try_mlbox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the data here (needs login): https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlbox.preprocessing import *\n",
    "from mlbox.optimisation import *\n",
    "from mlbox.prediction import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading csv : train.csv ...\n",
      "cleaning data...\n",
      "CPU time: 1.5729589462280273 seconds\n",
      "\n",
      "reading csv : test.csv ...\n",
      "cleaning data...\n",
      "CPU time: 0.14525485038757324 seconds\n",
      "\n",
      "number of common features : 11\n",
      "\n",
      "gathering and crunching for train and test datasets\n",
      "reindexing for train and test datasets\n",
      "dropping training duplicates\n",
      "dropping constant variables on training set\n",
      "\n",
      "number of categorical features : 7\n",
      "number of numerical features : 4\n",
      "number of training samples : 8523\n",
      "number of test samples : 5681\n",
      "\n",
      "Top sparse features (% missing values on train set):\n",
      "Outlet_Size    28.3\n",
      "Item_Weight    17.2\n",
      "dtype: float64\n",
      "\n",
      "task : regression\n"
     ]
    }
   ],
   "source": [
    "# After reading the data, it also cleans the data for you\n",
    "# But this step doesn't deal with missing data, it just tells you top sparse features\n",
    "\n",
    "df = Reader(sep=\",\").train_test_split(['train.csv', 'test.csv'], 'Item_Outlet_Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDA15</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>9.300</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Regular</td>\n",
       "      <td>DRC01</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>5.920</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDN15</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>Meat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>17.500</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.200</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCD19</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.930</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDP36</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.395</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDO10</td>\n",
       "      <td>57.6588</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.012741</td>\n",
       "      <td>13.650</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDP10</td>\n",
       "      <td>107.7622</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.127470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDH17</td>\n",
       "      <td>96.9726</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.016687</td>\n",
       "      <td>16.200</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDU28</td>\n",
       "      <td>187.8214</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>19.200</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDY07</td>\n",
       "      <td>45.5402</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.800</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDA03</td>\n",
       "      <td>144.1102</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.045464</td>\n",
       "      <td>18.500</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDX32</td>\n",
       "      <td>145.4786</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.100014</td>\n",
       "      <td>15.100</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDS46</td>\n",
       "      <td>119.6782</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.047257</td>\n",
       "      <td>17.600</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDF32</td>\n",
       "      <td>196.4426</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.068024</td>\n",
       "      <td>16.350</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDP49</td>\n",
       "      <td>56.3614</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>0.069089</td>\n",
       "      <td>9.000</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCB42</td>\n",
       "      <td>115.3492</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>0.008596</td>\n",
       "      <td>11.800</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDP49</td>\n",
       "      <td>54.3614</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>0.069196</td>\n",
       "      <td>9.000</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>DRI11</td>\n",
       "      <td>113.2834</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>0.034238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDU02</td>\n",
       "      <td>230.5352</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.102492</td>\n",
       "      <td>13.350</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDN22</td>\n",
       "      <td>250.8724</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.138190</td>\n",
       "      <td>18.850</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDW12</td>\n",
       "      <td>144.5444</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>0.035400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCB30</td>\n",
       "      <td>196.5084</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.025698</td>\n",
       "      <td>14.600</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDC37</td>\n",
       "      <td>107.6938</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>0.057557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDR28</td>\n",
       "      <td>165.0210</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.025896</td>\n",
       "      <td>13.850</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCD06</td>\n",
       "      <td>45.9060</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.099887</td>\n",
       "      <td>13.000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDV10</td>\n",
       "      <td>42.3112</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.066693</td>\n",
       "      <td>7.645</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>low fat</td>\n",
       "      <td>DRJ59</td>\n",
       "      <td>39.1164</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>0.019356</td>\n",
       "      <td>11.650</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDE51</td>\n",
       "      <td>45.5086</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.161467</td>\n",
       "      <td>5.925</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDC14</td>\n",
       "      <td>43.6454</td>\n",
       "      <td>Canned</td>\n",
       "      <td>0.072222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8493</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDP21</td>\n",
       "      <td>189.1872</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.025886</td>\n",
       "      <td>7.420</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8494</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCI54</td>\n",
       "      <td>110.4912</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.200</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8495</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDE22</td>\n",
       "      <td>160.4920</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.029567</td>\n",
       "      <td>9.695</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8496</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDJ57</td>\n",
       "      <td>185.3582</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>0.021696</td>\n",
       "      <td>7.420</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8497</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDT08</td>\n",
       "      <td>150.0050</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.049209</td>\n",
       "      <td>13.650</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8498</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCP54</td>\n",
       "      <td>124.5730</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.035293</td>\n",
       "      <td>15.350</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8499</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCK53</td>\n",
       "      <td>100.0042</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>0.037574</td>\n",
       "      <td>11.600</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8500</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCQ42</td>\n",
       "      <td>125.1678</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.350</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8501</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDW21</td>\n",
       "      <td>100.4358</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>5.340</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8502</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCH43</td>\n",
       "      <td>216.4192</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.070712</td>\n",
       "      <td>8.420</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8503</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDQ44</td>\n",
       "      <td>120.1756</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.036133</td>\n",
       "      <td>20.500</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8504</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCN18</td>\n",
       "      <td>111.7544</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.124111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8505</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDB46</td>\n",
       "      <td>210.8244</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.094146</td>\n",
       "      <td>10.500</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8506</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>DRF37</td>\n",
       "      <td>263.1910</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>0.084676</td>\n",
       "      <td>17.250</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8507</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDN28</td>\n",
       "      <td>101.7990</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.030242</td>\n",
       "      <td>5.880</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8508</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDW31</td>\n",
       "      <td>199.4742</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.043246</td>\n",
       "      <td>11.350</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8509</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDG45</td>\n",
       "      <td>213.9902</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.214306</td>\n",
       "      <td>8.100</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8510</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDN58</td>\n",
       "      <td>231.5984</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.056862</td>\n",
       "      <td>13.800</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8511</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDF05</td>\n",
       "      <td>262.5910</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.026980</td>\n",
       "      <td>17.500</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8512</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDR26</td>\n",
       "      <td>178.3028</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.042801</td>\n",
       "      <td>20.700</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8513</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDH31</td>\n",
       "      <td>99.9042</td>\n",
       "      <td>Meat</td>\n",
       "      <td>0.020407</td>\n",
       "      <td>12.000</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8514</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDA01</td>\n",
       "      <td>57.5904</td>\n",
       "      <td>Canned</td>\n",
       "      <td>0.054489</td>\n",
       "      <td>15.000</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8515</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDH24</td>\n",
       "      <td>157.5288</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>0.021518</td>\n",
       "      <td>20.700</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8516</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCJ19</td>\n",
       "      <td>58.7588</td>\n",
       "      <td>Others</td>\n",
       "      <td>0.118661</td>\n",
       "      <td>18.600</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8517</th>\n",
       "      <td>reg</td>\n",
       "      <td>FDF53</td>\n",
       "      <td>178.8318</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>0.083607</td>\n",
       "      <td>20.750</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDF22</td>\n",
       "      <td>214.5218</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.056783</td>\n",
       "      <td>6.865</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>High</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDS36</td>\n",
       "      <td>108.1570</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>0.046982</td>\n",
       "      <td>8.380</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NCJ29</td>\n",
       "      <td>85.1224</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>0.035186</td>\n",
       "      <td>10.600</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDN46</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>0.145221</td>\n",
       "      <td>7.210</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>DRG01</td>\n",
       "      <td>75.4670</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>0.044878</td>\n",
       "      <td>14.800</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Small</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Fat_Content Item_Identifier  Item_MRP              Item_Type  \\\n",
       "0             Low Fat           FDA15  249.8092                  Dairy   \n",
       "1             Regular           DRC01   48.2692            Soft Drinks   \n",
       "2             Low Fat           FDN15  141.6180                   Meat   \n",
       "3             Regular           FDX07  182.0950  Fruits and Vegetables   \n",
       "4             Low Fat           NCD19   53.8614              Household   \n",
       "5             Regular           FDP36   51.4008           Baking Goods   \n",
       "6             Regular           FDO10   57.6588            Snack Foods   \n",
       "7             Low Fat           FDP10  107.7622            Snack Foods   \n",
       "8             Regular           FDH17   96.9726           Frozen Foods   \n",
       "9             Regular           FDU28  187.8214           Frozen Foods   \n",
       "10            Low Fat           FDY07   45.5402  Fruits and Vegetables   \n",
       "11            Regular           FDA03  144.1102                  Dairy   \n",
       "12            Regular           FDX32  145.4786  Fruits and Vegetables   \n",
       "13            Regular           FDS46  119.6782            Snack Foods   \n",
       "14            Low Fat           FDF32  196.4426  Fruits and Vegetables   \n",
       "15            Regular           FDP49   56.3614              Breakfast   \n",
       "16            Low Fat           NCB42  115.3492     Health and Hygiene   \n",
       "17            Regular           FDP49   54.3614              Breakfast   \n",
       "18            Low Fat           DRI11  113.2834            Hard Drinks   \n",
       "19            Low Fat           FDU02  230.5352                  Dairy   \n",
       "20            Regular           FDN22  250.8724            Snack Foods   \n",
       "21            Regular           FDW12  144.5444           Baking Goods   \n",
       "22            Low Fat           NCB30  196.5084              Household   \n",
       "23            Low Fat           FDC37  107.6938           Baking Goods   \n",
       "24            Regular           FDR28  165.0210           Frozen Foods   \n",
       "25            Low Fat           NCD06   45.9060              Household   \n",
       "26            Regular           FDV10   42.3112            Snack Foods   \n",
       "27            low fat           DRJ59   39.1164            Hard Drinks   \n",
       "28            Regular           FDE51   45.5086                  Dairy   \n",
       "29            Regular           FDC14   43.6454                 Canned   \n",
       "...               ...             ...       ...                    ...   \n",
       "8493          Regular           FDP21  189.1872            Snack Foods   \n",
       "8494          Low Fat           NCI54  110.4912              Household   \n",
       "8495          Low Fat           FDE22  160.4920            Snack Foods   \n",
       "8496          Regular           FDJ57  185.3582                Seafood   \n",
       "8497          Low Fat           FDT08  150.0050  Fruits and Vegetables   \n",
       "8498          Low Fat           NCP54  124.5730              Household   \n",
       "8499          Low Fat           NCK53  100.0042     Health and Hygiene   \n",
       "8500          Low Fat           NCQ42  125.1678              Household   \n",
       "8501          Regular           FDW21  100.4358            Snack Foods   \n",
       "8502          Low Fat           NCH43  216.4192              Household   \n",
       "8503          Low Fat           FDQ44  120.1756  Fruits and Vegetables   \n",
       "8504          Low Fat           NCN18  111.7544              Household   \n",
       "8505          Regular           FDB46  210.8244            Snack Foods   \n",
       "8506          Low Fat           DRF37  263.1910            Soft Drinks   \n",
       "8507          Regular           FDN28  101.7990           Frozen Foods   \n",
       "8508          Regular           FDW31  199.4742  Fruits and Vegetables   \n",
       "8509          Low Fat           FDG45  213.9902  Fruits and Vegetables   \n",
       "8510          Regular           FDN58  231.5984            Snack Foods   \n",
       "8511          Low Fat           FDF05  262.5910           Frozen Foods   \n",
       "8512          Low Fat           FDR26  178.3028                  Dairy   \n",
       "8513          Regular           FDH31   99.9042                   Meat   \n",
       "8514          Regular           FDA01   57.5904                 Canned   \n",
       "8515          Low Fat           FDH24  157.5288           Baking Goods   \n",
       "8516          Low Fat           NCJ19   58.7588                 Others   \n",
       "8517              reg           FDF53  178.8318           Frozen Foods   \n",
       "8518          Low Fat           FDF22  214.5218            Snack Foods   \n",
       "8519          Regular           FDS36  108.1570           Baking Goods   \n",
       "8520          Low Fat           NCJ29   85.1224     Health and Hygiene   \n",
       "8521          Regular           FDN46  103.1332            Snack Foods   \n",
       "8522          Low Fat           DRG01   75.4670            Soft Drinks   \n",
       "\n",
       "      Item_Visibility  Item_Weight  Outlet_Establishment_Year  \\\n",
       "0            0.016047        9.300                     1999.0   \n",
       "1            0.019278        5.920                     2009.0   \n",
       "2            0.016760       17.500                     1999.0   \n",
       "3            0.000000       19.200                     1998.0   \n",
       "4            0.000000        8.930                     1987.0   \n",
       "5            0.000000       10.395                     2009.0   \n",
       "6            0.012741       13.650                     1987.0   \n",
       "7            0.127470          NaN                     1985.0   \n",
       "8            0.016687       16.200                     2002.0   \n",
       "9            0.094450       19.200                     2007.0   \n",
       "10           0.000000       11.800                     1999.0   \n",
       "11           0.045464       18.500                     1997.0   \n",
       "12           0.100014       15.100                     1999.0   \n",
       "13           0.047257       17.600                     1997.0   \n",
       "14           0.068024       16.350                     1987.0   \n",
       "15           0.069089        9.000                     1997.0   \n",
       "16           0.008596       11.800                     2009.0   \n",
       "17           0.069196        9.000                     1999.0   \n",
       "18           0.034238          NaN                     1985.0   \n",
       "19           0.102492       13.350                     2004.0   \n",
       "20           0.138190       18.850                     1987.0   \n",
       "21           0.035400          NaN                     1985.0   \n",
       "22           0.025698       14.600                     2004.0   \n",
       "23           0.057557          NaN                     1985.0   \n",
       "24           0.025896       13.850                     1997.0   \n",
       "25           0.099887       13.000                     2007.0   \n",
       "26           0.066693        7.645                     2004.0   \n",
       "27           0.019356       11.650                     1987.0   \n",
       "28           0.161467        5.925                     1998.0   \n",
       "29           0.072222          NaN                     1985.0   \n",
       "...               ...          ...                        ...   \n",
       "8493         0.025886        7.420                     2007.0   \n",
       "8494         0.000000       15.200                     2007.0   \n",
       "8495         0.029567        9.695                     2004.0   \n",
       "8496         0.021696        7.420                     2007.0   \n",
       "8497         0.049209       13.650                     2004.0   \n",
       "8498         0.035293       15.350                     2009.0   \n",
       "8499         0.037574       11.600                     2004.0   \n",
       "8500         0.000000       20.350                     2007.0   \n",
       "8501         0.005998        5.340                     2007.0   \n",
       "8502         0.070712        8.420                     2002.0   \n",
       "8503         0.036133       20.500                     2004.0   \n",
       "8504         0.124111          NaN                     1985.0   \n",
       "8505         0.094146       10.500                     2009.0   \n",
       "8506         0.084676       17.250                     2009.0   \n",
       "8507         0.030242        5.880                     2004.0   \n",
       "8508         0.043246       11.350                     2002.0   \n",
       "8509         0.214306        8.100                     1998.0   \n",
       "8510         0.056862       13.800                     2004.0   \n",
       "8511         0.026980       17.500                     2009.0   \n",
       "8512         0.042801       20.700                     1987.0   \n",
       "8513         0.020407       12.000                     2004.0   \n",
       "8514         0.054489       15.000                     2002.0   \n",
       "8515         0.021518       20.700                     2009.0   \n",
       "8516         0.118661       18.600                     2009.0   \n",
       "8517         0.083607       20.750                     1997.0   \n",
       "8518         0.056783        6.865                     1987.0   \n",
       "8519         0.046982        8.380                     2002.0   \n",
       "8520         0.035186       10.600                     2004.0   \n",
       "8521         0.145221        7.210                     2009.0   \n",
       "8522         0.044878       14.800                     1997.0   \n",
       "\n",
       "     Outlet_Identifier Outlet_Location_Type Outlet_Size        Outlet_Type  \n",
       "0               OUT049               Tier 1      Medium  Supermarket Type1  \n",
       "1               OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "2               OUT049               Tier 1      Medium  Supermarket Type1  \n",
       "3               OUT010               Tier 3         NaN      Grocery Store  \n",
       "4               OUT013               Tier 3        High  Supermarket Type1  \n",
       "5               OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "6               OUT013               Tier 3        High  Supermarket Type1  \n",
       "7               OUT027               Tier 3      Medium  Supermarket Type3  \n",
       "8               OUT045               Tier 2         NaN  Supermarket Type1  \n",
       "9               OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "10              OUT049               Tier 1      Medium  Supermarket Type1  \n",
       "11              OUT046               Tier 1       Small  Supermarket Type1  \n",
       "12              OUT049               Tier 1      Medium  Supermarket Type1  \n",
       "13              OUT046               Tier 1       Small  Supermarket Type1  \n",
       "14              OUT013               Tier 3        High  Supermarket Type1  \n",
       "15              OUT046               Tier 1       Small  Supermarket Type1  \n",
       "16              OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "17              OUT049               Tier 1      Medium  Supermarket Type1  \n",
       "18              OUT027               Tier 3      Medium  Supermarket Type3  \n",
       "19              OUT035               Tier 2       Small  Supermarket Type1  \n",
       "20              OUT013               Tier 3        High  Supermarket Type1  \n",
       "21              OUT027               Tier 3      Medium  Supermarket Type3  \n",
       "22              OUT035               Tier 2       Small  Supermarket Type1  \n",
       "23              OUT019               Tier 1       Small      Grocery Store  \n",
       "24              OUT046               Tier 1       Small  Supermarket Type1  \n",
       "25              OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "26              OUT035               Tier 2       Small  Supermarket Type1  \n",
       "27              OUT013               Tier 3        High  Supermarket Type1  \n",
       "28              OUT010               Tier 3         NaN      Grocery Store  \n",
       "29              OUT019               Tier 1       Small      Grocery Store  \n",
       "...                ...                  ...         ...                ...  \n",
       "8493            OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "8494            OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "8495            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8496            OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "8497            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8498            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8499            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8500            OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "8501            OUT017               Tier 2         NaN  Supermarket Type1  \n",
       "8502            OUT045               Tier 2         NaN  Supermarket Type1  \n",
       "8503            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8504            OUT027               Tier 3      Medium  Supermarket Type3  \n",
       "8505            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8506            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8507            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8508            OUT045               Tier 2         NaN  Supermarket Type1  \n",
       "8509            OUT010               Tier 3         NaN      Grocery Store  \n",
       "8510            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8511            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8512            OUT013               Tier 3        High  Supermarket Type1  \n",
       "8513            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8514            OUT045               Tier 2         NaN  Supermarket Type1  \n",
       "8515            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8516            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8517            OUT046               Tier 1       Small  Supermarket Type1  \n",
       "8518            OUT013               Tier 3        High  Supermarket Type1  \n",
       "8519            OUT045               Tier 2         NaN  Supermarket Type1  \n",
       "8520            OUT035               Tier 2       Small  Supermarket Type1  \n",
       "8521            OUT018               Tier 3      Medium  Supermarket Type2  \n",
       "8522            OUT046               Tier 1       Small  Supermarket Type1  \n",
       "\n",
       "[8523 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with missing data\n",
    "import numpy as np \n",
    "\n",
    "df['train']['Item_Weight'] = df['train']['Item_Weight'].fillna(np.nanmedian(df['train']['Item_Weight']))\n",
    "df['train']['Outlet_Size'] = df['train']['Outlet_Size'].fillna(df['train']['Outlet_Size'].mode().iloc[0])\n",
    "\n",
    "df['test']['Item_Weight'] = df['test']['Item_Weight'].fillna(np.nanmedian(df['test']['Item_Weight']))\n",
    "df['test']['Outlet_Size'] = df['test']['Outlet_Size'].fillna(df['test']['Outlet_Size'].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Medium', 'High', 'Small'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['train']['Outlet_Size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "computing drifts...\n",
      "CPU time: 0.6470849514007568 seconds\n",
      "\n",
      "Top 10 drifts\n",
      "\n",
      "('Item_Weight', 0.65609847148587663)\n",
      "('Item_Identifier', 0.51106910120235582)\n",
      "('Outlet_Establishment_Year', 0.50937181321141645)\n",
      "('Outlet_Identifier', 0.50936466518361112)\n",
      "('Item_Type', 0.50710733346937098)\n",
      "('Outlet_Type', 0.5068804417207915)\n",
      "('Item_MRP', 0.50626681759172498)\n",
      "('Outlet_Location_Type', 0.50406546024832555)\n",
      "('Item_Fat_Content', 0.50397593181172284)\n",
      "('Outlet_Size', 0.50290956941229747)\n",
      "\n",
      "deleted variables : []\n",
      "\n",
      "dumping drift coefficients into directory : save\n",
      "drift coefficients dumped\n"
     ]
    }
   ],
   "source": [
    "# It automatically detect and remove shifting features\n",
    "\n",
    "df=Drift_thresholder().fit_transform(df)  # 0.8 is the default shifting threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.24340838181811925}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1391282.33606\n",
      "VARIANCE : 88664.9284525 (fold 1 = -1424721.43356, fold 2 = -1348655.45321, fold 3 = -1416080.95793, fold 4 = -1249994.14259, fold 5 = -1516959.69298)\n",
      "CPU time: 38.442673206329346 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.259655482964065}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1616315.97098\n",
      "VARIANCE : 381582.545779 (fold 1 = -1496052.03068, fold 2 = -1377045.52064, fold 3 = -1520800.30717, fold 4 = -1322470.7294, fold 5 = -2365211.26703)\n",
      "CPU time: 123.07528305053711 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.09252636749438058}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1262728.70446\n",
      "VARIANCE : 90974.9616548 (fold 1 = -1328470.0293, fold 2 = -1246551.60169, fold 3 = -1315200.7206, fold 4 = -1091628.13793, fold 5 = -1331793.0328)\n",
      "CPU time: 23.051201105117798 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.19465908763257067}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1315593.91457\n",
      "VARIANCE : 93484.4530029 (fold 1 = -1361176.40834, fold 2 = -1274828.971, fold 3 = -1380600.38255, fold 4 = -1151659.36332, fold 5 = -1409704.44763)\n",
      "CPU time: 21.761613130569458 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.2928439578623288}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1210987.51282\n",
      "VARIANCE : 78354.3079837 (fold 1 = -1256443.16316, fold 2 = -1208105.55226, fold 3 = -1248988.50816, fold 4 = -1061310.38448, fold 5 = -1280089.95604)\n",
      "CPU time: 8.00858211517334 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.02758966238509085}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1240280.46376\n",
      "VARIANCE : 90956.235855 (fold 1 = -1301796.562, fold 2 = -1226240.62193, fold 3 = -1295416.72253, fold 4 = -1068394.0139, fold 5 = -1309554.39844)\n",
      "CPU time: 15.897170066833496 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.26337900867627656}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 3, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1256520.68653\n",
      "VARIANCE : 86944.8388357 (fold 1 = -1308147.03514, fold 2 = -1263097.88633, fold 3 = -1288388.87369, fold 4 = -1088976.76708, fold 5 = -1333992.87042)\n",
      "CPU time: 13.478636980056763 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.09195469902564313}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 3, 'n_estimators': 777, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1238127.04652\n",
      "VARIANCE : 91365.4366374 (fold 1 = -1285743.14318, fold 2 = -1243750.38165, fold 3 = -1267527.12583, fold 4 = -1064217.1491, fold 5 = -1329397.43283)\n",
      "CPU time: 15.068554878234863 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.14065441650865235}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1265230.96795\n",
      "VARIANCE : 78037.8980773 (fold 1 = -1340591.97455, fold 2 = -1237231.95449, fold 3 = -1285741.76633, fold 4 = -1128187.12618, fold 5 = -1334402.01821)\n",
      "CPU time: 13.981564044952393 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.1068024801057752}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 3, 'n_estimators': 777, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1210794.43705\n",
      "VARIANCE : 87492.4676639 (fold 1 = -1266994.39079, fold 2 = -1221303.41434, fold 3 = -1257415.69907, fold 4 = -1039215.50828, fold 5 = -1269043.17277)\n",
      "CPU time: 13.299551010131836 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.1475430322752079}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 777, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1345065.80375\n",
      "VARIANCE : 82153.5535971 (fold 1 = -1416497.1858, fold 2 = -1322495.76432, fold 3 = -1352989.70678, fold 4 = -1201653.37279, fold 5 = -1431692.98903)\n",
      "CPU time: 42.68111205101013 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.214903070391397}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 3, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1225187.96583\n",
      "VARIANCE : 87121.5299609 (fold 1 = -1291798.97175, fold 2 = -1232743.08103, fold 3 = -1270366.1224, fold 4 = -1055302.15973, fold 5 = -1275729.49424)\n",
      "CPU time: 15.422064065933228 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.015400712907574603}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1414896.67525\n",
      "VARIANCE : 94712.3870427 (fold 1 = -1472760.77648, fold 2 = -1368298.43806, fold 3 = -1492560.04602, fold 4 = -1249298.39652, fold 5 = -1491565.71918)\n",
      "CPU time: 39.21860313415527 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.12373387801221515}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1298951.06032\n",
      "VARIANCE : 80380.5829359 (fold 1 = -1368185.54819, fold 2 = -1279875.04113, fold 3 = -1313425.33793, fold 4 = -1155167.91838, fold 5 = -1378101.45595)\n",
      "CPU time: 29.077468156814575 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.22163613889968692}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 3, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1292140.4024\n",
      "VARIANCE : 260867.197829 (fold 1 = -1221626.85385, fold 2 = -1198579.78051, fold 3 = -1217838.59708, fold 4 = -1028836.29048, fold 5 = -1793820.49009)\n",
      "CPU time: 5.867727994918823 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.1466179583791516}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1264241.16494\n",
      "VARIANCE : 92366.7862511 (fold 1 = -1337171.79973, fold 2 = -1259365.85085, fold 3 = -1319850.66, fold 4 = -1087133.95373, fold 5 = -1317683.56038)\n",
      "CPU time: 20.403486013412476 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.093312207617748}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 777, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1347444.42336\n",
      "VARIANCE : 82540.1204365 (fold 1 = -1425193.15606, fold 2 = -1326456.69585, fold 3 = -1365570.11078, fold 4 = -1199442.36333, fold 5 = -1420559.79076)\n",
      "CPU time: 39.082828998565674 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.2637836705123948}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1293114.68941\n",
      "VARIANCE : 95849.6138459 (fold 1 = -1364429.5498, fold 2 = -1263704.2884, fold 3 = -1360479.2194, fold 4 = -1117077.71235, fold 5 = -1359882.67709)\n",
      "CPU time: 25.37029719352722 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.060013803226425494}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1387268.09487\n",
      "VARIANCE : 99640.9349038 (fold 1 = -1480569.05343, fold 2 = -1345572.68763, fold 3 = -1437406.52234, fold 4 = -1210880.72161, fold 5 = -1461911.48931)\n",
      "CPU time: 43.72024488449097 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.10802298841500145}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 999, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1377300.27355\n",
      "VARIANCE : 81080.5960909 (fold 1 = -1450544.41909, fold 2 = -1337567.5272, fold 3 = -1379113.83606, fold 4 = -1247166.23217, fold 5 = -1472109.35323)\n",
      "CPU time: 49.09121608734131 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.2911438537203328}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1210987.51282\n",
      "VARIANCE : 78354.3079837 (fold 1 = -1256443.16316, fold 2 = -1208105.55226, fold 3 = -1248988.50816, fold 4 = -1061310.38448, fold 5 = -1280089.95604)\n",
      "CPU time: 8.51688003540039 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.18091819239928936}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 777, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1333411.63625\n",
      "VARIANCE : 85837.6311441 (fold 1 = -1399628.85845, fold 2 = -1317027.05671, fold 3 = -1352565.40093, fold 4 = -1177590.59007, fold 5 = -1420246.27511)\n",
      "CPU time: 23.46346092224121 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.053231921350456}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 3, 'n_estimators': 777, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1223322.07086\n",
      "VARIANCE : 76764.0878569 (fold 1 = -1256445.31869, fold 2 = -1234156.23403, fold 3 = -1262212.81871, fold 4 = -1073951.94403, fold 5 = -1289844.03886)\n",
      "CPU time: 9.541645050048828 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.16719170695900007}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207455.87515\n",
      "VARIANCE : 90556.6579459 (fold 1 = -1255912.371, fold 2 = -1199214.80526, fold 3 = -1266427.89381, fold 4 = -1035000.80681, fold 5 = -1280723.49888)\n",
      "CPU time: 8.029596090316772 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.18352142335368926}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1206876.2304\n",
      "VARIANCE : 90072.7715528 (fold 1 = -1269096.04217, fold 2 = -1204865.69603, fold 3 = -1261984.78836, fold 4 = -1033018.95018, fold 5 = -1265415.67526)\n",
      "CPU time: 13.382926940917969 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.17674752238696306}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.766952276229858 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.193492934164765}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.981571197509766 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.20390177965239697}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.839221000671387 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.2283986726115822}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.991798162460327 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.23656179413402809}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.90871787071228 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.2509328574746459}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.866833209991455 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.25173826384562603}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.923513174057007 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.27798783721616976}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1318074.63702\n",
      "VARIANCE : 227638.316213 (fold 1 = -1274856.83219, fold 2 = -1235886.57334, fold 3 = -1251053.85662, fold 4 = -1077043.56496, fold 5 = -1751532.358)\n",
      "CPU time: 15.446709156036377 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'l1', 'threshold': 0.1908860553501464}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1207034.56499\n",
      "VARIANCE : 85027.9278413 (fold 1 = -1267879.015, fold 2 = -1208761.36581, fold 3 = -1249952.33893, fold 4 = -1042402.13741, fold 5 = -1266177.96781)\n",
      "CPU time: 10.977934122085571 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.16445880361943846}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1206876.2304\n",
      "VARIANCE : 90072.7715528 (fold 1 = -1269096.04217, fold 2 = -1204865.69603, fold 3 = -1261984.78836, fold 4 = -1033018.95018, fold 5 = -1265415.67526)\n",
      "CPU time: 13.780722856521606 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.16529354522296577}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1264241.16494\n",
      "VARIANCE : 92366.7862511 (fold 1 = -1337171.79973, fold 2 = -1259365.85085, fold 3 = -1319850.66, fold 4 = -1087133.95373, fold 5 = -1317683.56038)\n",
      "CPU time: 21.204238653182983 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.128693510882525}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1226511.25277\n",
      "VARIANCE : 80715.1720003 (fold 1 = -1272765.31472, fold 2 = -1239016.42759, fold 3 = -1262670.17014, fold 4 = -1068447.74086, fold 5 = -1289656.61053)\n",
      "CPU time: 16.04247784614563 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.0618163677015814}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 9, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1238704.38892\n",
      "VARIANCE : 89318.8669526 (fold 1 = -1306679.33961, fold 2 = -1220063.31915, fold 3 = -1289321.31804, fold 4 = -1071758.77222, fold 5 = -1305699.19555)\n",
      "CPU time: 17.288762092590332 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.21157697588483126}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 7, 'n_estimators': 500, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1429238.0031\n",
      "VARIANCE : 234125.743339 (fold 1 = -1379324.27283, fold 2 = -1323153.22165, fold 3 = -1351629.02198, fold 4 = -1209129.61494, fold 5 = -1882953.88408)\n",
      "CPU time: 29.631175756454468 seconds\n",
      "\n",
      "\n",
      "##################################################### testing hyper-parameters... ############################################################\n",
      "\n",
      ">>> NA ENCODER :{'numerical_strategy': 'median', 'categorical_strategy': nan}\n",
      "\n",
      ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
      "\n",
      ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.13068246788522075}\n",
      "\n",
      ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'n_estimators': 250, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.05, 'max_bin': 255, 'min_child_samples': 10, 'min_child_weight': 5, 'min_split_gain': 0, 'nthread': -1, 'num_leaves': 31, 'objective': 'regression', 'reg_alpha': 0, 'reg_lambda': 0, 'seed': 0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 50000, 'subsample_freq': 1}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MEAN SCORE : mean_squared_error = -1206876.2304\n",
      "VARIANCE : 90072.7715528 (fold 1 = -1269096.04217, fold 2 = -1204865.69603, fold 3 = -1261984.78836, fold 4 = -1033018.95018, fold 5 = -1265415.67526)\n",
      "CPU time: 13.204274892807007 seconds\n",
      "\n",
      "\n",
      "\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BEST HYPER-PARAMETERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "\n",
      "{'ce__strategy': 'label_encoding', 'est__max_depth': 5, 'est__n_estimators': 250, 'fs__strategy': 'rf_feature_importance', 'fs__threshold': 0.18352142335368926, 'ne__categorical_strategy': nan, 'ne__numerical_strategy': 'median'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/scorer.py:99: DeprecationWarning: Scoring method mean_squared_error was renamed to neg_mean_squared_error in version 0.18 and will be removed in 0.20.\n",
      "  sample_weight=sample_weight)\n"
     ]
    }
   ],
   "source": [
    "# setting the hyperparameter space\n",
    "space = {'ne__numerical_strategy':{\"search\":\"choice\",\"space\":['mean','median']},\n",
    "'ne__categorical_strategy':{\"search\":\"choice\",\"space\":[np.NaN]},\n",
    "'ce__strategy':{\"search\":\"choice\",\"space\":['label_encoding','entity_embedding','random_projection']},\n",
    "'fs__strategy':{\"search\":\"choice\",\"space\":['l1','variance','rf_feature_importance']},\n",
    "'fs__threshold':{\"search\":\"uniform\",\"space\":[0.01, 0.3]},\n",
    "'est__max_depth':{\"search\":\"choice\",\"space\":[3,5,7,9]},\n",
    "'est__n_estimators':{\"search\":\"choice\",\"space\":[250,500,777,999]}}\n",
    "\n",
    "\n",
    "# calculating the best hyper-parameter\n",
    "best=Optimiser(scoring=\"mean_squared_error\",n_folds=5).optimise(space,df,40)   # 40 is the number of iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "fitting the pipeline...\n",
      "CPU time: 3.6376922130584717 seconds\n",
      "\n",
      "dumping feature importances into directory : save\n"
     ]
    },
    {
     "data": {
      "image/png": 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hzHyr2ZRUgOGUli1fSWkJc+Nf54LMrF2Be5AkSZIkSVIL7rnnHi6//HKGDh1KbW0p5fLD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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11084f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "predicting...\n",
      "CPU time: 0.05679821968078613 seconds\n",
      "\n",
      "top 10 predictions :\n",
      "\n",
      "   Item_Outlet_Sales_predicted\n",
      "0                  1472.777624\n",
      "1                  1320.772321\n",
      "2                   609.170803\n",
      "3                  2510.855661\n",
      "4                  6366.136478\n",
      "5                  1903.204818\n",
      "6                   606.639674\n",
      "7                  2234.847714\n",
      "8                  1472.436550\n",
      "9                  3057.383588\n",
      "\n",
      "dumping predictions into directory : save\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlbox.prediction.predictor.Predictor at 0x10e7c7c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting on the test dataset\n",
    "Predictor().fit_predict(best,df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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