Marco tf-idf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "import string\n",
    "import re\n",
    "import h2o\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://www.cs.duke.edu/courses/spring14/compsci290/assignments/lab02.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passages</th>\n",
       "      <th>query</th>\n",
       "      <th>query_id</th>\n",
       "      <th>query_type</th>\n",
       "      <th>answers_yn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Row(is_selected=0, passage_text=u'In his youn...</td>\n",
       "      <td>was ronald reagan a democrat</td>\n",
       "      <td>19700</td>\n",
       "      <td>description</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Row(is_selected=0, passage_text=u'So, in the ...</td>\n",
       "      <td>do game shows pay their contestants</td>\n",
       "      <td>19749</td>\n",
       "      <td>description</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Row(is_selected=0, passage_text=u'Even though...</td>\n",
       "      <td>does peanut butter contain cholesterol</td>\n",
       "      <td>19761</td>\n",
       "      <td>description</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Row(is_selected=0, passage_text=u'When turnin...</td>\n",
       "      <td>can you take left hand lane to turn right on a...</td>\n",
       "      <td>19762</td>\n",
       "      <td>description</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Row(is_selected=0, passage_text=u'Assault on ...</td>\n",
       "      <td>is armed assault a felony</td>\n",
       "      <td>19774</td>\n",
       "      <td>description</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            passages  \\\n",
       "0  [Row(is_selected=0, passage_text=u'In his youn...   \n",
       "1  [Row(is_selected=0, passage_text=u'So, in the ...   \n",
       "2  [Row(is_selected=0, passage_text=u'Even though...   \n",
       "3  [Row(is_selected=0, passage_text=u'When turnin...   \n",
       "4  [Row(is_selected=0, passage_text=u'Assault on ...   \n",
       "\n",
       "                                               query  query_id   query_type  \\\n",
       "0                       was ronald reagan a democrat     19700  description   \n",
       "1                do game shows pay their contestants     19749  description   \n",
       "2             does peanut butter contain cholesterol     19761  description   \n",
       "3  can you take left hand lane to turn right on a...     19762  description   \n",
       "4                          is armed assault a felony     19774  description   \n",
       "\n",
       "  answers_yn  \n",
       "0        Yes  \n",
       "1        Yes  \n",
       "2         No  \n",
       "3         No  \n",
       "4        Yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df = pd.read_csv('D:/marco/vr_chat-master/Marco/train_yn.csv')\n",
    "tmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords \n",
    "\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "from nltk.stem.porter import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def tf_cln(sent = tmp_df.iloc[0][0],cln=True):\n",
    "\n",
    "    # sent = tmp_df.iloc[0][0]\n",
    "    \n",
    "    lowers= sent.lower()\n",
    "    # print lower\n",
    "    no_punctuation = lowers.translate(None, string.punctuation)\n",
    "    slt_sent = re.findall(r'passagetext(.*?)url',no_punctuation)\n",
    "    \n",
    "    if(cln==True):\n",
    "        slt_sent = re.findall(r'passage_text(.*?)url',sent)\n",
    "#         print slt_sent\n",
    "    else:\n",
    "        slt_sent = sent\n",
    "        \n",
    "    token = nltk.word_tokenize(str(slt_sent))\n",
    "    filtered_sentence = [w for w in token if not w in stop_words]\n",
    "#     print filtered_sentence\n",
    "\n",
    "    for i in filtered_sentence:\n",
    "        if re.findall(r'^\\W.*',str(i)):\n",
    "    #         print re.findall(r'^\\W',i)\n",
    "            stop_words.add(i)\n",
    "        \n",
    "#     for i in re.findall(r'\\W',str(filtered_sentence)):\n",
    "#         stop_words.add(i)\n",
    "#         print i\n",
    "\n",
    "    stop_words.add('``')\n",
    "    filtered_sentence = [w for w in token if not w in stop_words]\n",
    "\n",
    "    tf = nltk.FreqDist(filtered_sentence)\n",
    "    \n",
    "    return dict(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_tf_idf(dic):\n",
    "    transformer = TfidfTransformer(smooth_idf=False)\n",
    "    tf_idf=transformer.fit_transform(dic.values())\n",
    "    return tf_idf.toarray()\n",
    "#     return tf_idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def convt_lst(arr):\n",
    "#     a=get_tf_idf(tf_cln())\n",
    "    return (pd.DataFrame(arr).iloc[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lst_tfidf_psg=[]\n",
    "for i in tmp_df.iloc[:,0]:\n",
    "    lst_tfidf_psg.append(list(convt_lst(get_tf_idf(tf_cln(i)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lst_tfidf_qry = []\n",
    "for i in tmp_df.iloc[:,1]:\n",
    "    lst_tfidf_qry.append(list(convt_lst(get_tf_idf(tf_cln(i,False)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmp_df = tmp_df.replace(tmp_df['query_type'].unique(),range(0,len(tmp_df['query_type'].unique())))\n",
    "tmp_df['passages'] = lst_tfidf_psg\n",
    "tmp_df['query'] = lst_tfidf_qry\n",
    "tmp_df = tmp_df.replace(['yes', 'no'], ['Yes', 'No'])\n",
    "tmp_df = tmp_df.replace(['Yes', 'No'], ['1', '0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del(tmp_df['query_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passages</th>\n",
       "      <th>query</th>\n",
       "      <th>query_type</th>\n",
       "      <th>answers_yn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.034523833988, 0.069047667976, 0.03452383398...</td>\n",
       "      <td>[0.57735026919, 0.57735026919, 0.57735026919]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0283410100633, 0.510138181139, 0.1700460603...</td>\n",
       "      <td>[0.5, 0.5, 0.5, 0.5]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.0436020720197, 0.0872041440394, 0.087204144...</td>\n",
       "      <td>[0.5, 0.5, 0.5, 0.5]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.025515518154, 0.025515518154, 0.06378879538...</td>\n",
       "      <td>[0.333333333333, 0.333333333333, 0.33333333333...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.0286534127531, 0.0286534127531, 0.028653412...</td>\n",
       "      <td>[0.57735026919, 0.57735026919, 0.57735026919]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            passages  \\\n",
       "0  [0.034523833988, 0.069047667976, 0.03452383398...   \n",
       "1  [0.0283410100633, 0.510138181139, 0.1700460603...   \n",
       "2  [0.0436020720197, 0.0872041440394, 0.087204144...   \n",
       "3  [0.025515518154, 0.025515518154, 0.06378879538...   \n",
       "4  [0.0286534127531, 0.0286534127531, 0.028653412...   \n",
       "\n",
       "                                               query  query_type answers_yn  \n",
       "0      [0.57735026919, 0.57735026919, 0.57735026919]           0          1  \n",
       "1                               [0.5, 0.5, 0.5, 0.5]           0          1  \n",
       "2                               [0.5, 0.5, 0.5, 0.5]           0          0  \n",
       "3  [0.333333333333, 0.333333333333, 0.33333333333...           0          0  \n",
       "4      [0.57735026919, 0.57735026919, 0.57735026919]           0          1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>2 mins 46 secs</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.10.0.10</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>2 months and 11 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_user_5y0yr3</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>797 Mb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.11 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------\n",
       "H2O cluster uptime:         2 mins 46 secs\n",
       "H2O cluster version:        3.10.0.10\n",
       "H2O cluster version age:    2 months and 11 days\n",
       "H2O cluster name:           H2O_from_python_user_5y0yr3\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    797 Mb\n",
       "H2O cluster total cores:    2\n",
       "H2O cluster allowed cores:  2\n",
       "H2O cluster status:         locked, healthy\n",
       "H2O connection url:         http://localhost:54321\n",
       "H2O connection proxy:\n",
       "Python version:             2.7.11 final\n",
       "--------------------------  ---------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# tmp_df.iloc[0,0]\n",
    "# h2o.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf_tfidf_psg = h2o.H2OFrame(lst_tfidf_psg)\n",
    "hdf_tfidf_qry = h2o.H2OFrame(lst_tfidf_qry)\n",
    "\n",
    "hdf_tfidf = hdf_tfidf_psg.cbind(hdf_tfidf_qry)\n",
    "hdf_tfidf = hdf_tfidf.cbind(h2o.H2OFrame(tmp_df[tmp_df.columns[2:3]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf_target = h2o.H2OFrame(pd.DataFrame(tmp_df.answers_yn))\n",
    "hdf = h2o.H2OFrame.cbind(hdf_tfidf,h2o.H2OFrame.asfactor(hdf_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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style=\"text-align: right;\">     C106</th><th style=\"text-align: right;\">     C107</th><th style=\"text-align: right;\">     C108</th><th style=\"text-align: right;\">     C109</th><th style=\"text-align: right;\">     C110</th><th style=\"text-align: right;\">     C111</th><th style=\"text-align: right;\">     C112</th><th style=\"text-align: right;\">     C113</th><th style=\"text-align: right;\">    C114</th><th style=\"text-align: right;\">    C115</th><th style=\"text-align: right;\">     C116</th><th style=\"text-align: right;\">     C117</th><th style=\"text-align: right;\">     C118</th><th style=\"text-align: right;\">     C119</th><th style=\"text-align: right;\">     C120</th><th style=\"text-align: right;\">    C121</th><th style=\"text-align: right;\">     C122</th><th style=\"text-align: right;\">    C123</th><th style=\"text-align: right;\">     C124</th><th style=\"text-align: right;\">     C125</th><th style=\"text-align: right;\">    C126</th><th style=\"text-align: right;\">     C127</th><th style=\"text-align: right;\">     C128</th><th style=\"text-align: right;\">     C129</th><th style=\"text-align: right;\">     C130</th><th style=\"text-align: right;\">     C131</th><th style=\"text-align: right;\">     C132</th><th style=\"text-align: right;\">    C133</th><th style=\"text-align: right;\">     C134</th><th style=\"text-align: right;\">    C135</th><th style=\"text-align: right;\">     C136</th><th style=\"text-align: right;\">     C137</th><th style=\"text-align: right;\">     C138</th><th style=\"text-align: right;\">     C139</th><th style=\"text-align: right;\">     C140</th><th style=\"text-align: right;\">     C141</th><th style=\"text-align: right;\">     C142</th><th style=\"text-align: right;\">     C143</th><th style=\"text-align: right;\">    C144</th><th style=\"text-align: right;\">     C145</th><th style=\"text-align: right;\">     C146</th><th style=\"text-align: right;\">     C147</th><th style=\"text-align: right;\">     C148</th><th style=\"text-align: right;\">     C149</th><th style=\"text-align: right;\">     C150</th><th style=\"text-align: right;\">     C151</th><th style=\"text-align: right;\">     C152</th><th style=\"text-align: right;\">     C153</th><th style=\"text-align: right;\">     C154</th><th style=\"text-align: right;\">     C155</th><th style=\"text-align: right;\">     C156</th><th style=\"text-align: right;\">     C157</th><th style=\"text-align: right;\">     C158</th><th style=\"text-align: right;\">     C159</th><th style=\"text-align: right;\">     C160</th><th style=\"text-align: right;\">     C161</th><th style=\"text-align: right;\">     C162</th><th style=\"text-align: right;\">    C163</th><th style=\"text-align: right;\">     C164</th><th style=\"text-align: right;\">     C165</th><th style=\"text-align: right;\">     C166</th><th style=\"text-align: right;\">     C167</th><th style=\"text-align: right;\">    C168</th><th style=\"text-align: right;\">     C169</th><th style=\"text-align: right;\">     C170</th><th style=\"text-align: right;\">     C171</th><th style=\"text-align: right;\">  C172</th><th style=\"text-align: right;\">  C173</th><th style=\"text-align: right;\">  C174</th><th style=\"text-align: right;\">  C175</th><th style=\"text-align: right;\">  C176</th><th style=\"text-align: right;\">  C177</th><th style=\"text-align: right;\">  C178</th><th style=\"text-align: right;\">  C179</th><th style=\"text-align: right;\">  C180</th><th style=\"text-align: right;\">  C181</th><th style=\"text-align: right;\">  C182</th><th style=\"text-align: right;\">  C183</th><th style=\"text-align: right;\">  C184</th><th style=\"text-align: right;\">  C185</th><th style=\"text-align: right;\">  C186</th><th style=\"text-align: right;\">  C187</th><th style=\"text-align: right;\">  C188</th><th style=\"text-align: right;\">  C189</th><th style=\"text-align: right;\">  C190</th><th style=\"text-align: right;\">  C191</th><th style=\"text-align: right;\">  C192</th><th style=\"text-align: right;\">  C193</th><th style=\"text-align: right;\">  C194</th><th style=\"text-align: right;\">  C195</th><th style=\"text-align: right;\">  C196</th><th style=\"text-align: right;\">  C197</th><th style=\"text-align: right;\">  C198</th><th style=\"text-align: right;\">  C199</th><th style=\"text-align: right;\">  C200</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.310715</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.207143</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.172619</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.138095</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.207143</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.517858</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.172619</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0690477</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.103572</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.138095</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">0.0345238</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td><td style=\"text-align: right;\">   nan</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =5010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator\n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# len(hdf.col_names)-1\n",
    "# hdf.columns[0:len(hdf.col_names)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naivebayes Model Build progress: |████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "nb =H2ONaiveBayesEstimator(seed =1)\n",
    "nb.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.auc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model = H2OGradientBoostingEstimator(seed =1,  ntrees = 170, learn_rate = .05)\n",
    "gbm_model.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mfvaz0CYycSKcfDKsXRt3VCK1S7IJJM/MnjSzUWY2CpgebWsEfFl14YmU7sAD\n4eWX4Zhjwuy+Dz8cd0QitUeybSAGnAL0iTa9DDyWLg0PagMRCONGRo6Eww8P1VrNmsUdkUj6qrY2\nkOjuPA94DngWeFF3bEk3RxwBixZB8+Zh8OGzz8YdkUjNllQCMbMzgNeA04AzgFfN7LQkzz3ZzN4x\nsxVmVmKDu5kdYWZbE7sMm9n7ZrbUzBabmZYekjI1bBjWXf/HP2DUKPjVr+Cbb+KOSqRmSrYKaylw\nYuGYDzNrCTzj7l3LOC8LWAF8H/gQWAgMd/d3ijluLvAN8E93fzza/h5wuLt/Ucb7qEAku/j8c7jg\nAnjzzdDdt7sm3xHZrjq78WYVGTD4WZLn9gRWuvtqd98KTAaGFHPcL4FHCb27Elk5YhTZSfPmMHky\n/O53oZfWH/8I27bFHZVIzZHszfkpM3vazM4xs3OAmUAy09vtA6xJeP1BtG07M2sNDHX3vxMSRiIH\n5prZQjM7L8lYRbYzgzPPhNdfD20iffvCf/8bd1QiNUOyjeiXAHcBh0aPu9w9VQMI/8rOgxETk0hv\nd+8ODAB+YWZ9EKmANm1g7lw44ww46ii44w4NPhSprJxkD3T3x4DHynn9tcB+Ca/3jbYl6gFMjroK\ntwD6m9lWd5/u7h9F7/2JmT1BqBKbV9wbjRs3bvvz3NxccnNzyxmq1HRZWWG53BNPhB//OIwZufvu\nsJCVSE2Xl5dHXl5eSq9Z1nTumwjVSLvsIvTubVLqxcPa6e8SGtE/IvTkGuHuy0s4/l5ghrs/bmYN\nCW0vX0UDFucAV7v7nGLOUyO6lEt+Ptx6K9xwQ1iL/aKLIDs77qhEqk+VN6K7e2N3b1LMo3FZySM6\nPx+4kHDzfwuY7O7LzWy0mZ1f3CkJz1sB88xsMbCAkFh2SR4iFZGdDb/+NcyfD9OmhTm13n477qhE\nMovWA5Far6AA7rorTA8/dixccommiZeaTwtKRZRAJBX+9z84/3z4+GO4996wrK5ITVWd40BEarz9\n9oPZs8Po9ZNOCuNHvvsu7qhE0pcSiEgCszAFytKl8NZbYfT6q6/GHZVIelIVlkgJ3GHKlND198wz\n4dprw1xbIjWBqrBEqpBZGHj45puwbh0ceiikuBu9SEZTCUQkSTNmhMkZBw2Cm26CJmV2ZBdJXyqB\niFSjQYNg2TLYuhUOOQSeeiruiETipRKISAXMnRu6/PbtC3/+c5j5VySTqAQiEpMTTwxtI02ahNLI\nE0/EHZG09V8GAAARhUlEQVRI9VMJRKSS5s2Dn/wkLKN7222w555xRyRSNpVARNJAnz6wZAm0axdK\nIw8+GLoAi9R0KoGIpNDChWGq+P32C2uzt28fd0QixVMJRCTNHHFEWP3wmGOgZ0+47jpNhyI1lxKI\nSIrVrQuXXRYSyb//Haq15s6NOyqR1FMVlkgVe/LJsGBVz54wfjzss0/cEYmoCkskIwwcGAYgHnBA\n6Kn1l7/Atm1xRyVSeSqBiFSjd9+FX/wCPvkEbr8deveOOyKprbSgVEQJRDKJOzzySFhS9+STw7xa\nLVrEHZXUNqrCEslAZjBsGCxfHkayd+kC//hHWFpXJJOoBCISsyVL4Oc/Dwnk73+Hbt3ijkhqA5VA\nRGqAww4L06Gcd16o0hozBjZsiDsqkbIpgYikgaysMJ/WW2/B5s3QubOmRJH0pyoskTQ0f35YvKp5\nc/jb30JCEUklVWGJ1FC9eoVR7EOHhmlRrrgilExE0okSiEiayskJI9jfeANWrQq9taZPjzsqkR1U\nhSWSIZ55JgxC7NQJbr01TB8vUlGqwhKpRU44IZRGjjoqzPr7xz9qpl+JlxKISAapVy+0hyxcGBra\nO3eGe+6BrVvjjkxqIyUQkQzUrh3MmAH33Re6+x54YBjNvmVL3JFJbaIEIpLBjj0Wnn0WJk6EKVPC\njL933KGqLakeSiAiNUCfPjBnDkyeDFOnhkRy++1KJFK1lEBEapBeveCpp0Jp5MknoWNHuO02+Pbb\nuCOTmkgJRKQGOvJImDULHn8cnn4aOnQIXX+/+SbuyKQmUQIRqcGOOCI0tk+fDs89FxLJX/6iUe2S\nGkogIrXA4YfDtGkwcya8+GJIJOPHw9dfxx2ZZDIlEJFapFs3eOKJ0E4yf35IJDffDF99FXdkkomU\nQERqoa5d4dFHYe7cMGljhw5w442waVPckUkmUQIRqcUOOSSsz/7cc2FlxA4dwhQpGzfGHZlkAiUQ\nEeF73wtjSPLywqJWHTrAtddqZUQpnRKIiGzXpQs88EBYYnfFijCO5IYb1NguxVMCEZFddOoUpkd5\n6SVYvDgkkgkTNLJddqYEIiIlOuig0EYya1aYKuWAA+DuuzX7rwRaUEpEkjZ/Plx5JaxZA1dfDcOH\nQ5a+hmakVCwopQQiIuX27LMhkXz9NVx3HQweDFapW5FUNyWQiBKISPVzDxM2/u53UL9+SCQnnKBE\nkimUQCJKICLxKSgI7SS//z20bg3XXw+9e8cdlZRFa6KLSOyyskJbyNtvw9lnw5lnwg9+EHpvSc1W\n5QnEzE42s3fMbIWZ/baU444ws61mdkp5zxWR+OXkwI9/HMaP9O8fksjpp8Py5XFHJlWlShOImWUB\ntwH9gO8BI8zsoBKOuxF4urznikh6qVcPLrwQVq6EHj2gb18YNQpWrYo7Mkm1qi6B9ARWuvtqd98K\nTAaGFHPcL4FHgfUVOFdE0lCjRvDb34ZEsv/+IZn8/Ofw4YdxRyapUtUJZB9gTcLrD6Jt25lZa2Co\nu/8dsPKcKyLpr2nTMGbk3XdDUjn4YLj4Yvj007gjk8rKiTsA4K9Apds3xo0bt/15bm4uubm5lb2k\niKRQixbwpz/B2LGhy2+nTqGq6+KLoXHjuKOr+fLy8sjLy0vpNau0G6+ZHQWMc/eTo9eXAe7uNyUc\n817hU6AF8DVwPqE6q9RzE66hbrwiGWbVqtD197nnwsy/o0ZBdnbcUdUeaT8OxMyygXeB7wMfAa8B\nI9y92H4ZZnYvMMPdHy/PuUogIpnrtddCqWTz5rBeuyoPqkfajwNx93zgQmAO8BYw2d2Xm9loMzu/\nuFPKOrcq4xWR6tezZ5g+/vLL4dxz4Yc/hP/8J+6oJBkaiS4iaePbb0MpZPx4OOecME3K7rvHHVXN\nlPYlEBGR8qhfP5REli0LqyEedBDcfjts2xZ3ZFIclUBEJG0tXRraRz7+GP78Z+jXL+6Iao60b0Sv\nLkogIjWXO0yfHrr7HnBAqN7q3DnuqDKfqrBEpMYzgyFD4K234MQT4dhjw/gRDUSMnxKIiGSEunVD\nddby5SGpdO4cGty3bIk7stpLCUREMkqLFvB//wcvvABz54apUaZNC1VdUr3UBiIiGe3pp+HXv4a9\n9goN7V27xh1RZlAbiIjUev36hd5ap50GJ50E550H69bFHVXtoAQiIhkvJwcuuCDM+Nu0aajWuuGG\nMDBRqo4SiIjUGLvvDrfcAgsWwMKFoaF94kTIz487sppJbSAiUmO9+GIY2b5hQ5hCfsiQ0INLNJBw\nOyUQESmJO8yaBVdeGZbb/eMf4fvfjzuq+CmBRJRARKQsBQXwyCNw1VXQti1cfz0ceWTcUcVHvbBE\nRJKUlQXDh8Pbb8OwYaHX1tChYeJGqRglEBGpVerUCV19V66Evn1DddbIkfDf/8YdWeZRAhGRWql+\n/TA1ysqVYZLGI48MXYE//DDuyDKHEoiI1GpNmoS12d99F3bbDQ45BC69FD77LO7I0p8SiIgIsMce\n8Kc/wRtvwKZN0KkTXHtteC7FUwIREUmwzz7w97+HwYjvvgsdO4ZZfzWqfVdKICIixejYESZNgmee\nCTP/Hngg3H23ltdNpHEgIiJJWLAArrgCPvggVG2dfnroGpypNJAwogQiItXl2WdDIvnuuzAYccCA\nzJweRQkkogQiItXJPSxi9bvfQcOGIaEMHpxZJRIlkIgSiIjEoaAApk4N82t9802YuHH48DC9fLpT\nAokogYhInNzD8ro33ADvvx/GkZx7bhismK40F5aISBowC6shPv88PPggzJ4N7drBzTfDxo1xR1d1\nlEBERFKoVy+YPj2s1b5kCbRvH2YA/vTTuCNLPSUQEZEqcOihoTSyYAGsXx/GkfzqV7BmTdyRpY4S\niIhIFerYEe68M0wbn5MDXbvCT38aJnHMdEogIiLVoHXrsF77ypWw775w9NFhXZIlS+KOrOKUQERE\nqtEee8C4cfDee9CzJ/zgB2Ew4rx5cUdWfkogVSQvLy/uEHahmJKTjjFBesalmJJTXEyNG8NvfhMW\nsho6FEaNgmOPDT24MmVUghJIFcmUP+K4KabkpWNciik5pcVUvz6cf36Y+fdnPwtjSLp3hylTID+/\n+mKsCCUQEZE0kJMDZ54JS5fCNdfA+PHQpUtoN1m0KD2TiRKIiEgaycqCQYNg/ny4447QVvKjH0GL\nFjBkSOjNlS5qzFQmcccgIpJpNBeWiIjEQlVYIiJSIUogIiJSIRmdQMzsZDN7x8xWmNlvY4phXzN7\nzszeMrM3zeyiaHszM5tjZu+a2dNm1jSG2LLMbJGZTU+jmJqa2RQzWx59ZkfGHZeZjTWzZWb2hpk9\nYGZ1qzsmM7vHzD42szcStpUYg5ldbmYro8/xpGqM6eboPZeY2WNm1qQ6YyoproR9vzGzAjNrXp1x\nlRSTmf0yet83zezGuGMys65mNt/MFpvZa2bWo1IxuXtGPgjJ7z9AW6AOsAQ4KIY49gIOi57vBrwL\nHATcBFwabf8tcGMMsY0FJgHTo9fpENN9wLnR8xygaZxxAa2B94C60euHgVHVHRPQBzgMeCNhW7Ex\nAF2AxdHnt3/0/8CqKaYTgKzo+Y3ADdUZU0lxRdv3BZ4CVgHNo22dY/yscoE5QE70ukUaxPQ0cFL0\nvD/wfGX+/TK5BNITWOnuq919KzAZGFLdQbj7OndfEj3/ClhO+EMeAvwrOuxfwNDqjMvM9gUGAHcn\nbI47pibAMe5+L4C7b3P3DXHHBWQDjcwsB2gArK3umNx9HvBFkc0lxTAYmBx9fu8DKwn/H6o8Jnd/\nxt0LopcLCH/r1RZTSXFF/gJcUmTbkOqIq4SYLiAk/W3RMYUTuscZUwHhSxvA7oS/dajgv18mJ5B9\ngMSJkT+ItsXGzPYnZPwFQCt3/xhCkgH2rOZwCv8zJXazizumdsCnZnZvVLV2l5k1jDMud/8QGA/8\nj/CfaYO7PxNnTAn2LCGGon/7a4nnb//HwKzoeawxmdlgYI27v1lkV5xxHQgca2YLzOx5Mzs8DWIa\nC9xiZv8DbgYur0xMmZxA0oqZ7QY8CoyJSiJF+0dXW39pM/sB8HFUMiqtn3d19+HOAboDf3P37sDX\nwGXFxFGdn9XuhG+EbQnVWY3M7Kw4YypFOsQAgJldCWx194fSIJYGwBXAH+KOpYgcoJm7HwVcCkyJ\nOR4IpaIx7r4fIZn8szIXy+QEshbYL+H1vuwojlWrqOrjUWCiu0+LNn9sZq2i/XsB66sxpN7AYDN7\nD3gION7MJgLrYowJQilxjbv/O3r9GCGhxPlZnQC85+6fu3s+8ARwdMwxFSophrVAm4TjqvVv38zO\nIVSPnpmwOc6YOhDq7Zea2arovReZ2Z7Ee59YAzwO4O4LgXwz2yPmmEa5+9QopkeBI6LtFfr3y+QE\nshDoaGZtzawuMByYHlMs/wTedvdbE7ZNB86Jno8CphU9qaq4+xXuvp+7tyd8Ls+5+0hgRlwxRXF9\nDKwxswOjTd8H3iLGz4pQdXWUmdU3M4tiejummIydS4wlxTAdGB71FmsHdAReq46YzOxkQtXoYHf/\nrkis1RXTTnG5+zJ338vd27t7O8IXlW7uvj6Ka1gcnxUwFTgeIPqbr+vun8Uc01oz6xvF9H1CWwdU\n9N8v1S3/1fkATib0eloJXBZTDL2BfEIvsMXAoiiu5sAzUXxzgN1jiq8vO3phxR4T0JWQ/JcQvp01\njTsuQtXHcuANQmN1neqOCXgQ+BD4jpDUzgWalRQDoe76P1HcJ1VjTCuB1dHf+SLg9uqMqaS4iux/\nj6gXVsyfVQ4wEXgT+DfQNw1iOjqKZTEwn5BoKxyTpjIREZEKyeQqLBERiZESiIiIVIgSiIiIVIgS\niIiIVIgSiIiIVIgSiIiIVIgSiIiIVIgSiEgR0cR33avx/f4UrRdxUwn7h5jZQdUVj0iycuIOQKQm\nMbNsD3Nqlcd5hEn3ShrVOxR4EngnRe8nkhIqgUjGiuZBezuaFn6ZmT0VzWm1vQRhZntEE+xhZqPM\n7AkLq/y9Z2a/sLAa4SIzeyWambfQ2dGqbW+Y2RHR+Q2jVd4WmNnrZjYo4brTzOxZwtQjJcVbWNJY\namanR9umERYie71wW5FzehHWarg5irN99Pv9xcwWAheZWQsze9TMXo0eR5cRb5fouEUWVhbsUOl/\nDKmVVAKRTNcRGObu55vZZOBUSp+K/XuENVsaEub9ucTdu5vZn4GzgQnRcQ3cvZuZHUOYLPMQ4Erg\nWXf/iYXlZV8zs8KE0Q04xMMCWbsws1OAQ939kGiW2IVm9qK7DzGzjR6mt9+Fu8+3sCTxDHd/PLoW\nQB13L0xsDwB/dvdXzKwNYdW5LqXE+zPgr+7+UDSTdHapn7BICZRAJNOt8h2LCC0iTOtdmufdfTOw\n2cy+JFQNQZjw7pCE4x4CcPeXzKyxhdUUTwIGmVnhqnd12TEt99ySkkekT8I115tZHmEq7Scpfc2W\nkjyc8PwEoHM0mzDAbhYW6iop3vnAlRZWrXzC3f9TgfcXUQKRjJc4pXg+YUnabeyonq1fyvGe8LqA\nnf8/FFeKMeBUd1+ZuMPMjiIsjlUeiUmjIjOaJr6fAUd6WNo5MS4oJl7gXTNbAAwEZpnZ+e6eV4EY\npJZTG4hkuuK+vb8P9Iie79KukKRhAGbWh7DM7SZC1dBF29/Y7LByXO8lwhoQWWbWEjgGeLXwUmWc\nuwloUsr+OcCYhLi6Rk+LjdfM2rn7Knf/P8IaI4eW4/cQ2U4JRDJdcSWFW4ALzOx1wroeyZ6buP1b\nM1sE3E5Y+xvgWqBO1LC+DLgm6SDdnyCsN7KU0NB+ibt/UkYchSYDl0QN4e2LOX4M0CNqnF8GjI62\nX1dCvGdEnQ4WE9qE7k/29xBJpPVARESkQlQCERGRClEjukgKmdnBhGVMC4v2Bnzr7r2SOPcKQptN\nYYO9A1Pc/YYqClekUlSFJSIiFaIqLBERqRAlEBERqRAlEBERqRAlEBERqRAlEBERqZD/B+A7Q5dD\n/weLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12825ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x132d67f0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9nXOrKjlW2vXk582DSy6BUcMD9P6neu8iEr7a7snPArLNrIOZ1QMGApMrDKA9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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12399828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_valid = gbm_model.roc(valid=True)\n",
    "roc_train= gbm_model.roc()\n",
    "\n",
    "plt.plot(roc_valid[0],roc_valid[1])\n",
    "plt.plot(roc_train[0],roc_train[1])\n",
    "plt.plot(np.arange(0.0,1.1,0.1),np.arange(0.0,1.1,0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl = H2ODeepLearningEstimator(seed =1,epochs =15,hidden=[20,20,20], model_id='dl1')\n",
    "dl.train(x = hdf.columns[0:len(hdf.col_names)-1], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9Y3dfcven01ZV5e+voSQRkSRl3CJ6qaRgEBFJXsoX0TOzbYTTU/d5ijAPQZc1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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12399748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c5bf60>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jVQiRP+TJJH/t7jUauDXI/YW0sVuT6UlPlh/0YPGESGpJgxdC5CN5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\nIYQLkyYvhBAu7P8BEbflmHf7JSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123bf1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_valid = dl.roc(valid=True)\n",
    "roc_train= dl.roc()\n",
    "\n",
    "plt.plot(roc_valid[0],roc_valid[1])\n",
    "plt.plot(roc_train[0],roc_train[1])\n",
    "plt.plot(np.arange(0.0,1.1,0.1),np.arange(0.0,1.1,0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Machine Results\n",
      "==================================================================\n",
      "Training AUC: 0.98792935448 | Validation AUC:  0.526059892685\n",
      "\n",
      "Deep Learning Results\n",
      "==================================================================\n",
      "Training AUC: 0.65851085125 | Validation AUC:  0.536532629213\n"
     ]
    }
   ],
   "source": [
    "print \"Gradient Boosting Machine Results\"\n",
    "print '=================================================================='\n",
    "print 'Training AUC:', gbm_model.auc(train=True), '| Validation AUC: ', gbm_model.auc(valid=True)\n",
    "\n",
    "print \"\\nDeep Learning Results\"\n",
    "print '=================================================================='\n",
    "print 'Training AUC:', dl.auc(train=True), '| Validation AUC: ', dl.auc(valid=True)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}