Marco Bag of words pandas-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "import re\n",
    "import string\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": true
   },
   "outputs": [],
   "source": [
    "tmp_df = pd.read_csv('D:/marco/vr_chat-master/Marco/train_yn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords \n",
    "stop_words = set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {
    "collapsed": false
   },
   "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": 7,
   "metadata": {
    "collapsed": false
   },
   "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": 8,
   "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_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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del(tmp_df['query_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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>10 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_6s6cg0</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:         10 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_6s6cg0\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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# hdf=h2o.H2OFrame(tmp_df)\n",
    "\n",
    "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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# h2o.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =5010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    {
     "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nb.auc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 = ['query','query_type','passages'], y = 'answers_yn', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+6VZEvO02OOkkv6MyJrZFejr3UwKeJ5XkpsYURaVKbvXDJUvco0ULmDnTuv0a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177d29e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2236a4a8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Px5la8/LLTvX+5ptK8CIFlGeSN8YUA0YBtwLNgQHGmN+tGpI17g3ga28//JeT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\nIiIh7P8BiFqi9/9/KHEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x175d3f28>"
      ]
     },
     "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": 20,
   "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], 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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jMiCUCWM5UEtVk7zXscASVb3yvCLNZJYwskZiInToAJdcAgMGAEmJMKcT5CwE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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2243c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25619dd8>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y3kePVnoXkaAZY7DWmlDeG8yY/EKgpTGmuTEmHbgGmFqmA81wBf66igp8IsjP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22207320>"
      ]
     },
     "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": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Reterieve best result\n",
    "\n",
    "dl = H2ODeepLearningEstimator(seed =1,epochs =10,hidden=[20,20], model_id='dl1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Machine Results\n",
      "==================================================================\n",
      "Training AUC: 0.868026734277 | Validation AUC:  0.518675107633\n",
      "\n",
      "Deep Learning Results\n",
      "==================================================================\n",
      "Training AUC: 0.927639175037 | Validation AUC:  0.55693728659\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)"
   ]
  }
 ],
 "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
}