       "\n",
       "                                                      7     \\\n",
       "Gene Description       AFFX-CreX-5_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-CreX-5_at   \n",
       "1                                                     -176   \n",
       "2                                                     -168   \n",
       "3                                                     -367   \n",
       "\n",
       "                                                      8     \\\n",
       "Gene Description       AFFX-CreX-3_at (endogenous control)   \n",
       "Gene Accession Number                       AFFX-CreX-3_at   \n",
       "1                                                      252   \n",
       "2                                                      101   \n",
       "3                                                      206   \n",
       "\n",
       "                                                      9     \\\n",
       "Gene Description       AFFX-BioB-5_st (endogenous control)   \n",
       "Gene Accession Number                       AFFX-BioB-5_st   \n",
       "1                                                      206   \n",
       "2                                                       74   \n",
       "3                                                     -215   \n",
       "\n",
       "                                 ...              \\\n",
       "Gene Description                 ...               \n",
       "Gene Accession Number            ...               \n",
       "1                                ...               \n",
       "2                                ...               \n",
       "3                                ...               \n",
       "\n",
       "                                                            7119  \\\n",
       "Gene Description       Transcription factor Stat5b (stat5b) mRNA   \n",
       "Gene Accession Number                                  U48730_at   \n",
       "1                                                            185   \n",
       "2                                                            169   \n",
       "3                                                            315   \n",
       "\n",
       "                                                      7120  \\\n",
       "Gene Description       Breast epithelial antigen BA46 mRNA   \n",
       "Gene Accession Number                            U58516_at   \n",
       "1                                                      511   \n",
       "2                                                      837   \n",
       "3                                                     1199   \n",
       "\n",
       "                                                                    7121  \\\n",
       "Gene Description       GB DEF = Calcium/calmodulin-dependent protein ...   \n",
       "Gene Accession Number                                          U73738_at   \n",
       "1                                                                   -125   \n",
       "2                                                                    -36   \n",
       "3                                                                     33   \n",
       "\n",
       "                                        7122  \\\n",
       "Gene Description       TUBULIN ALPHA-4 CHAIN   \n",
       "Gene Accession Number              X06956_at   \n",
       "1                                        389   \n",
       "2                                        442   \n",
       "3                                        168   \n",
       "\n",
       "                                                                    7123  \\\n",
       "Gene Description       CYP4B1 Cytochrome P450; subfamily IVB; polypep...   \n",
       "Gene Accession Number                                          X16699_at   \n",
       "1                                                                    -37   \n",
       "2                                                                    -17   \n",
       "3                                                                     52   \n",
       "\n",
       "                                                                    7124  \\\n",
       "Gene Description       PTGER3 Prostaglandin E receptor 3 (subtype EP3...   \n",
       "Gene Accession Number                                          X83863_at   \n",
       "1                                                                    793   \n",
       "2                                                                    782   \n",
       "3                                                                   1138   \n",
       "\n",
       "                                                                    7125  \\\n",
       "Gene Description       HMG2 High-mobility group (nonhistone chromosom...   \n",
       "Gene Accession Number                                          Z17240_at   \n",
       "1                                                                    329   \n",
       "2                                                                    295   \n",
       "3                                                                    777   \n",
       "\n",
       "                                                                7126  \\\n",
       "Gene Description       RB1 Retinoblastoma 1 (including osteosarcoma)   \n",
       "Gene Accession Number                                    L49218_f_at   \n",
       "1                                                                 36   \n",
       "2                                                                 11   \n",
       "3                                                                 41   \n",
       "\n",
       "                                                                    7127  \\\n",
       "Gene Description       GB DEF = Glycophorin Sta (type A) exons 3 and ...   \n",
       "Gene Accession Number                                        M71243_f_at   \n",
       "1                                                                    191   \n",
       "2                                                                     76   \n",
       "3                                                                    228   \n",
       "\n",
       "                                            7128  \n",
       "Gene Description       GB DEF = mRNA (clone 1A7)  \n",
       "Gene Accession Number                Z78285_f_at  \n",
       "1                                            -37  \n",
       "2                                            -14  \n",
       "3                                            -41  \n",
       "\n",
       "[5 rows x 7129 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the columns and rows so that genes become features and rows become observations\n",
    "train = train.T\n",
    "test = test.T\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "15eefd6d-4962-4277-814a-95bfa7a06dc7",
    "_uuid": "d7a9b8fc01f3e0022f55394e87b651a962b99ab5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Gene Accession Number</th>\n",
       "      <th>AFFX-BioB-5_at</th>\n",
       "      <th>AFFX-BioB-M_at</th>\n",
       "      <th>AFFX-BioB-3_at</th>\n",
       "      <th>AFFX-BioC-5_at</th>\n",
       "      <th>AFFX-BioC-3_at</th>\n",
       "      <th>AFFX-BioDn-5_at</th>\n",
       "      <th>AFFX-BioDn-3_at</th>\n",
       "      <th>AFFX-CreX-5_at</th>\n",
       "      <th>AFFX-CreX-3_at</th>\n",
       "      <th>AFFX-BioB-5_st</th>\n",
       "      <th>...</th>\n",
       "      <th>U48730_at</th>\n",
       "      <th>U58516_at</th>\n",
       "      <th>U73738_at</th>\n",
       "      <th>X06956_at</th>\n",
       "      <th>X16699_at</th>\n",
       "      <th>X83863_at</th>\n",
       "      <th>Z17240_at</th>\n",
       "      <th>L49218_f_at</th>\n",
       "      <th>M71243_f_at</th>\n",
       "      <th>Z78285_f_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-214</td>\n",
       "      <td>-153</td>\n",
       "      <td>-58</td>\n",
       "      <td>88</td>\n",
       "      <td>-295</td>\n",
       "      <td>-558</td>\n",
       "      <td>199</td>\n",
       "      <td>-176</td>\n",
       "      <td>252</td>\n",
       "      <td>206</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>511</td>\n",
       "      <td>-125</td>\n",
       "      <td>389</td>\n",
       "      <td>-37</td>\n",
       "      <td>793</td>\n",
       "      <td>329</td>\n",
       "      <td>36</td>\n",
       "      <td>191</td>\n",
       "      <td>-37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-139</td>\n",
       "      <td>-73</td>\n",
       "      <td>-1</td>\n",
       "      <td>283</td>\n",
       "      <td>-264</td>\n",
       "      <td>-400</td>\n",
       "      <td>-330</td>\n",
       "      <td>-168</td>\n",
       "      <td>101</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>837</td>\n",
       "      <td>-36</td>\n",
       "      <td>442</td>\n",
       "      <td>-17</td>\n",
       "      <td>782</td>\n",
       "      <td>295</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-76</td>\n",
       "      <td>-49</td>\n",
       "      <td>-307</td>\n",
       "      <td>309</td>\n",
       "      <td>-376</td>\n",
       "      <td>-650</td>\n",
       "      <td>33</td>\n",
       "      <td>-367</td>\n",
       "      <td>206</td>\n",
       "      <td>-215</td>\n",
       "      <td>...</td>\n",
       "      <td>315</td>\n",
       "      <td>1199</td>\n",
       "      <td>33</td>\n",
       "      <td>168</td>\n",
       "      <td>52</td>\n",
       "      <td>1138</td>\n",
       "      <td>777</td>\n",
       "      <td>41</td>\n",
       "      <td>228</td>\n",
       "      <td>-41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-135</td>\n",
       "      <td>-114</td>\n",
       "      <td>265</td>\n",
       "      <td>12</td>\n",
       "      <td>-419</td>\n",
       "      <td>-585</td>\n",
       "      <td>158</td>\n",
       "      <td>-253</td>\n",
       "      <td>49</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>835</td>\n",
       "      <td>218</td>\n",
       "      <td>174</td>\n",
       "      <td>-110</td>\n",
       "      <td>627</td>\n",
       "      <td>170</td>\n",
       "      <td>-50</td>\n",
       "      <td>126</td>\n",
       "      <td>-91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-106</td>\n",
       "      <td>-125</td>\n",
       "      <td>-76</td>\n",
       "      <td>168</td>\n",
       "      <td>-230</td>\n",
       "      <td>-284</td>\n",
       "      <td>4</td>\n",
       "      <td>-122</td>\n",
       "      <td>70</td>\n",
       "      <td>252</td>\n",
       "      <td>...</td>\n",
       "      <td>156</td>\n",
       "      <td>649</td>\n",
       "      <td>57</td>\n",
       "      <td>504</td>\n",
       "      <td>-26</td>\n",
       "      <td>250</td>\n",
       "      <td>314</td>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Gene Accession Number  AFFX-BioB-5_at  AFFX-BioB-M_at  AFFX-BioB-3_at  \\\n",
       "1                                -214            -153             -58   \n",
       "2                                -139             -73              -1   \n",
       "3                                 -76             -49            -307   \n",
       "4                                -135            -114             265   \n",
       "5                                -106            -125             -76   \n",
       "\n",
       "Gene Accession Number  AFFX-BioC-5_at  AFFX-BioC-3_at  AFFX-BioDn-5_at  \\\n",
       "1                                  88            -295             -558   \n",
       "2                                 283            -264             -400   \n",
       "3                                 309            -376             -650   \n",
       "4                                  12            -419             -585   \n",
       "5                                 168            -230             -284   \n",
       "\n",
       "Gene Accession Number  AFFX-BioDn-3_at  AFFX-CreX-5_at  AFFX-CreX-3_at  \\\n",
       "1                                  199            -176             252   \n",
       "2                                 -330            -168             101   \n",
       "3                                   33            -367             206   \n",
       "4                                  158            -253              49   \n",
       "5                                    4            -122              70   \n",
       "\n",
       "Gene Accession Number  AFFX-BioB-5_st     ...       U48730_at  U58516_at  \\\n",
       "1                                 206     ...             185        511   \n",
       "2                                  74     ...             169        837   \n",
       "3                                -215     ...             315       1199   \n",
       "4                                  31     ...             240        835   \n",
       "5                                 252     ...             156        649   \n",
       "\n",
       "Gene Accession Number  U73738_at  X06956_at  X16699_at  X83863_at  Z17240_at  \\\n",
       "1                           -125        389        -37        793        329   \n",
       "2                            -36        442        -17        782        295   \n",
       "3                             33        168         52       1138        777   \n",
       "4                            218        174       -110        627        170   \n",
       "5                             57        504        -26        250        314   \n",
       "\n",
       "Gene Accession Number  L49218_f_at  M71243_f_at  Z78285_f_at  \n",
       "1                               36          191          -37  \n",
       "2                               11           76          -14  \n",
       "3                               41          228          -41  \n",
       "4                              -50          126          -91  \n",
       "5                               14           56          -25  \n",
       "\n",
       "[5 rows x 7129 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean up the column names for training data\n",
    "train.columns = train.iloc[1]\n",
    "train = train.drop([\"Gene Description\", \"Gene Accession Number\"]).apply(pd.to_numeric)\n",
    "\n",
    "# Clean up the column names for training data\n",
    "test.columns = test.iloc[1]\n",
    "test = test.drop([\"Gene Description\", \"Gene Accession Number\"]).apply(pd.to_numeric)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fa6672f2-c5a4-43cb-8077-6117c333cf3f",
    "_uuid": "66553b5ac208a39eb18fa9fcbfb2d1258484bab6"
   },
   "source": [
    "### Combine the data (gene expression) with class labels (patient numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "16fbe1c5-0952-4eb5-81b4-d452d0430c5e",
    "_kg_hide-input": false,
    "_uuid": "15906fae2bdd07f787d2b48a175ccc0859be1ded",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reset the index. The indexes of two dataframes need to be the same before you combine them\n",
    "train = train.reset_index(drop=True)\n",
    "\n",
    "# Subset the first 38 patient's cancer types\n",
    "pc_train = patient_cancer[patient_cancer.patient <= 38].reset_index(drop=True)\n",
    "\n",
    "# Combine dataframes for first 38 patients: Patient number + cancer type + gene expression values\n",
    "train = pd.concat([pc_train,train], axis=1)\n",
    "\n",
    "\n",
    "# Handle the test data for patients 38 through 72\n",
    "# Clean up the index\n",
    "test = test.reset_index(drop=True)\n",
    "\n",
    "# Subset the last patient's cancer types to test\n",
    "pc_test = patient_cancer[patient_cancer.patient > 38].reset_index(drop=True)\n",
    "\n",
    "# Combine dataframes for last patients: Patient number + cancer type + gene expression values\n",
    "test = pd.concat([pc_test,test], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "44cd4399-9cd3-49b8-aa3d-515ad2c04756",
    "_uuid": "8075fadf78a3b76b67b40f31a647e9ad42c5bbec",
    "collapsed": true
   },
   "source": [
    "# EDA\n",
    "---\n",
    "\n",
    "There's a bunch of data, so to speed things up, only using a small sample of the training data for the EDA.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "06a40eb4-0b38-48f5-83a6-e8a9a4fdd9c9",
    "_uuid": "38b9fb6750ddc2f90202977097eef7a3ef0e6a5f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X64877_at</th>\n",
       "      <th>L08895_at</th>\n",
       "      <th>M92424_at</th>\n",
       "      <th>M74826_at</th>\n",
       "      <th>J02611_at</th>\n",
       "      <th>M94547_at</th>\n",
       "      <th>U61166_at</th>\n",
       "      <th>U11877_at</th>\n",
       "      <th>X82877_at</th>\n",
       "      <th>M13194_at</th>\n",
       "      <th>...</th>\n",
       "      <th>U09477_at</th>\n",
       "      <th>M10321_s_at</th>\n",
       "      <th>U27460_at</th>\n",
       "      <th>U24704_at</th>\n",
       "      <th>U60808_s_at</th>\n",
       "      <th>X76180_at</th>\n",
       "      <th>L00972_at</th>\n",
       "      <th>M37457_at</th>\n",
       "      <th>L21998_at</th>\n",
       "      <th>M92934_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>-304.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>...</td>\n",
       "      <td>394.0</td>\n",
       "      <td>-98.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>-115.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>...</td>\n",
       "      <td>123.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-57.0</td>\n",
       "      <td>-135.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>-1206.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>-303.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>...</td>\n",
       "      <td>187.0</td>\n",
       "      <td>-280.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>-215.0</td>\n",
       "      <td>-291.0</td>\n",
       "      <td>-323.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>-24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>-466.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>302.0</td>\n",
       "      <td>-152.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>-143.0</td>\n",
       "      <td>-89.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>-204.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>372.0</td>\n",
       "      <td>-108.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>-104.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1538.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>142.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>-73.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>...</td>\n",
       "      <td>469.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>342.0</td>\n",
       "      <td>3822.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>905.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>704.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>4556.0</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>9105.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       X64877_at  L08895_at  M92424_at  M74826_at  J02611_at  M94547_at  \\\n",
       "count       38.0       38.0       38.0       38.0       38.0       38.0   \n",
       "mean       108.0      688.0      198.0      208.0      719.0     -304.0   \n",
       "std         79.0     1012.0      102.0       92.0      216.0      327.0   \n",
       "min        -57.0     -135.0       39.0        5.0      395.0    -1206.0   \n",
       "25%         55.0       -9.0      133.0      142.0      577.0     -466.0   \n",
       "50%        110.0      122.0      174.0      199.0      705.0     -204.0   \n",
       "75%        142.0     1130.0      239.0      264.0      802.0      -73.0   \n",
       "max        342.0     3822.0      482.0      449.0     1425.0       63.0   \n",
       "\n",
       "       U61166_at  U11877_at  X82877_at  M13194_at    ...      U09477_at  \\\n",
       "count       38.0       38.0       38.0       38.0    ...           38.0   \n",
       "mean       350.0       35.0      262.0      692.0    ...          394.0   \n",
       "std        162.0      177.0      104.0      313.0    ...          123.0   \n",
       "min        119.0     -303.0       72.0      213.0    ...          187.0   \n",
       "25%        227.0      -51.0      186.0      468.0    ...          302.0   \n",
       "50%        324.0       18.0      263.0      684.0    ...          372.0   \n",
       "75%        444.0      108.0      354.0      808.0    ...          469.0   \n",
       "max        905.0      679.0      444.0     1797.0    ...          704.0   \n",
       "\n",
       "       M10321_s_at  U27460_at  U24704_at  U60808_s_at  X76180_at  L00972_at  \\\n",
       "count         38.0       38.0       38.0         38.0       38.0       38.0   \n",
       "mean         -98.0      478.0      364.0       -115.0       -9.0      215.0   \n",
       "std           91.0      469.0      281.0         64.0      136.0      129.0   \n",
       "min         -280.0       48.0     -215.0       -291.0     -323.0        5.0   \n",
       "25%         -152.0      173.0      150.0       -143.0      -89.0      118.0   \n",
       "50%         -108.0      300.0      395.0       -104.0      -22.0      200.0   \n",
       "75%          -42.0      672.0      544.0        -79.0       48.0      297.0   \n",
       "max           95.0     2200.0     1092.0        -10.0      342.0      521.0   \n",
       "\n",
       "       M37457_at  L21998_at  M92934_at  \n",
       "count       38.0       38.0       38.0  \n",
       "mean      1592.0      579.0      767.0  \n",
       "std        724.0      373.0     1829.0  \n",
       "min        567.0     -167.0      -24.0  \n",
       "25%       1048.0      459.0       64.0  \n",
       "50%       1538.0      585.0      123.0  \n",
       "75%       1929.0      725.0      288.0  \n",
       "max       4556.0     1769.0     9105.0  \n",
       "\n",
       "[8 rows x 100 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = train.iloc[:,2:].sample(n=100, axis=1)\n",
    "sample[\"cancer\"] = train.cancer\n",
    "sample.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "dc7e9d78-5f66-40c4-8dc0-a8abe193a7be",
    "_uuid": "f60226125761e3b856a8e3868f702534b4eac119",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "302dd036-e213-4a52-812f-28060c151456",
    "_uuid": "43d84c88bfe44e3f63500a83ff11989ab575fff6"
   },
   "source": [
    "### Distribution of the random sample before standardizing\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "a11f2f62-7166-412f-adc5-24cdb7566259",
    "_uuid": "a9064b7a0c313ecdb224f7e91088e6a55b910695"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdab6d84630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD8CAYAAABKKbKtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX/uwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda97df9748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = sample.drop(\"cancer\", axis=1)\n",
    "sample.plot(kind=\"hist\", legend=None, bins=20, color='k')\n",
    "sample.plot(kind=\"kde\", legend=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1d98c27a-6ad0-4d98-945f-eff548e03e14",
    "_uuid": "ed323f625db8c06509c01098f2ed17f0917e2b90"
   },
   "source": [
    "### Distribution of the random sample after standardizing\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "5394ce94-d4c1-40aa-be25-258c5234e7e9",
    "_uuid": "2be76f45c36fcd1b5172d49930f6cd1593478be1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD8CAYAAACYebj1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaaa01ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD8CAYAAABthzNFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda92e990f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_scaled = pd.DataFrame(preprocessing.scale(sample))\n",
    "sample_scaled.plot(kind=\"hist\", normed=True, legend=None, bins=10, color='k')\n",
    "sample_scaled.plot(kind=\"kde\", legend=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5fca5861-3762-4f27-b18a-564098709935",
    "_uuid": "a118162f00c884a29e9159463e7e6dc53a37ee86"
   },
   "source": [
    "# Process the full set\n",
    "---\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "6747e63f-4aca-43d1-8ae9-3dae56519d8f",
    "_uuid": "4356fd7bba7318cf983b777cf7fbddd742b5e07c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# StandardScaler to remove mean and scale to unit variance\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "b26459eb-e2bb-4a06-bd71-60579d021e2f",
    "_uuid": "dabc60230623be1c1723862cc264316d8d43a943",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(train.iloc[:,2:])\n",
    "scaled_train = scaler.transform(train.iloc[:,2:])\n",
    "scaled_test = scaler.transform(test.iloc[:,2:])\n",
    "\n",
    "x_train = train.iloc[:,2:]\n",
    "y_train = train.iloc[:,1]\n",
    "x_test = test.iloc[:,2:]\n",
    "y_test = test.iloc[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2194e6ad-a24c-4835-a64d-a2354ae4e534",
    "_uuid": "f5244e36655b163a56b8e87f9a01e0f00d72af6d"
   },
   "source": [
    "\n",
    "# Classifiers\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "e679a209-d341-4be3-8cb4-96a331307208",
    "_uuid": "2e7fa7c6bc6517db7c42df9bec581ee50be14fb8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Grid Search for tuning parameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# RandomizedSearch for tuning (possibly faster than GridSearch)\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Bayessian optimization supposedly faster than GridSearch\n",
    "from bayes_opt import BayesianOptimization\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, log_loss\n",
    "\n",
    "## Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9efcd6c0-34ed-4b9b-bb78-86920ab14a34",
    "_uuid": "58ac80082fa2f28486225743dd9abbd69585eae0"
   },
   "source": [
    "# Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "5fe5062a-33ce-4504-8755-ce0820cf961f",
    "_uuid": "13cdd8de98183c59596132d9a7e50da910758b62",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CHERCHEZ FOR PARAMETERS\n",
    "def cherchez(estimator, param_grid, search):\n",
    "    \"\"\"\n",
    "    This is a helper function for tuning hyperparameters using teh two search methods.\n",
    "    Methods must be GridSearchCV or RandomizedSearchCV.\n",
    "    Inputs:\n",
    "        estimator: Logistic regression, SVM, KNN, etc\n",
    "        param_grid: Range of parameters to search\n",
    "        search: Grid search or Randomized search\n",
    "    Output:\n",
    "        Returns the estimator instance, clf\n",
    "    \n",
    "    \"\"\"   \n",
    "    try:\n",
    "        if search == \"grid\":\n",
    "            clf = GridSearchCV(\n",
    "                estimator=estimator, \n",
    "                param_grid=param_grid, \n",
    "                scoring=None,\n",
    "                n_jobs=-1, \n",
    "                cv=10, \n",
    "                verbose=0,\n",
    "                return_train_score=True\n",
    "            )\n",
    "        elif search == \"random\":           \n",
    "            clf = RandomizedSearchCV(\n",
    "                estimator=estimator,\n",
    "                param_distributions=param_grid,\n",
    "                n_iter=10,\n",
    "                n_jobs=-1,\n",
    "                cv=10,\n",
    "                verbose=0,\n",
    "                random_state=1,\n",
    "                return_train_score=True\n",
    "            )\n",
    "    except:\n",
    "        print('Search argument has to be \"grid\" or \"random\"')\n",
    "        sys.exit(0)\n",
    "        \n",
    "    # Fit the model\n",
    "    clf.fit(X=scaled_train, y=y_train)\n",
    "    \n",
    "    return clf   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "a27e8eec-79d4-45ee-a03c-9df9a8baab04",
    "_uuid": "e229a49752781655d6d5decd20e170b2feb0b9c8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function for plotting the confusion matrices\n",
    "def plot_confusion_matrix(cm, title=\"Confusion Matrix\"):\n",
    "    \"\"\"\n",
    "    Plots the confusion matrix. Modified verison from \n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    Inputs: \n",
    "        cm: confusion matrix\n",
    "        title: Title of plot\n",
    "    \"\"\"\n",
    "    classes=[\"AML\", \"ALL\"]    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.bone)\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    thresh = cm.mean()\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j]), \n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] < thresh else \"black\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f247ca48-0b70-481e-acfb-628d85d088db",
    "_uuid": "783035f0903958d75e3ef8059070d86c0d7c0c0e"
   },
   "source": [
    "# Models being tested\n",
    "1. Logisitc Regresison\n",
    "  - Using Grid search and Randomized search for tuning hyperparameters\n",
    "2. C-Support Vector Classification (SVM)\n",
    "  - Using Grid search and Randomized search for tuning hyperparameters\n",
    "3. K-Nearest Neighbors Classifier\n",
    "  - Using Grid search and Randomized search for tuning hyperparameters\n",
    "4. Decision Tree Classifier\n",
    "  - Using only Grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "1e4e148e-1c96-4f5b-bd6d-f85c3f3c35d5",
    "_uuid": "4cb5bedd07807e9e98e92e1d735f05a6ffd2d9cc",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Grid search results**\n",
      "Best training accuracy:\t 0.605263157895\n",
      "Test accuracy:\t 0.558823529412\n",
      "**Random search results**\n",
      "Best training accuracy:\t 0.710526315789\n",
      "Test accuracy:\t 0.529411764706\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEWCAYAAAD2GibgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda944c7940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "# Paramaters\n",
    "logreg_params = {} \n",
    "logreg_params[\"C\"] =  [0.01, 0.1, 10, 100]\n",
    "logreg_params[\"fit_intercept\"] =  [True, False]\n",
    "logreg_params[\"warm_start\"] = [True,False]\n",
    "logreg_params[\"random_state\"] = [1]\n",
    "\n",
    "lr_dist = {}\n",
    "lr_dist[\"C\"] = scipy.stats.expon(scale=.01)\n",
    "lr_dist[\"fit_intercept\"] =  [True, False]\n",
    "lr_dist[\"warm_start\"] = [True,False]\n",
    "lr_dist[\"random_state\"] = [1]\n",
    "\n",
    "logregression_grid = cherchez(LogisticRegression(), logreg_params, search=\"grid\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=logregression_grid.predict(scaled_test))\n",
    "cfmatrix_grid = confusion_matrix(y_true=y_test, y_pred=logregression_grid.predict(scaled_test))\n",
    "print(\"**Grid search results**\")\n",
    "print(\"Best training accuracy:\\t\", logregression_grid.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "logregression_random = cherchez(LogisticRegression(), lr_dist, search=\"random\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=logregression_random.predict(scaled_test))\n",
    "cfmatrix_rand = confusion_matrix(y_true=y_test, y_pred=logregression_random.predict(scaled_test))\n",
    "print(\"**Random search results**\")\n",
    "print(\"Best training accuracy:\\t\", logregression_random.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "plt.subplots(1,2)\n",
    "plt.subplots_adjust(left=-0.5, bottom=None, right=None, top=None, wspace=0.5, hspace=None)\n",
    "plot_confusion_matrix(cfmatrix_rand, title=\"Random Search Confusion Matrix\")\n",
    "plt.subplot(121)\n",
    "plot_confusion_matrix(cfmatrix_grid, title=\"Grid Search Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "9f8f1500-bf1a-4118-bd83-3c7b98263f9f",
    "_uuid": "35c0a28dffddca04064e270e9494a6bf430e9e98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Grid search results**\n",
      "Best training accuracy:\t 0.710526315789\n",
      "Test accuracy:\t 0.588235294118\n",
      "**Random search results**\n",
      "Best training accuracy:\t 0.710526315789\n",
      "Test accuracy:\t 0.588235294118\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEWCAYAAAD2GibgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nG02aQ6EmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda945754e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM\n",
    "svm_param = {\n",
    "    \"C\": [.01, .1, 1, 5, 10, 100],\n",
    "    \"gamma\": [0, .01, .1, 1, 5, 10, 100],\n",
    "    \"kernel\": [\"rbf\"],\n",
    "    \"random_state\": [1]\n",
    "}\n",
    "\n",
    "svm_dist = {\n",
    "    \"C\": scipy.stats.expon(scale=.01),\n",
    "    \"gamma\": scipy.stats.expon(scale=.01),\n",
    "    \"kernel\": [\"rbf\"],\n",
    "    \"random_state\": [1]\n",
    "}\n",
    "\n",
    "svm_grid = cherchez(SVC(), svm_param, \"grid\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=svm_grid.predict(scaled_test))\n",
    "cfmatrix_grid = confusion_matrix(y_true=y_test, y_pred=svm_grid.predict(scaled_test))\n",
    "print(\"**Grid search results**\")\n",
    "print(\"Best training accuracy:\\t\", svm_grid.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "svm_random = cherchez(SVC(), svm_dist, \"random\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=svm_random.predict(scaled_test))\n",
    "cfmatrix_rand = confusion_matrix(y_true=y_test, y_pred=svm_random.predict(scaled_test))\n",
    "print(\"**Random search results**\")\n",
    "print(\"Best training accuracy:\\t\", svm_random.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "plt.subplots(1,2)\n",
    "plt.subplots_adjust(left=-0.5, bottom=None, right=None, top=None, wspace=0.5, hspace=None)\n",
    "plot_confusion_matrix(cfmatrix_rand, title=\"Random Search Confusion Matrix\")\n",
    "plt.subplot(121)\n",
    "plot_confusion_matrix(cfmatrix_grid, title=\"Grid Search Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "34bf611b-063d-462f-a238-58d6c7c1e3d8",
    "_uuid": "3d16144e47b7a79d22e00608439e2ef87cdb676d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Grid search results**\n",
      "Best training accuracy:\t 0.894736842105\n",
      "Test accuracy:\t 0.558823529412\n",
      "**Random search results**\n",
      "Best training accuracy:\t 0.868421052632\n",
      "Test accuracy:\t 0.558823529412\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEWCAYAAAD2GibgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkqSKMQBKkiRVjAFQkiSpYv4/vWv4GS23NUQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaab040e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN\n",
    "knn_param = {\n",
    "    \"n_neighbors\": [i for i in range(1,30,5)],\n",
    "    \"weights\": [\"uniform\", \"distance\"],\n",
    "    \"algorithm\": [\"ball_tree\", \"kd_tree\", \"brute\"],\n",
    "    \"leaf_size\": [1, 10, 30],\n",
    "    \"p\": [1,2]\n",
    "}\n",
    "\n",
    "knn_dist = {\n",
    "    \"n_neighbors\": scipy.stats.randint(1,33),\n",
    "    \"weights\": [\"uniform\", \"distance\"],\n",
    "    \"algorithm\": [\"ball_tree\", \"kd_tree\", \"brute\"],\n",
    "    \"leaf_size\": scipy.stats.randint(1,1000),\n",
    "    \"p\": [1,2]\n",
    "}\n",
    "\n",
    "knn_grid = cherchez(KNeighborsClassifier(), knn_param, \"grid\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=knn_grid.predict(scaled_test))\n",
    "cfmatrix_grid = confusion_matrix(y_true=y_test, y_pred=svm_grid.predict(scaled_test))\n",
    "print(\"**Grid search results**\")\n",
    "print(\"Best training accuracy:\\t\", knn_grid.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "knn_random = cherchez(KNeighborsClassifier(), knn_dist, \"random\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=knn_random.predict(scaled_test))\n",
    "cfmatrix_rand = confusion_matrix(y_true=y_test, y_pred=knn_random.predict(scaled_test))\n",
    "print(\"**Random search results**\")\n",
    "print(\"Best training accuracy:\\t\", knn_random.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "plt.subplots(1,2)\n",
    "plt.subplots_adjust(left=-0.5, bottom=None, right=None, top=None, wspace=0.5, hspace=None)\n",
    "plot_confusion_matrix(cfmatrix_rand, title=\"Random Search Confusion Matrix\")\n",
    "plt.subplot(121)\n",
    "plot_confusion_matrix(cfmatrix_grid, title=\"Grid Search Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "98378072-dc27-435e-9d9c-5059e46c32fa",
    "_uuid": "06948cdc2f306a18894678a09184f4f6f4ac70ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Grid search results**\n",
      "Best training accuracy:\t 0.973684210526\n",
      "Test accuracy:\t 0.617647058824\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARUAAAEWCAYAAABIegNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAFllJREFUeJzt3X20VHW9x/H3BxCBEJSQbipyKjU0\nKi9at5uGimiZiZa6gmsqYlerW3a7ddW0B71pzw9LF101LBU102RZSEQPXkUxEcEeBFEx9QTiAwcN\nBTUQv/eP/RsdjufMGc75zdmc4+e11ixm7/2bvb97Zu/P/PZvZg6KCMzMculTdgFm1rs4VMwsK4eK\nmWXlUDGzrBwqZpaVQ8XMsnKoNJikX0s6sY526yS9uTtq6g0kDZL0K0lrJV3ThfWcKOnXOWsrg6Tf\nSjqu7DrAoQKApEckPS/pWUl/l/QHSZ+Q1OXnJyIOi4gr6mg3OCIe6ur2qqWgqtxeSvtYme6WA1DS\n8ZIWS1ov6bEUBO/NsOqPAsOA10fE5M6uJCKuiIjDMtSzGUkTJIWkn7eav0+a//s613OepMs7ahcR\nh0bE1Z0sNyuHyiuOiIjtgFHAN4EzgB+XW1LXpKAaHBGDgb9R7GNl3qsOQEn9cm5f0unAd4GvATtS\nPLc/Ao7MsPpRwP0R8WKGdTXKE8A4SdtXzTsReCDXBiT1yfHml1VEvOZvwCPAhFbz3g28BIxJ09tS\nnCB/ozhYLgYGVrU/EvgT8AzwV+ADaf4twMfT/d2AecBaoAW4turxAeyW7g8FZgCrgWbgS0CftGwK\nMD/V8jTwMHBYJ/fxPOBa4Brg2bTuPsBZaR9agJ8BO1Q9Zj9gAfD3tL/j2tneDsBzwIdr1DQAuBB4\nDHgU+D7QPy2bkGo+PT0Pq4AT0rLzgQ3ARmAdxYl6HnB51bp3Kw7vl6dPTut7FngImJTmfxy4pard\n/sCi9BotBP6latl84FzgD2k9c4Fh7exbpf5LgVPTvG3SfnwV+H1V22nAynTs3AW8N83/UKv9XFxV\nx9eAO4DngaY0b0paPp3Nj63vAb8B1C3nU9kn9NZwa+uES/P/Bnwy3f8BMIuiy70dcCPwjbTs3ekg\nPCSdlDsDo9OyW3glVK4Bzk5tBgD7V22rOlRmAL9M22mieGc7OS2bkg6yfwf6Ap9MB2rNA6atfUwn\n4gbgiFTTQODzwO1pHwakk+LK1H4ksAZ4f2r/AYrgeX0b26ucEH1q1PT1dILuCIwA7gS+mpZNAF5M\nJ+A2wERgPTCkqvbLW+1Lm6ECDEmvz+5p+o3AXun+y6ECDE/tJgP9gOPT/u6Qls8HlgO7A4OA24Dz\n2tm3SqiMA25P8yYCvwI+weahcjzFcdWPoof8KLBtW/tVVccjwJ7puenH5qEyGHgQ+BhwIEUo79Rd\n59PW1W3a+qwChkkScArwuYh4KiKepTghJqV2JwM/iYjfRcRLEfFoRNzXxvo2UnTbd4qIFyJifusG\nkvqm9X4xIp6NiEco3mmOr2rWHBHTI2ITcAXFSfKGTu7j/Ii4MdX9PMUBf1bahxco3pmPTV3sE4BZ\nEfGb1H4u8GeKcGnt9cCTEfFSjW0fB5wTEasj4kngf1rt5wsUJ+3GiJgF/APYo5P7GcAYSQMi4rGI\nuLeNNkcASyPimoh4MSKupOjVHF7V5scRsTwingN+DuzdwXZvA94o6S0Uz9+MVxUWcWU6rl4Evk0R\ngrt1sN6fRMSy9NxsdgkYEevSti5I2/tURKzqYH3ZOFRq2xl4iuKddBCwOA3k/p2i67tjajeS4nKh\nI6cDAhZKWippahtthlO8+zRXzWtOtVQ8XrmTDm4o3p06Y0Wr6V2BG6v28540fwRFIE6uLEvL3wPs\n1MZ61wAjOrje34na+9mSgrPiOTqxnxHxDEXv4z+AxyXNltRWOLWup62aHq+632E9qat0FfBZ4H0U\nPdDNSDpd0n2S1lJc0r6O4jiopfXr1todFD3tTcDMDtpm5VBph6R3URxM8ym6+M8Db4uI7dNtaBQD\noFC8wG/paJ0R8XhE/HtE7AScCvyvpNbvSC280qOp2JWiS9wIrX+mvhI4pGo/t4+IARHxOMV+XtZq\n2esi4jttrPd2isuXiTW2vYp8+7meIvgr/ql6YUT8OiImUPTqHgQuqaOertZUMYMi0Gal3t/LJB0E\n/BdwNLA9xVjUOoo3H3j160MH8ytOS+toobik7TYOlVYkDZH0IYoByqsi4p7UhZ8O/EDSiNRuZ0nv\nTw/7MXCSpIPTaPzOkka3se5jJe2SJp+mODA2uzxI78zXAedL2k7SKIqD7qoG7G5bLga+LmnXVPMI\nSZVguBL4sKRDJPWVNEDSQZJe1VOJiKcpLp0ukjRR0kBJ20g6XNI3U7NrgK9IGi5pR+DLdH4//wQc\nIGlk+rTlzMoCSW+UdISkQRTjPOtp9bwns4G3SfqopH6S/o3iMuRXnawJgIh4kGJs4yttLN6OInxb\nKHqo51D0VCqeAJrSJXhdJO2Z1nN8up0l6e2dKL1THCqvuFHSsxTvxmdTfBJxUtXyMyje4RZIegb4\nPfBWgIhYmNr+gGKgbx6vfscDeBdwp6R1FIO+n422v5vyGYoD/yGKntJPgZ90dQfr9H2KS7ub0vPx\nB4q6SeM7H6Y4+VdTdK8/TzvHUUR8i+J5O4ficmgFxcDyL1KTcynGZJYAf6EYqP1GJ+ueC9xAcbm2\nkOL5regL/DfFp0xrgPdS9Bxa17uaomd1Rmr3OeBDKSC7JCJui4jH2lg0h+JYWk4x+PpMqrPiWqA/\n8JSkhR1tR9I2FMF8fnpDvI8izK6U1L9re1EfpdFiM7Ms3FMxs6wcKmaWlUPFzLJyqJhZVll/QFaW\n4cOHR1NTU9ll2Ba4556lZZdgW2jDhhdaImLHjtr1ilBpampi0aJFZZdhW6CpaUzZJdgWam5e2vrb\nxm3y5Y+ZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XM\nsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4VM8vKoWJmWTlUzCwr\nh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XMsnKo\nmFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4VM8vKobKVmDp1KiNGjGDM\nmDEvz/vyl7/MO97xDvbee28OPfRQVq1aVWKFVsub39zEnDnXv3y7554FTJ36sbLLKoUiouwaumzf\nffeNRYsWlV1Gl9x6660MHjyYE044gSVLlgDwzDPPMGTIEAAuvPBC7r33Xi6++OIyy8ymqWlMx416\nqD59+nDnnf/HUUdN5tFHHyu7nGyam5cujoh9O2rnnspWYty4cQwbNmyzeZVAAVi/fj2Surss64T9\n9nsPzc0relWgbIl+ZRdgtZ199tnMmDGDoUOHcvPNN5ddjtXhiCMOY9asOWWXUZqG9lQkHSUpJI1O\n001p+ryqNsMlbZQ0LU2fI+kLjayrJzn//PNZsWIFxx13HNOmTSu7HOvANtv0Y8KEA5kz57dll1Ka\nRl/+TAbmp38rHgYOr5o+Flja4Dp6vOOOO46ZM2eWXYZ14MAD38eSJctoaVlTdimlaVioSBoM7A+c\nDEyqWvQcsExSZcDno8B1jaqjJ1u+fPnL93/5y18yevToEquxekyc+EFuvPG1e+kDjR1TORKYGxEP\nSFojaR+gEt8/AyZJegLYBKwCdtqSlUs6BTgFYNddd81XdUkmT57MLbfcQktLC7vssgvnnnsuc+bM\n4f7776dPnz6MGjWq13zy01sNHDiQ/ff/V84669yySylVwz5SljQbuCAififpNGBXYBowGxgL3AVc\nBawFNgD7RsSnJZ0DrIuI79a7rd7wkfJrTW/+SLm3qvcj5Yb0VCQNA8YDb5cUQF8ggB8CRMQGSYuB\nzwN7ARMbUYeZdb9GXf4cA1wZEadWZkiaB4ysavM9YF5EPOXvX5j1Ho0aqJ0M3NBq3kzgi5WJiFga\nEVe08/gvSVpZuTWoRjNrAH9N30rhMZWex1/TN7NSOFTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZ\nVg4VM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXl\nUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4V\nM8vKoWJmWTlUzCwrh4qZZeVQMbOs+rW3QNKNQLS3PCImNqQiM+vR2g0V4LvdVoWZ9RrthkpEzOvO\nQsysd6jVUwFA0u7AN4C9gAGV+RHx5gbWZWY9VD0DtZcBFwEvAgcBM4CrGlmUmfVc9YTKwIi4CVBE\nNEfEOcDhjS3LzHqqDi9/gH9I6gMsl/Rp4FFgcGPLMrOeqp6eymeBQcBpwD7A8cCJjSzKzHquDnsq\nEXFXursOOKmx5ZhZT1fPpz8308aX4CJifEMqMrMerZ4xlS9U3R8AHE3xSZCZ2avUc/mzuNWs2yUt\nbFA9ZtbD1XP5M6xqsg/FYO3QhlXUCYsXL0ZS2WXYFjjggElll2BbqLl5aV3t6rn8WUwxpiKKy56H\ngZM7XZmZ9Wr1hMqeEfFC9QxJ2zaoHjPr4er5nsof2ph3R+5CzKx3qPX3VP4J2BkYKOmfKS5/AIZQ\nfBnOzOxVal3+vB+YAuwCfI9XQuUZ4KzGlmVmPVWtv6dyBXCFpKMjYmY31mRmPVg9Yyr7SNq+MiFp\nB0nnNbAmM+vB6gmVwyLi75WJiHga+GDjSjKznqyeUOlb/RGypIGAP1I2szbV8z2Vq4GbJF1GMVg7\nBbiikUWZWc9Vz29/viXpz8AEim/W/gYY1ejCzKxnqvc/E3uCIlCOBcYDyxpWkZn1aLW+/LYHMDnd\nWoBrKf5O7UHdVJuZ9UC1Ln/uA24DPhQRDwJI+ly3VGVmPVaty5+PAI8BN0uaLulgXvlWrZlZm9oN\nlYj4RURMAkYDNwP/CYyQdJGkQ7urQDPrWTocqI2I9RHx04g4guJ3QH8Ezmh4ZWbWI9X76Q9QfJs2\nIn4UEQc3qiAz69m2KFTMzDriUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAx\ns6wcKmaWlUPFzLJyqJhZVg4VM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPL\nyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpaVQ8XMsupXdgHWtqFDh3LppZcyZswYIoKpU6eyYMGCssuy\nGo455jAOP3w8EDz00Aq+9a2L2bBhY9lldTv3VLZSF1xwAXPnzmXPPffkne98J8uWLSu7JKth+PAd\nOProD3DqqWdx0kmn06dPH8aP/9eyyyqFeypboSFDhjBu3DimTJkCwMaNG1m7dm25RVmH+vbty7bb\n9mfTpk0MGNCflpanyy6pFO6pbIXe9KY3sXr1ai677DLuvvtupk+fzqBBg8ouy2poaXmaa6+dzXXX\nTWPmzItYt+45Fi26p+yyStFtoSLpKEkhaXSabpK0pI12l0s6prvq2hr169ePsWPHctFFFzF27FjW\nr1/PmWeeWXZZVsPgwa9jv/32ZdKk0zj66E8xcOC2HHLI/mWXVYru7KlMBuanf62GlStXsnLlShYu\nXAjA9ddfz9ixY0uuymrZZ58xPPbYk6xd+yybNm3i1lvv4m1v26PsskrRLaEiaTCwP3AyMKk7ttmT\nPfHEE6xYsYI99igOyoMPPph777235KqsliefbGGvvXZn2237AzB27Biamx8tuapydNdA7ZHA3Ih4\nQNIaSfsAa7qyQkmnAKdkqW4r9JnPfIarr76a/v3789BDD3HSSSeVXZLVsGzZX5k3706mT/86mza9\nxPLljzB79k1ll1UKRUTjNyLNBi6IiN9JOg3YFZgGzI6IMa3aXp7mX78F62/8TlhWBxzgDmtPM2/e\nzxZHxL4dtWt4T0XSMGA88PZ08vcFAvhho7dtZt2vO8ZUjgGujIhREdEUESOBh4GR3bBtM+tm3REq\nk4EbWs2bCXwReKuklVW3Y9PyS6rm3dENNZpZJg2//ImIg9qYdyFwYTsP+XljKzKzRvI3as0sK4eK\nmWXlUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZ\nVg4VM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK4eKmWXl\nUDGzrBwqZpaVQ8XMsnKomFlWDhUzy8qhYmZZOVTMLCuHipll5VAxs6wcKmaWlUPFzLJyqJhZVg4V\nM8vKoWJmWTlUzCwrh4qZZeVQMbOsHCpmlpVDxcyycqiYWVYOFTPLyqFiZlk5VMwsK0VE2TV0maTV\nQHPZdTTIcKCl7CKsbr359RoVETt21KhXhEpvJmlRROxbdh1WH79evvwxs8wcKmaWlUNl6/ejsguw\nLfKaf708pmJmWbmnYmZZOVTMLCuHSokkHSUpJI1O001p+ryqNsMlbZQ0LU2fI+kLZdVs7b5uS9po\nd7mkY7q/wnI5VMo1GZif/q14GDi8avpYYGl3FmUdaut1s8ShUhJJg4H9gZOBSVWLngOWSap8geqj\nwHXdXJ61o8brZolDpTxHAnMj4gFgjaR9qpb9DJgkaSSwCVhVRoHWplqvm+FQKdNkivAg/VvdlZ4L\nHELxTnhtN9dltdV63QzoV3YBr0WShgHjgbdLCqAvEMAPASJig6TFwOeBvYCJZdVqr+jodbOCeyrl\nOAa4MiJGRURTRIykGKAdWdXme8AZEfFUKRVaW+p53V7zHCrlmAzc0GreTOCLlYmIWBoRV7Tz+C9J\nWlm5NapIe5Var9tbq18TScem5ZdUzbujW6stib+mb2ZZuadiZlk5VMwsK4eKmWXlUDGzrBwqZpaV\nQ8XaJWmTpD9JWiLp55IGdWFdB0qane5PlHRmjbbbS/pUJ7bhX3BvBRwqVsvzEbF3RIwBNgCfqF6o\nwhYfQxExKyK+WaPJ9sAWh4ptHRwqVq/bgN3S3w65X9IMYAkwUtKhku6QdHfq0QwGkPQBSfdJuhv4\nSGVFkqZU/X2YN0i6QdKf0+29wDeBt6Re0ndSu/+WdJekv0g6t2pdZ0t6QNJ84K3d9mxYu/zbH+uQ\npH7AYRQ/dATYHTgxIhZIGg58CZgQEeslnQH8l6RvA9MpfivzIO3/MPJCYF5EfFhSX2AwcCYwJiL2\nTts/NG3z3YCAWZLGAespfnS5N8WxfDewOO/e25ZyqFgtAyX9Kd2/DfgxsBPQHBEL0vz3UPzo8XZJ\nAP2BO4DRwMMRsRxA0lXAKW1sYzxwAkBEbALWStqhVZtD0+2PaXowRchsB9wQEc+lbczq0t5aFg4V\nq+X5Sm+hIgXH+upZwO8iYnKrdps9rosEfCMiLmm1jf/MuA3LxGMq1lULgP0k7QYg6XWS9gDuA5ok\nvSW1a+/vjtwEfDI9tq+kocCzFL2Qit8AU6vGanaWNAK4FThK0kBJ2wFHZN436wSHinVJRKwGpgDX\nSPoL6dInIl6guNz5VRqofbKdVXwWOEjSPRTjIXtFxBqKy6klkr4TEb8FfgrckdpdD2wXEXdTjNX8\nGfg1cFfDdtTq5l8pm1lW7qmYWVYOFTPLyqFiZlk5VMwsK4eKmWXlUDGzrBwqZpbV/wNc37KON9DX\nWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdaa9f7aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision tree classifier\n",
    "dtc_param = {\n",
    "    \"max_depth\": [None],\n",
    "    \"min_samples_split\": [2],\n",
    "    \"min_samples_leaf\": [1],\n",
    "    \"min_weight_fraction_leaf\": [0.],\n",
    "    \"max_features\": [None],\n",
    "    \"random_state\": [4],\n",
    "    \"max_leaf_nodes\": [None], # None = infinity or int\n",
    "    \"presort\": [True, False]\n",
    "}\n",
    "\n",
    "\n",
    "dtc_grid = cherchez(DecisionTreeClassifier(), dtc_param, \"grid\")\n",
    "acc = accuracy_score(y_true=y_test, y_pred=dtc_grid.predict(scaled_test))\n",
    "cfmatrix_grid = confusion_matrix(y_true=y_test, y_pred=dtc_grid.predict(scaled_test))\n",
    "print(\"**Grid search results**\")\n",
    "print(\"Best training accuracy:\\t\", dtc_grid.best_score_)\n",
    "print(\"Test accuracy:\\t\", acc)\n",
    "\n",
    "plot_confusion_matrix(cfmatrix_grid, title=\"Decision Tree Confusion Matrix\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}