Marco Bag of words and tf-idf pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "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": [
    "del tmp_df['query_id']\n",
    "del tmp_df['passages']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train_sub = tmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# del(tmp_df)\n",
    "# del(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>was ronald reagan a democrat</td>\n",
       "      <td>description</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>do game shows pay their contestants</td>\n",
       "      <td>description</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>does peanut butter contain cholesterol</td>\n",
       "      <td>description</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>can you take left hand lane to turn right on a...</td>\n",
       "      <td>description</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>is armed assault a felony</td>\n",
       "      <td>description</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query   query_type answers_yn\n",
       "0                       was ronald reagan a democrat  description        Yes\n",
       "1                do game shows pay their contestants  description        Yes\n",
       "2             does peanut butter contain cholesterol  description         No\n",
       "3  can you take left hand lane to turn right on a...  description         No\n",
       "4                          is armed assault a felony  description        Yes"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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>was ronald reagan a democrat</td>\n",
       "      <td>description</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>do game shows pay their contestants</td>\n",
       "      <td>description</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>does peanut butter contain cholesterol</td>\n",
       "      <td>description</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>can you take left hand lane to turn right on a...</td>\n",
       "      <td>description</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>is armed assault a felony</td>\n",
       "      <td>description</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query   query_type answers_yn\n",
       "0                       was ronald reagan a democrat  description          1\n",
       "1                do game shows pay their contestants  description          1\n",
       "2             does peanut butter contain cholesterol  description          0\n",
       "3  can you take left hand lane to turn right on a...  description          0\n",
       "4                          is armed assault a felony  description          1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert yes no to 1 o\n",
    "\n",
    "# df_train_sub=df_train_sub.na.replace(['yes', 'no'], ['Yes', 'No'], 'answers_yn')\n",
    "# df_train_sub=df_train_sub.na.replace(['Yes', 'No'], ['1', '0'], 'answers_yn')\n",
    "\n",
    "df_train_sub = df_train_sub.replace(['yes', 'no'], ['Yes', 'No'])\n",
    "df_train_sub = df_train_sub.replace(['Yes', 'No'], ['1', '0'])\n",
    "\n",
    "df_train_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# sentence = df_train_sub.select('query').limit(1).collect()\n",
    "\n",
    "sentence = df_train_sub.iloc[0,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "was ronald reagan a democrat\n",
      "['was', 'ronald', 'reagan', 'a', 'democrat']\n"
     ]
    }
   ],
   "source": [
    "print sentence\n",
    "token = nltk.word_tokenize(sentence)\n",
    "print token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'a': 1, 'democrat': 1, 'reagan': 1, 'ronald': 1, 'was': 1})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.FreqDist(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df_train_sub.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def term_fre(doc):\n",
    "    token = nltk.word_tokenize(doc)\n",
    "    tf = nltk.FreqDist(token)\n",
    "#     print tf\n",
    "    return dict(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lst_tf = []\n",
    "for i in df_train_sub.iloc[:,0]:\n",
    "    lst_tf.append(term_fre(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import h2o\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>1 min 25 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 4 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_user_62w4pr</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:         1 min 25 secs\n",
       "H2O cluster version:        3.10.0.10\n",
       "H2O cluster version age:    2 months and 4 days\n",
       "H2O cluster name:           H2O_from_python_user_62w4pr\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": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# h2o.shutdown()"
   ]
  },
  {
   "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(lst_tf)\n",
    "\n",
    "hdf_target = h2o.H2OFrame(pd.DataFrame(df_train_sub.answers_yn))\n",
    "\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>C1                                                                                                                                                             </th><th style=\"text-align: right;\">  answers_yn</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>{'ronald': 1, 'was': 1, 'reagan': 1, 'a': 1, 'democrat': 1}                                                                                                    </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'do': 1, 'game': 1, 'contestants': 1, 'pay': 1, 'their': 1, 'shows': 1}                                                                                       </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'butter': 1, 'peanut': 1, 'contain': 1, 'does': 1, 'cholesterol': 1}                                                                                          </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'a': 1, 'on': 1, 'lane': 1, 'right': 1, 'to': 1, 'carriageway': 1, 'hand': 1, 'turn': 1, 'can': 1, 'dual': 1, 'you': 1, 'left': 1, 'roundabout': 1, 'take': 1}</td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'a': 1, 'felony': 1, 'is': 1, 'armed': 1, 'assault': 1}                                                                                                       </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'the': 1, 'as': 1, 'is': 1, 'silverbeet': 1, 'same': 1, 'chard': 1}                                                                                           </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'harmful': 1, 'springtails': 1, 'are': 1}                                                                                                                     </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'good': 1, 'meat': 1, 'for': 1, 'angora': 1, 'goats': 1, 'are': 1}                                                                                            </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'good': 1, 'meat': 1, 'for': 1, 'angora': 1, 'goats': 1, 'are': 1}                                                                                            </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'does': 1, 'have': 1, 'signature': 1, 'assistance': 1, 'roadside': 1, 'visa': 1}                                                                              </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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =1)"
   ]
  },
  {
   "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"
   ]
  },
  {
   "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 = hdf.col_names[0], y = hdf.col_names[1], training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006909803758099552"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.auc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model = H2OGradientBoostingEstimator(seed =1)\n",
    "gbm_model.train(x = hdf.col_names[0], y = hdf.col_names[1], training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.752263706133\n",
      "accuracy:  [[0.6393085528420991, 0.79602402153655]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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FKcstWrUC1muGOhATGSxhGGOKE+zw5o8Dy0RkLq477SXAfWGLyvjGqqSMMcUJ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\nxgTFEoYxxpig/D9kNaY8P3X3igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4f3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"AUC: \", gbm_model.auc()\n",
    "print \"accuracy: \", gbm_model.accuracy()\n",
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model_2 = H2OGradientBoostingEstimator(ntrees=65,seed=1)\n",
    "gbm_model_2.train(x = hdf.col_names[0], y = hdf.col_names[1], training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.762872324658\n",
      "accuracy:  [[0.6623931204751395, 0.7983019258645683]]\n"
     ]
    }
   ],
   "source": [
    "print \"AUC: \", gbm_model_2.auc()\n",
    "print \"accuracy: \", gbm_model_2.accuracy()\n",
    "# gbm_model_2.plot()\n",
    "\n",
    "# H2OGradientBoostingEstimator.scoring_history(gbm_model_2)\n",
    "# plot(h2o.pe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "prediction = gbm_model.predict(hdf_validate[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pdf_pred=prediction.as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction unique values: [1 0]\n",
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "5    1\n",
      "6    1\n",
      "7    1\n",
      "8    1\n",
      "9    1\n",
      "Name: predict, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>C1                                                                                                                        </th><th style=\"text-align: right;\">  answers_yn</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>{'butter': 1, 'peanut': 1, 'contain': 1, 'does': 1, 'cholesterol': 1}                                                     </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'harmful': 1, 'springtails': 1, 'are': 1}                                                                                </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'does': 1, 'have': 1, 'signature': 1, 'assistance': 1, 'roadside': 1, 'visa': 1}                                         </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'tesla': 1, 'new': 1, 'is': 1, 'hybrid': 1, 'the': 1}                                                                    </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'a': 1, 'is': 1, 'plant': 1, 'moss': 1, 'spanish': 1}                                                                    </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'did': 1, 'on': 1, 'landed': 1, 'us': 1, 'moon': 1}                                                                      </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'farmhouse': 1, 'sinks': 1, 'laminate': 1, 'countertops': 1, 'with': 1, 'can': 1}                                        </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'on': 1, 'i': 1, 'saves': 1, 'xbox': 1, 'pc': 1, 'can': 1, 'put': 1, 'my': 1}                                            </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'for': 1, 'is': 1, 'there': 1, 'an': 1, 'limit': 1, 'learning': 1, 'speech': 1, 'age': 1}                                </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'counsel': 1, 'of': 1, 'an': 1, 'not': 1, 'does': 1, 'are': 1, 'employee': 1, 'partner': 1, 'you': 1, 'or': 1, 'mean': 1}</td><td style=\"text-align: right;\">           0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print 'prediction unique values:', pdf_pred['predict'].unique()\n",
    "print pdf_pred['predict'][0:10]\n",
    "print hdf_validate.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playing with data\n",
    "1. clean it\n",
    "2. then word cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tmp_str = str()\n",
    "for i in df_train_sub.iloc[:,0]:\n",
    "    tmp_str += i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wordcloud = WordCloud().generate(tmp_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_wordCloud(wordcloud):\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "token = nltk.word_tokenize(tmp_str)\n",
    "tf = nltk.FreqDist(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Remove punctuation and stop words\n",
    "from nltk.corpus import stopwords \n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "stop_words.add('?')\n",
    "\n",
    "\n",
    "filtered_sentence = [w for w in token if not w in stop_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf = nltk.FreqDist(filtered_sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf_lst = tf.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9LmZiIbEJVxGruxwrtQrEwMFVZ0oWOCZzHYN6UzLZkpyetFkRt5liGcyxTSZb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\nA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vgjeuMEVBBgLl+Kv30H3qZNowvZVQFn6+iDrT6rBn1SHNldCo0/BvgDjwcWNZhcPzdC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\nWvkgbRKLbyS5+AaC/q3ooEByyY2Y2RWU9v8XOixipFup3fQ5w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3Z5tOYlCVNQH9M5kg6p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73nGMGwQUfvuHcc/hSdjZ6rOzNVyUdC18lk7rzhgaeH7oYJhG2GrSewUhCV/CnnafPe1ZTHM+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf24e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud=WordCloud().generate_from_frequencies(tf_lst)\n",
    "\n",
    "plot_wordCloud(wordcloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df= pd.DataFrame(tf_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "165\n",
      "34\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>diseaseis</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>ira</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>take</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>I</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>caused</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0   1\n",
       "43    diseaseis  36\n",
       "437         ira  54\n",
       "1053       take  53\n",
       "1242          I  45\n",
       "1431     caused  33"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = df[df.columns[1]]>30\n",
    "\n",
    "print max(df[df.columns[1]])\n",
    "print len(df[idx][df.columns[1]])\n",
    "\n",
    "df[idx].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_idx = df[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lst_idx=[]\n",
    "for i in range (0,len(df_idx)):\n",
    "    tup = (df_idx.iloc[i,0], df_idx.iloc[i,1])\n",
    "    lst_idx.append(tup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YaZe6Z0cw1nSEsNJg9GbeVg2Bcgr664+KPnu0a0niLW5OtvGCGs9s8HrDtWDf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\n/g6J1xPTYkOOSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf24ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud=WordCloud().generate_from_frequencies(lst_idx)\n",
    "\n",
    "plot_wordCloud(wordcloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gwQKdPHlSkmQymbR27VqtXbtWktS7d29ZLBYFBgbq/fff1wcffKCvvvpK+fn5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xee31f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# objects = ('Python', 'C++', 'Java', 'Perl', 'Scala', 'Lisp')\n",
    "\n",
    "objects = tuple(df_idx[0])\n",
    "\n",
    "y_pos = np.arange(len(objects))\n",
    "\n",
    "performance = list(df_idx[1])\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Usage')\n",
    "plt.title('Programming language usage')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Build model after removing stops words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords \n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# stop_words.add('?')\n",
    "\n",
    "def rm_punct(token):\n",
    "    filtered_sentence = [w for w in token if not w in stop_words]\n",
    "    return filtered_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def term_fre(doc):\n",
    "    token = nltk.word_tokenize(doc)\n",
    "    tf = nltk.FreqDist(rm_punct(token))\n",
    "#     print tf\n",
    "    return dict(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lst_tf = []\n",
    "for i in df_train_sub.iloc[:,0]:\n",
    "    lst_tf.append(term_fre(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf_tf=h2o.H2OFrame(lst_tf)\n",
    "\n",
    "hdf_target = h2o.H2OFrame(pd.DataFrame(df_train_sub.answers_yn))\n",
    "\n",
    "hdf = h2o.H2OFrame.cbind(hdf_tf,h2o.H2OFrame.asfactor(hdf_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>C1                                                                                                               </th><th style=\"text-align: right;\">  answers_yn</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>{'ronald': 1, 'reagan': 1, 'democrat': 1}                                                                        </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'pay': 1, 'game': 1, 'shows': 1, 'contestants': 1}                                                              </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'butter': 1, 'peanut': 1, 'contain': 1, 'cholesterol': 1}                                                       </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'lane': 1, 'right': 1, 'carriageway': 1, 'hand': 1, 'turn': 1, 'take': 1, 'dual': 1, 'left': 1, 'roundabout': 1}</td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'felony': 1, 'assault': 1, 'armed': 1}                                                                          </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'silverbeet': 1, 'chard': 1}                                                                                    </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'harmful': 1, 'springtails': 1}                                                                                 </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'angora': 1, 'good': 1, 'goats': 1, 'meat': 1}                                                                  </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'angora': 1, 'good': 1, 'goats': 1, 'meat': 1}                                                                  </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'assistance': 1, 'visa': 1, 'signature': 1, 'roadside': 1}                                                      </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator\n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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.col_names[0], y = hdf.col_names[1], training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009103803952627752"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.auc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model = H2OGradientBoostingEstimator(ntrees=10,seed =1, model_id = 'gbm10')\n",
    "gbm_model.train(x = hdf.col_names[0], y = hdf.col_names[1], training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.629769183028\n",
      "accuracy:  [[0.7063278899952814, 0.765997100849037]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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169eP9R5XrlzJ6tWradSoET169ODw4cOsW7eOX3/9laNHj5I1a9an/qzMZJcB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\nAAAAcBAFNgAAAOCgL0UxknxDLZm/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd70a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"AUC: \", gbm_model.auc()\n",
    "print \"accuracy: \", gbm_model.accuracy()\n",
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "prediction = gbm_model.predict(hdf_validate[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pdf_pred=prediction.as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction unique values: [1]\n",
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "5    1\n",
      "6    1\n",
      "7    1\n",
      "8    1\n",
      "9    1\n",
      "Name: predict, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>C1                                                           </th><th style=\"text-align: right;\">  answers_yn</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>{'butter': 1, 'peanut': 1, 'contain': 1, 'cholesterol': 1}   </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'harmful': 1, 'springtails': 1}                             </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'assistance': 1, 'visa': 1, 'signature': 1, 'roadside': 1}  </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'tesla': 1, 'new': 1, 'hybrid': 1}                          </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'plant': 1, 'moss': 1, 'spanish': 1}                        </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'landed': 1, 'us': 1, 'moon': 1}                            </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'farmhouse': 1, 'laminate': 1, 'sinks': 1, 'countertops': 1}</td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'put': 1, 'pc': 1, 'saves': 1, 'xbox': 1}                   </td><td style=\"text-align: right;\">           1</td></tr>\n",
       "<tr><td>{'limit': 1, 'age': 1, 'speech': 1, 'learning': 1}           </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>{'employee': 1, 'partner': 1, 'counsel': 1, 'mean': 1}       </td><td style=\"text-align: right;\">           0</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print 'prediction unique values:', pdf_pred['predict'].unique()\n",
    "print pdf_pred['predict'][0:10]\n",
    "print hdf_validate.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.646719054372: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>52.0</td>\n",
       "<td>1133.0</td>\n",
       "<td>0.9561</td>\n",
       "<td> (1133.0/1185.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>0.0</td>\n",
       "<td>3644.0</td>\n",
       "<td>0.0</td>\n",
       "<td> (0.0/3644.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>52.0</td>\n",
       "<td>4777.0</td>\n",
       "<td>0.2346</td>\n",
       "<td> (1133.0/4829.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0    1     Error    Rate\n",
       "-----  ---  ----  -------  ---------------\n",
       "0      52   1133  0.9561   (1133.0/1185.0)\n",
       "1      0    3644  0        (0.0/3644.0)\n",
       "Total  52   4777  0.2346   (1133.0/4829.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_model.confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  gbm10\n",
      "Model Summary: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>number_of_trees</b></td>\n",
       "<td><b>number_of_internal_trees</b></td>\n",
       "<td><b>model_size_in_bytes</b></td>\n",
       "<td><b>min_depth</b></td>\n",
       "<td><b>max_depth</b></td>\n",
       "<td><b>mean_depth</b></td>\n",
       "<td><b>min_leaves</b></td>\n",
       "<td><b>max_leaves</b></td>\n",
       "<td><b>mean_leaves</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>10.0</td>\n",
       "<td>10.0</td>\n",
       "<td>5640.0</td>\n",
       "<td>5.0</td>\n",
       "<td>5.0</td>\n",
       "<td>5.0</td>\n",
       "<td>13.0</td>\n",
       "<td>20.0</td>\n",
       "<td>17.3</td></tr></table></div>"
      ],
      "text/plain": [
       "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
       "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
       "    10                 10                          5640                   5            5            5             13            20            17.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.178292338632\n",
      "RMSE: 0.422246774566\n",
      "LogLoss: 0.539488017897\n",
      "Mean Per-Class Error: 0.417101807723\n",
      "AUC: 0.629769183028\n",
      "Gini: 0.259538366056\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.646719054372: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>52.0</td>\n",
       "<td>1133.0</td>\n",
       "<td>0.9561</td>\n",
       "<td> (1133.0/1185.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>0.0</td>\n",
       "<td>3644.0</td>\n",
       "<td>0.0</td>\n",
       "<td> (0.0/3644.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>52.0</td>\n",
       "<td>4777.0</td>\n",
       "<td>0.2346</td>\n",
       "<td> (1133.0/4829.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0    1     Error    Rate\n",
       "-----  ---  ----  -------  ---------------\n",
       "0      52   1133  0.9561   (1133.0/1185.0)\n",
       "1      0    3644  0        (0.0/3644.0)\n",
       "Total  52   4777  0.2346   (1133.0/4829.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.6467191</td>\n",
       "<td>0.8654554</td>\n",
       "<td>91.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.6467191</td>\n",
       "<td>0.9414561</td>\n",
       "<td>91.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.7426043</td>\n",
       "<td>0.8068959</td>\n",
       "<td>69.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.7063279</td>\n",
       "<td>0.7659971</td>\n",
       "<td>84.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.8973293</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.6467191</td>\n",
       "<td>1.0</td>\n",
       "<td>91.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.8973293</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.7233171</td>\n",
       "<td>0.1833520</td>\n",
       "<td>78.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.7547104</td>\n",
       "<td>0.5397914</td>\n",
       "<td>61.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.7492397</td>\n",
       "<td>0.5828982</td>\n",
       "<td>62.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.646719     0.865455  91\n",
       "max f2                       0.646719     0.941456  91\n",
       "max f0point5                 0.742604     0.806896  69\n",
       "max accuracy                 0.706328     0.765997  84\n",
       "max precision                0.897329     1         0\n",
       "max recall                   0.646719     1         91\n",
       "max specificity              0.897329     1         0\n",
       "max absolute_mcc             0.723317     0.183352  78\n",
       "max min_per_class_accuracy   0.75471      0.539791  61\n",
       "max mean_per_class_accuracy  0.74924      0.582898  62"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate: 75.46 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0109754</td>\n",
       "<td>0.8740834</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0145445</td>\n",
       "<td>0.0145445</td>\n",
       "<td>32.5192097</td>\n",
       "<td>32.5192097</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0200870</td>\n",
       "<td>0.8386083</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0120746</td>\n",
       "<td>0.0266191</td>\n",
       "<td>32.5192097</td>\n",
       "<td>32.5192097</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0314765</td>\n",
       "<td>0.8300253</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0150933</td>\n",
       "<td>0.0417124</td>\n",
       "<td>32.5192097</td>\n",
       "<td>32.5192097</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0418306</td>\n",
       "<td>0.8264045</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.3251921</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0137212</td>\n",
       "<td>0.0554336</td>\n",
       "<td>32.5192097</td>\n",
       "<td>32.5192097</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.0503210</td>\n",
       "<td>0.7983582</td>\n",
       "<td>1.2928703</td>\n",
       "<td>1.3197386</td>\n",
       "<td>0.9756098</td>\n",
       "<td>0.9958848</td>\n",
       "<td>0.0109769</td>\n",
       "<td>0.0664105</td>\n",
       "<td>29.2870338</td>\n",
       "<td>31.9738631</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1644233</td>\n",
       "<td>0.7775160</td>\n",
       "<td>1.0846854</td>\n",
       "<td>1.1566223</td>\n",
       "<td>0.8185118</td>\n",
       "<td>0.8727960</td>\n",
       "<td>0.1237651</td>\n",
       "<td>0.1901756</td>\n",
       "<td>8.4685364</td>\n",
       "<td>15.6622321</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.2151584</td>\n",
       "<td>0.7665580</td>\n",
       "<td>1.1196521</td>\n",
       "<td>1.1479046</td>\n",
       "<td>0.8448980</td>\n",
       "<td>0.8662175</td>\n",
       "<td>0.0568057</td>\n",
       "<td>0.2469813</td>\n",
       "<td>11.9652098</td>\n",
       "<td>14.7904607</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.3392007</td>\n",
       "<td>0.7598765</td>\n",
       "<td>1.0353755</td>\n",
       "<td>1.1067538</td>\n",
       "<td>0.7813022</td>\n",
       "<td>0.8351648</td>\n",
       "<td>0.1284303</td>\n",
       "<td>0.3754116</td>\n",
       "<td>3.5375461</td>\n",
       "<td>10.6753839</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.5011390</td>\n",
       "<td>0.7569835</td>\n",
       "<td>1.0133822</td>\n",
       "<td>1.0765817</td>\n",
       "<td>0.7647059</td>\n",
       "<td>0.8123967</td>\n",
       "<td>0.1641054</td>\n",
       "<td>0.5395170</td>\n",
       "<td>1.3382192</td>\n",
       "<td>7.6581678</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.6289087</td>\n",
       "<td>0.7492397</td>\n",
       "<td>1.0180568</td>\n",
       "<td>1.0646917</td>\n",
       "<td>0.7682334</td>\n",
       "<td>0.8034244</td>\n",
       "<td>0.1300768</td>\n",
       "<td>0.6695939</td>\n",
       "<td>1.8056813</td>\n",
       "<td>6.4691708</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.7492234</td>\n",
       "<td>0.7460779</td>\n",
       "<td>0.9830599</td>\n",
       "<td>1.0515828</td>\n",
       "<td>0.7418244</td>\n",
       "<td>0.7935323</td>\n",
       "<td>0.1182766</td>\n",
       "<td>0.7878705</td>\n",
       "<td>-1.6940114</td>\n",
       "<td>5.1582783</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.9014289</td>\n",
       "<td>0.7426044</td>\n",
       "<td>0.9501717</td>\n",
       "<td>1.0344596</td>\n",
       "<td>0.7170068</td>\n",
       "<td>0.7806111</td>\n",
       "<td>0.1446213</td>\n",
       "<td>0.9324918</td>\n",
       "<td>-4.9828252</td>\n",
       "<td>3.4459624</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>1.0</td>\n",
       "<td>0.2882167</td>\n",
       "<td>0.6848682</td>\n",
       "<td>1.0</td>\n",
       "<td>0.5168067</td>\n",
       "<td>0.7546076</td>\n",
       "<td>0.0675082</td>\n",
       "<td>1.0</td>\n",
       "<td>-31.5131816</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.0109754                   0.874083           1.32519   1.32519            1                1                           0.0145445       0.0145445                  32.5192   32.5192\n",
       "    2        0.020087                    0.838608           1.32519   1.32519            1                1                           0.0120746       0.0266191                  32.5192   32.5192\n",
       "    3        0.0314765                   0.830025           1.32519   1.32519            1                1                           0.0150933       0.0417124                  32.5192   32.5192\n",
       "    4        0.0418306                   0.826405           1.32519   1.32519            1                1                           0.0137212       0.0554336                  32.5192   32.5192\n",
       "    5        0.050321                    0.798358           1.29287   1.31974            0.97561          0.995885                    0.0109769       0.0664105                  29.287    31.9739\n",
       "    6        0.164423                    0.777516           1.08469   1.15662            0.818512         0.872796                    0.123765        0.190176                   8.46854   15.6622\n",
       "    7        0.215158                    0.766558           1.11965   1.1479             0.844898         0.866218                    0.0568057       0.246981                   11.9652   14.7905\n",
       "    8        0.339201                    0.759877           1.03538   1.10675            0.781302         0.835165                    0.12843         0.375412                   3.53755   10.6754\n",
       "    9        0.501139                    0.756984           1.01338   1.07658            0.764706         0.812397                    0.164105        0.539517                   1.33822   7.65817\n",
       "    10       0.628909                    0.74924            1.01806   1.06469            0.768233         0.803424                    0.130077        0.669594                   1.80568   6.46917\n",
       "    11       0.749223                    0.746078           0.98306   1.05158            0.741824         0.793532                    0.118277        0.78787                    -1.69401  5.15828\n",
       "    12       0.901429                    0.742604           0.950172  1.03446            0.717007         0.780611                    0.144621        0.932492                   -4.98283  3.44596\n",
       "    13       1                           0.288217           0.684868  1                  0.516807         0.754608                    0.0675082       1                          -31.5132  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.179408753089\n",
      "RMSE: 0.423566704415\n",
      "LogLoss: 0.544453845588\n",
      "Mean Per-Class Error: 0.485139319548\n",
      "AUC: 0.516889325433\n",
      "Gini: 0.0337786508655\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.435075931487: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>0.0</td>\n",
       "<td>379.0</td>\n",
       "<td>1.0</td>\n",
       "<td> (379.0/379.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>0.0</td>\n",
       "<td>1233.0</td>\n",
       "<td>0.0</td>\n",
       "<td> (0.0/1233.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>0.0</td>\n",
       "<td>1612.0</td>\n",
       "<td>0.2351</td>\n",
       "<td> (379.0/1612.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0    1     Error    Rate\n",
       "-----  ---  ----  -------  --------------\n",
       "0      0    379   1        (379.0/379.0)\n",
       "1      0    1233  0        (0.0/1233.0)\n",
       "Total  0    1612  0.2351   (379.0/1612.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.4350759</td>\n",
       "<td>0.8667838</td>\n",
       "<td>63.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.4350759</td>\n",
       "<td>0.9420844</td>\n",
       "<td>63.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.6698231</td>\n",
       "<td>0.8046512</td>\n",
       "<td>58.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.6467191</td>\n",
       "<td>0.7655087</td>\n",
       "<td>59.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.8887874</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.4350759</td>\n",
       "<td>1.0</td>\n",
       "<td>63.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.8887874</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.6698231</td>\n",
       "<td>0.0745529</td>\n",
       "<td>58.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.7569835</td>\n",
       "<td>0.5125710</td>\n",
       "<td>36.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.7569835</td>\n",
       "<td>0.5148607</td>\n",
       "<td>36.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value      idx\n",
       "---------------------------  -----------  ---------  -----\n",
       "max f1                       0.435076     0.866784   63\n",
       "max f2                       0.435076     0.942084   63\n",
       "max f0point5                 0.669823     0.804651   58\n",
       "max accuracy                 0.646719     0.765509   59\n",
       "max precision                0.888787     1          0\n",
       "max recall                   0.435076     1          63\n",
       "max specificity              0.888787     1          0\n",
       "max absolute_mcc             0.669823     0.0745529  58\n",
       "max min_per_class_accuracy   0.756984     0.512571   36\n",
       "max mean_per_class_accuracy  0.756984     0.514861   36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate: 76.49 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0105459</td>\n",
       "<td>0.8347839</td>\n",
       "<td>0.8459520</td>\n",
       "<td>0.8459520</td>\n",
       "<td>0.6470588</td>\n",
       "<td>0.6470588</td>\n",
       "<td>0.0089213</td>\n",
       "<td>0.0089213</td>\n",
       "<td>-15.4047994</td>\n",
       "<td>-15.4047994</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0229529</td>\n",
       "<td>0.8157155</td>\n",
       "<td>1.2420114</td>\n",
       "<td>1.0600381</td>\n",
       "<td>0.95</td>\n",
       "<td>0.8108108</td>\n",
       "<td>0.0154096</td>\n",
       "<td>0.0243309</td>\n",
       "<td>24.2011354</td>\n",
       "<td>6.0038140</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0303970</td>\n",
       "<td>0.8064694</td>\n",
       "<td>1.0894836</td>\n",
       "<td>1.0672493</td>\n",
       "<td>0.8333333</td>\n",
       "<td>0.8163265</td>\n",
       "<td>0.0081103</td>\n",
       "<td>0.0324412</td>\n",
       "<td>8.9483644</td>\n",
       "<td>6.7249284</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0403226</td>\n",
       "<td>0.7981604</td>\n",
       "<td>1.0622466</td>\n",
       "<td>1.0660178</td>\n",
       "<td>0.8125</td>\n",
       "<td>0.8153846</td>\n",
       "<td>0.0105434</td>\n",
       "<td>0.0429846</td>\n",
       "<td>6.2246553</td>\n",
       "<td>6.6017843</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.0545906</td>\n",
       "<td>0.7928328</td>\n",
       "<td>1.2505377</td>\n",
       "<td>1.1142446</td>\n",
       "<td>0.9565217</td>\n",
       "<td>0.8522727</td>\n",
       "<td>0.0178427</td>\n",
       "<td>0.0608273</td>\n",
       "<td>25.0537748</td>\n",
       "<td>11.4244636</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1805211</td>\n",
       "<td>0.7775160</td>\n",
       "<td>0.9918058</td>\n",
       "<td>1.0288320</td>\n",
       "<td>0.7586207</td>\n",
       "<td>0.7869416</td>\n",
       "<td>0.1248986</td>\n",
       "<td>0.1857259</td>\n",
       "<td>-0.8194200</td>\n",
       "<td>2.8831977</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.2034739</td>\n",
       "<td>0.7753779</td>\n",
       "<td>0.9186997</td>\n",
       "<td>1.0164085</td>\n",
       "<td>0.7027027</td>\n",
       "<td>0.7774390</td>\n",
       "<td>0.0210868</td>\n",
       "<td>0.2068127</td>\n",
       "<td>-8.1300278</td>\n",
       "<td>1.6408522</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.3548387</td>\n",
       "<td>0.7598765</td>\n",
       "<td>1.0126840</td>\n",
       "<td>1.0148197</td>\n",
       "<td>0.7745902</td>\n",
       "<td>0.7762238</td>\n",
       "<td>0.1532847</td>\n",
       "<td>0.3600973</td>\n",
       "<td>1.2683978</td>\n",
       "<td>1.4819730</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.5055831</td>\n",
       "<td>0.7569835</td>\n",
       "<td>1.0114712</td>\n",
       "<td>1.0138213</td>\n",
       "<td>0.7736626</td>\n",
       "<td>0.7754601</td>\n",
       "<td>0.1524736</td>\n",
       "<td>0.5125710</td>\n",
       "<td>1.1471235</td>\n",
       "<td>1.3821345</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.6290323</td>\n",
       "<td>0.7492397</td>\n",
       "<td>0.9723231</td>\n",
       "<td>1.0056772</td>\n",
       "<td>0.7437186</td>\n",
       "<td>0.7692308</td>\n",
       "<td>0.1200324</td>\n",
       "<td>0.6326034</td>\n",
       "<td>-2.7676908</td>\n",
       "<td>0.5677210</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.7456576</td>\n",
       "<td>0.7460779</td>\n",
       "<td>0.9874894</td>\n",
       "<td>1.0028325</td>\n",
       "<td>0.7553191</td>\n",
       "<td>0.7670549</td>\n",
       "<td>0.1151663</td>\n",
       "<td>0.7477697</td>\n",
       "<td>-1.2510569</td>\n",
       "<td>0.2832532</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.9088089</td>\n",
       "<td>0.7426044</td>\n",
       "<td>1.0041477</td>\n",
       "<td>1.0030686</td>\n",
       "<td>0.7680608</td>\n",
       "<td>0.7672355</td>\n",
       "<td>0.1638281</td>\n",
       "<td>0.9115977</td>\n",
       "<td>0.4147663</td>\n",
       "<td>0.3068628</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>1.0</td>\n",
       "<td>0.4350759</td>\n",
       "<td>0.9694181</td>\n",
       "<td>1.0</td>\n",
       "<td>0.7414966</td>\n",
       "<td>0.7648883</td>\n",
       "<td>0.0884023</td>\n",
       "<td>1.0</td>\n",
       "<td>-3.0581900</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  --------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.0105459                   0.834784           0.845952  0.845952           0.647059         0.647059                    0.00892133      0.00892133                 -15.4048  -15.4048\n",
       "    2        0.0229529                   0.815716           1.24201   1.06004            0.95             0.810811                    0.0154096       0.0243309                  24.2011   6.00381\n",
       "    3        0.030397                    0.806469           1.08948   1.06725            0.833333         0.816327                    0.0081103       0.0324412                  8.94836   6.72493\n",
       "    4        0.0403226                   0.79816            1.06225   1.06602            0.8125           0.815385                    0.0105434       0.0429846                  6.22466   6.60178\n",
       "    5        0.0545906                   0.792833           1.25054   1.11424            0.956522         0.852273                    0.0178427       0.0608273                  25.0538   11.4245\n",
       "    6        0.180521                    0.777516           0.991806  1.02883            0.758621         0.786942                    0.124899        0.185726                   -0.81942  2.8832\n",
       "    7        0.203474                    0.775378           0.9187    1.01641            0.702703         0.777439                    0.0210868       0.206813                   -8.13003  1.64085\n",
       "    8        0.354839                    0.759877           1.01268   1.01482            0.77459          0.776224                    0.153285        0.360097                   1.2684    1.48197\n",
       "    9        0.505583                    0.756984           1.01147   1.01382            0.773663         0.77546                     0.152474        0.512571                   1.14712   1.38213\n",
       "    10       0.629032                    0.74924            0.972323  1.00568            0.743719         0.769231                    0.120032        0.632603                   -2.76769  0.567721\n",
       "    11       0.745658                    0.746078           0.987489  1.00283            0.755319         0.767055                    0.115166        0.74777                    -1.25106  0.283253\n",
       "    12       0.908809                    0.742604           1.00415   1.00307            0.768061         0.767235                    0.163828        0.911598                   0.414766  0.306863\n",
       "    13       1                           0.435076           0.969418  1                  0.741497         0.764888                    0.0884023       1                          -3.05819  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
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       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
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       "<td><b>number_of_trees</b></td>\n",
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       "<td><b>validation_lift</b></td>\n",
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       "<tr><td></td>\n",
       "<td>2017-01-12 11:18:49</td>\n",
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       "<td>2017-01-12 11:18:49</td>\n",
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       "<td>3.0</td>\n",
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       "<td>2017-01-12 11:18:49</td>\n",
       "<td> 0.201 sec</td>\n",
       "<td>5.0</td>\n",
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       "<td>0.5483729</td>\n",
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       "<td>2017-01-12 11:18:49</td>\n",
       "<td> 0.244 sec</td>\n",
       "<td>6.0</td>\n",
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       "<td>2017-01-12 11:18:49</td>\n",
       "<td> 0.289 sec</td>\n",
       "<td>7.0</td>\n",
       "<td>0.4242403</td>\n",
       "<td>0.5439365</td>\n",
       "<td>0.6058840</td>\n",
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       "<td>2017-01-12 11:18:49</td>\n",
       "<td> 0.332 sec</td>\n",
       "<td>8.0</td>\n",
       "<td>0.4239245</td>\n",
       "<td>0.5431717</td>\n",
       "<td>0.6171001</td>\n",
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       "<tr><td></td>\n",
       "<td>2017-01-12 11:18:50</td>\n",
       "<td> 0.393 sec</td>\n",
       "<td>9.0</td>\n",
       "<td>0.4226861</td>\n",
       "<td>0.5405404</td>\n",
       "<td>0.6183451</td>\n",
       "<td>1.3251921</td>\n",
       "<td>0.2344171</td>\n",
       "<td>0.4234680</td>\n",
       "<td>0.5442229</td>\n",
       "<td>0.5176394</td>\n",
       "<td>0.8459520</td>\n",
       "<td>0.2351117</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-12 11:18:50</td>\n",
       "<td> 0.440 sec</td>\n",
       "<td>10.0</td>\n",
       "<td>0.4222468</td>\n",
       "<td>0.5394880</td>\n",
       "<td>0.6297692</td>\n",
       "<td>1.3251921</td>\n",
       "<td>0.2346241</td>\n",
       "<td>0.4235667</td>\n",
       "<td>0.5444538</td>\n",
       "<td>0.5168893</td>\n",
       "<td>0.8459520</td>\n",
       "<td>0.2351117</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration    number_of_trees    training_rmse    training_logloss    training_auc    training_lift    training_classification_error    validation_rmse    validation_logloss    validation_auc    validation_lift    validation_classification_error\n",
       "--  -------------------  ----------  -----------------  ---------------  ------------------  --------------  ---------------  -------------------------------  -----------------  --------------------  ----------------  -----------------  ---------------------------------\n",
       "    2017-01-12 11:18:49  0.022 sec   0                  0.43032          0.557216            0.5             1                0.245392                         0.424193           0.545668              0.5               1                  0.235112\n",
       "    2017-01-12 11:18:49  0.064 sec   1                  0.429675         0.555778            0.548105        1.277            0.243322                         0.42401            0.545272              0.514479          1.00232            0.235112\n",
       "    2017-01-12 11:18:49  0.094 sec   2                  0.429157         0.554676            0.549935        1.277            0.243322                         0.423979           0.545219              0.510284          1.00568            0.235112\n",
       "    2017-01-12 11:18:49  0.126 sec   3                  0.42873          0.55378             0.551408        1.277            0.243322                         0.423985           0.545241              0.507709          0.96843            0.235112\n",
       "    2017-01-12 11:18:49  0.161 sec   4                  0.428383         0.553048            0.551436        1.27869          0.243322                         0.424092           0.545448              0.507542          1.00568            0.235112\n",
       "    2017-01-12 11:18:49  0.201 sec   5                  0.426293         0.548373            0.591095        1.32519          0.235038                         0.423747           0.544731              0.514153          1.00568            0.235112\n",
       "    2017-01-12 11:18:49  0.244 sec   6                  0.425833         0.547335            0.59994         1.32519          0.234624                         0.423916           0.545088              0.510918          0.769047           0.235112\n",
       "    2017-01-12 11:18:49  0.289 sec   7                  0.42424          0.543937            0.605884        1.32519          0.234624                         0.42367            0.544626              0.510273          0.769047           0.235112\n",
       "    2017-01-12 11:18:49  0.332 sec   8                  0.423924         0.543172            0.6171          1.32519          0.234624                         0.423725           0.54472               0.515294          0.769047           0.235112\n",
       "    2017-01-12 11:18:50  0.393 sec   9                  0.422686         0.54054             0.618345        1.32519          0.234417                         0.423468           0.544223              0.517639          0.845952           0.235112\n",
       "    2017-01-12 11:18:50  0.440 sec   10                 0.422247         0.539488            0.629769        1.32519          0.234624                         0.423567           0.544454              0.516889          0.845952           0.235112"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Importances: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>variable</b></td>\n",
       "<td><b>relative_importance</b></td>\n",
       "<td><b>scaled_importance</b></td>\n",
       "<td><b>percentage</b></td></tr>\n",
       "<tr><td>C1</td>\n",
       "<td>1764.7327881</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td></tr></table></div>"
      ],
      "text/plain": [
       "variable    relative_importance    scaled_importance    percentage\n",
       "----------  ---------------------  -------------------  ------------\n",
       "C1          1764.73                1                    1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<bound method ?.recall of >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_model.recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr, tpr = gbm_model.roc(valid=True)\n",
    "roc_valid = gbm_model.roc(valid=True)\n",
    "roc_train= gbm_model.roc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1077c8d0>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yldBQs4717FlYuRJaNIyCLl6mT2DIEPj0U8iRw+4yJQNI1knrunXrcHR0xNX1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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12443b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": [
    "# include Description column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "des_df = pd.read_csv('D:/marco/vr_chat-master/Marco/train_yn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['description' 'entity' 'numeric' 'person' 'location']\n",
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "print des_df['query_type'].unique()\n",
    "print range(0,len(des_df['query_type'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "des_df=des_df.replace(des_df['query_type'].unique(),range(0,len(des_df['query_type'].unique())))\n",
    "# des_df['query_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "hdf=h2o.H2OFrame.cbind(hdf, h2o.H2OFrame(list(des_df['query_type'])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hdf_train, hdf_validate=hdf.split_frame(ratios=[0.75], seed =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator\n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'C1', u'answers_yn', u'C10']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "hdf.col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "gbm_model = H2OGradientBoostingEstimator(ntrees=10,seed =1, model_id = 'gbm10')\n",
    "gbm_model.train(x = ['C1','C10'], y = hdf.col_names[1], training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.627045672442\n",
      "accuracy:  [[0.6579287757987202, 0.7664112652723131]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vz9atWzl//jyGYVC0aFEGDBhAkyZNktTTrl07cuXKxeLFi5k8eTJPPPEE7dq1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\nRxNWDf0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xee23358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"AUC: \", gbm_model.auc()\n",
    "print \"accuracy: \", gbm_model.accuracy()\n",
    "gbm_model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpr, tpr = gbm_model.roc(valid=True)\n",
    "roc_valid = gbm_model.roc(valid=True)\n",
    "roc_train= gbm_model.roc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1242ab38>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyMNItpKZu38u5aeWZ+6BuXze+HMODzxM86dT2i0kd2JZ5kSrxo3huefMaNVa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10740668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": 1
}