MLND_Capstone_ Sberbank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chelimin/miniconda2/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n",
      "/Users/chelimin/miniconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import model_selection, preprocessing\n",
    "import xgboost as xgb\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('input/train.csv')\n",
    "test = pd.read_csv('input/test.csv')\n",
    "macro = pd.read_csv('input/macro.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 292)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2484, 100)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 391)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=pd.merge(macro, train)\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>oil_urals</th>\n",
       "      <th>gdp_quart</th>\n",
       "      <th>gdp_quart_growth</th>\n",
       "      <th>cpi</th>\n",
       "      <th>ppi</th>\n",
       "      <th>gdp_deflator</th>\n",
       "      <th>balance_trade</th>\n",
       "      <th>balance_trade_growth</th>\n",
       "      <th>usdrub</th>\n",
       "      <th>eurrub</th>\n",
       "      <th>...</th>\n",
       "      <th>cafe_count_5000_price_2500</th>\n",
       "      <th>cafe_count_5000_price_4000</th>\n",
       "      <th>cafe_count_5000_price_high</th>\n",
       "      <th>big_church_count_5000</th>\n",
       "      <th>church_count_5000</th>\n",
       "      <th>mosque_count_5000</th>\n",
       "      <th>leisure_count_5000</th>\n",
       "      <th>sport_count_5000</th>\n",
       "      <th>market_count_5000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>30471.000000</td>\n",
       "      <td>3.047100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97.704786</td>\n",
       "      <td>18216.990660</td>\n",
       "      <td>1.444098</td>\n",
       "      <td>411.660490</td>\n",
       "      <td>492.213019</td>\n",
       "      <td>110.397005</td>\n",
       "      <td>17.488725</td>\n",
       "      <td>22.612130</td>\n",
       "      <td>38.029219</td>\n",
       "      <td>48.759814</td>\n",
       "      <td>...</td>\n",
       "      <td>32.058318</td>\n",
       "      <td>10.783860</td>\n",
       "      <td>1.771783</td>\n",
       "      <td>15.045552</td>\n",
       "      <td>30.251518</td>\n",
       "      <td>0.442421</td>\n",
       "      <td>8.648814</td>\n",
       "      <td>52.796593</td>\n",
       "      <td>5.987070</td>\n",
       "      <td>7.123035e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.996897</td>\n",
       "      <td>1668.958493</td>\n",
       "      <td>1.702220</td>\n",
       "      <td>32.436292</td>\n",
       "      <td>28.597338</td>\n",
       "      <td>7.422593</td>\n",
       "      <td>2.426106</td>\n",
       "      <td>18.372021</td>\n",
       "      <td>9.553469</td>\n",
       "      <td>9.412958</td>\n",
       "      <td>...</td>\n",
       "      <td>73.465611</td>\n",
       "      <td>28.385679</td>\n",
       "      <td>5.418807</td>\n",
       "      <td>29.118668</td>\n",
       "      <td>47.347938</td>\n",
       "      <td>0.609269</td>\n",
       "      <td>20.580741</td>\n",
       "      <td>46.292660</td>\n",
       "      <td>4.889219</td>\n",
       "      <td>4.780111e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>46.341400</td>\n",
       "      <td>14313.700000</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>420.700000</td>\n",
       "      <td>86.721000</td>\n",
       "      <td>12.785000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>28.808200</td>\n",
       "      <td>38.433700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>93.740000</td>\n",
       "      <td>17015.100000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>388.500000</td>\n",
       "      <td>472.200000</td>\n",
       "      <td>108.299000</td>\n",
       "      <td>15.748000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>31.926700</td>\n",
       "      <td>41.593100</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.740002e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>107.199100</td>\n",
       "      <td>18410.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>408.300000</td>\n",
       "      <td>487.900000</td>\n",
       "      <td>113.465000</td>\n",
       "      <td>16.954000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>34.376800</td>\n",
       "      <td>46.821000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.274411e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>108.650000</td>\n",
       "      <td>19566.500000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>428.600000</td>\n",
       "      <td>510.100000</td>\n",
       "      <td>113.465000</td>\n",
       "      <td>19.639000</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>38.590700</td>\n",
       "      <td>50.220000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.300000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>122.520000</td>\n",
       "      <td>21514.700000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>489.500000</td>\n",
       "      <td>575.900000</td>\n",
       "      <td>123.661000</td>\n",
       "      <td>21.939000</td>\n",
       "      <td>75.800000</td>\n",
       "      <td>69.466600</td>\n",
       "      <td>84.960500</td>\n",
       "      <td>...</td>\n",
       "      <td>377.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.111111e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 372 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          oil_urals     gdp_quart  gdp_quart_growth           cpi  \\\n",
       "count  30471.000000  30471.000000      30471.000000  30471.000000   \n",
       "mean      97.704786  18216.990660          1.444098    411.660490   \n",
       "std       18.996897   1668.958493          1.702220     32.436292   \n",
       "min       46.341400  14313.700000         -2.800000    353.000000   \n",
       "25%       93.740000  17015.100000          0.700000    388.500000   \n",
       "50%      107.199100  18410.700000          0.900000    408.300000   \n",
       "75%      108.650000  19566.500000          2.100000    428.600000   \n",
       "max      122.520000  21514.700000          5.200000    489.500000   \n",
       "\n",
       "                ppi  gdp_deflator  balance_trade  balance_trade_growth  \\\n",
       "count  30471.000000  30471.000000   30471.000000          30471.000000   \n",
       "mean     492.213019    110.397005      17.488725             22.612130   \n",
       "std       28.597338      7.422593       2.426106             18.372021   \n",
       "min      420.700000     86.721000      12.785000              4.600000   \n",
       "25%      472.200000    108.299000      15.748000              7.600000   \n",
       "50%      487.900000    113.465000      16.954000             16.800000   \n",
       "75%      510.100000    113.465000      19.639000             32.900000   \n",
       "max      575.900000    123.661000      21.939000             75.800000   \n",
       "\n",
       "             usdrub        eurrub      ...       cafe_count_5000_price_2500  \\\n",
       "count  30471.000000  30471.000000      ...                     30471.000000   \n",
       "mean      38.029219     48.759814      ...                        32.058318   \n",
       "std        9.553469      9.412958      ...                        73.465611   \n",
       "min       28.808200     38.433700      ...                         0.000000   \n",
       "25%       31.926700     41.593100      ...                         2.000000   \n",
       "50%       34.376800     46.821000      ...                         8.000000   \n",
       "75%       38.590700     50.220000      ...                        21.000000   \n",
       "max       69.466600     84.960500      ...                       377.000000   \n",
       "\n",
       "       cafe_count_5000_price_4000  cafe_count_5000_price_high  \\\n",
       "count                30471.000000                30471.000000   \n",
       "mean                    10.783860                    1.771783   \n",
       "std                     28.385679                    5.418807   \n",
       "min                      0.000000                    0.000000   \n",
       "25%                      1.000000                    0.000000   \n",
       "50%                      2.000000                    0.000000   \n",
       "75%                      5.000000                    1.000000   \n",
       "max                    147.000000                   30.000000   \n",
       "\n",
       "       big_church_count_5000  church_count_5000  mosque_count_5000  \\\n",
       "count           30471.000000       30471.000000       30471.000000   \n",
       "mean               15.045552          30.251518           0.442421   \n",
       "std                29.118668          47.347938           0.609269   \n",
       "min                 0.000000           0.000000           0.000000   \n",
       "25%                 2.000000           9.000000           0.000000   \n",
       "50%                 7.000000          16.000000           0.000000   \n",
       "75%                12.000000          28.000000           1.000000   \n",
       "max               151.000000         250.000000           2.000000   \n",
       "\n",
       "       leisure_count_5000  sport_count_5000  market_count_5000     price_doc  \n",
       "count        30471.000000      30471.000000       30471.000000  3.047100e+04  \n",
       "mean             8.648814         52.796593           5.987070  7.123035e+06  \n",
       "std             20.580741         46.292660           4.889219  4.780111e+06  \n",
       "min              0.000000          0.000000           0.000000  1.000000e+05  \n",
       "25%              0.000000         11.000000           1.000000  4.740002e+06  \n",
       "50%              2.000000         48.000000           5.000000  6.274411e+06  \n",
       "75%              7.000000         76.000000          10.000000  8.300000e+06  \n",
       "max            106.000000        218.000000          21.000000  1.111111e+08  \n",
       "\n",
       "[8 rows x 372 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['timestamp',\n",
       " 'oil_urals',\n",
       " 'gdp_quart',\n",
       " 'gdp_quart_growth',\n",
       " 'cpi',\n",
       " 'ppi',\n",
       " 'gdp_deflator',\n",
       " 'balance_trade',\n",
       " 'balance_trade_growth',\n",
       " 'usdrub',\n",
       " 'eurrub',\n",
       " 'brent',\n",
       " 'net_capital_export',\n",
       " 'gdp_annual',\n",
       " 'gdp_annual_growth',\n",
       " 'average_provision_of_build_contract',\n",
       " 'average_provision_of_build_contract_moscow',\n",
       " 'rts',\n",
       " 'micex',\n",
       " 'micex_rgbi_tr',\n",
       " 'micex_cbi_tr',\n",
       " 'deposits_value',\n",
       " 'deposits_growth',\n",
       " 'deposits_rate',\n",
       " 'mortgage_value',\n",
       " 'mortgage_growth',\n",
       " 'mortgage_rate',\n",
       " 'grp',\n",
       " 'grp_growth',\n",
       " 'income_per_cap',\n",
       " 'real_dispos_income_per_cap_growth',\n",
       " 'salary',\n",
       " 'salary_growth',\n",
       " 'fixed_basket',\n",
       " 'retail_trade_turnover',\n",
       " 'retail_trade_turnover_per_cap',\n",
       " 'retail_trade_turnover_growth',\n",
       " 'labor_force',\n",
       " 'unemployment',\n",
       " 'employment',\n",
       " 'invest_fixed_capital_per_cap',\n",
       " 'invest_fixed_assets',\n",
       " 'profitable_enterpr_share',\n",
       " 'unprofitable_enterpr_share',\n",
       " 'share_own_revenues',\n",
       " 'overdue_wages_per_cap',\n",
       " 'fin_res_per_cap',\n",
       " 'marriages_per_1000_cap',\n",
       " 'divorce_rate',\n",
       " 'construction_value',\n",
       " 'invest_fixed_assets_phys',\n",
       " 'pop_natural_increase',\n",
       " 'pop_migration',\n",
       " 'pop_total_inc',\n",
       " 'childbirth',\n",
       " 'mortality',\n",
       " 'housing_fund_sqm',\n",
       " 'lodging_sqm_per_cap',\n",
       " 'water_pipes_share',\n",
       " 'baths_share',\n",
       " 'sewerage_share',\n",
       " 'gas_share',\n",
       " 'hot_water_share',\n",
       " 'electric_stove_share',\n",
       " 'heating_share',\n",
       " 'old_house_share',\n",
       " 'average_life_exp',\n",
       " 'infant_mortarity_per_1000_cap',\n",
       " 'perinatal_mort_per_1000_cap',\n",
       " 'incidence_population',\n",
       " 'rent_price_4+room_bus',\n",
       " 'rent_price_3room_bus',\n",
       " 'rent_price_2room_bus',\n",
       " 'rent_price_1room_bus',\n",
       " 'rent_price_3room_eco',\n",
       " 'rent_price_2room_eco',\n",
       " 'rent_price_1room_eco',\n",
       " 'load_of_teachers_preschool_per_teacher',\n",
       " 'child_on_acc_pre_school',\n",
       " 'load_of_teachers_school_per_teacher',\n",
       " 'students_state_oneshift',\n",
       " 'modern_education_share',\n",
       " 'old_education_build_share',\n",
       " 'provision_doctors',\n",
       " 'provision_nurse',\n",
       " 'load_on_doctors',\n",
       " 'power_clinics',\n",
       " 'hospital_beds_available_per_cap',\n",
       " 'hospital_bed_occupancy_per_year',\n",
       " 'provision_retail_space_sqm',\n",
       " 'provision_retail_space_modern_sqm',\n",
       " 'turnover_catering_per_cap',\n",
       " 'theaters_viewers_per_1000_cap',\n",
       " 'seats_theather_rfmin_per_100000_cap',\n",
       " 'museum_visitis_per_100_cap',\n",
       " 'bandwidth_sports',\n",
       " 'population_reg_sports_share',\n",
       " 'students_reg_sports_share',\n",
       " 'apartment_build',\n",
       " 'apartment_fund_sqm',\n",
       " 'id',\n",
       " 'full_sq',\n",
       " 'life_sq',\n",
       " 'floor',\n",
       " 'max_floor',\n",
       " 'material',\n",
       " 'build_year',\n",
       " 'num_room',\n",
       " 'kitch_sq',\n",
       " 'state',\n",
       " 'product_type',\n",
       " 'sub_area',\n",
       " 'area_m',\n",
       " 'raion_popul',\n",
       " 'green_zone_part',\n",
       " 'indust_part',\n",
       " 'children_preschool',\n",
       " 'preschool_quota',\n",
       " 'preschool_education_centers_raion',\n",
       " 'children_school',\n",
       " 'school_quota',\n",
       " 'school_education_centers_raion',\n",
       " 'school_education_centers_top_20_raion',\n",
       " 'hospital_beds_raion',\n",
       " 'healthcare_centers_raion',\n",
       " 'university_top_20_raion',\n",
       " 'sport_objects_raion',\n",
       " 'additional_education_raion',\n",
       " 'culture_objects_top_25',\n",
       " 'culture_objects_top_25_raion',\n",
       " 'shopping_centers_raion',\n",
       " 'office_raion',\n",
       " 'thermal_power_plant_raion',\n",
       " 'incineration_raion',\n",
       " 'oil_chemistry_raion',\n",
       " 'radiation_raion',\n",
       " 'railroad_terminal_raion',\n",
       " 'big_market_raion',\n",
       " 'nuclear_reactor_raion',\n",
       " 'detention_facility_raion',\n",
       " 'full_all',\n",
       " 'male_f',\n",
       " 'female_f',\n",
       " 'young_all',\n",
       " 'young_male',\n",
       " 'young_female',\n",
       " 'work_all',\n",
       " 'work_male',\n",
       " 'work_female',\n",
       " 'ekder_all',\n",
       " 'ekder_male',\n",
       " 'ekder_female',\n",
       " '0_6_all',\n",
       " '0_6_male',\n",
       " '0_6_female',\n",
       " '7_14_all',\n",
       " '7_14_male',\n",
       " '7_14_female',\n",
       " '0_17_all',\n",
       " '0_17_male',\n",
       " '0_17_female',\n",
       " '16_29_all',\n",
       " '16_29_male',\n",
       " '16_29_female',\n",
       " '0_13_all',\n",
       " '0_13_male',\n",
       " '0_13_female',\n",
       " 'raion_build_count_with_material_info',\n",
       " 'build_count_block',\n",
       " 'build_count_wood',\n",
       " 'build_count_frame',\n",
       " 'build_count_brick',\n",
       " 'build_count_monolith',\n",
       " 'build_count_panel',\n",
       " 'build_count_foam',\n",
       " 'build_count_slag',\n",
       " 'build_count_mix',\n",
       " 'raion_build_count_with_builddate_info',\n",
       " 'build_count_before_1920',\n",
       " 'build_count_1921-1945',\n",
       " 'build_count_1946-1970',\n",
       " 'build_count_1971-1995',\n",
       " 'build_count_after_1995',\n",
       " 'ID_metro',\n",
       " 'metro_min_avto',\n",
       " 'metro_km_avto',\n",
       " 'metro_min_walk',\n",
       " 'metro_km_walk',\n",
       " 'kindergarten_km',\n",
       " 'school_km',\n",
       " 'park_km',\n",
       " 'green_zone_km',\n",
       " 'industrial_km',\n",
       " 'water_treatment_km',\n",
       " 'cemetery_km',\n",
       " 'incineration_km',\n",
       " 'railroad_station_walk_km',\n",
       " 'railroad_station_walk_min',\n",
       " 'ID_railroad_station_walk',\n",
       " 'railroad_station_avto_km',\n",
       " 'railroad_station_avto_min',\n",
       " 'ID_railroad_station_avto',\n",
       " 'public_transport_station_km',\n",
       " 'public_transport_station_min_walk',\n",
       " 'water_km',\n",
       " 'water_1line',\n",
       " 'mkad_km',\n",
       " 'ttk_km',\n",
       " 'sadovoe_km',\n",
       " 'bulvar_ring_km',\n",
       " 'kremlin_km',\n",
       " 'big_road1_km',\n",
       " 'ID_big_road1',\n",
       " 'big_road1_1line',\n",
       " 'big_road2_km',\n",
       " 'ID_big_road2',\n",
       " 'railroad_km',\n",
       " 'railroad_1line',\n",
       " 'zd_vokzaly_avto_km',\n",
       " 'ID_railroad_terminal',\n",
       " 'bus_terminal_avto_km',\n",
       " 'ID_bus_terminal',\n",
       " 'oil_chemistry_km',\n",
       " 'nuclear_reactor_km',\n",
       " 'radiation_km',\n",
       " 'power_transmission_line_km',\n",
       " 'thermal_power_plant_km',\n",
       " 'ts_km',\n",
       " 'big_market_km',\n",
       " 'market_shop_km',\n",
       " 'fitness_km',\n",
       " 'swim_pool_km',\n",
       " 'ice_rink_km',\n",
       " 'stadium_km',\n",
       " 'basketball_km',\n",
       " 'hospice_morgue_km',\n",
       " 'detention_facility_km',\n",
       " 'public_healthcare_km',\n",
       " 'university_km',\n",
       " 'workplaces_km',\n",
       " 'shopping_centers_km',\n",
       " 'office_km',\n",
       " 'additional_education_km',\n",
       " 'preschool_km',\n",
       " 'big_church_km',\n",
       " 'church_synagogue_km',\n",
       " 'mosque_km',\n",
       " 'theater_km',\n",
       " 'museum_km',\n",
       " 'exhibition_km',\n",
       " 'catering_km',\n",
       " 'ecology',\n",
       " 'green_part_500',\n",
       " 'prom_part_500',\n",
       " 'office_count_500',\n",
       " 'office_sqm_500',\n",
       " 'trc_count_500',\n",
       " 'trc_sqm_500',\n",
       " 'cafe_count_500',\n",
       " 'cafe_sum_500_min_price_avg',\n",
       " 'cafe_sum_500_max_price_avg',\n",
       " 'cafe_avg_price_500',\n",
       " 'cafe_count_500_na_price',\n",
       " 'cafe_count_500_price_500',\n",
       " 'cafe_count_500_price_1000',\n",
       " 'cafe_count_500_price_1500',\n",
       " 'cafe_count_500_price_2500',\n",
       " 'cafe_count_500_price_4000',\n",
       " 'cafe_count_500_price_high',\n",
       " 'big_church_count_500',\n",
       " 'church_count_500',\n",
       " 'mosque_count_500',\n",
       " 'leisure_count_500',\n",
       " 'sport_count_500',\n",
       " 'market_count_500',\n",
       " 'green_part_1000',\n",
       " 'prom_part_1000',\n",
       " 'office_count_1000',\n",
       " 'office_sqm_1000',\n",
       " 'trc_count_1000',\n",
       " 'trc_sqm_1000',\n",
       " 'cafe_count_1000',\n",
       " 'cafe_sum_1000_min_price_avg',\n",
       " 'cafe_sum_1000_max_price_avg',\n",
       " 'cafe_avg_price_1000',\n",
       " 'cafe_count_1000_na_price',\n",
       " 'cafe_count_1000_price_500',\n",
       " 'cafe_count_1000_price_1000',\n",
       " 'cafe_count_1000_price_1500',\n",
       " 'cafe_count_1000_price_2500',\n",
       " 'cafe_count_1000_price_4000',\n",
       " 'cafe_count_1000_price_high',\n",
       " 'big_church_count_1000',\n",
       " 'church_count_1000',\n",
       " 'mosque_count_1000',\n",
       " 'leisure_count_1000',\n",
       " 'sport_count_1000',\n",
       " 'market_count_1000',\n",
       " 'green_part_1500',\n",
       " 'prom_part_1500',\n",
       " 'office_count_1500',\n",
       " 'office_sqm_1500',\n",
       " 'trc_count_1500',\n",
       " 'trc_sqm_1500',\n",
       " 'cafe_count_1500',\n",
       " 'cafe_sum_1500_min_price_avg',\n",
       " 'cafe_sum_1500_max_price_avg',\n",
       " 'cafe_avg_price_1500',\n",
       " 'cafe_count_1500_na_price',\n",
       " 'cafe_count_1500_price_500',\n",
       " 'cafe_count_1500_price_1000',\n",
       " 'cafe_count_1500_price_1500',\n",
       " 'cafe_count_1500_price_2500',\n",
       " 'cafe_count_1500_price_4000',\n",
       " 'cafe_count_1500_price_high',\n",
       " 'big_church_count_1500',\n",
       " 'church_count_1500',\n",
       " 'mosque_count_1500',\n",
       " 'leisure_count_1500',\n",
       " 'sport_count_1500',\n",
       " 'market_count_1500',\n",
       " 'green_part_2000',\n",
       " 'prom_part_2000',\n",
       " 'office_count_2000',\n",
       " 'office_sqm_2000',\n",
       " 'trc_count_2000',\n",
       " 'trc_sqm_2000',\n",
       " 'cafe_count_2000',\n",
       " 'cafe_sum_2000_min_price_avg',\n",
       " 'cafe_sum_2000_max_price_avg',\n",
       " 'cafe_avg_price_2000',\n",
       " 'cafe_count_2000_na_price',\n",
       " 'cafe_count_2000_price_500',\n",
       " 'cafe_count_2000_price_1000',\n",
       " 'cafe_count_2000_price_1500',\n",
       " 'cafe_count_2000_price_2500',\n",
       " 'cafe_count_2000_price_4000',\n",
       " 'cafe_count_2000_price_high',\n",
       " 'big_church_count_2000',\n",
       " 'church_count_2000',\n",
       " 'mosque_count_2000',\n",
       " 'leisure_count_2000',\n",
       " 'sport_count_2000',\n",
       " 'market_count_2000',\n",
       " 'green_part_3000',\n",
       " 'prom_part_3000',\n",
       " 'office_count_3000',\n",
       " 'office_sqm_3000',\n",
       " 'trc_count_3000',\n",
       " 'trc_sqm_3000',\n",
       " 'cafe_count_3000',\n",
       " 'cafe_sum_3000_min_price_avg',\n",
       " 'cafe_sum_3000_max_price_avg',\n",
       " 'cafe_avg_price_3000',\n",
       " 'cafe_count_3000_na_price',\n",
       " 'cafe_count_3000_price_500',\n",
       " 'cafe_count_3000_price_1000',\n",
       " 'cafe_count_3000_price_1500',\n",
       " 'cafe_count_3000_price_2500',\n",
       " 'cafe_count_3000_price_4000',\n",
       " 'cafe_count_3000_price_high',\n",
       " 'big_church_count_3000',\n",
       " 'church_count_3000',\n",
       " 'mosque_count_3000',\n",
       " 'leisure_count_3000',\n",
       " 'sport_count_3000',\n",
       " 'market_count_3000',\n",
       " 'green_part_5000',\n",
       " 'prom_part_5000',\n",
       " 'office_count_5000',\n",
       " 'office_sqm_5000',\n",
       " 'trc_count_5000',\n",
       " 'trc_sqm_5000',\n",
       " 'cafe_count_5000',\n",
       " 'cafe_sum_5000_min_price_avg',\n",
       " 'cafe_sum_5000_max_price_avg',\n",
       " 'cafe_avg_price_5000',\n",
       " 'cafe_count_5000_na_price',\n",
       " 'cafe_count_5000_price_500',\n",
       " 'cafe_count_5000_price_1000',\n",
       " 'cafe_count_5000_price_1500',\n",
       " 'cafe_count_5000_price_2500',\n",
       " 'cafe_count_5000_price_4000',\n",
       " 'cafe_count_5000_price_high',\n",
       " 'big_church_count_5000',\n",
       " 'church_count_5000',\n",
       " 'mosque_count_5000',\n",
       " 'leisure_count_5000',\n",
       " 'sport_count_5000',\n",
       " 'market_count_5000',\n",
       " 'price_doc']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list (train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>timestamp</th>\n",
       "      <th>oil_urals</th>\n",
       "      <th>gdp_quart</th>\n",
       "      <th>gdp_quart_growth</th>\n",
       "      <th>cpi</th>\n",
       "      <th>ppi</th>\n",
       "      <th>gdp_deflator</th>\n",
       "      <th>balance_trade</th>\n",
       "      <th>balance_trade_growth</th>\n",
       "      <th>usdrub</th>\n",
       "      <th>...</th>\n",
       "      <th>cafe_count_5000_price_2500</th>\n",
       "      <th>cafe_count_5000_price_4000</th>\n",
       "      <th>cafe_count_5000_price_high</th>\n",
       "      <th>big_church_count_5000</th>\n",
       "      <th>church_count_5000</th>\n",
       "      <th>mosque_count_5000</th>\n",
       "      <th>leisure_count_5000</th>\n",
       "      <th>sport_count_5000</th>\n",
       "      <th>market_count_5000</th>\n",
       "      <th>price_doc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-08-20</td>\n",
       "      <td>109.31</td>\n",
       "      <td>14313.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>354.0</td>\n",
       "      <td>420.7</td>\n",
       "      <td>86.721</td>\n",
       "      <td>15.459</td>\n",
       "      <td>10.1</td>\n",
       "      <td>29.0048</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>5850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-08-23</td>\n",
       "      <td>109.31</td>\n",
       "      <td>14313.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>354.0</td>\n",
       "      <td>420.7</td>\n",
       "      <td>86.721</td>\n",
       "      <td>15.459</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28.9525</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>6000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-08-27</td>\n",
       "      <td>109.31</td>\n",
       "      <td>14313.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>354.0</td>\n",
       "      <td>420.7</td>\n",
       "      <td>86.721</td>\n",
       "      <td>15.459</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28.8082</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>5700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-09-01</td>\n",
       "      <td>111.29</td>\n",
       "      <td>14313.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>353.2</td>\n",
       "      <td>434.4</td>\n",
       "      <td>86.721</td>\n",
       "      <td>15.386</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28.9655</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>13100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-09-05</td>\n",
       "      <td>111.29</td>\n",
       "      <td>14313.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>353.2</td>\n",
       "      <td>434.4</td>\n",
       "      <td>86.721</td>\n",
       "      <td>15.386</td>\n",
       "      <td>10.1</td>\n",
       "      <td>29.4625</td>\n",
       "      <td>...</td>\n",
       "      <td>319</td>\n",
       "      <td>108</td>\n",
       "      <td>17</td>\n",
       "      <td>135</td>\n",
       "      <td>236</td>\n",
       "      <td>2</td>\n",
       "      <td>91</td>\n",
       "      <td>195</td>\n",
       "      <td>14</td>\n",
       "      <td>16331452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 391 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp  oil_urals  gdp_quart  gdp_quart_growth    cpi    ppi  \\\n",
       "0  2011-08-20     109.31    14313.7               3.3  354.0  420.7   \n",
       "1  2011-08-23     109.31    14313.7               3.3  354.0  420.7   \n",
       "2  2011-08-27     109.31    14313.7               3.3  354.0  420.7   \n",
       "3  2011-09-01     111.29    14313.7               3.3  353.2  434.4   \n",
       "4  2011-09-05     111.29    14313.7               3.3  353.2  434.4   \n",
       "\n",
       "   gdp_deflator  balance_trade  balance_trade_growth   usdrub    ...      \\\n",
       "0        86.721         15.459                  10.1  29.0048    ...       \n",
       "1        86.721         15.459                  10.1  28.9525    ...       \n",
       "2        86.721         15.459                  10.1  28.8082    ...       \n",
       "3        86.721         15.386                  10.1  28.9655    ...       \n",
       "4        86.721         15.386                  10.1  29.4625    ...       \n",
       "\n",
       "   cafe_count_5000_price_2500  cafe_count_5000_price_4000  \\\n",
       "0                           9                           4   \n",
       "1                          15                           3   \n",
       "2                          10                           3   \n",
       "3                          11                           2   \n",
       "4                         319                         108   \n",
       "\n",
       "   cafe_count_5000_price_high  big_church_count_5000  church_count_5000  \\\n",
       "0                           0                     13                 22   \n",
       "1                           0                     15                 29   \n",
       "2                           0                     11                 27   \n",
       "3                           1                      4                  4   \n",
       "4                          17                    135                236   \n",
       "\n",
       "   mosque_count_5000  leisure_count_5000  sport_count_5000  market_count_5000  \\\n",
       "0                  1                   0                52                  4   \n",
       "1                  1                  10                66                 14   \n",
       "2                  0                   4                67                 10   \n",
       "3                  0                   0                26                  3   \n",
       "4                  2                  91               195                 14   \n",
       "\n",
       "   price_doc  \n",
       "0    5850000  \n",
       "1    6000000  \n",
       "2    5700000  \n",
       "3   13100000  \n",
       "4   16331452  \n",
       "\n",
       "[5 rows x 391 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 390)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df=pd.merge(macro, test)\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y688o4DVgiKR/RcS/gJWBQ8hMO/MA/5a0TZM2ZgH+DJwrqbGQVuO+KwInkkW3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\ncGB74qLxdCIp91TgWuC35TO7lAo/hwLvAI8QOQC7l/0uQqT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aWs7wpxn1Q+B0SQsRM/GH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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106fba390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#missing values in train_df\n",
    "\n",
    "missing = train_df.isnull().sum(0).reset_index()\n",
    "missing.columns = ['column', 'count']\n",
    "missing = missing.sort_values(by = 'count', ascending = False).loc[missing['count'] > 0]\n",
    "missing['percentage'] = missing['count'] / float(train_df.shape[0]) * 100\n",
    "ind = np.arange(missing.shape[0])\n",
    "width = 0.9\n",
    "fig, ax = plt.subplots(figsize=(10,18))\n",
    "rects = ax.barh(ind, missing.percentage.values, color='r')\n",
    "ax.set_yticks(ind)\n",
    "ax.set_yticklabels(missing.column.values, rotation='horizontal')\n",
    "ax.set_xlabel(\"Precentage of missing values %\", fontsize = 14)\n",
    "ax.set_title(\"Number of missing values in each column\", fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#drop features with more than 30% missing values from train and test\n",
    "\n",
    "delete_col = missing.loc[missing['percentage'] >= 30].column.values\n",
    "train_set = train_df.drop(delete_col, axis=1)\n",
    "test_set = test_df.drop(delete_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7n6Jcly1LvY317AKsDWwhaVirz17T1/lzrWvuo2pwP1WH+6oa3E/V0Nf95AH/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\nrIn9HyjHGJ6hoxvWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ae7250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#missing values in test_df\n",
    "\n",
    "missing = test_df.isnull().sum(0).reset_index()\n",
    "missing.columns = ['column', 'count']\n",
    "missing = missing.sort_values(by = 'count', ascending = False).loc[missing['count'] > 0]\n",
    "missing['percentage'] = missing['count'] / float(test_df.shape[0]) * 100\n",
    "ind = np.arange(missing.shape[0])\n",
    "width = 0.9\n",
    "fig, ax = plt.subplots(figsize=(10,18))\n",
    "rects = ax.barh(ind, missing.percentage.values, color='r')\n",
    "ax.set_yticks(ind)\n",
    "ax.set_yticklabels(missing.column.values, rotation='horizontal')\n",
    "ax.set_xlabel(\"Precentage of missing values %\", fontsize = 14)\n",
    "ax.set_title(\"Number of missing values in each column\", fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#drop features with more than 30% missing values from train and test\n",
    "\n",
    "delete_col = missing.loc[missing['percentage'] > 30].column.values\n",
    "train_set = train_df.drop(delete_col, axis=1)\n",
    "test_set = test_df.drop(delete_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 322)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 321)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Column Type</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>int64</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>float64</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Column Type  Count\n",
       "0       int64    175\n",
       "1     float64    147"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for f in train_set.columns:\n",
    "    if train_set[f].dtype=='object':\n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(train_set[f].values)) \n",
    "        train_set[f] = lbl.transform(list(train_set[f].values))\n",
    "dtype_df = train_set.dtypes.reset_index()\n",
    "dtype_df.columns = [\"Count\", \"Column Type\"]\n",
    "dtype_df.groupby(\"Column Type\").aggregate('count').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Imputing missing values in train_set\n",
    "\n",
    "imputer = preprocessing.Imputer(missing_values=np.nan, strategy = 'median', axis =1)\n",
    "\n",
    "train_data = pd.DataFrame(imputer.fit_transform(train_set))\n",
    "train_data.columns = train_set.columns\n",
    "train_data.index = train_set.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Normalize train_data...can't normalize test_data? \n",
    "\n",
    "# train_data = pd.DataFrame(preprocessing.normalize(train_data, axis =1, copy=True, return_norm=False))\n",
    "# train_data.columns = train_set.columns\n",
    "# train_data.index = train_set.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_train = train_data[\"price_doc\"]\n",
    "x_train = train_data.drop([\"id\",\"timestamp\", \"price_doc\"], axis=1)\n",
    "x_test = test_set.drop([\"id\",\"timestamp\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for c in x_train.columns:\n",
    "    if x_train[c].dtype == 'object':\n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(x_train[c].values)) \n",
    "        x_train[c] = lbl.transform(list(x_train[c].values))\n",
    "        #x_train.drop(c,axis=1,inplace=True)\n",
    "        \n",
    "for c in x_test.columns:\n",
    "    if x_test[c].dtype == 'object':\n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(x_test[c].values)) \n",
    "        x_test[c] = lbl.transform(list(x_test[c].values))\n",
    "        #x_test.drop(c,axis=1,inplace=True)  \n",
    "        \n",
    "# lbl = preprocessing.LabelEncoder()\n",
    "# lbl.fit(y_train)\n",
    "# y_train = lbl.transform(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30471, 322)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Split data into train and test leaving last 100 rows for the final evaluation\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_train.iloc[:-100], y_train.iloc[:-100], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database size: 30471 \n",
      "Train set size: 24296 \n",
      "Test set size: 6075 \n",
      "Validate set size: 100\n"
     ]
    }
   ],
   "source": [
    "print('Database size: {:g} \\nTrain set size: {:g} \\nTest set size: {:g} \\nValidate set size: {:g}'\n",
    "      .format(len(train_data), len(y_train), len(y_test), len(train_data)-(len(y_train)+len(y_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chelimin/miniconda2/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn import linear_model\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import KFold, cross_val_score\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib import learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train & evaluate model performance\n",
    "def train_and_evaluate(model, X, y):\n",
    "    model.fit(X, y)\n",
    "    print('Score on training set: {:g}'.format(model.score(X, y)))\n",
    "    # k-fold cross validation iterator of 5 folds\n",
    "    cv = KFold(X.shape[0], 5, shuffle=True)\n",
    "    scores = cross_val_score(model, X, y, cv=cv)\n",
    "    print('Score using 5-fold cross-validation: {:g} +/- {:g}'.format(np.mean(scores), np.std(scores)*2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training set: 0.403294\n",
      "Score using 5-fold cross-validation: -2.85851 +/- 8.70918\n"
     ]
    }
   ],
   "source": [
    "# Ordinary least squares Linear Regression\n",
    "\n",
    "from sklearn import linear_model\n",
    "clf_lr = linear_model.LinearRegression()\n",
    "train_and_evaluate(clf_lr, X_train, y_train.ravel())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.429148\n",
      "Explained variance score: 0.429503\n",
      "Mean squared error: 1.19546e+13\n"
     ]
    }
   ],
   "source": [
    "# Test model prediction on test data\n",
    "y_pred = clf_lr.predict(X_test)\n",
    "print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vL/JEh1zpfY25i0ix6aqSRUd7kDNPbeDpF8Oa0FYkXT0xunpCPLwhxNGH+Zhx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\nUoUUwEVERKqQAriIiEgV+v9dekOdHNnH/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11295ef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residual plot\n",
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "ax.set_title('Ordinary least squares (residuals)')\n",
    "ax.scatter(y_test, y_pred-y_test, color='royalblue', s=30)\n",
    "ax.set_ylabel('Residuals')\n",
    "ax.hlines(0, y_test.min(), y_test.max())\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM using GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Support Vector Regression - takes super loooong to run! \n",
    "# svr = SVR()\n",
    "# params = {'C':[0.01, 0.1, 1., 10., 100., 1000.],\n",
    "#           'kernel':['linear', 'poly', 'rbf'],\n",
    "#           'epsilon':[0.1, 1.]}\n",
    "\n",
    "# Grid search with cross-validation for optimal parameters\n",
    "# grid_obj = GridSearchCV(estimator=svr, param_grid=params, cv=5, n_jobs=-1)\n",
    "# grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Test model prediction on test data\n",
    "# y_pred = mach.predict(X_test)\n",
    "\n",
    "# print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "# print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "# print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# clf = svm.SVC(kernel='rbf') - takes too long to run! \n",
    "# clf.fit(X_train, y_train)\n",
    "\n",
    "# # Test model prediction on test data\n",
    "# y_pred = clf.predict(X_test)\n",
    "\n",
    "# print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "# print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "# print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training set: 0.937491\n",
      "Score using 5-fold cross-validation: 0.630476 +/- 0.055627\n"
     ]
    }
   ],
   "source": [
    "# RandomForestRegressor\n",
    "\n",
    "forest = RandomForestRegressor()\n",
    "train_and_evaluate(forest, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.639537\n",
      "Explained variance score: 0.639538\n",
      "Mean squared error: 7.54866e+12\n"
     ]
    }
   ],
   "source": [
    "# Test model prediction on test data\n",
    "\n",
    "y_pred = forest.predict(X_test)\n",
    "print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training set: 0.819193\n",
      "Score using 5-fold cross-validation: 0.660798 +/- 0.0404566\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting Regressor\n",
    "clf_gb = GradientBoostingRegressor(loss='lad', max_depth=10, n_estimators=100)\n",
    "train_and_evaluate(clf_gb, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.679413\n",
      "Explained variance score: 0.682344\n",
      "Mean squared error: 6.71361e+12\n"
     ]
    }
   ],
   "source": [
    "# Test model prediction on test data\n",
    "y_pred = clf_gb.predict(X_test)\n",
    "print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i9aHYsm0dJFpLXdIPRGL9ylIjvBJmtgMSw/NRfftzyLn+A6ptrwYuRuUAM5Dz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OnuH5jZbaigVy2KBN+DiootiZ8AV0Zed3vghLDOP4iiq+MpRmVvBW4LAf4KgLs3mNlxwANm\nthhFp5+KeTwf85tGsbjYCDP7CxKnuPtbn3PepdwL7Nbk2T0EDI6ocye0GbALErA3mdncmNMIJL6f\nBv4Zx5Sed7e7zzKz0us1/e5OWsLc3kIR6ldRxfanYqxGFJEvtILbD7Uyu8Pdm21ZZmZbot7uTwOP\nxziXAHcDu5vZcyiafXjkiTcnml9GkXpQpffmvqcPAdx9spmdCTwRVdvfA05FmypJkiRJkiRJkiT/\nZ6qmzVry/z9m1o9oA7ey57ICyDZrSVmoxJyqpDrJtZSUi1xLSbnItZSUi0pcS9lmLTCzNsAjzXzk\n7n7Mip7P58XMfkmxRVkph7v7h828v0Io9/M1s31RkcCmXOLudy/jGBX5rJIkSZIkSZIkSUrJCHqS\nrBwygp6UhUrcEU6qk1xLSbnItZSUi1xLSbmoxLXUUgS9UorEJUmSJEmSJEmSJMkXmhToSZIkSZIk\nSZIkSVIBpEBPkiRJkiRJkiRJkgogBXqSJEmSJEmSJEmSVAAp0JMkSZIkSZIkSZKkAkiBniRJkiRJ\nkiRJkiQVQAr0JEmSJEmSJEmSJKkAUqAnSZIkSZIkSZIkSQWQAj1JkiRJkiRJkiRJKoAU6EmSJEmS\nJEmSJElSAaRAT5IkSZIkSZIkSZIKIAV6kiRJkiRJkiRJklQAKdCTJEmSJEmSJEmSpAJIgZ4kSZIk\nSZIkSZIkFUAK9CRJkiRJkiRJkiSpAFKgJ0mSJEmSJEmSJEkFkAI9SZIkSZIkSZIkSSqAmsbGxpU9\nhyRJkiRJkiRJkiT5wpMR9CRJkiRJkiRJkiSpAFKgJ0mSJEmSJEmSJEkFkAI9SZIkSZIkSZIkSSqA\nFOhJkiRJkiRJkiRJUgGkQE+SJEmSJEmSJEmSCiAFepIkSZIkSZIkSZJUACnQkyRJkiRJkiRJkqQC\naLWyJ5AkXxTMrBa4AtgMmA8c5e7vrdxZJdWCmbUGrgf6AW2Bc4ERwA1AI/Bv4Hh3b1hJU0yqDDNb\nFXgF2B1YRK6l5HNiZqcB+wJt0P/ODSPXU7KcxP/O3Yj+d24x8H3yv03JcmJmg4DfuvtgM1uPZtaP\nmZ0J7IPW14nu/uJKm3AzZAQ9SVYc+wHt3H074FTgwpU8n6S6OASY4u47AXsClwMXAWfEezXAkJU4\nv6SKiP8jfDUwL97KtZR8LsxsMLA9sAOwM7AGuZ6Sz8feQCt33x74FXAeuZaS5cDMfgZcC7SLt/5j\n/ZjZlui/VYOAg4A/roy5LokU6Emy4tgReAjA3YcDW6/c6SRVxu3AL+LvGrTruxWKVAE8COy2EuaV\nVCcXAFcB4+N1rqXk87IH8BZwN3AfcD+5npLPxztAq3AcdgEWkmspWT7eB75Z8rq59bMj8Ii7N7r7\nGLTmeq3YaS6ZFOhJsuLoAswoeb3YzDLNJFkm3H22u88ys87AHcAZQI27N8Yhs4CuK22CSdVgZkOB\nSe7+cMnbuZaSz0tPtOF8AHAs8FegNtdT8jmYjezto4BrgEvJ/zYly4G738n/a+/OY+ys6jCOf2ew\nDULRArVICbLFPgq1Ims1AQqBUAigDcoShKQKqFAQwr5YKxYhQUDqBhiBUlKWStWCBVRqU4pi2WoD\nwiNGwYUEqBQFtULp+Mc5Y2+vM+3MdJnbzPNJbnLvO+857++992Tm/t7fOe+UCzuduho/zd/HW25c\nJUGP2HD+AWzR8Lrd9vL+CiY2PpK2B34BTLc9A2hch7cF8Fq/BBYbm88Ah0iaB+wO3AoMb/h5xlL0\nxt+AB2y/advAMlb9spvxFD11NmUsjaTcr2ca5b4GnTKWore6+p7U/H285cZVEvSIDedhyvoqJI2h\nTAmM6BFJ2wA/BS6wfVPd/GRd/wlwGPBQf8QWGxfb+9s+wPZYYBFwEnBfxlL00QJgnKQ2SSOAzYEH\nM56iD5aysrL5KjCI/J2LtdPV+HkYOFRSu6T3UQpmS/orwK5kem3EhvNDStXql5Q1xBP6OZ7YuFwM\nbAl8SVLnWvQvAlMlDQaeoUx9j+iLc4DvZSxFb9m+V9L+wEJK4ed04I9kPEXvXQvcJOkhSuX8YuAx\nMpai7/7vb5vtt+sY+xUrf2e1lLaOjo417xURERERERER61WmuEdERERERES0gCToERERERERES0g\nCXpEREREREREC0iCHhEREREREdECkqBHREREREREtID8m7WIiIhYbyTtCPwO+G3Tj460/ede9rUT\ncKntz66j8Dr73ZFVY2wH3gVMs/3lNbSbZ3vH1eyzD3C07QskHQXsZXvSWsbbYbttbfro5fFuBibb\nfmFDHTMiYqBKgh4RERHr24u2d18H/ewA7LIO+unKKjFKGgE8J+kO28+sRb+7AtsA2J4NzF67MPvF\ngcBX+juIiIiBIAl6RERE9AtJ2wA3ANsDK4CLbP9c0nbA94GhwLbA7bYvBKYCO0v6NjCTUtUdW/u6\nBZhXH/cDS4BlwKHAVcBYYBPgFtvX9iC8bYE24PXa/4XAMbWPB4ALms5lFPBNYAgwHLgauBW4DBgi\n6RLgrzWOWcCpto+obScCI4GzexOrpLHAJTXOXYAfAH8HPlG3HW77JUmvAPcCe9bzOcH285LGANcB\nm9b363O2fy9pHvAqsBtwMzACmCNpP+Ag4BzgnfVxsu35tc1CYD/gPcAZtu+TtEPtYzjwr7r/Ykkn\nAWdRZis8Dpxue9nqPpCIiIEga9AjIiJifRshaVHD47y6/TrgJtt7AkcBN0jaAjiekpSPAUYDp0ka\nBpwJPGb79DUcT8CnbR8MnAJgew9gH+DjNdHsLsZnJS0BpgDjbf9F0jhKcrs38BFgO+CEpvYnA1Ns\n702pOF9u+zVgEjDb9uUN+94H7CFpy/r6eOC2XsTaaF9gAiWZ/gLwiu29gMXAcXWfYZSp+KOBO4Cp\nkgbX5xNtfxi4Hri9od/FtmX7SuBF4HBgKfB54Ija5krgvIY2g21/lHKhYUrd9h3gbtujgMnApZJ2\nq+f6sTpr4WXg3DWcZ0TEgJAKekRERKxv3U1xPxj4gKTL6utBwC62vy7pQEnnAqOAwcDmvTjey7af\nbzjG7pIOqq+HAB8CHuoqRkntlOr3aGBuQx/7Uiq9UCrHfwIWNLQ/Bxgn6aLadkh3wdl+S9Is4GhJ\nPwO2tr1Q0vk9jLXRU51r+euFhQfr9heAzgsAyyjVfIBpwBWUiv1S24/WmGZKulHSu+t+v+4i7hWS\nxgNHShKl0v92wy73d8YEbFWfH0C5AIHtOZRK/ETg/cAjpRsGA0+s5hwjIgaMJOgRERHRXzYBDrL9\nKvxv3fdLkq4GdgZmAD+iJMjNN0XraNo2qOH5v5uOcb7tWfUYw4B/dhdQTULPAxZRqrpX1D6+Yfua\n2sdQYDmlMt3pLkqF+R5KZfo4Vu824KuUJHpGX2Kt3mx6vbyLfVbY7qjP2+s+Xc2ibKsxwKrvITWe\nIcCjwHRgPqVKP7Fhl84p6o2fzVsN7duAD9Zj3GX7zIZ+8500IoJMcY+IiIj+Mxc4DUDSrpSEbzPg\nEOAq2zMp69O3oyR1y1mZyC2hrEffVNJWlLXP3R3jFEmDaiK4gFIN75bt5ZTk/GJJ7619nChpiKR3\nUC4afLKp2SHAJNs/plSNkdQcc+MxHqGs7T6Rkqz3KdYe2kzSkfX5BMoUewNbS9q7xnoM8ELnxZIm\nnecwknKvgK/VWA9jZULfnfmsvFhxMHAj5T4B4yUNr0n7dynr0SMiBrwk6BEREdFfzgDGSFoM3Amc\naPt1StV6uqTHKWucHwN2Ap4Bhkqabvtp4CfA05QbxnU3Dfx64DngydrPzbbnrSkw2/cDj1DWld8D\n3E2Z9v0Upbo+ranJZGCBpCcoN6Z7vsa8sJ7jlV0c5k7gDdt/WJtYe+hT9X0+FDjL9n+AY4FvSXqK\nUgk/tpu29wJzKDegWwQ8S5mS/gblzvqrM5EylX8R5U7wp9r+TX0+l/L5tVPWs0dEDHhtHR0da94r\nIiIiIjZKG/r/pkdERN+lgh4RERERERHRAlJBj4iIiIiIiGgBqaBHREREREREtIAk6BEREREREREt\nIAl6RERERERERAtIgh4RERERERHRApKgR0RERERERLSA/wJ+lpJnwyu2GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122c1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature importance\n",
    "feature_importance = clf_gb.feature_importances_\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "fig, ax = plt.subplots(figsize=(14,8))\n",
    "ax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, train_data.columns[sorted_idx])\n",
    "ax.set_xlabel('Feature Relative Importance')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:8.25291e+06\ttest-rmse:8.25684e+06\n",
      "[50]\ttrain-rmse:2.49484e+06\ttest-rmse:2.963e+06\n",
      "[100]\ttrain-rmse:2.12425e+06\ttest-rmse:2.77468e+06\n",
      "[150]\ttrain-rmse:1.97699e+06\ttest-rmse:2.72605e+06\n",
      "[200]\ttrain-rmse:1.87631e+06\ttest-rmse:2.70282e+06\n",
      "[250]\ttrain-rmse:1.79353e+06\ttest-rmse:2.69075e+06\n",
      "[300]\ttrain-rmse:1.72117e+06\ttest-rmse:2.68251e+06\n",
      "[350]\ttrain-rmse:1.6595e+06\ttest-rmse:2.67788e+06\n",
      "[400]\ttrain-rmse:1.60202e+06\ttest-rmse:2.67342e+06\n",
      "[450]\ttrain-rmse:1.54887e+06\ttest-rmse:2.67203e+06\n",
      "[500]\ttrain-rmse:1.49747e+06\ttest-rmse:2.67065e+06\n",
      "R2 score: 0.699328\n",
      "Explained variance score: 0.699656\n",
      "Mean squared error: 6.29656e+12\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c7a0d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtrain = xgb.DMatrix(X_train, y_train)\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "\n",
    "xgb_params = {\n",
    "    'eta': 0.05,\n",
    "    'max_depth': 5,\n",
    "    'subsample': 0.7,\n",
    "    'colsample_bytree': 0.7,\n",
    "    'objective': 'reg:linear',\n",
    "    'eval_metric': 'rmse',\n",
    "    'silent': 1\n",
    "}\n",
    "\n",
    "cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=1000, early_stopping_rounds=20,\n",
    "    verbose_eval=50, show_stdv=False)\n",
    "cv_output[['train-rmse-mean', 'test-rmse-mean']].plot()\n",
    "\n",
    "num_boost_rounds = len(cv_output)\n",
    "clf_xgb = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round= num_boost_rounds)\n",
    "y_pred = clf_xgb.predict(dtest)\n",
    "\n",
    "print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp9BIfvX\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x11564ab50>, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}\n",
      "WARNING:tensorflow:From <ipython-input-32-aa21497781eb>:13: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-32-aa21497781eb>:13: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-32-aa21497781eb>:13: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with batch_size is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  equality = a == b\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n",
      "Instructions for updating:\n",
      "Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp9BIfvX/model.ckpt.\n",
      "INFO:tensorflow:loss = 7.26339e+13, step = 1\n",
      "INFO:tensorflow:global_step/sec: 23.7321\n",
      "INFO:tensorflow:loss = 6.44903e+13, step = 101\n",
      "INFO:tensorflow:global_step/sec: 22.2374\n",
      "INFO:tensorflow:loss = 4.15067e+13, step = 201\n",
      "INFO:tensorflow:global_step/sec: 25.4554\n",
      "INFO:tensorflow:loss = 2.60568e+13, step = 301\n",
      "INFO:tensorflow:global_step/sec: 23.2029\n",
      "INFO:tensorflow:loss = 4.25001e+13, step = 401\n",
      "INFO:tensorflow:global_step/sec: 24.4543\n",
      "INFO:tensorflow:loss = 2.0821e+13, step = 501\n",
      "INFO:tensorflow:global_step/sec: 25.251\n",
      "INFO:tensorflow:loss = 1.3834e+13, step = 601\n",
      "INFO:tensorflow:global_step/sec: 25.9677\n",
      "INFO:tensorflow:loss = 1.25633e+13, step = 701\n",
      "INFO:tensorflow:global_step/sec: 25.1908\n",
      "INFO:tensorflow:loss = 1.80771e+13, step = 801\n",
      "INFO:tensorflow:global_step/sec: 25.2344\n",
      "INFO:tensorflow:loss = 1.05718e+13, step = 901\n",
      "INFO:tensorflow:global_step/sec: 22.9809\n",
      "INFO:tensorflow:loss = 1.48468e+13, step = 1001\n",
      "INFO:tensorflow:global_step/sec: 25.5669\n",
      "INFO:tensorflow:loss = 9.61567e+12, step = 1101\n",
      "INFO:tensorflow:global_step/sec: 25.8694\n",
      "INFO:tensorflow:loss = 1.332e+13, step = 1201\n",
      "INFO:tensorflow:global_step/sec: 25.9918\n",
      "INFO:tensorflow:loss = 9.29212e+12, step = 1301\n",
      "INFO:tensorflow:global_step/sec: 25.9805\n",
      "INFO:tensorflow:loss = 1.82098e+13, step = 1401\n",
      "INFO:tensorflow:global_step/sec: 25.5255\n",
      "INFO:tensorflow:loss = 1.39119e+13, step = 1501\n",
      "INFO:tensorflow:global_step/sec: 25.6709\n",
      "INFO:tensorflow:loss = 1.47629e+13, step = 1601\n",
      "INFO:tensorflow:global_step/sec: 25.6824\n",
      "INFO:tensorflow:loss = 1.57091e+13, step = 1701\n",
      "INFO:tensorflow:global_step/sec: 25.2146\n",
      "INFO:tensorflow:loss = 1.17899e+13, step = 1801\n",
      "INFO:tensorflow:global_step/sec: 24.7012\n",
      "INFO:tensorflow:loss = 2.2654e+13, step = 1901\n",
      "INFO:tensorflow:global_step/sec: 24.3385\n",
      "INFO:tensorflow:loss = 1.06787e+13, step = 2001\n",
      "INFO:tensorflow:global_step/sec: 22.2779\n",
      "INFO:tensorflow:loss = 2.06675e+13, step = 2101\n",
      "INFO:tensorflow:global_step/sec: 24.9178\n",
      "INFO:tensorflow:loss = 3.44198e+13, step = 2201\n",
      "INFO:tensorflow:global_step/sec: 25.0224\n",
      "INFO:tensorflow:loss = 8.1645e+12, step = 2301\n",
      "INFO:tensorflow:global_step/sec: 25.3461\n",
      "INFO:tensorflow:loss = 1.20773e+13, step = 2401\n",
      "INFO:tensorflow:global_step/sec: 25.3484\n",
      "INFO:tensorflow:loss = 2.35754e+13, step = 2501\n",
      "INFO:tensorflow:global_step/sec: 25.0607\n",
      "INFO:tensorflow:loss = 1.28226e+13, step = 2601\n",
      "INFO:tensorflow:global_step/sec: 24.7687\n",
      "INFO:tensorflow:loss = 2.10797e+13, step = 2701\n",
      "INFO:tensorflow:global_step/sec: 24.0258\n",
      "INFO:tensorflow:loss = 7.24047e+12, step = 2801\n",
      "INFO:tensorflow:global_step/sec: 24.8529\n",
      "INFO:tensorflow:loss = 1.22955e+13, step = 2901\n",
      "INFO:tensorflow:global_step/sec: 24.574\n",
      "INFO:tensorflow:loss = 1.02887e+13, step = 3001\n",
      "INFO:tensorflow:global_step/sec: 24.7379\n",
      "INFO:tensorflow:loss = 1.2287e+13, step = 3101\n",
      "INFO:tensorflow:global_step/sec: 24.9735\n",
      "INFO:tensorflow:loss = 1.30295e+13, step = 3201\n",
      "INFO:tensorflow:global_step/sec: 25.3925\n",
      "INFO:tensorflow:loss = 1.00702e+13, step = 3301\n",
      "INFO:tensorflow:global_step/sec: 24.3989\n",
      "INFO:tensorflow:loss = 9.70062e+12, step = 3401\n",
      "INFO:tensorflow:global_step/sec: 24.7652\n",
      "INFO:tensorflow:loss = 1.68246e+13, step = 3501\n",
      "INFO:tensorflow:global_step/sec: 24.8382\n",
      "INFO:tensorflow:loss = 1.20667e+13, step = 3601\n",
      "INFO:tensorflow:global_step/sec: 25.4996\n",
      "INFO:tensorflow:loss = 8.84234e+12, step = 3701\n",
      "INFO:tensorflow:global_step/sec: 24.8784\n",
      "INFO:tensorflow:loss = 9.01779e+12, step = 3801\n",
      "INFO:tensorflow:global_step/sec: 25.3217\n",
      "INFO:tensorflow:loss = 1.34644e+13, step = 3901\n",
      "INFO:tensorflow:global_step/sec: 25.2275\n",
      "INFO:tensorflow:loss = 1.76138e+13, step = 4001\n",
      "INFO:tensorflow:global_step/sec: 25.0765\n",
      "INFO:tensorflow:loss = 8.68688e+12, step = 4101\n",
      "INFO:tensorflow:global_step/sec: 25.342\n",
      "INFO:tensorflow:loss = 1.20109e+13, step = 4201\n",
      "INFO:tensorflow:global_step/sec: 25.6114\n",
      "INFO:tensorflow:loss = 1.62866e+13, step = 4301\n",
      "INFO:tensorflow:global_step/sec: 25.3536\n",
      "INFO:tensorflow:loss = 9.4715e+12, step = 4401\n",
      "INFO:tensorflow:global_step/sec: 25.1365\n",
      "INFO:tensorflow:loss = 1.63984e+13, step = 4501\n",
      "INFO:tensorflow:global_step/sec: 25.1925\n",
      "INFO:tensorflow:loss = 1.28153e+13, step = 4601\n",
      "INFO:tensorflow:global_step/sec: 25.3653\n",
      "INFO:tensorflow:loss = 1.79831e+13, step = 4701\n",
      "INFO:tensorflow:global_step/sec: 25.0496\n",
      "INFO:tensorflow:loss = 1.28873e+13, step = 4801\n",
      "INFO:tensorflow:global_step/sec: 25.0242\n",
      "INFO:tensorflow:loss = 1.27597e+13, step = 4901\n",
      "INFO:tensorflow:global_step/sec: 25.5961\n",
      "INFO:tensorflow:loss = 1.39462e+13, step = 5001\n",
      "INFO:tensorflow:global_step/sec: 21.4179\n",
      "INFO:tensorflow:loss = 7.99943e+12, step = 5101\n",
      "INFO:tensorflow:global_step/sec: 23.6418\n",
      "INFO:tensorflow:loss = 8.51444e+12, step = 5201\n",
      "INFO:tensorflow:global_step/sec: 24.0463\n",
      "INFO:tensorflow:loss = 9.37363e+12, step = 5301\n",
      "INFO:tensorflow:global_step/sec: 25.0126\n",
      "INFO:tensorflow:loss = 1.11416e+13, step = 5401\n",
      "INFO:tensorflow:global_step/sec: 24.3428\n",
      "INFO:tensorflow:loss = 8.82753e+12, step = 5501\n",
      "INFO:tensorflow:global_step/sec: 22.9248\n",
      "INFO:tensorflow:loss = 8.49064e+12, step = 5601\n",
      "INFO:tensorflow:global_step/sec: 25.5698\n",
      "INFO:tensorflow:loss = 5.84219e+12, step = 5701\n",
      "INFO:tensorflow:global_step/sec: 25.4126\n",
      "INFO:tensorflow:loss = 1.27044e+13, step = 5801\n",
      "INFO:tensorflow:global_step/sec: 25.4617\n",
      "INFO:tensorflow:loss = 1.0978e+13, step = 5901\n",
      "INFO:tensorflow:global_step/sec: 25.9222\n",
      "INFO:tensorflow:loss = 8.23108e+12, step = 6001\n",
      "INFO:tensorflow:global_step/sec: 25.2339\n",
      "INFO:tensorflow:loss = 8.97763e+12, step = 6101\n",
      "INFO:tensorflow:global_step/sec: 25.5539\n",
      "INFO:tensorflow:loss = 3.19309e+13, step = 6201\n",
      "INFO:tensorflow:global_step/sec: 25.4745\n",
      "INFO:tensorflow:loss = 1.86911e+13, step = 6301\n",
      "INFO:tensorflow:global_step/sec: 24.5532\n",
      "INFO:tensorflow:loss = 1.24701e+13, step = 6401\n",
      "INFO:tensorflow:global_step/sec: 22.6282\n",
      "INFO:tensorflow:loss = 1.36412e+13, step = 6501\n",
      "INFO:tensorflow:global_step/sec: 24.6768\n",
      "INFO:tensorflow:loss = 1.78772e+13, step = 6601\n",
      "INFO:tensorflow:global_step/sec: 24.987\n",
      "INFO:tensorflow:loss = 8.13034e+12, step = 6701\n",
      "INFO:tensorflow:global_step/sec: 24.4494\n",
      "INFO:tensorflow:loss = 1.68498e+13, step = 6801\n",
      "INFO:tensorflow:global_step/sec: 23.4\n",
      "INFO:tensorflow:loss = 1.34888e+13, step = 6901\n",
      "INFO:tensorflow:global_step/sec: 24.9961\n",
      "INFO:tensorflow:loss = 1.83569e+13, step = 7001\n",
      "INFO:tensorflow:global_step/sec: 23.5577\n",
      "INFO:tensorflow:loss = 8.28931e+12, step = 7101\n",
      "INFO:tensorflow:global_step/sec: 19.6664\n",
      "INFO:tensorflow:loss = 2.87941e+13, step = 7201\n",
      "INFO:tensorflow:global_step/sec: 20.9327\n",
      "INFO:tensorflow:loss = 1.07749e+13, step = 7301\n",
      "INFO:tensorflow:global_step/sec: 19.5948\n",
      "INFO:tensorflow:loss = 1.2441e+13, step = 7401\n",
      "INFO:tensorflow:global_step/sec: 21.1043\n",
      "INFO:tensorflow:loss = 7.40483e+12, step = 7501\n",
      "INFO:tensorflow:global_step/sec: 25.038\n",
      "INFO:tensorflow:loss = 9.72226e+12, step = 7601\n",
      "INFO:tensorflow:global_step/sec: 22.9093\n",
      "INFO:tensorflow:loss = 1.101e+13, step = 7701\n",
      "INFO:tensorflow:global_step/sec: 22.1899\n",
      "INFO:tensorflow:loss = 8.77709e+12, step = 7801\n",
      "INFO:tensorflow:global_step/sec: 22.4225\n",
      "INFO:tensorflow:loss = 8.31254e+12, step = 7901\n",
      "INFO:tensorflow:global_step/sec: 23.3358\n",
      "INFO:tensorflow:loss = 1.38866e+13, step = 8001\n",
      "INFO:tensorflow:global_step/sec: 22.4206\n",
      "INFO:tensorflow:loss = 7.50463e+12, step = 8101\n",
      "INFO:tensorflow:global_step/sec: 23.7499\n",
      "INFO:tensorflow:loss = 7.40542e+12, step = 8201\n",
      "INFO:tensorflow:global_step/sec: 24.3869\n",
      "INFO:tensorflow:loss = 1.28132e+13, step = 8301\n",
      "INFO:tensorflow:global_step/sec: 25.2431\n",
      "INFO:tensorflow:loss = 1.5661e+13, step = 8401\n",
      "INFO:tensorflow:global_step/sec: 23.3251\n",
      "INFO:tensorflow:loss = 5.84192e+12, step = 8501\n",
      "INFO:tensorflow:global_step/sec: 20.501\n",
      "INFO:tensorflow:loss = 8.19145e+12, step = 8601\n",
      "INFO:tensorflow:global_step/sec: 23.0009\n",
      "INFO:tensorflow:loss = 9.98333e+12, step = 8701\n",
      "INFO:tensorflow:global_step/sec: 22.1262\n",
      "INFO:tensorflow:loss = 1.6951e+13, step = 8801\n",
      "INFO:tensorflow:global_step/sec: 21.8605\n",
      "INFO:tensorflow:loss = 1.16577e+13, step = 8901\n",
      "INFO:tensorflow:global_step/sec: 25.0598\n",
      "INFO:tensorflow:loss = 1.18238e+13, step = 9001\n",
      "INFO:tensorflow:global_step/sec: 23.4495\n",
      "INFO:tensorflow:loss = 8.25957e+12, step = 9101\n",
      "INFO:tensorflow:global_step/sec: 23.7753\n",
      "INFO:tensorflow:loss = 1.86282e+13, step = 9201\n",
      "INFO:tensorflow:global_step/sec: 22.3794\n",
      "INFO:tensorflow:loss = 1.34354e+13, step = 9301\n",
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      "INFO:tensorflow:loss = 1.34115e+13, step = 9501\n",
      "INFO:tensorflow:global_step/sec: 21.1625\n",
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      "INFO:tensorflow:global_step/sec: 25.2941\n",
      "INFO:tensorflow:loss = 1.049e+13, step = 9801\n",
      "INFO:tensorflow:global_step/sec: 22.6061\n",
      "INFO:tensorflow:loss = 7.15803e+12, step = 9901\n",
      "INFO:tensorflow:global_step/sec: 23.8896\n",
      "INFO:tensorflow:loss = 1.10636e+13, step = 10001\n",
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      "INFO:tensorflow:global_step/sec: 25.6551\n",
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      "INFO:tensorflow:global_step/sec: 25.6807\n",
      "INFO:tensorflow:loss = 1.16745e+13, step = 10501\n",
      "INFO:tensorflow:global_step/sec: 24.7047\n",
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      "INFO:tensorflow:global_step/sec: 25.703\n",
      "INFO:tensorflow:loss = 8.95826e+12, step = 10801\n",
      "INFO:tensorflow:global_step/sec: 25.4612\n",
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      "INFO:tensorflow:global_step/sec: 25.5459\n",
      "INFO:tensorflow:loss = 1.43481e+13, step = 11001\n",
      "INFO:tensorflow:global_step/sec: 21.9425\n",
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      "INFO:tensorflow:loss = 8.76759e+12, step = 11201\n",
      "INFO:tensorflow:global_step/sec: 23.326\n",
      "INFO:tensorflow:loss = 8.04298e+12, step = 11301\n",
      "INFO:tensorflow:global_step/sec: 21.9922\n",
      "INFO:tensorflow:loss = 6.46065e+12, step = 11401\n",
      "INFO:tensorflow:global_step/sec: 24.4431\n",
      "INFO:tensorflow:loss = 1.00738e+13, step = 11501\n",
      "INFO:tensorflow:global_step/sec: 22.4842\n",
      "INFO:tensorflow:loss = 8.43077e+12, step = 11601\n",
      "INFO:tensorflow:global_step/sec: 25.2893\n",
      "INFO:tensorflow:loss = 1.27194e+13, step = 11701\n",
      "INFO:tensorflow:global_step/sec: 24.3387\n",
      "INFO:tensorflow:loss = 1.15886e+13, step = 11801\n",
      "INFO:tensorflow:global_step/sec: 24.4566\n",
      "INFO:tensorflow:loss = 6.87102e+12, step = 11901\n",
      "INFO:tensorflow:global_step/sec: 24.7478\n",
      "INFO:tensorflow:loss = 7.04839e+12, step = 12001\n",
      "INFO:tensorflow:global_step/sec: 25.3233\n",
      "INFO:tensorflow:loss = 1.00651e+13, step = 12101\n",
      "INFO:tensorflow:global_step/sec: 23.3768\n",
      "INFO:tensorflow:loss = 1.90669e+13, step = 12201\n",
      "INFO:tensorflow:global_step/sec: 21.9537\n",
      "INFO:tensorflow:loss = 1.41136e+13, step = 12301\n",
      "INFO:tensorflow:global_step/sec: 22.352\n",
      "INFO:tensorflow:loss = 8.24427e+12, step = 12401\n",
      "INFO:tensorflow:global_step/sec: 20.8499\n",
      "INFO:tensorflow:loss = 7.28878e+12, step = 12501\n",
      "INFO:tensorflow:global_step/sec: 19.1605\n",
      "INFO:tensorflow:loss = 7.61374e+12, step = 12601\n",
      "INFO:tensorflow:global_step/sec: 25.3318\n",
      "INFO:tensorflow:loss = 8.54272e+12, step = 12701\n",
      "INFO:tensorflow:global_step/sec: 22.4249\n",
      "INFO:tensorflow:loss = 8.47802e+12, step = 12801\n",
      "INFO:tensorflow:global_step/sec: 20.4048\n",
      "INFO:tensorflow:loss = 1.27439e+13, step = 12901\n",
      "INFO:tensorflow:global_step/sec: 23.4797\n",
      "INFO:tensorflow:loss = 9.82067e+12, step = 13001\n",
      "INFO:tensorflow:global_step/sec: 22.5029\n",
      "INFO:tensorflow:loss = 1.22868e+13, step = 13101\n",
      "INFO:tensorflow:global_step/sec: 19.9373\n",
      "INFO:tensorflow:loss = 1.19561e+13, step = 13201\n",
      "INFO:tensorflow:global_step/sec: 22.6611\n",
      "INFO:tensorflow:loss = 7.61515e+12, step = 13301\n",
      "INFO:tensorflow:global_step/sec: 22.9039\n",
      "INFO:tensorflow:loss = 9.86994e+12, step = 13401\n",
      "INFO:tensorflow:global_step/sec: 16.3524\n",
      "INFO:tensorflow:loss = 9.08123e+12, step = 13501\n",
      "INFO:tensorflow:global_step/sec: 20.0814\n",
      "INFO:tensorflow:loss = 9.01599e+12, step = 13601\n",
      "INFO:tensorflow:global_step/sec: 24.2286\n",
      "INFO:tensorflow:loss = 6.3068e+12, step = 13701\n",
      "INFO:tensorflow:global_step/sec: 25.351\n",
      "INFO:tensorflow:loss = 6.85614e+12, step = 13801\n",
      "INFO:tensorflow:global_step/sec: 24.9116\n",
      "INFO:tensorflow:loss = 2.65045e+13, step = 13901\n",
      "INFO:tensorflow:global_step/sec: 23.1031\n",
      "INFO:tensorflow:loss = 9.40478e+12, step = 14001\n",
      "INFO:tensorflow:global_step/sec: 21.0679\n",
      "INFO:tensorflow:loss = 6.92314e+12, step = 14101\n",
      "INFO:tensorflow:global_step/sec: 22.347\n",
      "INFO:tensorflow:loss = 9.97315e+12, step = 14201\n",
      "INFO:tensorflow:Saving checkpoints for 14252 into /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp9BIfvX/model.ckpt.\n",
      "INFO:tensorflow:global_step/sec: 17.5303\n",
      "INFO:tensorflow:loss = 8.13326e+12, step = 14301\n",
      "INFO:tensorflow:global_step/sec: 20.3363\n",
      "INFO:tensorflow:loss = 7.71583e+12, step = 14401\n",
      "INFO:tensorflow:global_step/sec: 21.4738\n",
      "INFO:tensorflow:loss = 1.02296e+13, step = 14501\n",
      "INFO:tensorflow:global_step/sec: 24.3816\n",
      "INFO:tensorflow:loss = 9.66753e+12, step = 14601\n",
      "INFO:tensorflow:global_step/sec: 22.7741\n",
      "INFO:tensorflow:loss = 9.00079e+12, step = 14701\n",
      "INFO:tensorflow:global_step/sec: 22.2188\n",
      "INFO:tensorflow:loss = 2.70686e+13, step = 14801\n",
      "INFO:tensorflow:global_step/sec: 24.8232\n",
      "INFO:tensorflow:loss = 5.84922e+12, step = 14901\n",
      "INFO:tensorflow:Saving checkpoints for 15000 into /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp9BIfvX/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 8.19865e+12.\n",
      "WARNING:tensorflow:From /Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:692: calling predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow Regression using DNN\n",
    "features = learn.infer_real_valued_columns_from_input(X_train)\n",
    "\n",
    "# Optimizer algorithm\n",
    "adam = tf.train.AdamOptimizer(learning_rate=0.001)\n",
    "\n",
    "# Build a multi-layer DNN for regression\n",
    "model_nn = learn.DNNRegressor(feature_columns=features, hidden_units=[400, 200, 100], \n",
    "                              optimizer=adam, activation_fn=tf.nn.relu,\n",
    "                              )\n",
    "\n",
    "# Fit and predict\n",
    "model_nn.fit(X_train, y_train, steps=15000, batch_size=512)\n",
    "y_pred = model_nn.predict(X_test)\n",
    "\n",
    "# print('R2 score: {:g}'.format(metrics.r2_score(y_test, y_pred)))\n",
    "# print('Explained variance score: {:g}'.format(metrics.explained_variance_score(y_test, y_pred)))\n",
    "# print('Mean squared error: {:g}'.format(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:692: calling predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n"
     ]
    }
   ],
   "source": [
    "y_lr = list(clf_lr.predict(X_test))     # Linear \n",
    "y_rf = list(forest.predict(X_test))     # Random Forest\n",
    "y_gb = list(clf_gb.predict(X_test))     # Gradient Boosting\n",
    "\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "y_xgb = list(clf_xgb.predict(dtest))    # XGBoost\n",
    "\n",
    "y_nn = list(model_nn.predict(X_test))   # DNN\n",
    "\n",
    "stack = pd.DataFrame({'lr':y_lr, 'gb':y_gb, 'xgb':y_xgb,'rf':y_rf,'nn':y_nn, 'true':y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data.items(), columns=['Model', 'Values'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TheilSenRegressor(copy_X=True, fit_intercept=False, max_iter=300,\n",
       "         max_subpopulation=10000, n_jobs=-1, n_subsamples=None,\n",
       "         random_state=None, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stacking & Ensembling multiple regressors\n",
    "stack_type = 'TheilSan'\n",
    "\n",
    "if stack_type == 'OLS':\n",
    "    # Ordinary least squares Linear Regression (OLS)\n",
    "    stacker = linear_model.LinearRegression(fit_intercept=False)\n",
    "elif stack_type == 'Ridge':\n",
    "    # Linear least squares with L2 regularization (Ridge)\n",
    "    stacker = linear_model.Ridge(fit_intercept=False)\n",
    "elif stack_type == 'TheilSan':\n",
    "    # Theil-Sen Estimator: robust multivariate regression model\n",
    "    stacker = linear_model.TheilSenRegressor(fit_intercept=False, n_jobs=-1)\n",
    "\n",
    "# RF + GBR + XGB + NN\n",
    "stacker.fit(X=stack.loc[:, lambda df: ['gb','rf','nn','xgb']], y=stack['true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp1cbAqK\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_type': None, '_environment': 'local', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x13b9bd710>, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_task_id': 0, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_evaluation_master': '', '_keep_checkpoint_every_n_hours': 10000, '_master': ''}\n",
      "WARNING:tensorflow:From <ipython-input-42-dea9b3e196dd>:14: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-42-dea9b3e196dd>:14: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-42-dea9b3e196dd>:14: calling fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with batch_size is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
      "WARNING:tensorflow:From /Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n",
      "Instructions for updating:\n",
      "Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp1cbAqK/model.ckpt.\n",
      "INFO:tensorflow:loss = 1.58351e+14, step = 1\n",
      "INFO:tensorflow:global_step/sec: 153.811\n",
      "INFO:tensorflow:loss = 5.37849e+12, step = 101\n",
      "INFO:tensorflow:global_step/sec: 155.336\n",
      "INFO:tensorflow:loss = 3.36739e+12, step = 201\n",
      "INFO:tensorflow:global_step/sec: 130.967\n",
      "INFO:tensorflow:loss = 3.97338e+12, step = 301\n",
      "INFO:tensorflow:global_step/sec: 147.095\n",
      "INFO:tensorflow:loss = 5.55173e+12, step = 401\n",
      "INFO:tensorflow:global_step/sec: 144.622\n",
      "INFO:tensorflow:loss = 4.80779e+12, step = 501\n",
      "INFO:tensorflow:global_step/sec: 144.335\n",
      "INFO:tensorflow:loss = 3.61124e+12, step = 601\n",
      "INFO:tensorflow:global_step/sec: 159.448\n",
      "INFO:tensorflow:loss = 7.77922e+12, step = 701\n",
      "INFO:tensorflow:global_step/sec: 147.127\n",
      "INFO:tensorflow:loss = 7.58009e+12, step = 801\n",
      "INFO:tensorflow:global_step/sec: 152.385\n",
      "INFO:tensorflow:loss = 4.39747e+12, step = 901\n",
      "INFO:tensorflow:global_step/sec: 135.861\n",
      "INFO:tensorflow:loss = 4.2773e+12, step = 1001\n",
      "INFO:tensorflow:global_step/sec: 159.937\n",
      "INFO:tensorflow:loss = 5.66657e+12, step = 1101\n",
      "INFO:tensorflow:global_step/sec: 159.41\n",
      "INFO:tensorflow:loss = 5.43891e+12, step = 1201\n",
      "INFO:tensorflow:global_step/sec: 151.437\n",
      "INFO:tensorflow:loss = 8.11542e+12, step = 1301\n",
      "INFO:tensorflow:global_step/sec: 153.424\n",
      "INFO:tensorflow:loss = 4.05716e+12, step = 1401\n",
      "INFO:tensorflow:global_step/sec: 154.601\n",
      "INFO:tensorflow:loss = 5.74656e+12, step = 1501\n",
      "INFO:tensorflow:global_step/sec: 153.833\n",
      "INFO:tensorflow:loss = 8.08061e+12, step = 1601\n",
      "INFO:tensorflow:global_step/sec: 149.604\n",
      "INFO:tensorflow:loss = 7.1268e+12, step = 1701\n",
      "INFO:tensorflow:global_step/sec: 139.112\n",
      "INFO:tensorflow:loss = 3.61917e+12, step = 1801\n",
      "INFO:tensorflow:global_step/sec: 151.926\n",
      "INFO:tensorflow:loss = 7.50961e+12, step = 1901\n",
      "INFO:tensorflow:global_step/sec: 158.52\n",
      "INFO:tensorflow:loss = 8.69518e+12, step = 2001\n",
      "INFO:tensorflow:global_step/sec: 155.854\n",
      "INFO:tensorflow:loss = 5.97284e+12, step = 2101\n",
      "INFO:tensorflow:global_step/sec: 156.812\n",
      "INFO:tensorflow:loss = 4.94138e+12, step = 2201\n",
      "INFO:tensorflow:global_step/sec: 141.156\n",
      "INFO:tensorflow:loss = 9.74399e+12, step = 2301\n",
      "INFO:tensorflow:global_step/sec: 111.812\n",
      "INFO:tensorflow:loss = 9.92201e+12, step = 2401\n",
      "INFO:tensorflow:global_step/sec: 140.419\n",
      "INFO:tensorflow:loss = 5.67929e+12, step = 2501\n",
      "INFO:tensorflow:global_step/sec: 145.861\n",
      "INFO:tensorflow:loss = 5.29498e+12, step = 2601\n",
      "INFO:tensorflow:global_step/sec: 151.545\n",
      "INFO:tensorflow:loss = 7.26236e+12, step = 2701\n",
      "INFO:tensorflow:global_step/sec: 157.994\n",
      "INFO:tensorflow:loss = 7.49694e+12, step = 2801\n",
      "INFO:tensorflow:global_step/sec: 144.534\n",
      "INFO:tensorflow:loss = 3.84612e+12, step = 2901\n",
      "INFO:tensorflow:global_step/sec: 156.422\n",
      "INFO:tensorflow:loss = 5.09248e+12, step = 3001\n",
      "INFO:tensorflow:global_step/sec: 141.985\n",
      "INFO:tensorflow:loss = 7.68544e+12, step = 3101\n",
      "INFO:tensorflow:global_step/sec: 119.738\n",
      "INFO:tensorflow:loss = 6.48624e+12, step = 3201\n",
      "INFO:tensorflow:global_step/sec: 137.477\n",
      "INFO:tensorflow:loss = 8.90375e+12, step = 3301\n",
      "INFO:tensorflow:global_step/sec: 144.92\n",
      "INFO:tensorflow:loss = 6.09948e+12, step = 3401\n",
      "INFO:tensorflow:global_step/sec: 133.752\n",
      "INFO:tensorflow:loss = 7.13899e+12, step = 3501\n",
      "INFO:tensorflow:global_step/sec: 156.099\n",
      "INFO:tensorflow:loss = 6.64745e+12, step = 3601\n",
      "INFO:tensorflow:global_step/sec: 158.389\n",
      "INFO:tensorflow:loss = 4.86131e+12, step = 3701\n",
      "INFO:tensorflow:global_step/sec: 151.141\n",
      "INFO:tensorflow:loss = 6.21548e+12, step = 3801\n",
      "INFO:tensorflow:global_step/sec: 148.733\n",
      "INFO:tensorflow:loss = 6.66134e+12, step = 3901\n",
      "INFO:tensorflow:global_step/sec: 153.211\n",
      "INFO:tensorflow:loss = 4.06249e+12, step = 4001\n",
      "INFO:tensorflow:global_step/sec: 155.659\n",
      "INFO:tensorflow:loss = 4.16485e+12, step = 4101\n",
      "INFO:tensorflow:global_step/sec: 156.452\n",
      "INFO:tensorflow:loss = 4.24985e+12, step = 4201\n",
      "INFO:tensorflow:global_step/sec: 152.077\n",
      "INFO:tensorflow:loss = 4.29649e+12, step = 4301\n",
      "INFO:tensorflow:global_step/sec: 154.618\n",
      "INFO:tensorflow:loss = 8.70415e+12, step = 4401\n",
      "INFO:tensorflow:global_step/sec: 147.806\n",
      "INFO:tensorflow:loss = 6.33405e+12, step = 4501\n",
      "INFO:tensorflow:global_step/sec: 146.034\n",
      "INFO:tensorflow:loss = 4.2324e+12, step = 4601\n",
      "INFO:tensorflow:global_step/sec: 147.082\n",
      "INFO:tensorflow:loss = 4.83159e+12, step = 4701\n",
      "INFO:tensorflow:global_step/sec: 144.118\n",
      "INFO:tensorflow:loss = 6.55419e+12, step = 4801\n",
      "INFO:tensorflow:global_step/sec: 150.788\n",
      "INFO:tensorflow:loss = 6.7048e+12, step = 4901\n",
      "INFO:tensorflow:global_step/sec: 153.667\n",
      "INFO:tensorflow:loss = 5.53747e+12, step = 5001\n",
      "INFO:tensorflow:global_step/sec: 156.357\n",
      "INFO:tensorflow:loss = 5.1661e+12, step = 5101\n",
      "INFO:tensorflow:global_step/sec: 149.411\n",
      "INFO:tensorflow:loss = 4.82212e+12, step = 5201\n",
      "INFO:tensorflow:global_step/sec: 140.987\n",
      "INFO:tensorflow:loss = 7.60126e+12, step = 5301\n",
      "INFO:tensorflow:global_step/sec: 144.138\n",
      "INFO:tensorflow:loss = 3.94195e+12, step = 5401\n",
      "INFO:tensorflow:global_step/sec: 132.82\n",
      "INFO:tensorflow:loss = 3.84389e+12, step = 5501\n",
      "INFO:tensorflow:global_step/sec: 153.646\n",
      "INFO:tensorflow:loss = 7.26689e+12, step = 5601\n",
      "INFO:tensorflow:global_step/sec: 116.147\n",
      "INFO:tensorflow:loss = 7.6745e+12, step = 5701\n",
      "INFO:tensorflow:global_step/sec: 154.415\n",
      "INFO:tensorflow:loss = 8.49219e+12, step = 5801\n",
      "INFO:tensorflow:global_step/sec: 157.51\n",
      "INFO:tensorflow:loss = 4.27006e+12, step = 5901\n",
      "INFO:tensorflow:global_step/sec: 150.515\n",
      "INFO:tensorflow:loss = 7.37874e+12, step = 6001\n",
      "INFO:tensorflow:global_step/sec: 143.318\n",
      "INFO:tensorflow:loss = 5.44131e+12, step = 6101\n",
      "INFO:tensorflow:global_step/sec: 149.215\n",
      "INFO:tensorflow:loss = 1.11898e+13, step = 6201\n",
      "INFO:tensorflow:global_step/sec: 156.107\n",
      "INFO:tensorflow:loss = 5.12606e+12, step = 6301\n",
      "INFO:tensorflow:global_step/sec: 133.729\n",
      "INFO:tensorflow:loss = 4.96715e+12, step = 6401\n",
      "INFO:tensorflow:global_step/sec: 134.362\n",
      "INFO:tensorflow:loss = 4.43579e+12, step = 6501\n",
      "INFO:tensorflow:global_step/sec: 121.378\n",
      "INFO:tensorflow:loss = 3.93933e+12, step = 6601\n",
      "INFO:tensorflow:global_step/sec: 147.995\n",
      "INFO:tensorflow:loss = 3.79341e+12, step = 6701\n",
      "INFO:tensorflow:global_step/sec: 140.921\n",
      "INFO:tensorflow:loss = 3.97772e+12, step = 6801\n",
      "INFO:tensorflow:global_step/sec: 131.051\n",
      "INFO:tensorflow:loss = 7.77849e+12, step = 6901\n",
      "INFO:tensorflow:global_step/sec: 155.061\n",
      "INFO:tensorflow:loss = 5.38831e+12, step = 7001\n",
      "INFO:tensorflow:global_step/sec: 152.645\n",
      "INFO:tensorflow:loss = 9.31397e+12, step = 7101\n",
      "INFO:tensorflow:global_step/sec: 154.883\n",
      "INFO:tensorflow:loss = 4.86892e+12, step = 7201\n",
      "INFO:tensorflow:global_step/sec: 156.246\n",
      "INFO:tensorflow:loss = 1.24269e+13, step = 7301\n",
      "INFO:tensorflow:global_step/sec: 158.664\n",
      "INFO:tensorflow:loss = 4.44762e+12, step = 7401\n",
      "INFO:tensorflow:global_step/sec: 143.345\n",
      "INFO:tensorflow:loss = 3.11791e+12, step = 7501\n",
      "INFO:tensorflow:global_step/sec: 157.112\n",
      "INFO:tensorflow:loss = 7.43321e+12, step = 7601\n",
      "INFO:tensorflow:global_step/sec: 152.884\n",
      "INFO:tensorflow:loss = 7.19097e+12, step = 7701\n",
      "INFO:tensorflow:global_step/sec: 155.956\n",
      "INFO:tensorflow:loss = 1.02733e+13, step = 7801\n",
      "INFO:tensorflow:global_step/sec: 153.942\n",
      "INFO:tensorflow:loss = 6.42878e+12, step = 7901\n",
      "INFO:tensorflow:Saving checkpoints for 8000 into /var/folders/7g/zyrh78dj0_n0_bfwvzhtymhr0000gn/T/tmp1cbAqK/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 4.66099e+12.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DNNRegressor(params={'head': <tensorflow.contrib.learn.python.learn.estimators.head._RegressionHead object at 0x13b9bd450>, 'hidden_units': [20, 10], 'feature_columns': (_RealValuedColumn(column_name='', dimension=4, default_value=None, dtype=tf.float64, normalizer=None),), 'embedding_lr_multipliers': None, 'optimizer': <tensorflow.python.training.adam.AdamOptimizer object at 0x13bc56c90>, 'dropout': None, 'gradient_clip_norm': None, 'activation_fn': <function relu at 0x1147bd5f0>, 'input_layer_min_slice_size': None})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.contrib import learn\n",
    "\n",
    "# Stacking multiple regressors using DNN\n",
    "features_blend = learn.infer_real_valued_columns_from_input(stack[['gb','rf','nn','xgb']])\n",
    "\n",
    "# Optimizer algorithm\n",
    "adam = tf.train.AdamOptimizer(learning_rate=0.001)\n",
    "\n",
    "# Build multi-layer DNN for regression\n",
    "blend_nn = learn.DNNRegressor(feature_columns=features_blend, hidden_units=[20, 10], \n",
    "                              optimizer=adam, activation_fn=tf.nn.relu)\n",
    "#                               config=learn.estimators.RunConfig(num_cores=8))\n",
    "# Fit DNN\n",
    "blend_nn.fit(x=stack[['gb','rf','nn','xgb']], y=stack['true'], steps=8000, batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pedict price for new apartments (not seen before)\n",
    "x_new = x_train.iloc[-100:]  # example\n",
    "# Scale data\n",
    "x_new = scaler.transform(x_new)\n",
    "# True values (actual prices)\n",
    "true_values = y_train.iloc[-100:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TheilSenRegressor(copy_X=True, fit_intercept=False, max_iter=300,\n",
       "         max_subpopulation=10000, n_jobs=-1, n_subsamples=None,\n",
       "         random_state=None, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stacking & Ensembling multiple regressors\n",
    "stack_type = 'TheilSan'\n",
    "\n",
    "if stack_type == 'OLR':\n",
    "    # Ordinary least squares Linear Regression (OLR)\n",
    "    stacker = linear_model.LinearRegression(fit_intercept=False)\n",
    "elif stack_type == 'XGBoost':\n",
    "    # Linear least squares with L2 regularization (Ridge)\n",
    "    stacker = linear_model.Ridge(fit_intercept=False)\n",
    "elif stack_type == 'TheilSan':\n",
    "    # Theil-Sen Estimator: robust multivariate regression model\n",
    "    stacker = linear_model.TheilSenRegressor(fit_intercept=False, n_jobs=-1)\n",
    "\n",
    "# rf+gb+xgb+nn\n",
    "stacker.fit(X=stack[['rf', 'gb', 'xgb', 'nn']], y=stack['true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:692: calling predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n"
     ]
    }
   ],
   "source": [
    "# PREDICT: Ordinary least squares Linear Regression\n",
    "y_new_lr = list(clf_lr.predict(x_new))\n",
    "\n",
    "# PREDICT: RandomForestRegressor\n",
    "y_new_rf = list(forest.predict(x_new))\n",
    "\n",
    "# PREDICT: Gradient Boosting for regression\n",
    "y_new_gb = list(clf_gb.predict(x_new))\n",
    "\n",
    "# PREDICT: XGBoost\n",
    "dtest = xgb.DMatrix(x_new)\n",
    "y_new_xgb = list(clf_xgb.predict(dtest) )\n",
    "\n",
    "# PREDICT: TensorFlow DNNRegressor\n",
    "# y_new_nn = np.around(model_nn.predict(x_new), decimals=2)\n",
    "y_new_nn = list(model_nn.predict(x_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Relative errors (%) for predictions \n",
    "\n",
    "lr_error = (1.-y_new_lr/true_values)*100.\n",
    "rf_error = (1.-y_new_rf/true_values)*100.\n",
    "gb_error = (1.-y_new_gb/true_values)*100.\n",
    "xgb_error = (1.-y_new_xgb/true_values)*100.\n",
    "nn_error = (1.-y_new_nn/true_values)*100. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame(data={'True Values':y_train.iloc[-100:].values, \n",
    "                                 'OLR':y_new_lr, 'OLR Err':lr_error,   # OLR - Ordinary Linear Regression\n",
    "                                 'RFR':y_new_rf, 'RFR Err':rf_error,   # RFR - Random Forrest regressor\n",
    "                                 'GBR':y_new_gb, 'GBR Err':gb_error,   # GBR - Gradient Boost regresor\n",
    "                                 'XGB':y_new_xgb,'XGB Err':xgb_error,  # XGB - XGBoost\n",
    "                                 'DNN':y_new_nn, 'DNN Err':nn_error})  # DNN - Deep Neural Network\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OLR: RMSE = 0.762365\n",
      "RFR: RMSE = 0.776548\n",
      "GBR: RMSE = 0.78121\n",
      "XGB: RMSE = 0.799111\n"
     ]
    }
   ],
   "source": [
    "algos_predicted = predictions[['OLR', 'RFR', 'GBR', 'XGB']]\n",
    "for col in algos_predicted:\n",
    "    rms_log = metrics.mean_squared_error(np.log(predictions['True Values']), np.log(algos_predicted[col]))\n",
    "    print('{:s}: RMSE = {:g}'.format(col, np.sqrt(rms_log)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OLR: RMSE = 0.762365\n",
      "DNN: RMSE = 0.786273\n",
      "RFR: RMSE = 0.776548\n",
      "GBR: RMSE = 0.78121\n",
      "XGB: RMSE = 0.799111\n"
     ]
    }
   ],
   "source": [
    "#Stacking is using the output of the stacked models (RFR, GBR, XGB, DNN) as input\n",
    "\n",
    "# Root Mean Square Error (between the logarithm of predicted and true values)\n",
    "algos_predicted = predictions[['OLR', 'DNN', 'RFR', 'GBR', 'XGB']]\n",
    "for col in algos_predicted:\n",
    "    rms_log = metrics.mean_squared_error(np.log(predictions['True Values']), np.log(algos_predicted[col]))\n",
    "    print('{:s}: RMSE = {:g}'.format(col, np.sqrt(rms_log)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# RF + Gradient Boosting + XGBoost + DNN\n",
    "new_stack = pd.DataFrame(data={'rf':y_new_rf, 'gb':y_new_gb, 'xgb':y_new_xgb, 'nn':y_new_nn, 'true':true_values})\n",
    "\n",
    "# PREDICT: Stacked regressors (Stack)\n",
    "y_new_st = stacker.predict(new_stack[['rf', 'gb', 'xgb', 'nn']])\n",
    "\n",
    "# Relative errors (%) for predictions\n",
    "st_error = (1.-y_new_st/true_values)*100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/chelimin/miniconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:692: calling predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n"
     ]
    }
   ],
   "source": [
    "# PREDICT: Stacked regressors using DNN (Ensamble)\n",
    "y_new_bl = list(blend_nn.predict(new_stack[['rf', 'gb', 'xgb', 'nn']]))\n",
    "bl_error = (1.-y_new_bl/true_values)*100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Averageing best predictions from different regressors\n",
    "# RF + Gradient Boosting + XGBoost + DNN\n",
    "w = [1, 1, 1, 1]  # weights\n",
    "y_new_agg = np.c_[y_new_rf, y_new_gb, y_new_xgb,y_new_nn]\n",
    "y_new_avr = np.average(y_new_agg, axis=1, weights=w)\n",
    "avr_error = (1.-y_new_avr/true_values)*100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "predictions = (('True Values',y_test.iloc[-100:].values), \n",
    "               ('Average',y_new_avr), ('Average Err',avr_error),\n",
    "               ('Stack',y_new_st), ('Stack Err',st_error),\n",
    "               ('Ensemble',y_new_bl), ('Ensemble Err',bl_error))\n",
    "preds_avr = pd.DataFrame(data=OrderedDict(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Average: RMSE = 0.810983\n",
      "    Stack: RMSE = 0.818542\n",
      " Ensemble: RMSE = 0.790231\n"
     ]
    }
   ],
   "source": [
    "# Root Mean Square Error (RMSE) between the logarithm of the  \n",
    "# predicted value and the logarithm of the observed sales price\n",
    "rms_log = metrics.mean_squared_error(np.log(preds_avr['True Values']), np.log(preds_avr['Average']))\n",
    "print('  Average: RMSE = {:g}'.format(np.sqrt(rms_log)))\n",
    "rms_log = metrics.mean_squared_error(np.log(preds_avr['True Values']), np.log(preds_avr['Stack']))\n",
    "print('    Stack: RMSE = {:g}'.format(np.sqrt(rms_log)))\n",
    "rms_log = metrics.mean_squared_error(np.log(preds_avr['True Values']), np.log(preds_avr['Ensemble']))\n",
    "print(' Ensemble: RMSE = {:g}'.format(np.sqrt(rms_log)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking 2  - not working! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from random import randrange\n",
    "\n",
    "# Convert string column to float\n",
    "def str_column_to_float(dataset, column):\n",
    "\tfor row in dataset:\n",
    "\t\trow[column] = float(row[column].strip())\n",
    "\n",
    "# Convert string column to integer\n",
    "def str_column_to_int(dataset, column):\n",
    "\tclass_values = [row[column] for row in dataset]\n",
    "\tunique = set(class_values)\n",
    "\tlookup = dict()\n",
    "\tfor i, value in enumerate(unique):\n",
    "\t\tlookup[value] = i\n",
    "\tfor row in dataset:\n",
    "\t\trow[column] = lookup[row[column]]\n",
    "\treturn lookup\n",
    "\n",
    "# Split a dataset into k folds\n",
    "def cross_validation_split(dataset, n_folds):\n",
    "\tdataset_split = list()\n",
    "\tdataset_copy = list(dataset)\n",
    "\tfold_size = int(len(dataset) / n_folds)\n",
    "\tfor i in range(n_folds):\n",
    "\t\tfold = list()\n",
    "\t\twhile len(fold) < fold_size:\n",
    "\t\t\tindex = randrange(len(dataset_copy))\n",
    "\t\t\tfold.append(dataset_copy.pop(index))\n",
    "\t\tdataset_split.append(fold)\n",
    "\treturn dataset_split\n",
    "\n",
    "# Calculate accuracy percentage\n",
    "def accuracy_metric(actual, predicted):\n",
    "\tcorrect = 0\n",
    "\tfor i in range(len(actual)):\n",
    "\t\tif actual[i] == predicted[i]:\n",
    "\t\t\tcorrect += 1\n",
    "\treturn correct / float(len(actual)) * 100.0\n",
    "\n",
    "# Evaluate an algorithm using a cross validation split\n",
    "def evaluate_algorithm(dataset, algorithm, n_folds, *args):\n",
    "\tfolds = cross_validation_split(dataset, n_folds)\n",
    "\tscores = list()\n",
    "\tfor fold in folds:\n",
    "\t\ttrain_set = list(folds)\n",
    "\t\ttrain_set.remove(fold)\n",
    "\t\ttrain_set = sum(train_set, [])\n",
    "\t\ttest_set = list()\n",
    "\t\tfor row in fold:\n",
    "\t\t\trow_copy = list(row)\n",
    "\t\t\ttest_set.append(row_copy)\n",
    "\t\t\trow_copy[-1] = None\n",
    "\t\tpredicted = algorithm(train_set, test_set, *args)\n",
    "\t\tactual = [row[-1] for row in fold]\n",
    "\t\taccuracy = accuracy_metric(actual, predicted)\n",
    "\t\tscores.append(accuracy)\n",
    "\treturn scores\n",
    "\n",
    "# Make a prediction with coefficients\n",
    "def logistic_regression_predict(model, row):\n",
    "\tyhat = model[0]\n",
    "\tfor i in range(len(row)-1):\n",
    "\t\tyhat += model[i + 1] * row[i]\n",
    "\treturn 1.0 / (1.0 + exp(-yhat))\n",
    "\n",
    "# Estimate logistic regression coefficients using stochastic gradient descent\n",
    "def logistic_regression_model(train, l_rate=0.01, n_epoch=5000):\n",
    "\tcoef = [0.0 for i in range(len(train[0]))]\n",
    "\tfor epoch in range(n_epoch):\n",
    "\t\tfor row in train:\n",
    "\t\t\tyhat = logistic_regression_predict(coef, row)\n",
    "\t\t\terror = row[-1] - yhat\n",
    "\t\t\tcoef[0] = coef[0] + l_rate * error * yhat * (1.0 - yhat)\n",
    "\t\t\tfor i in range(len(row)-1):\n",
    "\t\t\t\tcoef[i + 1] = coef[i + 1] + l_rate * error * yhat * (1.0 - yhat) * row[i]\n",
    "\treturn coef\n",
    "\n",
    "# Make predictions with sub-models and construct a new stacked row\n",
    "def to_stacked_row(models, predict_list, row):\n",
    "\tstacked_row = list()\n",
    "\tfor i in range(len(models)):\n",
    "\t\tprediction = predict_list[i](models[i], row)\n",
    "\t\tstacked_row.append(prediction)\n",
    "\tstacked_row.append(row[-1])\n",
    "\treturn row[0:len(row)-1] + stacked_row\n",
    "\n",
    "# Stacked Generalization Algorithm\n",
    "def stacking(train, test):\n",
    "\tmodel_list = [forest, clf_gb]\n",
    "\tpredict_list = [y_rf, y_gb]\n",
    "\tmodels = list()\n",
    "\tfor i in range(len(model_list)):\n",
    "\t\tmodel = model_list[i](train)\n",
    "\t\tmodels.append(model)\n",
    "\tstacked_dataset = list()\n",
    "\tfor row in train:\n",
    "\t\tstacked_row = to_stacked_row(models, predict_list, row)\n",
    "\t\tstacked_dataset.append(stacked_row)\n",
    "\tstacked_model = logistic_regression_model(stacked_dataset)\n",
    "\tpredictions = list()\n",
    "\tfor row in test:\n",
    "\t\tstacked_row = to_stacked_row(models, predict_list, row)\n",
    "\t\tstacked_dataset.append(stacked_row)\n",
    "\t\tprediction = logistic_regression_predict(stacked_model, stacked_row)\n",
    "\t\tprediction = round(prediction)\n",
    "\t\tpredictions.append(prediction)\n",
    "\treturn predictions\n",
    "\n",
    "# convert string attributes to integers\n",
    "# for i in range(len(dataset[0])-1):\n",
    "# \tstr_column_to_float(dataset, i)\n",
    "    \n",
    "# convert class column to integers\n",
    "# str_column_to_int(dataset, len(dataset[0])-1)\n",
    "\n",
    "# load and prepare data\n",
    "dataset = train_data\n",
    "n_folds = 3\n",
    "scores = evaluate_algorithm(dataset, stacking, n_folds)\n",
    "\n",
    "print('Scores: %s' % scores)\n",
    "print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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