Bayes_Calibrated_Baseline-V2_Reg_Class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import os\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.preprocessing import  OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "app_events = pd.read_csv('../Data/app_events.csv')\n",
    "app_labels = pd.read_csv('../Data/app_labels.csv')\n",
    "events = pd.read_csv('../Data/events.csv')\n",
    "gender_age_train = pd.read_csv('../Data/gender_age_train.csv')\n",
    "gender_age_test = pd.read_csv('../Data/gender_age_test.csv')\n",
    "label_categories = pd.read_csv('../Data/label_categories.csv')\n",
    "phone_brand_device_model = pd.read_csv('../Data/phone_brand_device_model.csv',encoding='utf-8')\n",
    "phone_brand_device_model = phone_brand_device_model.drop_duplicates('device_id',keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Categorical Features:Device Brand(Cardinality - 131) and Device Model (Cardinality - 1599)\n",
    "phone_brand_master = gender_age_train.merge(phone_brand_device_model[['device_id','phone_brand','device_model']], how='left',on='device_id')\n",
    "phone_brand_master['brand_model'] = phone_brand_master['phone_brand'] + ' ' + phone_brand_master['device_model']\n",
    "phone_brand_master.brand_model.fillna('',inplace=True)\n",
    "phone_brand_master_test = gender_age_test.merge(phone_brand_device_model[['device_id','phone_brand','device_model']], how='left',on='device_id')\n",
    "phone_brand_master_test['brand_model'] = phone_brand_master_test['phone_brand'] + ' ' + phone_brand_master_test['device_model']\n",
    "phone_brand_master_test.brand_model.fillna('',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "def imputeDFColsUsingMedian(dataFrame,cols):\n",
    "    for col in cols:\n",
    "        medianOfCol=np.nanmedian(dataFrame[col])\n",
    "        dataFrame[col].fillna(medianOfCol,inplace=True)\n",
    "def imputeDFColsUsingMean(dataFrame,cols):\n",
    "    for col in cols:\n",
    "        meanOfCol=np.nanmean(dataFrame[col])\n",
    "        dataFrame[col].fillna(meanOfCol,inplace=True)\n",
    "def scaleFeature(dataFrame,col):\n",
    "    maxVal=np.max(dataFrame[col])\n",
    "    minVal=np.min(dataFrame[col])\n",
    "    scaledDenom=maxVal-minVal\n",
    "    dataFrame[col]=(dataFrame[col]-minVal)/scaledDenom\n",
    "def labelEncodeFeats(dataFrame,listOfFeats):\n",
    "    for feat in listOfFeats:\n",
    "        #print(\"Encoding On \",feat,\" Shape \",dataFrame[feat].shape)\n",
    "        labelEncoder=LabelEncoder()\n",
    "        encodedFeatValues=labelEncoder.fit_transform(dataFrame[feat])\n",
    "        dataFrame[feat]=encodedFeatValues\n",
    "def OneHotEncodeFeats(dataFrame,listOfFeats,ctgrcl_ftrs_msk):\n",
    "    #print(\"List of Feats to Encode \",listOfFeats)\n",
    "    labelEncodeFeats(dataFrame,listOfFeats)\n",
    "    #print(\"Label Encoding Done\")\n",
    "    oneHotEncoder=OneHotEncoder(categorical_features=ctgrcl_ftrs_msk,sparse=False)\n",
    "    #print(\"Got Object of OneHot\")\n",
    "    print(\"Total Cols \",dataFrame.columns)\n",
    "    oneHotEncodedFeats=oneHotEncoder.fit_transform(dataFrame)\n",
    "    print(\"Shape \",oneHotEncodedFeats.shape)\n",
    "    return oneHotEncodedFeats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "events.timestamp=events.timestamp.map(lambda x:pd.Timestamp(x).value)\n",
    "deviceEvents=events.groupby('device_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def avgEventDuration(listOfTimeStamps):\n",
    "    if(len(listOfTimeStamps)<1):\n",
    "        return 0\n",
    "    return (np.max(listOfTimeStamps)-np.min(listOfTimeStamps))/len(listOfTimeStamps)\n",
    "def stdEventDuration(listOfTimeStamps):\n",
    "    if(len(set(listOfTimeStamps))<=1):\n",
    "        return 0\n",
    "    return np.std(listOfTimeStamps)\n",
    "def avglongChangeFreq(listOfLongs):\n",
    "    if(len(listOfLongs)<1):\n",
    "        return 0\n",
    "    return len(set(listOfLongs))/float(len(listOfLongs))\n",
    "def avgSqrdlongChangeAmt(listOfLongs):\n",
    "    if(len(listOfLongs)<1):\n",
    "        return 0\n",
    "    return np.sum(np.diff(listOfLongs)**2)/float(len(listOfLongs))\n",
    "eventBasedAggregates=deviceEvents.aggregate({'timestamp':[np.count_nonzero,avgEventDuration,stdEventDuration],\n",
    "                                            'longitude':[avglongChangeFreq,avgSqrdlongChangeAmt]})\n",
    "eventBasedAggregatesFeats=['num_of_evnts','avg_evnt_drtn','std_evnt_drtn','avgLongtdChgFrq','avgSqrdLongtdChgAmt']\n",
    "modelFeatures=eventBasedAggregatesFeats.copy()\n",
    "eventBasedAggregates.columns=eventBasedAggregatesFeats\n",
    "eventBasedAggregates['device_id']=eventBasedAggregates.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "                      num_of_evnts  avg_evnt_drtn  std_evnt_drtn  \\\n",
       "device_id                                                          \n",
       "-9222956879900151005      0.030769    3156.504025             65   \n",
       "-9222661944218806987      0.125000       0.000000              8   \n",
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       "\n",
       "                      avgLongtdChgFrq  avgSqrdLongtdChgAmt  \\\n",
       "device_id                                                    \n",
       "-9222956879900151005    1758338461538       37541752542466   \n",
       "-9222661944218806987   72986000000000      193413022978676   \n",
       "-9222399302879214035   37490800000000      101030283478568   \n",
       "\n",
       "                                device_id  \n",
       "device_id                                  \n",
       "-9222956879900151005 -9222956879900151005  \n",
       "-9222661944218806987 -9222661944218806987  \n",
       "-9222399302879214035 -9222399302879214035  "
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "eventBasedAggregates[0:3]"
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  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>group</th>\n",
       "      <th>num_of_evnts</th>\n",
       "      <th>avg_evnt_drtn</th>\n",
       "      <th>std_evnt_drtn</th>\n",
       "      <th>avgLongtdChgFrq</th>\n",
       "      <th>avgSqrdLongtdChgAmt</th>\n",
       "      <th>phone_brand</th>\n",
       "      <th>device_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>MI 2</td>\n",
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       "    <tr>\n",
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       "      <td>小米</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>35</td>\n",
       "      <td>M32-38</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>小米</td>\n",
       "      <td>MI 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             device_id gender  age   group  num_of_evnts  avg_evnt_drtn  \\\n",
       "0 -8076087639492063270      M   35  M32-38      0.236296    1081.402878   \n",
       "1 -2897161552818060146      M   35  M32-38      0.236296    1081.402878   \n",
       "2 -8260683887967679142      M   35  M32-38      1.000000       0.000000   \n",
       "\n",
       "   std_evnt_drtn  avgLongtdChgFrq  avgSqrdLongtdChgAmt phone_brand  \\\n",
       "0      52.151315     2.412437e+13         1.116697e+14          小米   \n",
       "1      52.151315     2.412437e+13         1.116697e+14          小米   \n",
       "2       1.000000     0.000000e+00         0.000000e+00          小米   \n",
       "\n",
       "  device_model  \n",
       "0         MI 2  \n",
       "1         MI 2  \n",
       "2         MI 2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventsData_train =gender_age_train.merge(eventBasedAggregates, how='left',on='device_id')\n",
    "imputeDFColsUsingMean(eventsData_train,eventBasedAggregatesFeats)\n",
    "eventsData_train =eventsData_train.merge(phone_brand_master[['phone_brand','device_id']], how='left',on='device_id')\n",
    "eventsData_train =eventsData_train.merge(phone_brand_master[['device_model','device_id']], how='left',on='device_id')\n",
    "eventsData_train[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avgSqrdLongtdChgAmt</th>\n",
       "      <th>std_evnt_drtn</th>\n",
       "      <th>avgLongtdChgFrq</th>\n",
       "      <th>avg_evnt_drtn</th>\n",
       "      <th>num_of_evnts</th>\n",
       "      <th>device_model</th>\n",
       "      <th>phone_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.012329</td>\n",
       "      <td>0.081663</td>\n",
       "      <td>0.028765</td>\n",
       "      <td>0.235807</td>\n",
       "      <td>MI 2</td>\n",
       "      <td>小米</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.37801</td>\n",
       "      <td>0.012329</td>\n",
       "      <td>0.081663</td>\n",
       "      <td>0.028765</td>\n",
       "      <td>0.235807</td>\n",
       "      <td>MI 2</td>\n",
       "      <td>小米</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>MI 2</td>\n",
       "      <td>小米</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   avgSqrdLongtdChgAmt  std_evnt_drtn  avgLongtdChgFrq  avg_evnt_drtn  \\\n",
       "0              0.37801       0.012329         0.081663       0.028765   \n",
       "1              0.37801       0.012329         0.081663       0.028765   \n",
       "2              0.00000       0.000000         0.000000       0.000000   \n",
       "\n",
       "   num_of_evnts device_model phone_brand  \n",
       "0      0.235807         MI 2          小米  \n",
       "1      0.235807         MI 2          小米  \n",
       "2      1.000000         MI 2          小米  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelFeatures.extend(['phone_brand','device_model'])\n",
    "modelFeatures=list(set(modelFeatures))\n",
    "eventsData_train_final=eventsData_train[modelFeatures]\n",
    "eventBasedAggregatesFeats=list(set(eventBasedAggregatesFeats))\n",
    "for feat in eventBasedAggregatesFeats:\n",
    "    scaleFeature(eventsData_train_final,feat)\n",
    "categorical_features_mask=[False,False,False,False,False,True,True]\n",
    "featured_model_train=OneHotEncodeFeats(eventsData_train_final,['phone_brand','device_model'],categorical_features_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/numpy/lib/arraysetops.py:200: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
      "  flag = np.concatenate(([True], aux[1:] != aux[:-1]))\n",
      "/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cols  Index(['avgSqrdLongtdChgAmt', 'std_evnt_drtn', 'avgLongtdChgFrq',\n",
      "       'avg_evnt_drtn', 'num_of_evnts', 'device_model', 'phone_brand'],\n",
      "      dtype='object')\n",
      "Shape  (112071, 7)\n"
     ]
    }
   ],
   "source": [
    "eventsData_test =gender_age_test.merge(eventBasedAggregates, how='left',on='device_id')\n",
    "imputeDFColsUsingMean(eventsData_test,eventBasedAggregatesFeats)\n",
    "eventsData_test =eventsData_test.merge(phone_brand_master[['phone_brand','device_id']], how='left',on='device_id')\n",
    "eventsData_test =eventsData_test.merge(phone_brand_master[['device_model','device_id']], how='left',on='device_id')\n",
    "eventsData_test_final=eventsData_test[modelFeatures]\n",
    "for feat in eventBasedAggregatesFeats:\n",
    "    scaleFeature(eventsData_test_final,feat)\n",
    "featured_model_test=OneHotEncodeFeats(eventsData_test_final,['phone_brand','device_model'],categorical_features_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112071, 7), (74645, 1563))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featured_model_test.shape,featured_model_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(74645, 1563) (74645, 7)\n",
      "[ 0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.01232859  0.08166277  0.37801029  0.23580696  0.02876541]\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_of_evnts</th>\n",
       "      <th>avg_evnt_drtn</th>\n",
       "      <th>std_evnt_drtn</th>\n",
       "      <th>avgLongtdChgFrq</th>\n",
       "      <th>avgSqrdLongtdChgAmt</th>\n",
       "      <th>phone_brand</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.012329</td>\n",
       "      <td>0.081663</td>\n",
       "      <td>0.37801</td>\n",
       "      <td>0.235807</td>\n",
       "      <td>0.028765</td>\n",
       "      <td>47</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_of_evnts  avg_evnt_drtn  std_evnt_drtn  avgLongtdChgFrq  \\\n",
       "0      0.012329       0.081663        0.37801         0.235807   \n",
       "\n",
       "   avgSqrdLongtdChgAmt  phone_brand  device_model  \n",
       "0             0.028765           47           677  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on coo_matrix in module scipy.sparse.coo object:\n",
      "\n",
      "class coo_matrix(scipy.sparse.data._data_matrix, scipy.sparse.data._minmax_mixin)\n",
      " |  A sparse matrix in COOrdinate format.\n",
      " |  \n",
      " |  Also known as the 'ijv' or 'triplet' format.\n",
      " |  \n",
      " |  This can be instantiated in several ways:\n",
      " |      coo_matrix(D)\n",
      " |          with a dense matrix D\n",
      " |  \n",
      " |      coo_matrix(S)\n",
      " |          with another sparse matrix S (equivalent to S.tocoo())\n",
      " |  \n",
      " |      coo_matrix((M, N), [dtype])\n",
      " |          to construct an empty matrix with shape (M, N)\n",
      " |          dtype is optional, defaulting to dtype='d'.\n",
      " |  \n",
      " |      coo_matrix((data, (i, j)), [shape=(M, N)])\n",
      " |          to construct from three arrays:\n",
      " |              1. data[:]   the entries of the matrix, in any order\n",
      " |              2. i[:]      the row indices of the matrix entries\n",
      " |              3. j[:]      the column indices of the matrix entries\n",
      " |  \n",
      " |          Where ``A[i[k], j[k]] = data[k]``.  When shape is not\n",
      " |          specified, it is inferred from the index arrays\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  dtype : dtype\n",
      " |      Data type of the matrix\n",
      " |  shape : 2-tuple\n",
      " |      Shape of the matrix\n",
      " |  ndim : int\n",
      " |      Number of dimensions (this is always 2)\n",
      " |  nnz\n",
      " |      Number of nonzero elements\n",
      " |  data\n",
      " |      COO format data array of the matrix\n",
      " |  row\n",
      " |      COO format row index array of the matrix\n",
      " |  col\n",
      " |      COO format column index array of the matrix\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  \n",
      " |  Sparse matrices can be used in arithmetic operations: they support\n",
      " |  addition, subtraction, multiplication, division, and matrix power.\n",
      " |  \n",
      " |  Advantages of the COO format\n",
      " |      - facilitates fast conversion among sparse formats\n",
      " |      - permits duplicate entries (see example)\n",
      " |      - very fast conversion to and from CSR/CSC formats\n",
      " |  \n",
      " |  Disadvantages of the COO format\n",
      " |      - does not directly support:\n",
      " |          + arithmetic operations\n",
      " |          + slicing\n",
      " |  \n",
      " |  Intended Usage\n",
      " |      - COO is a fast format for constructing sparse matrices\n",
      " |      - Once a matrix has been constructed, convert to CSR or\n",
      " |        CSC format for fast arithmetic and matrix vector operations\n",
      " |      - By default when converting to CSR or CSC format, duplicate (i,j)\n",
      " |        entries will be summed together.  This facilitates efficient\n",
      " |        construction of finite element matrices and the like. (see example)\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from scipy.sparse import coo_matrix\n",
      " |  >>> coo_matrix((3, 4), dtype=np.int8).toarray()\n",
      " |  array([[0, 0, 0, 0],\n",
      " |         [0, 0, 0, 0],\n",
      " |         [0, 0, 0, 0]], dtype=int8)\n",
      " |  \n",
      " |  >>> row  = np.array([0, 3, 1, 0])\n",
      " |  >>> col  = np.array([0, 3, 1, 2])\n",
      " |  >>> data = np.array([4, 5, 7, 9])\n",
      " |  >>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray()\n",
      " |  array([[4, 0, 9, 0],\n",
      " |         [0, 7, 0, 0],\n",
      " |         [0, 0, 0, 0],\n",
      " |         [0, 0, 0, 5]])\n",
      " |  \n",
      " |  >>> # example with duplicates\n",
      " |  >>> row  = np.array([0, 0, 1, 3, 1, 0, 0])\n",
      " |  >>> col  = np.array([0, 2, 1, 3, 1, 0, 0])\n",
      " |  >>> data = np.array([1, 1, 1, 1, 1, 1, 1])\n",
      " |  >>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray()\n",
      " |  array([[3, 0, 1, 0],\n",
      " |         [0, 2, 0, 0],\n",
      " |         [0, 0, 0, 0],\n",
      " |         [0, 0, 0, 1]])\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      coo_matrix\n",
      " |      scipy.sparse.data._data_matrix\n",
      " |      scipy.sparse.base.spmatrix\n",
      " |      scipy.sparse.data._minmax_mixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, arg1, shape=None, dtype=None, copy=False)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  diagonal(self)\n",
      " |      Returns the main diagonal of the matrix\n",
      " |  \n",
      " |  getnnz(self, axis=None)\n",
      " |      Get the count of explicitly-stored values (nonzeros)\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      axis : None, 0, or 1\n",
      " |          Select between the number of values across the whole matrix, in\n",
      " |          each column, or in each row.\n",
      " |  \n",
      " |  sum_duplicates(self)\n",
      " |      Eliminate duplicate matrix entries by adding them together\n",
      " |      \n",
      " |      This is an *in place* operation\n",
      " |  \n",
      " |  toarray(self, order=None, out=None)\n",
      " |      See the docstring for `spmatrix.toarray`.\n",
      " |  \n",
      " |  tocoo(self, copy=False)\n",
      " |  \n",
      " |  tocsc(self)\n",
      " |      Return a copy of this matrix in Compressed Sparse Column format\n",
      " |      \n",
      " |      Duplicate entries will be summed together.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> from numpy import array\n",
      " |      >>> from scipy.sparse import coo_matrix\n",
      " |      >>> row  = array([0, 0, 1, 3, 1, 0, 0])\n",
      " |      >>> col  = array([0, 2, 1, 3, 1, 0, 0])\n",
      " |      >>> data = array([1, 1, 1, 1, 1, 1, 1])\n",
      " |      >>> A = coo_matrix((data, (row, col)), shape=(4, 4)).tocsc()\n",
      " |      >>> A.toarray()\n",
      " |      array([[3, 0, 1, 0],\n",
      " |             [0, 2, 0, 0],\n",
      " |             [0, 0, 0, 0],\n",
      " |             [0, 0, 0, 1]])\n",
      " |  \n",
      " |  tocsr(self)\n",
      " |      Return a copy of this matrix in Compressed Sparse Row format\n",
      " |      \n",
      " |      Duplicate entries will be summed together.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> from numpy import array\n",
      " |      >>> from scipy.sparse import coo_matrix\n",
      " |      >>> row  = array([0, 0, 1, 3, 1, 0, 0])\n",
      " |      >>> col  = array([0, 2, 1, 3, 1, 0, 0])\n",
      " |      >>> data = array([1, 1, 1, 1, 1, 1, 1])\n",
      " |      >>> A = coo_matrix((data, (row, col)), shape=(4, 4)).tocsr()\n",
      " |      >>> A.toarray()\n",
      " |      array([[3, 0, 1, 0],\n",
      " |             [0, 2, 0, 0],\n",
      " |             [0, 0, 0, 0],\n",
      " |             [0, 0, 0, 1]])\n",
      " |  \n",
      " |  todia(self)\n",
      " |  \n",
      " |  todok(self)\n",
      " |  \n",
      " |  transpose(self, copy=False)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  nnz\n",
      " |      Get the count of explicitly-stored values (nonzeros)\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      axis : None, 0, or 1\n",
      " |          Select between the number of values across the whole matrix, in\n",
      " |          each column, or in each row.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from scipy.sparse.data._data_matrix:\n",
      " |  \n",
      " |  __abs__(self)\n",
      " |  \n",
      " |  __imul__(self, other)\n",
      " |  \n",
      " |  __itruediv__(self, other)\n",
      " |  \n",
      " |  __neg__(self)\n",
      " |  \n",
      " |  arcsin(self)\n",
      " |      Element-wise arcsin.\n",
      " |      \n",
      " |      See numpy.arcsin for more information.\n",
      " |  \n",
      " |  arcsinh(self)\n",
      " |      Element-wise arcsinh.\n",
      " |      \n",
      " |      See numpy.arcsinh for more information.\n",
      " |  \n",
      " |  arctan(self)\n",
      " |      Element-wise arctan.\n",
      " |      \n",
      " |      See numpy.arctan for more information.\n",
      " |  \n",
      " |  arctanh(self)\n",
      " |      Element-wise arctanh.\n",
      " |      \n",
      " |      See numpy.arctanh for more information.\n",
      " |  \n",
      " |  astype(self, t)\n",
      " |  \n",
      " |  ceil(self)\n",
      " |      Element-wise ceil.\n",
      " |      \n",
      " |      See numpy.ceil for more information.\n",
      " |  \n",
      " |  conj(self)\n",
      " |  \n",
      " |  copy(self)\n",
      " |  \n",
      " |  deg2rad(self)\n",
      " |      Element-wise deg2rad.\n",
      " |      \n",
      " |      See numpy.deg2rad for more information.\n",
      " |  \n",
      " |  expm1(self)\n",
      " |      Element-wise expm1.\n",
      " |      \n",
      " |      See numpy.expm1 for more information.\n",
      " |  \n",
      " |  floor(self)\n",
      " |      Element-wise floor.\n",
      " |      \n",
      " |      See numpy.floor for more information.\n",
      " |  \n",
      " |  log1p(self)\n",
      " |      Element-wise log1p.\n",
      " |      \n",
      " |      See numpy.log1p for more information.\n",
      " |  \n",
      " |  power(self, n, dtype=None)\n",
      " |      This function performs element-wise power.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      n : n is a scalar\n",
      " |      \n",
      " |      dtype : If dtype is not specified, the current dtype will be preserved.\n",
      " |  \n",
      " |  rad2deg(self)\n",
      " |      Element-wise rad2deg.\n",
      " |      \n",
      " |      See numpy.rad2deg for more information.\n",
      " |  \n",
      " |  rint(self)\n",
      " |      Element-wise rint.\n",
      " |      \n",
      " |      See numpy.rint for more information.\n",
      " |  \n",
      " |  sign(self)\n",
      " |      Element-wise sign.\n",
      " |      \n",
      " |      See numpy.sign for more information.\n",
      " |  \n",
      " |  sin(self)\n",
      " |      Element-wise sin.\n",
      " |      \n",
      " |      See numpy.sin for more information.\n",
      " |  \n",
      " |  sinh(self)\n",
      " |      Element-wise sinh.\n",
      " |      \n",
      " |      See numpy.sinh for more information.\n",
      " |  \n",
      " |  sqrt(self)\n",
      " |      Element-wise sqrt.\n",
      " |      \n",
      " |      See numpy.sqrt for more information.\n",
      " |  \n",
      " |  tan(self)\n",
      " |      Element-wise tan.\n",
      " |      \n",
      " |      See numpy.tan for more information.\n",
      " |  \n",
      " |  tanh(self)\n",
      " |      Element-wise tanh.\n",
      " |      \n",
      " |      See numpy.tanh for more information.\n",
      " |  \n",
      " |  trunc(self)\n",
      " |      Element-wise trunc.\n",
      " |      \n",
      " |      See numpy.trunc for more information.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from scipy.sparse.data._data_matrix:\n",
      " |  \n",
      " |  dtype\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from scipy.sparse.base.spmatrix:\n",
      " |  \n",
      " |  __add__(self, other)\n",
      " |  \n",
      " |  __bool__(self)\n",
      " |  \n",
      " |  __div__(self, other)\n",
      " |  \n",
      " |  __eq__(self, other)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, other)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattr__(self, attr)\n",
      " |  \n",
      " |  __gt__(self, other)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, other)\n",
      " |  \n",
      " |  __idiv__(self, other)\n",
      " |  \n",
      " |  __isub__(self, other)\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |  \n",
      " |  __le__(self, other)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |      # What should len(sparse) return? For consistency with dense matrices,\n",
      " |      # perhaps it should be the number of rows?  But for some uses the number of\n",
      " |      # non-zeros is more important.  For now, raise an exception!\n",
      " |  \n",
      " |  __lt__(self, other)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __matmul__(self, other)\n",
      " |  \n",
      " |  __mul__(self, other)\n",
      " |      interpret other and call one of the following\n",
      " |      \n",
      " |      self._mul_scalar()\n",
      " |      self._mul_vector()\n",
      " |      self._mul_multivector()\n",
      " |      self._mul_sparse_matrix()\n",
      " |  \n",
      " |  __ne__(self, other)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __nonzero__ = __bool__(self)\n",
      " |  \n",
      " |  __numpy_ufunc__(self, func, method, pos, inputs, **kwargs)\n",
      " |      Method for compatibility with NumPy's ufuncs and dot\n",
      " |      functions.\n",
      " |  \n",
      " |  __pow__(self, other)\n",
      " |  \n",
      " |  __radd__(self, other)\n",
      " |  \n",
      " |  __rdiv__(self, other)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __rmatmul__(self, other)\n",
      " |  \n",
      " |  __rmul__(self, other)\n",
      " |  \n",
      " |  __rsub__(self, other)\n",
      " |  \n",
      " |  __rtruediv__(self, other)\n",
      " |  \n",
      " |  __str__(self)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  __sub__(self, other)\n",
      " |  \n",
      " |  __truediv__(self, other)\n",
      " |  \n",
      " |  asformat(self, format)\n",
      " |      Return this matrix in a given sparse format\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      format : {string, None}\n",
      " |          desired sparse matrix format\n",
      " |              - None for no format conversion\n",
      " |              - \"csr\" for csr_matrix format\n",
      " |              - \"csc\" for csc_matrix format\n",
      " |              - \"lil\" for lil_matrix format\n",
      " |              - \"dok\" for dok_matrix format and so on\n",
      " |  \n",
      " |  asfptype(self)\n",
      " |      Upcast matrix to a floating point format (if necessary)\n",
      " |  \n",
      " |  conjugate(self)\n",
      " |  \n",
      " |  dot(self, other)\n",
      " |      Ordinary dot product\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> import numpy as np\n",
      " |      >>> from scipy.sparse import csr_matrix\n",
      " |      >>> A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])\n",
      " |      >>> v = np.array([1, 0, -1])\n",
      " |      >>> A.dot(v)\n",
      " |      array([ 1, -3, -1], dtype=int64)\n",
      " |  \n",
      " |  getH(self)\n",
      " |      # Renamed conjtranspose() -> getH() for compatibility with dense matrices\n",
      " |  \n",
      " |  get_shape(self)\n",
      " |  \n",
      " |  getcol(self, j)\n",
      " |      Returns a copy of column j of the matrix, as an (m x 1) sparse\n",
      " |      matrix (column vector).\n",
      " |  \n",
      " |  getformat(self)\n",
      " |  \n",
      " |  getmaxprint(self)\n",
      " |  \n",
      " |  getrow(self, i)\n",
      " |      Returns a copy of row i of the matrix, as a (1 x n) sparse\n",
      " |      matrix (row vector).\n",
      " |  \n",
      " |  maximum(self, other)\n",
      " |  \n",
      " |  mean(self, axis=None)\n",
      " |      Average the matrix over the given axis.  If the axis is None,\n",
      " |      average over both rows and columns, returning a scalar.\n",
      " |  \n",
      " |  minimum(self, other)\n",
      " |  \n",
      " |  multiply(self, other)\n",
      " |      Point-wise multiplication by another matrix\n",
      " |  \n",
      " |  nonzero(self)\n",
      " |      nonzero indices\n",
      " |      \n",
      " |      Returns a tuple of arrays (row,col) containing the indices\n",
      " |      of the non-zero elements of the matrix.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> from scipy.sparse import csr_matrix\n",
      " |      >>> A = csr_matrix([[1,2,0],[0,0,3],[4,0,5]])\n",
      " |      >>> A.nonzero()\n",
      " |      (array([0, 0, 1, 2, 2]), array([0, 1, 2, 0, 2]))\n",
      " |  \n",
      " |  reshape(self, shape)\n",
      " |  \n",
      " |  set_shape(self, shape)\n",
      " |  \n",
      " |  setdiag(self, values, k=0)\n",
      " |      Set diagonal or off-diagonal elements of the array.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      values : array_like\n",
      " |          New values of the diagonal elements.\n",
      " |      \n",
      " |          Values may have any length.  If the diagonal is longer than values,\n",
      " |          then the remaining diagonal entries will not be set.  If values if\n",
      " |          longer than the diagonal, then the remaining values are ignored.\n",
      " |      \n",
      " |          If a scalar value is given, all of the diagonal is set to it.\n",
      " |      \n",
      " |      k : int, optional\n",
      " |          Which off-diagonal to set, corresponding to elements a[i,i+k].\n",
      " |          Default: 0 (the main diagonal).\n",
      " |  \n",
      " |  sum(self, axis=None)\n",
      " |      Sum the matrix over the given axis.  If the axis is None, sum\n",
      " |      over both rows and columns, returning a scalar.\n",
      " |  \n",
      " |  tobsr(self, blocksize=None)\n",
      " |  \n",
      " |  todense(self, order=None, out=None)\n",
      " |      Return a dense matrix representation of this matrix.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      order : {'C', 'F'}, optional\n",
      " |          Whether to store multi-dimensional data in C (row-major)\n",
      " |          or Fortran (column-major) order in memory. The default\n",
      " |          is 'None', indicating the NumPy default of C-ordered.\n",
      " |          Cannot be specified in conjunction with the `out`\n",
      " |          argument.\n",
      " |      \n",
      " |      out : ndarray, 2-dimensional, optional\n",
      " |          If specified, uses this array (or `numpy.matrix`) as the\n",
      " |          output buffer instead of allocating a new array to\n",
      " |          return. The provided array must have the same shape and\n",
      " |          dtype as the sparse matrix on which you are calling the\n",
      " |          method.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      arr : numpy.matrix, 2-dimensional\n",
      " |          A NumPy matrix object with the same shape and containing\n",
      " |          the same data represented by the sparse matrix, with the\n",
      " |          requested memory order. If `out` was passed and was an\n",
      " |          array (rather than a `numpy.matrix`), it will be filled\n",
      " |          with the appropriate values and returned wrapped in a\n",
      " |          `numpy.matrix` object that shares the same memory.\n",
      " |  \n",
      " |  tolil(self)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from scipy.sparse.base.spmatrix:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  shape\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from scipy.sparse.base.spmatrix:\n",
      " |  \n",
      " |  __array_priority__ = 10.1\n",
      " |  \n",
      " |  __hash__ = None\n",
      " |  \n",
      " |  ndim = 2\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from scipy.sparse.data._minmax_mixin:\n",
      " |  \n",
      " |  max(self, axis=None)\n",
      " |      Maximum of the elements of this matrix.\n",
      " |      \n",
      " |      This takes all elements into account, not just the non-zero ones.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      amax : self.dtype\n",
      " |          Maximum element.\n",
      " |  \n",
      " |  min(self, axis=None)\n",
      " |      Minimum of the elements of this matrix.\n",
      " |      \n",
      " |      This takes all elements into account, not just the non-zero ones.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      amin : self.dtype\n",
      " |          Minimum element.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(enCodeFeats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eventsData_test =gender_age_test.merge(eventBasedAggregates, how='left',on='device_id')\n",
    "imputeDFColsUsingMean(eventsData_test,eventBasedAggregatesFeats)\n",
    "eventsData_test_final=eventsData_test[eventBasedAggregatesFeats]\n",
    "eventsData_test_final=scaleFeatures(eventsData_test_final,eventBasedAggregatesFeats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'device_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1944\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1945\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1946\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'device_id'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-904ae0611a66>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0meventsData_train\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0meventsData_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphone_brand_master\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'phone_brand'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'device_id'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#eventsData_train =eventsData_train.merge(phone_brand_master[['device_model']], how='left',on='device_id')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#phone_brand_master[['device_model']]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[0;32m   4435\u001b[0m                      \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4436\u001b[0m                      \u001b[0mright_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4437\u001b[1;33m                      copy=copy, indicator=indicator)\n\u001b[0m\u001b[0;32m   4438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4439\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/tools/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[0;32m     36\u001b[0m                          \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m                          \u001b[0mright_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m                          copy=copy, indicator=indicator)\n\u001b[0m\u001b[0;32m     39\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/tools/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[0;32m    208\u001b[0m         (self.left_join_keys,\n\u001b[0;32m    209\u001b[0m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 210\u001b[1;33m          self.join_names) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/tools/merge.py\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    432\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_rkey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 434\u001b[1;33m                         \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mlk\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mrk\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m                             \u001b[1;31m# avoid key upcast in corner case (length-0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1995\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1996\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1997\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1998\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1999\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2002\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2003\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2004\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2006\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1348\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1349\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1350\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1351\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1352\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3289\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3290\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3291\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3292\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/alokkumary2j/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   1945\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1946\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1947\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1949\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'device_id'"
     ]
    }
   ],
   "source": [
    "eventsData_train =eventsData_train.merge(phone_brand_master[['phone_brand']], how='left',on='device_id')\n",
    "#eventsData_train =eventsData_train.merge(phone_brand_master[['device_model']], how='left',on='device_id')\n",
    "#phone_brand_master[['device_model']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(ngram_range=(1,2),min_df=0.0)\n",
    "vect_matrix = vectorizer.fit_transform(phone_brand_master['brand_model'])\n",
    "test_vect_matrix = vectorizer.transform(phone_brand_master_test['brand_model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validateModel(X,y, model):\n",
    "    kf = KFold(X.shape[0], n_folds=5, shuffle=True, random_state=0)\n",
    "    for itrain, itest in kf:\n",
    "        if type(X)==type(pd.DataFrame()):\n",
    "            train=X.ix[itrain]\n",
    "            test=X.ix[itest]\n",
    "        else:\n",
    "            train = X[itrain,:]\n",
    "            test = X[itest,:]\n",
    "        ytrain, ytest = y[itrain], y[itest]\n",
    "        clf = model.fit(train,ytrain)\n",
    "        ypred = clf.predict_proba(test)\n",
    "        print(ypred.shape)\n",
    "        print(log_loss(ytest, ypred))\n",
    "        \n",
    "def getModelOutput(X,y,X2, model):\n",
    "    kf = KFold(X.shape[0], n_folds=5, shuffle=True, random_state=0)\n",
    "    for itrain, itest in kf:\n",
    "        if type(X)==type(pd.DataFrame()):\n",
    "            train=X.ix[itrain]\n",
    "            test=X.ix[itest]\n",
    "        else:\n",
    "            train = X[itrain,:]\n",
    "            test = X[itest,:]\n",
    "        ytrain, ytest = y[itrain], y[itest]\n",
    "        clf = model.fit(train,ytrain)\n",
    "        ypred = clf.predict_proba(X2)\n",
    "        return ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm  import LinearSVC\n",
    "#class CalibModel(object):\n",
    "class CalibModel(object):\n",
    "    def __init__(self,clf):\n",
    "        #clf = MultinomialNB()\n",
    "        print(\"Obtained Classifier Instance \",clf)\n",
    "        self.clf = CalibratedClassifierCV(clf, cv=2, method='sigmoid')\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        self.clf.fit(X,y)\n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return self.clf.predict(X)\n",
    "    \n",
    "    def predict_proba(self, X):\n",
    "        return self.clf.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtained Classifier Instance  MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n",
      "(14929, 12)\n",
      "2.40430254546\n",
      "(14929, 12)\n",
      "2.40689362128\n",
      "(14929, 12)\n",
      "2.3995671297\n",
      "(14929, 12)\n",
      "2.40416462513\n",
      "(14929, 12)\n",
      "2.4056362231\n",
      "Obtained Classifier Instance  GaussianNB()\n",
      "(14929, 12)\n",
      "2.42672411397\n",
      "(14929, 12)\n",
      "2.43003467875\n",
      "(14929, 12)\n",
      "2.42232043542\n",
      "(14929, 12)\n",
      "2.42918764186\n",
      "(14929, 12)\n",
      "2.42587252809\n"
     ]
    }
   ],
   "source": [
    "validateModel(vect_matrix, phone_brand_master['group'], CalibModel(MultinomialNB()))\n",
    "#validateModel(vect_matrix, phone_brand_master['group'], CalibModel(LinearSVC()))\n",
    "validateModel(eventsData_train_final,eventsData_train.group,CalibModel(GaussianNB()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "validateModel(vect_matrix, phone_brand_master['group'], CalibModel(Dec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "targetencoder = LabelEncoder().fit(phone_brand_master.group)\n",
    "y = targetencoder.transform(phone_brand_master.group)\n",
    "pred = pd.DataFrame(getModelOutput(vect_matrix, phone_brand_master['group'], test_vect_matrix, CalibModel()), index = phone_brand_master_test.device_id, columns=targetencoder.classes_)\n",
    "pred.to_csv('submit_1.csv',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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