try_genetic_alg_through_TPOT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TPOT uses genetic algorithm for feature selection and model selection, and it does all these automatically\n",
    "# want to try it\n",
    "\n",
    "# Data can be down loaded here (need sign in): \n",
    "## https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn import preprocessing \n",
    "from sklearn.metrics import mean_squared_error \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tpot import TPOTRegressor\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train_GA.csv\")\n",
    "test = pd.read_csv(\"test_GA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              False\n",
       "Item_Weight                   True\n",
       "Item_Fat_Content             False\n",
       "Item_Visibility              False\n",
       "Item_Type                    False\n",
       "Item_MRP                     False\n",
       "Outlet_Identifier            False\n",
       "Outlet_Establishment_Year    False\n",
       "Outlet_Size                   True\n",
       "Outlet_Location_Type         False\n",
       "Outlet_Type                  False\n",
       "Item_Outlet_Sales            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(train).sum() > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              False\n",
       "Item_Weight                   True\n",
       "Item_Fat_Content             False\n",
       "Item_Visibility              False\n",
       "Item_Type                    False\n",
       "Item_MRP                     False\n",
       "Outlet_Identifier            False\n",
       "Outlet_Establishment_Year    False\n",
       "Outlet_Size                   True\n",
       "Outlet_Location_Type         False\n",
       "Outlet_Type                  False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(test).sum() > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train.Item_Weight = train.Item_Weight.fillna(np.nanmedian(train.Item_Weight))\n",
    "test.Item_Weight = test.Item_Weight.fillna(np.nanmedian(test.Item_Weight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Medium' nan 'High' 'Small']\n",
      "['Medium' nan 'Small' 'High']\n"
     ]
    }
   ],
   "source": [
    "print train.Outlet_Size.unique()\n",
    "print test.Outlet_Size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.Outlet_Size = train.Outlet_Size.fillna(train.Outlet_Size.mode().iloc[0])\n",
    "test.Outlet_Size = test.Outlet_Size.fillna(train.Outlet_Size.mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_Weight                  float64\n",
       "Item_Fat_Content              object\n",
       "Item_Visibility              float64\n",
       "Item_Type                     object\n",
       "Item_MRP                     float64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Establishment_Year      int64\n",
       "Outlet_Size                   object\n",
       "Outlet_Location_Type          object\n",
       "Outlet_Type                   object\n",
       "Item_Outlet_Sales            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Low Fat' 'Regular' 'low fat' 'LF' 'reg']\n",
      "['Low Fat' 'reg' 'Regular' 'LF' 'low fat']\n",
      "['Dairy' 'Soft Drinks' 'Meat' 'Fruits and Vegetables' 'Household'\n",
      " 'Baking Goods' 'Snack Foods' 'Frozen Foods' 'Breakfast'\n",
      " 'Health and Hygiene' 'Hard Drinks' 'Canned' 'Breads' 'Starchy Foods'\n",
      " 'Others' 'Seafood']\n",
      "['Snack Foods' 'Dairy' 'Others' 'Fruits and Vegetables' 'Baking Goods'\n",
      " 'Health and Hygiene' 'Breads' 'Hard Drinks' 'Seafood' 'Soft Drinks'\n",
      " 'Household' 'Frozen Foods' 'Meat' 'Canned' 'Starchy Foods' 'Breakfast']\n",
      "['OUT049' 'OUT018' 'OUT010' 'OUT013' 'OUT027' 'OUT045' 'OUT017' 'OUT046'\n",
      " 'OUT035' 'OUT019']\n",
      "['OUT049' 'OUT017' 'OUT010' 'OUT027' 'OUT046' 'OUT018' 'OUT045' 'OUT019'\n",
      " 'OUT013' 'OUT035']\n",
      "['Medium' 'High' 'Small']\n",
      "['Medium' 'Small' 'High']\n",
      "['Tier 1' 'Tier 3' 'Tier 2']\n",
      "['Tier 1' 'Tier 2' 'Tier 3']\n",
      "['Supermarket Type1' 'Supermarket Type2' 'Grocery Store'\n",
      " 'Supermarket Type3']\n",
      "['Supermarket Type1' 'Grocery Store' 'Supermarket Type3'\n",
      " 'Supermarket Type2']\n"
     ]
    }
   ],
   "source": [
    "print train.Item_Fat_Content.unique()\n",
    "print test.Item_Fat_Content.unique()\n",
    "print train.Item_Type.unique()\n",
    "print test.Item_Type.unique()\n",
    "print train.Outlet_Identifier.unique()\n",
    "print test.Outlet_Identifier.unique()\n",
    "print train.Outlet_Size.unique()\n",
    "print test.Outlet_Size.unique()\n",
    "print train.Outlet_Location_Type.unique()\n",
    "print test.Outlet_Location_Type.unique()\n",
    "print train.Outlet_Type.unique()\n",
    "print test.Outlet_Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.Item_Fat_Content = train.Item_Fat_Content.replace(['low fat', 'LF'], ['Low Fat', 'Low Fat'])\n",
    "test.Item_Fat_Content = test.Item_Fat_Content.replace(['low fat', 'LF'], ['Low Fat', 'Low Fat'])\n",
    "train.Item_Fat_Content = train.Item_Fat_Content.replace(['reg'], ['Regular'])\n",
    "test.Item_Fat_Content = test.Item_Fat_Content.replace(['reg'], ['Regular'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Low Fat' 'Regular']\n",
      "['Low Fat' 'Regular']\n"
     ]
    }
   ],
   "source": [
    "print train.Item_Fat_Content.unique()\n",
    "print test.Item_Fat_Content.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009\n",
      "1985\n"
     ]
    }
   ],
   "source": [
    "print train.Outlet_Establishment_Year.max()\n",
    "print train.Outlet_Establishment_Year.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.Outlet_Establishment_Year = 2017 - train.Outlet_Establishment_Year\n",
    "test.Outlet_Establishment_Year = 2017 - test.Outlet_Establishment_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# label encoding, do this by combining train and test together\n",
    "\n",
    "test['Item_Outlet_Sales'] = 0\n",
    "combi = train.append(test)\n",
    "number = LabelEncoder()\n",
    "\n",
    "for i in combi.columns:\n",
    "    if (combi[i].dtype == 'object'):\n",
    "        combi[i] = number.fit_transform(combi[i].astype('str'))\n",
    "        combi[i] = combi[i].astype('object')\n",
    "        \n",
    "train = combi[:train.shape[0]]\n",
    "test = combi[train.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1114</td>\n",
       "      <td>20.750</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>13</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1078</td>\n",
       "      <td>8.300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>4</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1420</td>\n",
       "      <td>14.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>11</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>817</td>\n",
       "      <td>7.315</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>13</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1197</td>\n",
       "      <td>12.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>4</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility Item_Type  \\\n",
       "0            1114       20.750                0         0.007565        13   \n",
       "1            1078        8.300                1         0.038428         4   \n",
       "2            1420       14.600                0         0.099575        11   \n",
       "3             817        7.315                0         0.015388        13   \n",
       "4            1197       12.500                1         0.118599         4   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  107.8622                 9                         18           1   \n",
       "1   87.3198                 2                         10           1   \n",
       "2  241.7538                 0                         19           1   \n",
       "3  155.0340                 2                         10           1   \n",
       "4  234.2300                 5                         32           1   \n",
       "\n",
       "  Outlet_Location_Type Outlet_Type  Item_Outlet_Sales  \n",
       "0                    0           1                0.0  \n",
       "1                    1           1                0.0  \n",
       "2                    2           0                0.0  \n",
       "3                    1           1                0.0  \n",
       "4                    2           3                0.0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = test.drop('Item_Outlet_Sales',axis=1)\n",
    "\n",
    "# remove id and those with more levels\n",
    "tpot_train = train.drop('Item_Identifier',axis=1)\n",
    "tpot_test = test.drop('Item_Identifier',axis=1)\n",
    "target = tpot_train['Item_Outlet_Sales']\n",
    "tpot_train = tpot_train.drop('Item_Outlet_Sales',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Python/2.7/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "Optimization Progress:  46%|████▌     | 138/300 [02:35<01:59,  1.36pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 1156077.3302\n",
      "Generation 2 - Current best internal CV score: 1156077.3302"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  60%|██████    | 181/300 [03:49<02:55,  1.47s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 3 - Current best internal CV score: 1153466.11981"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  77%|███████▋  | 230/300 [04:57<01:09,  1.01pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 4 - Current best internal CV score: 1153466.11981"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  93%|█████████▎| 279/300 [06:10<00:22,  1.06s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 5 - Current best internal CV score: 1151908.128"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "          \r",
      "\r",
      "          \r",
      "Optimization Progress:  93%|█████████▎| 279/300 [06:10<00:22,  1.06s/pipeline]\r",
      "                                                                              "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Best pipeline: ExtraTreesRegressor(input_matrix, ExtraTreesRegressor__bootstrap=True, ExtraTreesRegressor__max_features=1.0, ExtraTreesRegressor__min_samples_leaf=13, ExtraTreesRegressor__min_samples_split=20, ExtraTreesRegressor__n_estimators=100)\n",
      "1210911.02967\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# build the model with tpot\n",
    "# finally building model using tpot library\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(tpot_train, target,\n",
    " train_size=0.75, test_size=0.25)\n",
    "\n",
    "tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\n",
    "tpot.fit(X_train, y_train)\n",
    "print(tpot.score(X_test, y_test))   # mean squared error (MSE)\n",
    "tpot.export('tpot_boston_pipeline.py')"
   ]
  }
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