Imbalance Class.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": "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 = tmp_df.replace(['yes', 'no'], ['Yes', 'No'])\n",
    "tmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def print_dataDis(df=tmp_df):\n",
    "    print df['answers_yn'].unique()\n",
    "    print len(df[df['answers_yn'] == 'Yes']), '|', len(df[tmp_df['answers_yn'] == 'No'])\n",
    "    print 'fraction of No: ', len(df[df['answers_yn'] == 'No'])/ float(len(df[df['answers_yn'] == 'No']) + \n",
    "                                                                  float(len(df[df['answers_yn'] == 'Yes'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Yes' 'No']\n",
      "4877 | 1564\n",
      "fraction of No:  0.242819437975\n"
     ]
    }
   ],
   "source": [
    "print_dataDis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plt_bar(df =tmp_df):\n",
    "    yes=len(df[df['answers_yn'] == 'Yes'])\n",
    "    no=len(df[df['answers_yn'] == 'No'])\n",
    "    plt.bar([10,11], [yes, no] )\n",
    "    plt.xticks([10.5,11.5],['Yes','No'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2d46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def over_samp_fra(df=tmp_df, fraction =0.7):\n",
    "    over_samp = df[df['answers_yn'] == 'No'].sample(frac=fraction, replace=True)\n",
    "#     print len(over_samp)\n",
    "    return df.append(over_samp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Yes' 'No']\n",
      "4877 |"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DATO\\Anaconda2\\envs\\gl-env\\lib\\site-packages\\ipykernel\\__main__.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2659\n",
      "fraction of No:  0.35283970276\n"
     ]
    }
   ],
   "source": [
    "over_samp_df= over_samp_fra()\n",
    "print_dataDis(over_samp_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z+kZU1TeTfAr4r6o6nuQ9wNXAFzJ8g+8FwFcZvuNpTZIHgO1VtWN0q9ZSUlXfSDIB3AX8\nd+9D7+TlsH8C+O3FXlvrDH1bTnYPGP5Uz8erasvUSUl+CFgP3J7kp6rq5xZxjVra/oDhvzwf7o1N\nfeufbwWcZ+7Rt2snsCHJGwCSvD7JxUneCJxXVX8FbAGuHOUitWQEoKq+DmwDNvY+9o/A+7rj9wOf\nXdyltc87+kZV1T8nuQ/Y2f166P9h+O6ck8DHuu2ck8CvjnCZWjr6d+m/C9zRG7sTeDjJLwNfAz6w\nyGtrnj8wJUmNc+tGkhpn6CWpcYZekhpn6CWpcYZekhpn6CWpcYZekhpn6CWpcf8H8qXPqNOULu4A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdba63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_bar(over_samp_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tf-idf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tf_cln(sent = tmp_df.iloc[0][0],cln=True):\n",
    "\n",
    "    lowers= sent.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",
    "    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",
    "\n",
    "    for i in filtered_sentence:\n",
    "        if re.findall(r'^\\W.*',str(i)):\n",
    "            stop_words.add(i)\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": 45,
   "metadata": {
    "collapsed": true
   },
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convt_lst(arr):\n",
    "    return (pd.DataFrame(arr).iloc[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#########save over_samp_df to tmp_df to upcoming var assign#########\n",
    "tmp_df = over_samp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "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": 53,
   "metadata": {
    "collapsed": true
   },
   "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": 54,
   "metadata": {
    "collapsed": true
   },
   "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": 56,
   "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>[0.034523833988, 0.069047667976, 0.03452383398...</td>\n",
       "      <td>[0.57735026919, 0.57735026919, 0.57735026919]</td>\n",
       "      <td>19700</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>19749</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>19761</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>19762</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>19774</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[0.0229900244936, 0.0459800489872, 0.022990024...</td>\n",
       "      <td>[0.707106781187, 0.707106781187]</td>\n",
       "      <td>19784</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.023131050103, 0.0462621002059, 0.0231310501...</td>\n",
       "      <td>[0.707106781187, 0.707106781187]</td>\n",
       "      <td>19813</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[0.0209151935046, 0.0209151935046, 0.020915193...</td>\n",
       "      <td>[0.5, 0.5, 0.5, 0.5]</td>\n",
       "      <td>19829</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[0.0209151935046, 0.0209151935046, 0.020915193...</td>\n",
       "      <td>[0.5, 0.5, 0.5, 0.5]</td>\n",
       "      <td>19829</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[0.0506369683542, 0.0506369683542, 0.050636968...</td>\n",
       "      <td>[0.5, 0.5, 0.5, 0.5]</td>\n",
       "      <td>19830</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",
       "5  [0.0229900244936, 0.0459800489872, 0.022990024...   \n",
       "6  [0.023131050103, 0.0462621002059, 0.0231310501...   \n",
       "7  [0.0209151935046, 0.0209151935046, 0.020915193...   \n",
       "8  [0.0209151935046, 0.0209151935046, 0.020915193...   \n",
       "9  [0.0506369683542, 0.0506369683542, 0.050636968...   \n",
       "\n",
       "                                               query  query_id  query_type  \\\n",
       "0      [0.57735026919, 0.57735026919, 0.57735026919]     19700           0   \n",
       "1                               [0.5, 0.5, 0.5, 0.5]     19749           0   \n",
       "2                               [0.5, 0.5, 0.5, 0.5]     19761           0   \n",
       "3  [0.333333333333, 0.333333333333, 0.33333333333...     19762           0   \n",
       "4      [0.57735026919, 0.57735026919, 0.57735026919]     19774           0   \n",
       "5                   [0.707106781187, 0.707106781187]     19784           0   \n",
       "6                   [0.707106781187, 0.707106781187]     19813           0   \n",
       "7                               [0.5, 0.5, 0.5, 0.5]     19829           0   \n",
       "8                               [0.5, 0.5, 0.5, 0.5]     19829           0   \n",
       "9                               [0.5, 0.5, 0.5, 0.5]     19830           0   \n",
       "\n",
       "  answers_yn  \n",
       "0          1  \n",
       "1          1  \n",
       "2          0  \n",
       "3          0  \n",
       "4          1  \n",
       "5          1  \n",
       "6          0  \n",
       "7          1  \n",
       "8          0  \n",
       "9          1  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>07 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 15 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_user_mt10a9</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>896 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>accepting new members, 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:         07 secs\n",
       "H2O cluster version:        3.10.0.10\n",
       "H2O cluster version age:    2 months and 15 days\n",
       "H2O cluster name:           H2O_from_python_user_mt10a9\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    896 Mb\n",
       "H2O cluster total cores:    2\n",
       "H2O cluster allowed cores:  2\n",
       "H2O cluster status:         accepting new members, 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": 59,
   "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": 60,
   "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": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =5010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "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": 65,
   "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": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.auc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model = H2OGradientBoostingEstimator(seed =1,  ntrees = 500, 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": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IO7rPTbmfttElm8zEkrYJDnqvj+51JZmEmlC6ZlZyCdxKnRl1Y2IsgXiiKYGA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\nOGAF0BFIBeYBfVV1WcAxCaq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tdtyaDbmN6u0JfAosC9H9jAkJK4GYqOXNg7ZERF4TkcUi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\nY0yBWAIxxhhTIJZAjDHGFMj/AwS6B0R8KaAGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150c42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x155844e0>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb77b9b0>"
      ]
     },
     "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": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl = H2ODeepLearningEstimator(seed =1,epochs =700,hidden=[50,50], 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": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TIstXRNbVBHbHJZmIiIQi1jEGAy4HekVWvQ+8Upad/hpjEBEpuriNMbi7m9kc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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ac2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14ee6390>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z8OKL0L692UzRqxu8pHfhAyTJ30S32d14oP4DvNXxLatLcakNG2DAALOD5Mcf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x153210b8>"
      ]
     },
     "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": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Machine Results\n",
      "==================================================================\n",
      "Training AUC: 0.998309651253 | Validation AUC:  0.77203932615\n",
      "\n",
      "Deep Learning Results\n",
      "==================================================================\n",
      "Training AUC: 0.763406841554 | Validation AUC:  0.61442471276\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords \n",
    "stop_words = set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tf_cln(sent = tmp_df.iloc[0][0],cln=True):\n",
    "\n",
    "    lowers= sent.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",
    "    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",
    "\n",
    "    for i in filtered_sentence:\n",
    "        if re.findall(r'^\\W.*',str(i)):\n",
    "            stop_words.add(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": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#########save over_samp_df to tmp_df to upcoming var assign#########\n",
    "tmp_df = over_samp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst_tf_psg=[]\n",
    "for i in tmp_df.iloc[:,0]:\n",
    "    lst_tf_psg.append(tf_cln(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst_tf_qry = []\n",
    "for i in tmp_df.iloc[:,1]:\n",
    "    lst_tf_qry.append(tf_cln(i,False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "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_tf_psg\n",
    "tmp_df['query'] = lst_tf_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": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del(tmp_df['query_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>{u'Wilson': 1, u'United': 2, u'switched': 1, u...</td>\n",
       "      <td>{u'ronald': 1, u'reagan': 1, u'democrat': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{u'United': 1, u'show': 18, u'money': 6, u'Jeo...</td>\n",
       "      <td>{u'pay': 1, u'game': 1, u'shows': 1, u'contest...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{u'serving': 1, u'saturated': 2, u'unhealthy':...</td>\n",
       "      <td>{u'butter': 1, u'peanut': 1, u'contain': 1, u'...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{u'already': 2, u'show': 2, u'When': 5, u'mark...</td>\n",
       "      <td>{u'lane': 1, u'right': 1, u'carriageway': 1, u...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{u'sentence': 1, u'Deadly': 1, u'rob': 1, u'mi...</td>\n",
       "      <td>{u'felony': 1, u'assault': 1, u'armed': 1}</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            passages  \\\n",
       "0  {u'Wilson': 1, u'United': 2, u'switched': 1, u...   \n",
       "1  {u'United': 1, u'show': 18, u'money': 6, u'Jeo...   \n",
       "2  {u'serving': 1, u'saturated': 2, u'unhealthy':...   \n",
       "3  {u'already': 2, u'show': 2, u'When': 5, u'mark...   \n",
       "4  {u'sentence': 1, u'Deadly': 1, u'rob': 1, u'mi...   \n",
       "\n",
       "                                               query  query_type answers_yn  \n",
       "0       {u'ronald': 1, u'reagan': 1, u'democrat': 1}           0          1  \n",
       "1  {u'pay': 1, u'game': 1, u'shows': 1, u'contest...           0          1  \n",
       "2  {u'butter': 1, u'peanut': 1, u'contain': 1, u'...           0          0  \n",
       "3  {u'lane': 1, u'right': 1, u'carriageway': 1, u...           0          0  \n",
       "4         {u'felony': 1, u'assault': 1, u'armed': 1}           0          1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>22 mins 31 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 15 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_user_mt10a9</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>848 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:         22 mins 31 secs\n",
       "H2O cluster version:        3.10.0.10\n",
       "H2O cluster version age:    2 months and 15 days\n",
       "H2O cluster name:           H2O_from_python_user_mt10a9\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    848 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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf_tf=h2o.H2OFrame(tmp_df[tmp_df.columns[0:3]])\n",
    "hdf_target = h2o.H2OFrame(pd.DataFrame(tmp_df.answers_yn))\n",
    "hdf = h2o.H2OFrame.cbind(hdf_tf,h2o.H2OFrame.asfactor(hdf_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>passages                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   </th><th>query                                    </th><th style=\"text-align: right;\">  query_type</th><th style=\"text-align: right;\">  answers_yn</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>{'Wilson': 1, 'United': 2, 'switched': 1, 'votes\\\\\\\\u2014the': 1, 'supported': 1, 'years': 2, 'four': 1, 'radio': 1, '1962': 2, 'Ford': 1, '1948': 1, 'voter': 1, 'candidate': 3, 'former': 3, 'Ronald': 9, 'video.duration.momentjs': 1, 'candidates': 1, 'small-town': 1, '8': 1, 'campaigned': 2, 'views': 1, 'Re-Election': 1, 'early': 2, 'party': 3, 'Illinois': 1, 'presidential': 1, 'defected': 1, 'witched': 1, 'Raised': 1, 'Republican': 6, 'Liberal': 1, 'commentator': 1, '51': 1, '50': 1, 'disillusioned': 1, 'either': 1, 'found': 1, '538': 1, 'working-class': 1, 'From': 1, 'people': 1, 'Gerald': 1, 'Wikipedia': 1, '1960s': 2, 'challenger': 1, 'economic': 1, 'election': 2, 'Listen': 1, 'even': 1, 'ultimately': 1, 'said': 1, 'fundamentally': 1, 'electoral': 1, 'conservative': 2, '525': 1, 'mate': 2, 'During': 1, 'America': 1, 'ever': 1, 'Mondale': 3, 'He': 2, 'U.S.': 5, 'Bill': 1, 'however': 1, 'free': 1, 'Democrat': 3, 'Ferraro': 2, 'Northerner': 1, 'served': 3, 'put': 1, 'path': 1, '1935-': 2, 'Nixon': 1, 'received': 1, 'Jimmy': 1, 'Clinton': 1, 'carried': 1, 'country': 1, 'changed': 2, 'became': 3, '1970s': 1, 'Yes': 2, 'think': 1, 'first': 2, 'major': 2, 'useful': 1, 'political': 3, 'When': 1, 'number': 2, 'officially': 1, 'female': 2, 'parties': 1, 'old': 1, 'Hollywood': 1, '1984': 4, '1980': 2, '1981': 2, 'trajectory': 1, 'support': 1, 'June': 1, '1989': 2, 'California': 3, 'way': 2, 'vice-presidential': 2, 'white': 1, '40th': 2, 'life': 1, 're-elected': 1, '6': 1, 'American': 2, 'dramatic': 1, 'Harry': 1, '1911': 1, 'Democratic': 6, 'November': 3, 'Carter': 1, 'grew': 1, 'politician': 1, 'presidency': 1, 'reelected': 2, 'Reagan': 15, 'elections': 1, 'governor': 3, 'recording': 1, 'time': 1, 'landslide': 3, 'age': 1, '20s': 1, 'Richard': 1, 'voters': 1, 'states': 1, 'ready': 1, 'Party': 5, 'sounding': 1, 'Walter': 3, 'States': 2, 'different': 2, 'traditionally': 1, 'us': 1, '49': 1, 'rather': 1, 'began': 1, '1967': 1, 'actor': 3, 'member': 2, '5': 1, 'largest': 1, 'President': 2, 'A': 1, 'February': 1, 'I': 1, 'Geraldine': 2, 'earlier': 1, 'running': 2, 'president': 1, 'Democrat\\\\': 1, 'desert': 1, 'younger': 1, 'Truman': 1, 'especially': 1, '1975': 1, 'later': 1, 'defeating': 3, 'deserted': 1, 'encyclopedia': 1, 'Democrats': 1, '2004': 1, 'In': 4, 'Duration': 1, 'audio': 1, '1911-2004': 1}</td><td>{'ronald': 1, 'reagan': 1, 'democrat': 1}</td><td style=\"text-align: right;\">           0</td><td style=\"text-align: right;\">           1</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =5010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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 = ['query','query_type','passages'], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04080739473947178"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.auc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model = H2OGradientBoostingEstimator(seed =1,  ntrees = 2500, learn_rate = .05)\n",
    "gbm_model.train(x = ['query','query_type','passages'], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UkZdegnPOgdmzoVGjuKsRkfpOLZMcdeSR0K0bPPhg3JWIiKSfWiZ16M034dRT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\nLl8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x318c0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x218aa908>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I6V0kbinJAwsXumWEFy70uhKfUXoXiXtK8oC1kJqq2TSHUXoXCQQleWD3bkjS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x349dc160>"
      ]
     },
     "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": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl = H2ODeepLearningEstimator(seed =1,epochs =50,hidden=[20,20,20], model_id='dl1')\n",
    "dl.train(x = ['query','query_type','passages'], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/NTHYLHXaXTQK+5yMi73Ijh1IzfZJNG7XAXE53yzxn//AihUwa5bTkRQj7xJs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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c0eb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3271e898>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiMROTV5EJMPU5EVEMkxNXkQkw9TkRUQyTE1eRCTD1ORFRDJMTV5EJMP+P9cK\nHRMYxICtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1de978>"
      ]
     },
     "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": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Machine Results\n",
      "==================================================================\n",
      "Training AUC: 0.954606465088 | Validation AUC:  0.742288062267\n",
      "\n",
      "Deep Learning Results\n",
      "==================================================================\n",
      "Training AUC: 0.998617715615 | Validation AUC:  0.802913758689\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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