model-35-predict-lgb-remove_outlier_03-0.001-kfolds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change Log:\n",
    "- v32 - remove building with too large percent error\n",
    "- v20 - lower learning rate = 0.0008, add height_r and top_floor index\n",
    "- v16 - change parameters (some from model 05), rm reduce to 68\n",
    "- v14 - change random seed\n",
    "- v10 - remove features based on feature importance results (exp-l04-per-area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pickle\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from utilities import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'model-35-lgb-remove_outlier_03-2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_log_target = True\n",
    "is_per_area_target = True\n",
    "rm_outlier = True\n",
    "outlier_pct = 3 # 1, 3, 5\n",
    "\n",
    "map_target = {\n",
    "    (True, True):   'log_per_area_total_price',\n",
    "    (True, False):  'log_total_price',\n",
    "    (False, True):  'per_area_total_price',\n",
    "    (False, False): 'total_price'\n",
    "}\n",
    "col_target = map_target[(is_log_target, is_per_area_target)]\n",
    "\n",
    "#cols_feats_add = ['encoded_latlon', 'building_per_land_area'] # add feats into model\n",
    "cols_num_add = ['building_per_land_area', 'height_r']\n",
    "cols_cat_add = ['encoded_latlon']\n",
    "\n",
    "#cols_feats_rm = [] # TODO: implement before using it\n",
    "#['day_in_year_txn_dt', 'day_in_year_building_complete_dt', 'dt_diff_building_complete_dt_txn_dt'] # dt num feats\n",
    "#['dow_txn_dt', 'dow_building_complete_dt', 'month_txn_dt', 'month_building_complete_dt',\n",
    "# 'quarter_txn_dt', 'quarter_building_complete_dt', 'year_txn_dt', 'year_building_complete_dt'] # dt cat feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_processing(df, cols_num, cols_cat): #, cols_feats\n",
    "    # Convert types\n",
    "    df[cols_num] = df[cols_num].astype('float32')\n",
    "    \n",
    "    # generate target\n",
    "    if 'total_price' in df:\n",
    "        df['total_price'] = df['total_price'].astype('float32')\n",
    "        df['log_total_price'] = np.log1p(df['total_price'])\n",
    "        df['per_area_total_price'] = df['total_price'] / df['building_area']\n",
    "        df['log_per_area_total_price'] = np.log1p(df['per_area_total_price'])\n",
    "        df['target'] = df[col_target]\n",
    "\n",
    "    # generate lat/lon encoded\n",
    "    kmeans = pickle.load(open('./object_kmeans_k6_combined.pkl', 'rb'))\n",
    "    df['encoded_latlon'] = kmeans.predict(df[['lat','lon']])\n",
    "    \n",
    "    # generate dt feats\n",
    "    df = create_dt_feats(df, 'txn_dt')\n",
    "    df = create_dt_feats(df, 'building_complete_dt')\n",
    "    df['dt_diff_building_complete_dt_txn_dt'] = df['txn_dt'] - df['building_complete_dt']\n",
    "    \n",
    "    # generate building to land ratio\n",
    "    df['building_per_land_area'] = df['building_area'] / df['land_area']\n",
    " \n",
    "    #cols_feats = cols_num + cols_cat + cols_feats_add\n",
    "    \n",
    "    # fix town and city\n",
    "    df['town'] = df['city'].astype('str')+'-'+df['town'].astype('str')\n",
    "    df['village'] = df['town'].astype('str')+'-'+df['village'].astype('str')\n",
    "    \n",
    "    # height ratio\n",
    "    df['height_r'] = df['txn_floor'] / df['total_floor']\n",
    "    df['top_floor'] = df['txn_floor'] == df['total_floor']\n",
    "    \n",
    "    return df  #, cols_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_engineering import CategoricalColumnsEncoder\n",
    "from feature_engineering import TargetMeanEncoding\n",
    "from feature_engineering import MultiLabelEncoding\n",
    "\n",
    "class FeatureEngineering():\n",
    "    def __init__(self, cols_num, cols_cat, cols_feats, col_target, n_ran_encode=4):\n",
    "        # cols\n",
    "        self.cols_num = cols_num\n",
    "        self.cols_cat = cols_cat\n",
    "        self.cols_feats = cols_feats\n",
    "        self.col_target = col_target\n",
    "        \n",
    "        # Numbers\n",
    "        self.n_ran_encode = n_ran_encode\n",
    "        \n",
    "        # encoders\n",
    "        #self.tar_encoder = TargetMeanEncoding(col_target = self.col_target)\n",
    "        #self.ran_encoder = MultiLabelEncoding(self.n_ran_encode)\n",
    "        self.cat_astyper = CategoricalColumnsEncoder(mode='pandas')\n",
    "        \n",
    "    def fit_transform(self, df):\n",
    "        df = df.copy()\n",
    "    \n",
    "#        encoded1 = self.tar_encoder.fit_transform(df[self.cols_cat], df[self.col_target])\n",
    "#        encoded2 = self.ran_encoder.fit_transform(df[self.cols_cat])\n",
    "        self.cat_astyper.fit_transform(df, self.cols_cat)    \n",
    "        return df\n",
    "#        return pd.concat([df, encoded1, encoded2], axis=1)\n",
    "    \n",
    "    def transform(self, df):\n",
    "        df = df.copy()\n",
    "#        encoded1 = self.tar_encoder.transform(df[self.cols_cat])\n",
    "#        encoded2 = self.ran_encoder.transform(df[self.cols_cat])     # TODO: add generated feats into cols_feats\n",
    "        self.cat_astyper.transform(df)\n",
    "        return df\n",
    "#        return pd.concat([df, encoded1, encoded2], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_processing(y_pred, df):\n",
    "    \"\"\"Args: \n",
    "          y_pred (1d np.array): raw prediction y\n",
    "          df (pd.Dataframe): input feature df\n",
    "       Return: np.array\n",
    "    \"\"\"\n",
    "    y_pred_final = y_pred.copy()\n",
    "    \n",
    "    if is_log_target:\n",
    "        y_pred_final = np.expm1(y_pred_final)\n",
    "        y_pred_final = np.clip(y_pred_final, 0, None)\n",
    "\n",
    "    if is_per_area_target:\n",
    "        y_pred_final = y_pred_final * df['building_area'].values\n",
    "        \n",
    "    return y_pred_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_num = ['txn_dt', 'total_floor', 'building_complete_dt', 'parking_area', 'parking_price', 'txn_floor',\n",
    "            'land_area', 'building_area', 'lat', 'lon', 'village_income_median', 'town_population', 'town_area',\n",
    "            'town_population_density', 'doc_rate', 'master_rate', 'bachelor_rate', 'jobschool_rate',\n",
    "            'highschool_rate', 'junior_rate', 'elementary_rate', 'born_rate', 'death_rate', 'marriage_rate',\n",
    "            'divorce_rate', 'N_50', 'N_500', 'N_1000', 'N_5000', 'N_10000', 'I_10', 'I_50', 'I_100', 'I_250',\n",
    "            'I_500', 'I_1000', 'I_5000', 'I_10000', 'I_MIN', 'II_10', 'II_50', 'II_100', 'II_250', 'II_500',\n",
    "            'II_1000', 'II_5000', 'II_10000', 'II_MIN', 'III_10', 'III_50', 'III_100', 'III_250', 'III_500',\n",
    "            'III_1000', 'III_5000', 'III_10000', 'III_MIN', 'IV_10', 'IV_50', 'IV_100', 'IV_250', 'IV_500',\n",
    "            'IV_1000', 'IV_5000', 'IV_10000', 'IV_MIN', 'V_10', 'V_50', 'V_100', 'V_250', 'V_500', 'V_1000',\n",
    "            'V_5000', 'V_10000', 'V_MIN', 'VI_50', 'VI_100', 'VI_250', 'VI_500', 'VI_1000', 'VI_5000', 'VI_10000',\n",
    "            'VI_MIN', 'VII_10', 'VII_50', 'VII_100', 'VII_250', 'VII_500', 'VII_1000', 'VII_5000', 'VII_10000',\n",
    "            'VII_MIN', 'VIII_10', 'VIII_50', 'VIII_100', 'VIII_250', 'VIII_500', 'VIII_1000', 'VIII_5000',\n",
    "            'VIII_10000', 'VIII_MIN', 'IX_10', 'IX_50', 'IX_100', 'IX_250', 'IX_500', 'IX_1000', 'IX_5000',\n",
    "            'IX_10000', 'IX_MIN', 'X_10', 'X_50', 'X_100', 'X_250', 'X_500', 'X_1000', 'X_5000', 'X_10000', 'X_MIN',\n",
    "            'XI_10', 'XI_50', 'XI_100', 'XI_250', 'XI_500', 'XI_1000', 'XI_5000', 'XI_10000', 'XI_MIN', 'XII_10',\n",
    "            'XII_50', 'XII_100', 'XII_250', 'XII_500', 'XII_1000', 'XII_5000', 'XII_10000', 'XII_MIN', 'XIII_10',\n",
    "            'XIII_50', 'XIII_100', 'XIII_250', 'XIII_500', 'XIII_1000', 'XIII_5000', 'XIII_10000', 'XIII_MIN',\n",
    "            'XIV_10', 'XIV_50', 'XIV_100', 'XIV_250', 'XIV_500', 'XIV_1000', 'XIV_5000', 'XIV_10000', 'XIV_MIN']\n",
    "cols_cat = ['building_material', 'city', 'building_type', 'building_use', 'parking_way', 'town', 'village',\n",
    "            'I_index_50', 'I_index_500', 'I_index_1000', 'II_index_50', 'II_index_500', 'II_index_1000',\n",
    "            'III_index_50', 'III_index_500', 'III_index_1000', 'IV_index_50', 'IV_index_500', 'IV_index_1000',\n",
    "            'IV_index_5000', 'V_index_50', 'V_index_500', 'V_index_1000', 'VI_10', 'VI_index_50', 'VI_index_500',\n",
    "            'VI_index_1000', 'VII_index_50', 'VII_index_500', 'VII_index_1000', 'VIII_index_50', 'VIII_index_500',\n",
    "            'VIII_index_1000', 'IX_index_50', 'IX_index_500', 'IX_index_1000', 'IX_index_5000', 'X_index_50',\n",
    "            'X_index_500', 'X_index_1000', 'XI_index_50', 'XI_index_500', 'XI_index_1000', 'XI_index_5000',\n",
    "            'XI_index_10000', 'XII_index_50', 'XII_index_500', 'XII_index_1000', 'XIII_index_50', 'XIII_index_500',\n",
    "            'XIII_index_1000', 'XIII_index_5000', 'XIII_index_10000', 'XIV_index_50', 'XIV_index_500',\n",
    "            'XIV_index_1000']\n",
    "cols_feats = cols_num + cols_cat\n",
    "\n",
    "# informational cols\n",
    "cols_dt = ['txn_dt', 'building_complete_dt']\n",
    "cols_bin = ['I_index_50', 'I_index_500', 'I_index_1000', 'II_index_50', 'II_index_500', 'II_index_1000',\n",
    "            'III_index_50', 'III_index_500', 'III_index_1000', 'IV_index_50', 'IV_index_500', 'IV_index_1000',\n",
    "            'IV_index_5000', 'V_index_50', 'V_index_500', 'V_index_1000', 'VI_10', 'VI_index_50', 'VI_index_500',\n",
    "            'VI_index_1000', 'VII_index_50', 'VII_index_500', 'VII_index_1000', 'VIII_index_50', 'VIII_index_500',\n",
    "            'VIII_index_1000', 'IX_index_50', 'IX_index_500', 'IX_index_1000', 'IX_index_5000', 'X_index_50',\n",
    "            'X_index_500', 'X_index_1000', 'XI_index_50', 'XI_index_500', 'XI_index_1000', 'XI_index_5000',\n",
    "            'XI_index_10000', 'XII_index_50', 'XII_index_500', 'XII_index_1000', 'XIII_index_50', 'XIII_index_500',\n",
    "            'XIII_index_1000', 'XIII_index_5000', 'XIII_index_10000', 'XIV_index_50', 'XIV_index_500',\n",
    "            'XIV_index_1000']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>building_material</th>\n",
       "      <th>city</th>\n",
       "      <th>txn_dt</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>building_type</th>\n",
       "      <th>building_use</th>\n",
       "      <th>building_complete_dt</th>\n",
       "      <th>parking_way</th>\n",
       "      <th>parking_area</th>\n",
       "      <th>...</th>\n",
       "      <th>XIV_500</th>\n",
       "      <th>XIV_index_500</th>\n",
       "      <th>XIV_1000</th>\n",
       "      <th>XIV_index_1000</th>\n",
       "      <th>XIV_5000</th>\n",
       "      <th>XIV_index_5000</th>\n",
       "      <th>XIV_10000</th>\n",
       "      <th>XIV_index_10000</th>\n",
       "      <th>XIV_MIN</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e3mMIMR3JJqCaXz1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>2483</td>\n",
       "      <td>1</td>\n",
       "      <td>6011</td>\n",
       "      <td>1</td>\n",
       "      <td>34.469802848528126</td>\n",
       "      <td>647603.7704719775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LgwzgklNvy4QCtq5</td>\n",
       "      <td>8</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>7885</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>15872</td>\n",
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       "      <td>32221</td>\n",
       "      <td>1</td>\n",
       "      <td>40.07357348973039</td>\n",
       "      <td>3321452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ucIR2NLLsC3T650L</td>\n",
       "      <td>8</td>\n",
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       "      <td>19289</td>\n",
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       "      <td>2</td>\n",
       "      <td>6028</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>15760</td>\n",
       "      <td>1</td>\n",
       "      <td>32228</td>\n",
       "      <td>1</td>\n",
       "      <td>54.46208108983661</td>\n",
       "      <td>9570885.316206373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jre1pJhcQj91Kdky</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>20385</td>\n",
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       "      <td>2</td>\n",
       "      <td>18325</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>rQpYpY9nRG7X5mmr</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>18</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>7442</td>\n",
       "      <td>1</td>\n",
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       "      <td>762711.9757135696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 235 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        building_id building_material city txn_dt total_floor building_type  \\\n",
       "0  e3mMIMR3JJqCaXz1                 8   21  18674           4             3   \n",
       "1  LgwzgklNvy4QCtq5                 8    7  18800           5             1   \n",
       "2  ucIR2NLLsC3T650L                 8    7  19289           4             1   \n",
       "3  jre1pJhcQj91Kdky                 8   21  20385          24             0   \n",
       "4  rQpYpY9nRG7X5mmr                 1   21  20657           2             4   \n",
       "\n",
       "  building_use building_complete_dt parking_way parking_area  ... XIV_500  \\\n",
       "0            2                 6271           2          NaN  ...      58   \n",
       "1            2                 7885           2          NaN  ...      28   \n",
       "2            2                 6028           2          NaN  ...      78   \n",
       "3            2                18325           0          NaN  ...      20   \n",
       "4            2                 6880           2          NaN  ...      18   \n",
       "\n",
       "  XIV_index_500 XIV_1000 XIV_index_1000 XIV_5000 XIV_index_5000 XIV_10000  \\\n",
       "0             1      157              1     2483              1      6011   \n",
       "1             1      115              1    15872              1     32221   \n",
       "2             1      212              1    15760              1     32228   \n",
       "3             1      125              1     2568              1      7271   \n",
       "4             1       47              1     2587              1      7442   \n",
       "\n",
       "  XIV_index_10000             XIV_MIN         total_price  \n",
       "0               1  34.469802848528126   647603.7704719775  \n",
       "1               1   40.07357348973039           3321452.0  \n",
       "2               1   54.46208108983661   9570885.316206373  \n",
       "3               1   99.62896580763416  14215011.127100045  \n",
       "4               1   124.1312362968124   762711.9757135696  \n",
       "\n",
       "[5 rows x 235 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>building_material</th>\n",
       "      <th>city</th>\n",
       "      <th>txn_dt</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>building_type</th>\n",
       "      <th>building_use</th>\n",
       "      <th>building_complete_dt</th>\n",
       "      <th>parking_way</th>\n",
       "      <th>parking_area</th>\n",
       "      <th>...</th>\n",
       "      <th>XIV_250</th>\n",
       "      <th>XIV_500</th>\n",
       "      <th>XIV_index_500</th>\n",
       "      <th>XIV_1000</th>\n",
       "      <th>XIV_index_1000</th>\n",
       "      <th>XIV_5000</th>\n",
       "      <th>XIV_index_5000</th>\n",
       "      <th>XIV_10000</th>\n",
       "      <th>XIV_index_10000</th>\n",
       "      <th>XIV_MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X5gsdTWGS3W7JJQB</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>18634</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1857</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>3991</td>\n",
       "      <td>1</td>\n",
       "      <td>8409</td>\n",
       "      <td>1</td>\n",
       "      <td>105.4622312795739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTshNOJyKHnT2YIT</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>20199</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>16011</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "      <td>1</td>\n",
       "      <td>10725</td>\n",
       "      <td>1</td>\n",
       "      <td>100.81980868401074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dhdymr0lV8N5kZOT</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>19769</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>18294</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>3707</td>\n",
       "      <td>1</td>\n",
       "      <td>19459</td>\n",
       "      <td>1</td>\n",
       "      <td>259.8005430183116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VEwyGGMcD56w5BOc</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>20479</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17837</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>6487</td>\n",
       "      <td>1</td>\n",
       "      <td>29400</td>\n",
       "      <td>1</td>\n",
       "      <td>22.941906168328487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wmUeMoJZfsqaSX9b</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>18164</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13272</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1346</td>\n",
       "      <td>1</td>\n",
       "      <td>3280</td>\n",
       "      <td>1</td>\n",
       "      <td>181.21309507601745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 234 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        building_id building_material city txn_dt total_floor building_type  \\\n",
       "0  X5gsdTWGS3W7JJQB                 1   13  18634           2             4   \n",
       "1  BTshNOJyKHnT2YIT                 8    7  20199          10             3   \n",
       "2  dhdymr0lV8N5kZOT                 8   12  19769           4             4   \n",
       "3  VEwyGGMcD56w5BOc                10    7  20479          19             2   \n",
       "4  wmUeMoJZfsqaSX9b                 8    3  18164          13             0   \n",
       "\n",
       "  building_use building_complete_dt parking_way parking_area  ... XIV_250  \\\n",
       "0            2                 1857           2          NaN  ...       7   \n",
       "1            2                16011           2          NaN  ...      18   \n",
       "2           10                18294           2          NaN  ...       0   \n",
       "3            0                17837           2          NaN  ...      16   \n",
       "4            2                13272           2          NaN  ...       4   \n",
       "\n",
       "  XIV_500 XIV_index_500 XIV_1000 XIV_index_1000 XIV_5000 XIV_index_5000  \\\n",
       "0      21             1      129              1     3991              1   \n",
       "1      44             1       67              1     1950              1   \n",
       "2       3             1       35              1     3707              1   \n",
       "3      60             1      144              1     6487              1   \n",
       "4      18             1       76              1     1346              1   \n",
       "\n",
       "  XIV_10000 XIV_index_10000             XIV_MIN  \n",
       "0      8409               1   105.4622312795739  \n",
       "1     10725               1  100.81980868401074  \n",
       "2     19459               1   259.8005430183116  \n",
       "3     29400               1  22.941906168328487  \n",
       "4      3280               1  181.21309507601745  \n",
       "\n",
       "[5 rows x 234 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = pd.read_csv('dataset/train.csv', dtype=object)\n",
    "test = pd.read_csv('dataset/test.csv', dtype=object)\n",
    "display(train.head(5))\n",
    "display(test.head(5))\n",
    "\n",
    "id_cv = []\n",
    "for i in range(5):\n",
    "    id_one_fold = pd.read_csv(f'dataset/id_cv{i}.csv', dtype=object)['building_id']\n",
    "    id_cv.append(id_one_fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N outliers removed: 1800\n"
     ]
    }
   ],
   "source": [
    "# Only train on data in this list\n",
    "if rm_outlier:\n",
    "    training_buildings = pd.read_csv('dataset/building_we_care_%d.csv'%outlier_pct)\n",
    "    print('N outliers removed:', len(train) - len(training_buildings))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/skyhuang/.local/lib/python3.6/site-packages/sklearn/base.py:306: UserWarning: Trying to unpickle estimator KMeans from version 0.20.3 when using version 0.21.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/home/skyhuang/.local/lib/python3.6/site-packages/sklearn/base.py:306: UserWarning: Trying to unpickle estimator KMeans from version 0.20.3 when using version 0.21.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "train = pre_processing(train, cols_num, cols_cat)\n",
    "test = pre_processing(test, cols_num, cols_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_num = cols_num + [c for c in cols_num_add if c not in cols_num]\n",
    "cols_cat = cols_cat + [c for c in cols_cat_add if c not in cols_cat]\n",
    "cols_feats = cols_num + cols_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove feats based on shuffle results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 'divorce_rate' 5709.20035 3.333296666667593]\n",
      " [1 'I_250' 5709.200316666666 3.333263333333889]\n",
      " [2 'XI_100' 5708.200346666667 2.33329333333495]\n",
      " [3 'VI_index_500' 5707.867050000001 1.9999966666682667]\n",
      " [4 'IX_index_50' 5707.86704 1.9999866666676098]\n",
      " [5 'VI_500' 5707.866983333333 1.9999300000008589]\n",
      " [6 'XIV_10' 5707.533710000001 1.666656666668132]\n",
      " [7 'XI_index_500' 5707.200386666666 1.3333333333339397]\n",
      " [8 'VI_index_1000' 5707.200383333334 1.3333300000012969]\n",
      " [9 'V_100' 5707.200373333333 1.33332000000064]\n",
      " [10 'VII_10' 5707.200363333334 1.333310000001802]\n",
      " [11 'X_100' 5707.200336666666 1.3332833333333838]\n",
      " [12 'XII_index_50' 5706.867056666667 1.0000033333344618]\n",
      " [15 'X_index_500' 5706.867050000001 0.9999966666682667]\n",
      " [14 'X_10' 5706.867050000001 0.9999966666682667]\n",
      " [13 'II_10' 5706.867050000001 0.9999966666682667]\n",
      " [16 'II_index_500' 5706.867046666666 0.9999933333338049]\n",
      " [17 'V_index_1000' 5706.867046666666 0.9999933333338049]\n",
      " [18 'XIII_10' 5706.867043333335 0.9999900000020716]\n",
      " [19 'XIV_index_50' 5706.867043333335 0.9999900000020716]\n",
      " [20 'VIII_index_500' 5706.86704 0.9999866666676098]\n",
      " [21 'IX_50' 5706.867016666667 0.9999633333345628]\n",
      " [22 'XI_index_1000' 5706.533726666666 0.6666733333331649]\n",
      " [23 'XI_10' 5706.533719999999 0.6666666666669698]\n",
      " [24 'II_index_1000' 5706.533719999999 0.6666666666669698]\n",
      " [25 'VII_index_500' 5706.533719999999 0.6666666666669698]\n",
      " [26 'X_index_1000' 5706.533719999999 0.6666666666669698]\n",
      " [30 'XIII_index_1000' 5706.533716666668 0.6666633333352365]\n",
      " [31 'XIV_index_500' 5706.533716666668 0.6666633333352365]\n",
      " [27 'VI_100' 5706.533716666668 0.6666633333352365]\n",
      " [29 'IX_index_500' 5706.533716666668 0.6666633333352365]\n",
      " [28 'I_index_1000' 5706.533716666668 0.6666633333352365]\n",
      " [32 'XIII_index_500' 5706.533713333333 0.6666600000007747]\n",
      " [33 'V_10' 5706.533706666666 0.6666533333336702]\n",
      " [34 'VII_index_50' 5706.533706666666 0.6666533333336702]\n",
      " [35 'I_500' 5706.533573333333 0.6665200000006735]\n",
      " [36 'IV_index_50' 5706.200386666666 0.33333333333393966]\n",
      " [37 'V_index_500' 5706.200386666666 0.33333333333393966]\n",
      " [38 'VI_index_50' 5706.200386666666 0.33333333333393966]\n",
      " [39 'XIII_index_5000' 5706.200386666666 0.33333333333393966]\n",
      " [40 'VI_50' 5706.200383333334 0.33333000000129687]\n",
      " [41 'III_index_1000' 5706.200383333334 0.33333000000129687]\n",
      " [44 'I_index_50' 5706.200379999999 0.3333266666668351]\n",
      " [42 'I_50' 5706.200379999999 0.3333266666668351]\n",
      " [43 'IV_50' 5706.200379999999 0.3333266666668351]\n",
      " [45 'III_index_50' 5706.200370000001 0.3333166666689067]\n",
      " [46 'VI_250' 5706.200366666667 0.3333133333344449]\n",
      " [47 'XI_index_50' 5706.200366666667 0.3333133333344449]\n",
      " [48 'VI_1000' 5706.200263333333 0.33321000000069034]\n",
      " [49 'XII_index_500' 5705.867056666667 3.333334461785853e-06]\n",
      " [59 'XIV_index_1000' 5705.8670533333325 0.0]\n",
      " [58 'XIII_index_50' 5705.8670533333325 0.0]\n",
      " [57 'XII_index_1000' 5705.8670533333325 0.0]\n",
      " [55 'IX_index_5000' 5705.8670533333325 0.0]\n",
      " [56 'XI_index_10000' 5705.8670533333325 0.0]\n",
      " [53 'VI_10' 5705.8670533333325 0.0]\n",
      " [54 'VII_index_1000' 5705.8670533333325 0.0]\n",
      " [52 'IV_index_5000' 5705.8670533333325 0.0]\n",
      " [51 'I_10' 5705.8670533333325 0.0]\n",
      " [50 'none' 5705.8670533333325 0.0]\n",
      " [60 'VIII_index_1000' 5705.867050000001 -3.3333317333017476e-06]\n",
      " [61 'XI_index_5000' 5705.867050000001 -3.3333317333017476e-06]\n",
      " [62 'I_index_500' 5705.86704 -1.3333332390175201e-05]\n",
      " [63 'II_index_50' 5705.86704 -1.3333332390175201e-05]\n",
      " [64 'IX_index_1000' 5705.867036666667 -1.666666503297165e-05]\n",
      " [65 'I_100' 5705.86703 -2.3333332137553953e-05]\n",
      " [66 'II_50' 5705.8670133333335 -3.9999998989515007e-05]\n",
      " [67 'N_500' 5705.866976666667 -7.666666533623356e-05]\n",
      " [68 'N_5000' 5705.533719999999 -0.33333333333303017]\n",
      " [69 'IX_10' 5705.533713333333 -0.33333999999922526]\n",
      " [70 'IV_index_1000' 5705.533713333333 -0.33333999999922526]\n",
      " [71 'X_index_50' 5705.533713333333 -0.33333999999922526]\n",
      " [72 'IV_10' 5705.533710000001 -0.33334333333186805]\n",
      " [73 'XIII_index_10000' 5705.533710000001 -0.33334333333186805]\n",
      " [74 'XII_10' 5705.533630000001 -0.33342333333166607]\n",
      " [75 'III_10' 5705.200383333334 -0.6666699999987031]\n",
      " [76 'X_50' 5705.200370000001 -0.6666833333310933]\n",
      " [77 'IV_index_500' 5705.200370000001 -0.6666833333310933]\n",
      " [78 'VIII_index_50' 5705.200363333334 -0.6666899999981979]\n",
      " [79 'XI_50' 5705.20035 -0.666703333332407]\n",
      " [80 'N_10000' 5705.200326666666 -0.6667266666663636]\n",
      " [81 'V_index_50' 5704.867036666667 -1.000016666665033]\n",
      " [82 'X_500' 5704.86689 -1.0001633333322388]\n",
      " [83 'X_250' 5704.866873333333 -1.0001799999990908]\n",
      " [84 'III_index_500' 5704.533719999999 -1.3333333333330302]\n",
      " [85 'XIII_50' 5704.533710000001 -1.333343333331868]\n",
      " [86 'XIII_100' 5704.533710000001 -1.333343333331868]\n",
      " [87 'V_50' 5704.200356666667 -1.6666966666653025]\n",
      " [88 'IX_100' 5704.2002600000005 -1.6667933333319525]\n",
      " [89 'XIV_250' 5704.200026666666 -1.667026666666061]\n",
      " [90 'IV_100' 5703.867026666667 -2.00002666666569]\n",
      " [91 'IV_250' 5703.866953333333 -2.0000999999992928]\n",
      " [92 'III_100' 5703.533653333333 -2.3333999999995285]\n",
      " [93 'XIV_100' 5703.533503333333 -2.333549999999377]\n",
      " [94 'XIII_250' 5703.200316666666 -2.666736666666111]\n",
      " [95 'XI_250' 5703.200193333333 -2.6668599999993603]\n",
      " [96 'VIII_10' 5702.866996666667 -3.0000566666658415]\n",
      " [97 'IX_250' 5702.200013333334 -3.667039999998451]\n",
      " [98 'IV_500' 5701.866936666666 -4.000116666666145]\n",
      " [99 'IX_5000' 5701.533173333333 -4.333879999999226]\n",
      " [100 'XII_100' 5701.2001666666665 -4.66688666666596]\n",
      " [101 'II_100' 5700.866966666667 -5.000086666665084]\n",
      " [102 'N_1000' 5700.866936666666 -5.000116666666145]\n",
      " [103 'born_rate' 5700.86682 -5.0002333333322895]\n",
      " [104 'junior_rate' 5700.5335000000005 -5.33355333333202]\n",
      " [105 'VIII_50' 5700.533383333334 -5.333669999998165]\n",
      " [106 'marriage_rate' 5700.533316666667 -5.333736666665573]\n",
      " [107 'XIV_50' 5699.533613333333 -6.3334399999994275]\n",
      " [108 'VII_50' 5699.533456666667 -6.333596666665471]\n",
      " [109 'IV_1000' 5699.199853333333 -6.667199999999866]\n",
      " [110 'II_250' 5698.53337 -7.333683333332374]\n",
      " [111 'XIII_500' 5698.200293333332 -7.6667600000000675]\n",
      " [112 'III_50' 5698.200069999999 -7.666983333333519]\n",
      " [113 'II_10000' 5698.199886666666 -7.667166666666162]\n",
      " [114 'IV_5000' 5697.866536666667 -8.000516666665135]\n",
      " [115 'XIII_1000' 5697.533463333333 -8.333589999999276]\n",
      " [116 'III_250' 5697.200150000001 -8.666903333331902]\n",
      " [117 'VIII_100' 5696.866476666667 -9.000576666665438]\n",
      " [118 'XII_50' 5696.200313333335 -9.666739999997844]\n",
      " [119 'N_50' 5696.19989 -9.66716333333261]\n",
      " [120 'VIII_5000' 5695.533113333334 -10.33393999999862]\n",
      " [121 'I_1000' 5694.866853333333 -11.000199999999495]\n",
      " [122 'VI_MIN' 5694.866663333334 -11.000389999998333]\n",
      " [123 'XIV_500' 5694.866446666667 -11.00060666666559]\n",
      " [124 'VII_100' 5694.53343 -11.33362333333207]\n",
      " [125 'XI_500' 5693.8668800000005 -12.000173333331986]\n",
      " [126 'IV_10000' 5693.532886666667 -12.334166666665624]\n",
      " [127 'V_MIN' 5692.86606 -13.00099333333219]\n",
      " [128 'death_rate' 5692.5331633333335 -13.333889999998974]\n",
      " [129 'VIII_10000' 5692.199853333333 -13.667199999999866]\n",
      " [130 'XIII_MIN' 5691.533060000001 -14.333993333331819]\n",
      " [131 'V_500' 5690.199906666668 -15.667146666664848]\n",
      " [132 'I_MIN' 5690.19978 -15.66727333333256]\n",
      " [133 'V_250' 5689.200093333333 -16.666959999999563]\n",
      " [134 'IV_MIN' 5689.1995566666665 -16.66749666666601]\n",
      " [135 'III_500' 5688.866743333333 -17.000309999999445]\n",
      " [136 'IX_500' 5688.86639 -17.00066333333234]\n",
      " [137 'XI_5000' 5688.533226666666 -17.333826666666027]\n",
      " [138 'XII_250' 5688.533106666667 -17.333946666665724]\n",
      " [139 'III_1000' 5687.866279999999 -18.0007733333332]\n",
      " [140 'XI_MIN' 5687.532873333334 -18.334179999998923]\n",
      " [141 'X_10000' 5687.532800000001 -18.334253333331617]\n",
      " [142 'II_500' 5686.532996666666 -19.334056666666584]\n",
      " [143 'III_5000' 5684.866556666667 -21.00049666666564]\n",
      " [144 'V_1000' 5683.866256666667 -22.000796666665337]\n",
      " [145 'III_10000' 5683.5324900000005 -22.33456333333197]\n",
      " [146 'VII_MIN' 5683.199209999999 -22.66784333333362]\n",
      " [147 'XII_500' 5683.199173333334 -22.66787999999815]\n",
      " [148 'III_MIN' 5682.532556666666 -23.334496666666382]\n",
      " [149 'VIII_250' 5681.86623 -24.000823333332846]\n",
      " [150 'XIV_1000' 5680.866440000001 -25.000613333331785]\n",
      " [151 'town_area' 5680.533066666667 -25.333986666665623]\n",
      " [152 'XIV_MIN' 5680.19969 -25.667363333332105]\n",
      " [153 'II_MIN' 5677.53274 -28.33431333333283]\n",
      " [154 'XII_5000' 5676.8655733333335 -29.001479999998992]\n",
      " [155 'building_material' 5676.865563333333 -29.00148999999965]\n",
      " [156 'X_1000' 5675.865993333333 -30.001059999999597]\n",
      " [157 'I_5000' 5675.532426666668 -30.334626666664917]\n",
      " [158 'XI_10000' 5675.199393333333 -30.66765999999916]\n",
      " [159 'XII_10000' 5674.865669999999 -31.00138333333325]\n",
      " [160 'elementary_rate' 5674.532066666667 -31.334986666665827]\n",
      " [161 'town_population_density' 5674.199496666667 -31.667556666665405]\n",
      " [162 'IX_MIN' 5673.5324900000005 -32.33456333333197]\n",
      " [163 'XII_MIN' 5672.199463333333 -33.66758999999911]\n",
      " [164 'X_MIN' 5672.199316666666 -33.667736666666315]\n",
      " [165 'VII_250' 5671.865873333333 -34.001179999999295]\n",
      " [166 'XIV_10000' 5669.865906666667 -36.00114666666559]\n",
      " [167 'X_5000' 5669.865403333333 -36.001649999999245]\n",
      " [168 'parking_area' 5665.866163333333 -40.000889999999345]\n",
      " [169 'IX_10000' 5665.53269 -40.334363333332476]\n",
      " [170 'highschool_rate' 5664.532736666667 -41.33431666666547]\n",
      " [171 'II_5000' 5662.532619999999 -43.334433333333436]\n",
      " [172 'V_5000' 5661.19843 -44.66862333333211]\n",
      " [173 'VIII_MIN' 5660.532049999999 -45.33500333333359]\n",
      " [174 'VIII_500' 5658.531816666667 -47.33523666666588]\n",
      " [175 'VIII_1000' 5657.86419 -48.00286333333224]\n",
      " [176 'I_10000' 5655.531346666667 -50.33570666666583]\n",
      " [177 'building_use' 5652.19898 -53.66807333333236]\n",
      " [178 'XII_1000' 5644.19792 -61.669133333332866]\n",
      " [179 'VI_5000' 5641.86575 -64.00130333333254]\n",
      " [180 'lat' 5637.531133333333 -68.33591999999953]\n",
      " [181 'VII_5000' 5631.530966666666 -74.33608666666623]\n",
      " [182 'VII_500' 5629.531536666666 -76.33551666666608]\n",
      " [183 'town_population' 5626.198586666666 -79.66846666666606]\n",
      " [184 'city' 5624.864426666667 -81.00262666666549]\n",
      " [185 'II_1000' 5618.53164 -87.33541333333233]\n",
      " [186 'IX_1000' 5609.530946666667 -96.33610666666573]\n",
      " [187 'XI_1000' 5600.864693333333 -105.0023599999995]\n",
      " [188 'jobschool_rate' 5577.52952 -128.33753333333243]\n",
      " [189 'bachelor_rate' 5564.530006666667 -141.33704666666563]\n",
      " [190 'XIV_5000' 5544.529993333334 -161.33705999999893]\n",
      " [191 'V_10000' 5527.5279433333335 -178.33910999999898]\n",
      " [192 'VII_1000' 5483.195199999999 -222.67185333333327]\n",
      " [193 'parking_way' 5481.528866666667 -224.33818666666502]\n",
      " [194 'XIII_5000' 5403.193223333333 -302.6738299999997]\n",
      " [195 'lon' 5361.52429 -344.3427633333322]\n",
      " [196 'parking_price' 5360.190783333333 -345.67626999999993]\n",
      " [197 'master_rate' 5210.856313333333 -495.0107399999997]\n",
      " [198 'building_type' 5193.523143333333 -512.3439099999996]\n",
      " [199 'village_income_median' 5009.84994 -696.0171133333324]\n",
      " [200 'total_floor' 4984.18339 -721.6836633333323]\n",
      " [201 'building_area' 4936.506333333334 -769.3607199999988]\n",
      " [202 'doc_rate' 4886.513366666666 -819.3536866666664]\n",
      " [203 'village' 4852.180716666666 -853.6863366666666]\n",
      " [204 'VII_10000' 4688.84273 -1017.0243233333322]\n",
      " [205 'land_area' 4551.80975 -1154.0573033333321]\n",
      " [206 'VI_10000' 4376.823850000001 -1329.0432033333318]\n",
      " [207 'txn_floor' 4149.4685 -1556.3985533333325]\n",
      " [208 'txn_dt' 3962.7975066666663 -1743.0695466666662]\n",
      " [209 'building_complete_dt' 3433.7606266666667 -2272.106426666666]\n",
      " [210 'XIII_10000' 2914.651653333333 -2791.2153999999996]\n",
      " [211 'town' 2516.730063333333 -3189.1369899999995]]\n",
      "70\n",
      "['divorce_rate', 'I_250', 'XI_100', 'VI_index_500', 'IX_index_50', 'VI_500', 'XIV_10', 'XI_index_500', 'VI_index_1000', 'V_100', 'VII_10', 'X_100', 'XII_index_50', 'X_index_500', 'X_10', 'II_10', 'II_index_500', 'V_index_1000', 'XIII_10', 'XIV_index_50', 'VIII_index_500', 'IX_50', 'XI_index_1000', 'XI_10', 'II_index_1000', 'VII_index_500', 'X_index_1000', 'XIII_index_1000', 'XIV_index_500', 'VI_100', 'IX_index_500', 'I_index_1000', 'XIII_index_500', 'V_10', 'VII_index_50', 'I_500', 'IV_index_50', 'V_index_500', 'VI_index_50', 'XIII_index_5000', 'VI_50', 'III_index_1000', 'I_index_50', 'I_50', 'IV_50', 'III_index_50', 'VI_250', 'XI_index_50', 'VI_1000', 'XII_index_500', 'XIV_index_1000', 'XIII_index_50', 'XII_index_1000', 'IX_index_5000', 'XI_index_10000', 'VI_10', 'VII_index_1000', 'IV_index_5000', 'I_10', 'VIII_index_1000', 'XI_index_5000', 'I_index_500', 'II_index_50', 'IX_index_1000', 'I_100', 'II_50', 'N_500', 'N_5000', 'IX_10', 'IV_index_1000']\n",
      "N feats (before): 214\n",
      "N feats (after): 144\n",
      "['VI_10000', 'IX_MIN', 'doc_rate', 'III_50', 'XIV_50', 'X_500', 'XIV_MIN', 'junior_rate', 'XIII_250', 'XII_250', 'town_population', 'X_250', 'II_5000', 'N_1000', 'marriage_rate', 'jobschool_rate', 'lat', 'XI_250', 'XIII_100', 'X_MIN', 'IV_1000', 'XII_500', 'III_250', 'VII_100', 'XII_MIN', 'town_area', 'parking_area', 'IX_250', 'VIII_500', 'building_use', 'lon', 'IV_5000', 'VII_10000', 'VII_50', 'XI_10000', 'land_area', 'IV_500', 'X_5000', 'III_500', 'III_5000', 'V_index_50', 'III_10000', 'village', 'VIII_5000', 'III_index_500', 'XIV_250', 'encoded_latlon', 'height_r', 'parking_price', 'XII_50', 'IV_index_500', 'death_rate', 'XII_10', 'bachelor_rate', 'II_1000', 'X_1000', 'III_MIN', 'II_MIN', 'VIII_MIN', 'building_area', 'total_floor', 'II_500', 'II_100', 'village_income_median', 'III_10', 'V_5000', 'town', 'IV_MIN', 'I_10000', 'XII_1000', 'VIII_index_50', 'XIV_10000', 'XIV_5000', 'VI_5000', 'txn_dt', 'XIII_50', 'I_MIN', 'XIV_100', 'IV_10', 'XI_1000', 'VI_MIN', 'V_500', 'XIII_5000', 'IX_500', 'XI_MIN', 'I_5000', 'XIII_index_10000', 'XII_10000', 'IV_10000', 'building_material', 'VIII_250', 'IX_5000', 'III_1000', 'XIII_10000', 'VII_1000', 'VIII_50', 'VII_MIN', 'II_250', 'XI_50', 'born_rate', 'X_10000', 'IV_100', 'XIII_MIN', 'VIII_10000', 'VIII_100', 'XI_5000', 'V_10000', 'IX_100', 'XII_100', 'VIII_1000', 'XIV_500', 'parking_way', 'city', 'I_1000', 'IX_1000', 'building_per_land_area', 'VII_250', 'X_50', 'XIV_1000', 'elementary_rate', 'X_index_50', 'N_50', 'VII_5000', 'XIII_500', 'XII_5000', 'V_250', 'master_rate', 'V_1000', 'N_10000', 'V_MIN', 'IX_10000', 'XIII_1000', 'III_100', 'VII_500', 'XI_500', 'highschool_rate', 'building_type', 'town_population_density', 'V_50', 'IV_250', 'VIII_10', 'txn_floor', 'II_10000', 'building_complete_dt']\n"
     ]
    }
   ],
   "source": [
    "# PARS #\n",
    "N_feats_rm = 70\n",
    "# PARS #\n",
    "\n",
    "from vars_03 import score_base_per_area, shuffle_results_per_area\n",
    "\n",
    "feat_imp = pd.DataFrame({'feats': shuffle_results_per_area[:,0],\n",
    "                         'avg':shuffle_results_per_area[:,1]})\n",
    "feat_imp['diff'] = feat_imp['avg'] - score_base_per_area\n",
    "feat_imp = feat_imp.sort_values(by='diff', ascending=False)\n",
    "print(feat_imp.reset_index().values)\n",
    "\n",
    "feats_rm = list(feat_imp.iloc[:(N_feats_rm+1)]['feats'].values)\n",
    "feats_rm.remove('none')\n",
    "print(len(feats_rm))\n",
    "print(feats_rm)\n",
    "\n",
    "print('N feats (before):', len(cols_feats))\n",
    "cols_feats = list(set(cols_feats) - set(feats_rm))\n",
    "print('N feats (after):', len(cols_feats))\n",
    "print(cols_feats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# params\n",
    "# parameters from model 10: 24, 300, 0.5, 10, 0.1, 0.0\n",
    "# parameters from model 5: 24, 258, 0.4, 6, 0.04, 0.02\n",
    "params = {'task': 'train',\n",
    "          'boosting_type': 'gbdt',\n",
    "          'objective': 'mse',\n",
    "          'metric': 'mape',\n",
    "          'learning_rate': 0.001, # 0.001\n",
    "          'max_depth': 24,\n",
    "          'num_leaves': 279,           # may reduce in dim-reduction exp\n",
    "          'feature_fraction': 0.45,\n",
    "          'min_data_in_leaf': 8,\n",
    "          'lambda_l1': 0.07,\n",
    "          'lambda_l2': 0.01,\n",
    "          'seed': 666,\n",
    "          'bagging_freq': 10,\n",
    "          'bagging_fraction': 0.9, \n",
    "         }\n",
    "\n",
    "lgb_other_params = {'num_boost_round': 1000000,  # expected to stop 250000\n",
    "                    'verbose_eval': 2000,\n",
    "                    'early_stopping_rounds': 1000,}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kfold cross-predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0156812\n",
      "[4000]\tvalid_0's mape: 0.0113693\n",
      "[6000]\tvalid_0's mape: 0.010446\n",
      "[8000]\tvalid_0's mape: 0.0100298\n",
      "[10000]\tvalid_0's mape: 0.00978329\n",
      "[12000]\tvalid_0's mape: 0.00962048\n",
      "[14000]\tvalid_0's mape: 0.00950517\n",
      "[16000]\tvalid_0's mape: 0.00942001\n",
      "[18000]\tvalid_0's mape: 0.00935428\n",
      "[20000]\tvalid_0's mape: 0.00930161\n",
      "[22000]\tvalid_0's mape: 0.00925879\n",
      "[24000]\tvalid_0's mape: 0.00922314\n",
      "[26000]\tvalid_0's mape: 0.00919302\n",
      "[28000]\tvalid_0's mape: 0.00916689\n",
      "[30000]\tvalid_0's mape: 0.00914461\n",
      "[32000]\tvalid_0's mape: 0.0091255\n",
      "[34000]\tvalid_0's mape: 0.00910859\n",
      "[36000]\tvalid_0's mape: 0.00909381\n",
      "[38000]\tvalid_0's mape: 0.00908066\n",
      "[40000]\tvalid_0's mape: 0.00906917\n",
      "[42000]\tvalid_0's mape: 0.00905862\n",
      "[44000]\tvalid_0's mape: 0.00904919\n",
      "[46000]\tvalid_0's mape: 0.00904073\n",
      "[48000]\tvalid_0's mape: 0.00903292\n",
      "[50000]\tvalid_0's mape: 0.00902601\n",
      "[52000]\tvalid_0's mape: 0.00901962\n",
      "[54000]\tvalid_0's mape: 0.00901378\n",
      "[56000]\tvalid_0's mape: 0.00900869\n",
      "[58000]\tvalid_0's mape: 0.00900376\n",
      "[60000]\tvalid_0's mape: 0.00899908\n",
      "[62000]\tvalid_0's mape: 0.00899494\n",
      "[64000]\tvalid_0's mape: 0.00899118\n",
      "[66000]\tvalid_0's mape: 0.00898764\n",
      "[68000]\tvalid_0's mape: 0.00898432\n",
      "[70000]\tvalid_0's mape: 0.00898119\n",
      "[72000]\tvalid_0's mape: 0.00897839\n",
      "[74000]\tvalid_0's mape: 0.00897581\n",
      "[76000]\tvalid_0's mape: 0.00897344\n",
      "[78000]\tvalid_0's mape: 0.00897117\n",
      "[80000]\tvalid_0's mape: 0.00896891\n",
      "[82000]\tvalid_0's mape: 0.00896679\n",
      "[84000]\tvalid_0's mape: 0.00896504\n",
      "[86000]\tvalid_0's mape: 0.0089632\n",
      "[88000]\tvalid_0's mape: 0.00896168\n",
      "[90000]\tvalid_0's mape: 0.0089601\n",
      "[92000]\tvalid_0's mape: 0.00895847\n",
      "[94000]\tvalid_0's mape: 0.00895701\n",
      "[96000]\tvalid_0's mape: 0.00895565\n",
      "[98000]\tvalid_0's mape: 0.00895433\n",
      "[100000]\tvalid_0's mape: 0.00895312\n",
      "[102000]\tvalid_0's mape: 0.00895199\n",
      "[104000]\tvalid_0's mape: 0.00895095\n",
      "[106000]\tvalid_0's mape: 0.00894999\n",
      "[108000]\tvalid_0's mape: 0.00894894\n",
      "[110000]\tvalid_0's mape: 0.00894798\n",
      "[112000]\tvalid_0's mape: 0.00894709\n",
      "[114000]\tvalid_0's mape: 0.00894626\n",
      "[116000]\tvalid_0's mape: 0.00894546\n",
      "[118000]\tvalid_0's mape: 0.00894472\n",
      "[120000]\tvalid_0's mape: 0.00894406\n",
      "[122000]\tvalid_0's mape: 0.00894336\n",
      "[124000]\tvalid_0's mape: 0.00894267\n",
      "[126000]\tvalid_0's mape: 0.00894206\n",
      "[128000]\tvalid_0's mape: 0.0089414\n",
      "[130000]\tvalid_0's mape: 0.00894081\n",
      "[132000]\tvalid_0's mape: 0.00894022\n",
      "[134000]\tvalid_0's mape: 0.00893969\n",
      "[136000]\tvalid_0's mape: 0.00893921\n",
      "[138000]\tvalid_0's mape: 0.00893874\n",
      "[140000]\tvalid_0's mape: 0.00893824\n",
      "[142000]\tvalid_0's mape: 0.00893779\n",
      "[144000]\tvalid_0's mape: 0.00893736\n",
      "[146000]\tvalid_0's mape: 0.00893693\n",
      "[148000]\tvalid_0's mape: 0.00893652\n",
      "[150000]\tvalid_0's mape: 0.0089361\n",
      "[152000]\tvalid_0's mape: 0.00893576\n",
      "[154000]\tvalid_0's mape: 0.0089354\n",
      "[156000]\tvalid_0's mape: 0.00893508\n",
      "[158000]\tvalid_0's mape: 0.00893474\n",
      "[160000]\tvalid_0's mape: 0.00893446\n",
      "[162000]\tvalid_0's mape: 0.00893417\n",
      "[164000]\tvalid_0's mape: 0.00893388\n",
      "[166000]\tvalid_0's mape: 0.00893359\n",
      "[168000]\tvalid_0's mape: 0.00893334\n",
      "[170000]\tvalid_0's mape: 0.00893308\n",
      "[172000]\tvalid_0's mape: 0.00893283\n",
      "[174000]\tvalid_0's mape: 0.00893261\n",
      "[176000]\tvalid_0's mape: 0.00893235\n",
      "[178000]\tvalid_0's mape: 0.00893207\n",
      "[180000]\tvalid_0's mape: 0.00893182\n",
      "[182000]\tvalid_0's mape: 0.00893162\n",
      "[184000]\tvalid_0's mape: 0.00893143\n",
      "[186000]\tvalid_0's mape: 0.00893119\n",
      "[188000]\tvalid_0's mape: 0.00893101\n",
      "[190000]\tvalid_0's mape: 0.00893083\n",
      "[192000]\tvalid_0's mape: 0.00893065\n",
      "[194000]\tvalid_0's mape: 0.00893047\n",
      "[196000]\tvalid_0's mape: 0.0089303\n",
      "[198000]\tvalid_0's mape: 0.00893012\n",
      "[200000]\tvalid_0's mape: 0.00892996\n",
      "[202000]\tvalid_0's mape: 0.00892979\n",
      "[204000]\tvalid_0's mape: 0.00892965\n",
      "[206000]\tvalid_0's mape: 0.0089295\n",
      "[208000]\tvalid_0's mape: 0.00892932\n",
      "[210000]\tvalid_0's mape: 0.00892919\n",
      "[212000]\tvalid_0's mape: 0.00892905\n",
      "[214000]\tvalid_0's mape: 0.00892891\n",
      "[216000]\tvalid_0's mape: 0.00892878\n",
      "[218000]\tvalid_0's mape: 0.00892867\n",
      "[220000]\tvalid_0's mape: 0.00892854\n",
      "[222000]\tvalid_0's mape: 0.00892843\n",
      "[224000]\tvalid_0's mape: 0.00892831\n",
      "[226000]\tvalid_0's mape: 0.00892818\n",
      "[228000]\tvalid_0's mape: 0.00892809\n",
      "[230000]\tvalid_0's mape: 0.00892798\n",
      "[232000]\tvalid_0's mape: 0.00892785\n",
      "[234000]\tvalid_0's mape: 0.00892775\n",
      "[236000]\tvalid_0's mape: 0.00892764\n",
      "[238000]\tvalid_0's mape: 0.00892756\n",
      "[240000]\tvalid_0's mape: 0.00892743\n",
      "[242000]\tvalid_0's mape: 0.00892734\n",
      "[244000]\tvalid_0's mape: 0.00892726\n",
      "[246000]\tvalid_0's mape: 0.00892717\n",
      "[248000]\tvalid_0's mape: 0.00892709\n",
      "[250000]\tvalid_0's mape: 0.00892701\n",
      "[252000]\tvalid_0's mape: 0.00892692\n",
      "[254000]\tvalid_0's mape: 0.00892684\n",
      "[256000]\tvalid_0's mape: 0.00892674\n",
      "[258000]\tvalid_0's mape: 0.00892665\n",
      "[260000]\tvalid_0's mape: 0.00892658\n",
      "[262000]\tvalid_0's mape: 0.00892654\n",
      "[264000]\tvalid_0's mape: 0.00892648\n",
      "[266000]\tvalid_0's mape: 0.0089264\n",
      "[268000]\tvalid_0's mape: 0.00892634\n",
      "[270000]\tvalid_0's mape: 0.00892625\n",
      "[272000]\tvalid_0's mape: 0.0089262\n",
      "[274000]\tvalid_0's mape: 0.00892614\n",
      "[276000]\tvalid_0's mape: 0.00892608\n",
      "[278000]\tvalid_0's mape: 0.008926\n",
      "[280000]\tvalid_0's mape: 0.00892594\n",
      "[282000]\tvalid_0's mape: 0.00892586\n",
      "[284000]\tvalid_0's mape: 0.0089258\n",
      "[286000]\tvalid_0's mape: 0.00892577\n",
      "[288000]\tvalid_0's mape: 0.0089257\n",
      "[290000]\tvalid_0's mape: 0.00892564\n",
      "[292000]\tvalid_0's mape: 0.00892558\n",
      "[294000]\tvalid_0's mape: 0.00892553\n",
      "[296000]\tvalid_0's mape: 0.00892547\n",
      "[298000]\tvalid_0's mape: 0.0089254\n",
      "[300000]\tvalid_0's mape: 0.00892534\n",
      "[302000]\tvalid_0's mape: 0.0089253\n",
      "[304000]\tvalid_0's mape: 0.00892525\n",
      "[306000]\tvalid_0's mape: 0.0089252\n",
      "[308000]\tvalid_0's mape: 0.00892515\n",
      "[310000]\tvalid_0's mape: 0.00892511\n",
      "[312000]\tvalid_0's mape: 0.00892505\n",
      "[314000]\tvalid_0's mape: 0.00892501\n",
      "[316000]\tvalid_0's mape: 0.00892498\n",
      "[318000]\tvalid_0's mape: 0.00892493\n",
      "[320000]\tvalid_0's mape: 0.0089249\n",
      "[322000]\tvalid_0's mape: 0.00892485\n",
      "[324000]\tvalid_0's mape: 0.00892481\n",
      "[326000]\tvalid_0's mape: 0.00892476\n",
      "[328000]\tvalid_0's mape: 0.00892472\n",
      "[330000]\tvalid_0's mape: 0.00892467\n",
      "[332000]\tvalid_0's mape: 0.00892463\n",
      "[334000]\tvalid_0's mape: 0.00892457\n",
      "[336000]\tvalid_0's mape: 0.00892453\n",
      "[338000]\tvalid_0's mape: 0.00892449\n",
      "[340000]\tvalid_0's mape: 0.00892446\n",
      "[342000]\tvalid_0's mape: 0.00892443\n",
      "[344000]\tvalid_0's mape: 0.0089244\n",
      "[346000]\tvalid_0's mape: 0.00892437\n",
      "[348000]\tvalid_0's mape: 0.00892433\n",
      "[350000]\tvalid_0's mape: 0.0089243\n",
      "[352000]\tvalid_0's mape: 0.00892427\n",
      "[354000]\tvalid_0's mape: 0.00892424\n",
      "[356000]\tvalid_0's mape: 0.00892421\n",
      "[358000]\tvalid_0's mape: 0.00892419\n",
      "[360000]\tvalid_0's mape: 0.00892416\n",
      "[362000]\tvalid_0's mape: 0.00892413\n",
      "[364000]\tvalid_0's mape: 0.0089241\n",
      "[366000]\tvalid_0's mape: 0.00892408\n",
      "[368000]\tvalid_0's mape: 0.00892405\n",
      "[370000]\tvalid_0's mape: 0.00892402\n",
      "[372000]\tvalid_0's mape: 0.008924\n",
      "Early stopping, best iteration is:\n",
      "[372876]\tvalid_0's mape: 0.00892398\n",
      "(Fold 0) score, mse, mae, mape: 5917.87783, 0.03533, 0.11872, 0.00892.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0158048\n",
      "[4000]\tvalid_0's mape: 0.0114403\n",
      "[6000]\tvalid_0's mape: 0.0105265\n",
      "[8000]\tvalid_0's mape: 0.0101121\n",
      "[10000]\tvalid_0's mape: 0.00985841\n",
      "[12000]\tvalid_0's mape: 0.0096896\n",
      "[14000]\tvalid_0's mape: 0.00956494\n",
      "[16000]\tvalid_0's mape: 0.00947193\n",
      "[18000]\tvalid_0's mape: 0.0094009\n",
      "[20000]\tvalid_0's mape: 0.00934441\n",
      "[22000]\tvalid_0's mape: 0.00929856\n",
      "[24000]\tvalid_0's mape: 0.00926095\n",
      "[26000]\tvalid_0's mape: 0.00922927\n",
      "[28000]\tvalid_0's mape: 0.00920271\n",
      "[30000]\tvalid_0's mape: 0.00917962\n",
      "[32000]\tvalid_0's mape: 0.00915948\n",
      "[34000]\tvalid_0's mape: 0.00914166\n",
      "[36000]\tvalid_0's mape: 0.0091262\n",
      "[38000]\tvalid_0's mape: 0.00911254\n",
      "[40000]\tvalid_0's mape: 0.00910068\n",
      "[42000]\tvalid_0's mape: 0.00908995\n",
      "[44000]\tvalid_0's mape: 0.00908056\n",
      "[46000]\tvalid_0's mape: 0.00907179\n",
      "[48000]\tvalid_0's mape: 0.00906382\n",
      "[50000]\tvalid_0's mape: 0.00905683\n",
      "[52000]\tvalid_0's mape: 0.00905035\n",
      "[54000]\tvalid_0's mape: 0.00904455\n",
      "[56000]\tvalid_0's mape: 0.00903922\n",
      "[58000]\tvalid_0's mape: 0.00903444\n",
      "[60000]\tvalid_0's mape: 0.00902995\n",
      "[62000]\tvalid_0's mape: 0.00902587\n",
      "[64000]\tvalid_0's mape: 0.00902213\n",
      "[66000]\tvalid_0's mape: 0.00901865\n",
      "[68000]\tvalid_0's mape: 0.00901523\n",
      "[70000]\tvalid_0's mape: 0.00901216\n",
      "[72000]\tvalid_0's mape: 0.00900945\n",
      "[74000]\tvalid_0's mape: 0.00900702\n",
      "[76000]\tvalid_0's mape: 0.00900462\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[78000]\tvalid_0's mape: 0.00900239\n",
      "[80000]\tvalid_0's mape: 0.00900026\n",
      "[82000]\tvalid_0's mape: 0.00899823\n",
      "[84000]\tvalid_0's mape: 0.00899637\n",
      "[86000]\tvalid_0's mape: 0.00899468\n",
      "[88000]\tvalid_0's mape: 0.00899296\n",
      "[90000]\tvalid_0's mape: 0.00899141\n",
      "[92000]\tvalid_0's mape: 0.00898996\n",
      "[94000]\tvalid_0's mape: 0.00898853\n",
      "[96000]\tvalid_0's mape: 0.00898718\n",
      "[98000]\tvalid_0's mape: 0.00898592\n",
      "[100000]\tvalid_0's mape: 0.00898475\n",
      "[102000]\tvalid_0's mape: 0.00898371\n",
      "[104000]\tvalid_0's mape: 0.00898269\n",
      "[106000]\tvalid_0's mape: 0.00898171\n",
      "[108000]\tvalid_0's mape: 0.0089808\n",
      "[110000]\tvalid_0's mape: 0.00897996\n",
      "[112000]\tvalid_0's mape: 0.00897918\n",
      "[114000]\tvalid_0's mape: 0.00897837\n",
      "[116000]\tvalid_0's mape: 0.00897757\n",
      "[118000]\tvalid_0's mape: 0.00897685\n",
      "[120000]\tvalid_0's mape: 0.00897616\n",
      "[122000]\tvalid_0's mape: 0.00897555\n",
      "[124000]\tvalid_0's mape: 0.00897492\n",
      "[126000]\tvalid_0's mape: 0.00897438\n",
      "[128000]\tvalid_0's mape: 0.00897384\n",
      "[130000]\tvalid_0's mape: 0.0089733\n",
      "[132000]\tvalid_0's mape: 0.00897277\n",
      "[134000]\tvalid_0's mape: 0.00897231\n",
      "[136000]\tvalid_0's mape: 0.00897188\n",
      "[138000]\tvalid_0's mape: 0.00897145\n",
      "[140000]\tvalid_0's mape: 0.00897103\n",
      "[142000]\tvalid_0's mape: 0.00897069\n",
      "[144000]\tvalid_0's mape: 0.00897028\n",
      "[146000]\tvalid_0's mape: 0.00896987\n",
      "[148000]\tvalid_0's mape: 0.00896951\n",
      "[150000]\tvalid_0's mape: 0.00896917\n",
      "[152000]\tvalid_0's mape: 0.00896883\n",
      "[154000]\tvalid_0's mape: 0.00896851\n",
      "[156000]\tvalid_0's mape: 0.00896824\n",
      "[158000]\tvalid_0's mape: 0.00896799\n",
      "[160000]\tvalid_0's mape: 0.00896768\n",
      "[162000]\tvalid_0's mape: 0.00896739\n",
      "[164000]\tvalid_0's mape: 0.0089671\n",
      "[166000]\tvalid_0's mape: 0.00896687\n",
      "[168000]\tvalid_0's mape: 0.00896666\n",
      "[170000]\tvalid_0's mape: 0.00896642\n",
      "[172000]\tvalid_0's mape: 0.00896623\n",
      "[174000]\tvalid_0's mape: 0.00896599\n",
      "[176000]\tvalid_0's mape: 0.00896579\n",
      "[178000]\tvalid_0's mape: 0.00896559\n",
      "[180000]\tvalid_0's mape: 0.00896539\n",
      "[182000]\tvalid_0's mape: 0.00896526\n",
      "[184000]\tvalid_0's mape: 0.00896504\n",
      "[186000]\tvalid_0's mape: 0.00896489\n",
      "[188000]\tvalid_0's mape: 0.00896472\n",
      "[190000]\tvalid_0's mape: 0.00896454\n",
      "[192000]\tvalid_0's mape: 0.00896436\n",
      "[194000]\tvalid_0's mape: 0.00896425\n",
      "[196000]\tvalid_0's mape: 0.00896412\n",
      "[198000]\tvalid_0's mape: 0.00896398\n",
      "[200000]\tvalid_0's mape: 0.00896383\n",
      "[202000]\tvalid_0's mape: 0.0089637\n",
      "[204000]\tvalid_0's mape: 0.00896356\n",
      "[206000]\tvalid_0's mape: 0.00896341\n",
      "[208000]\tvalid_0's mape: 0.00896327\n",
      "[210000]\tvalid_0's mape: 0.00896315\n",
      "[212000]\tvalid_0's mape: 0.00896305\n",
      "[214000]\tvalid_0's mape: 0.00896293\n",
      "[216000]\tvalid_0's mape: 0.00896281\n",
      "[218000]\tvalid_0's mape: 0.00896271\n",
      "[220000]\tvalid_0's mape: 0.00896258\n",
      "[222000]\tvalid_0's mape: 0.00896249\n",
      "[224000]\tvalid_0's mape: 0.00896237\n",
      "[226000]\tvalid_0's mape: 0.00896228\n",
      "[228000]\tvalid_0's mape: 0.0089622\n",
      "[230000]\tvalid_0's mape: 0.00896211\n",
      "[232000]\tvalid_0's mape: 0.008962\n",
      "[234000]\tvalid_0's mape: 0.00896192\n",
      "[236000]\tvalid_0's mape: 0.00896183\n",
      "[238000]\tvalid_0's mape: 0.00896173\n",
      "[240000]\tvalid_0's mape: 0.00896167\n",
      "[242000]\tvalid_0's mape: 0.00896158\n",
      "[244000]\tvalid_0's mape: 0.00896151\n",
      "[246000]\tvalid_0's mape: 0.0089614\n",
      "[248000]\tvalid_0's mape: 0.00896133\n",
      "[250000]\tvalid_0's mape: 0.00896125\n",
      "[252000]\tvalid_0's mape: 0.00896119\n",
      "[254000]\tvalid_0's mape: 0.00896113\n",
      "[256000]\tvalid_0's mape: 0.00896107\n",
      "[258000]\tvalid_0's mape: 0.00896101\n",
      "[260000]\tvalid_0's mape: 0.00896094\n",
      "[262000]\tvalid_0's mape: 0.00896085\n",
      "[264000]\tvalid_0's mape: 0.00896077\n",
      "[266000]\tvalid_0's mape: 0.00896073\n",
      "[268000]\tvalid_0's mape: 0.00896066\n",
      "[270000]\tvalid_0's mape: 0.0089606\n",
      "[272000]\tvalid_0's mape: 0.00896054\n",
      "[274000]\tvalid_0's mape: 0.00896048\n",
      "[276000]\tvalid_0's mape: 0.00896044\n",
      "[278000]\tvalid_0's mape: 0.0089604\n",
      "[280000]\tvalid_0's mape: 0.00896034\n",
      "[282000]\tvalid_0's mape: 0.00896029\n",
      "[284000]\tvalid_0's mape: 0.00896022\n",
      "[286000]\tvalid_0's mape: 0.0089602\n",
      "[288000]\tvalid_0's mape: 0.00896015\n",
      "[290000]\tvalid_0's mape: 0.0089601\n",
      "[292000]\tvalid_0's mape: 0.00896007\n",
      "[294000]\tvalid_0's mape: 0.00896001\n",
      "[296000]\tvalid_0's mape: 0.00895997\n",
      "[298000]\tvalid_0's mape: 0.00895992\n",
      "[300000]\tvalid_0's mape: 0.00895987\n",
      "[302000]\tvalid_0's mape: 0.00895984\n",
      "[304000]\tvalid_0's mape: 0.00895982\n",
      "[306000]\tvalid_0's mape: 0.00895977\n",
      "[308000]\tvalid_0's mape: 0.00895973\n",
      "[310000]\tvalid_0's mape: 0.00895968\n",
      "[312000]\tvalid_0's mape: 0.00895966\n",
      "[314000]\tvalid_0's mape: 0.00895961\n",
      "[316000]\tvalid_0's mape: 0.00895959\n",
      "[318000]\tvalid_0's mape: 0.00895956\n",
      "[320000]\tvalid_0's mape: 0.00895951\n",
      "[322000]\tvalid_0's mape: 0.00895948\n",
      "[324000]\tvalid_0's mape: 0.00895947\n",
      "[326000]\tvalid_0's mape: 0.00895944\n",
      "[328000]\tvalid_0's mape: 0.00895941\n",
      "[330000]\tvalid_0's mape: 0.00895937\n",
      "[332000]\tvalid_0's mape: 0.00895933\n",
      "[334000]\tvalid_0's mape: 0.00895929\n",
      "[336000]\tvalid_0's mape: 0.00895925\n",
      "[338000]\tvalid_0's mape: 0.00895923\n",
      "[340000]\tvalid_0's mape: 0.0089592\n",
      "Early stopping, best iteration is:\n",
      "[339811]\tvalid_0's mape: 0.0089592\n",
      "(Fold 1) score, mse, mae, mape: 6024.87714, 0.03788, 0.11942, 0.00896.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0155966\n",
      "[4000]\tvalid_0's mape: 0.0113222\n",
      "[6000]\tvalid_0's mape: 0.0104483\n",
      "[8000]\tvalid_0's mape: 0.0100585\n",
      "[10000]\tvalid_0's mape: 0.00982498\n",
      "[12000]\tvalid_0's mape: 0.0096655\n",
      "[14000]\tvalid_0's mape: 0.00955163\n",
      "[16000]\tvalid_0's mape: 0.0094663\n",
      "[18000]\tvalid_0's mape: 0.00940067\n",
      "[20000]\tvalid_0's mape: 0.00934924\n",
      "[22000]\tvalid_0's mape: 0.00930789\n",
      "[24000]\tvalid_0's mape: 0.00927387\n",
      "[26000]\tvalid_0's mape: 0.00924371\n",
      "[28000]\tvalid_0's mape: 0.00921822\n",
      "[30000]\tvalid_0's mape: 0.00919612\n",
      "[32000]\tvalid_0's mape: 0.00917709\n",
      "[34000]\tvalid_0's mape: 0.00916032\n",
      "[36000]\tvalid_0's mape: 0.00914575\n",
      "[38000]\tvalid_0's mape: 0.00913218\n",
      "[40000]\tvalid_0's mape: 0.00912052\n",
      "[42000]\tvalid_0's mape: 0.00911015\n",
      "[44000]\tvalid_0's mape: 0.00910109\n",
      "[46000]\tvalid_0's mape: 0.00909266\n",
      "[48000]\tvalid_0's mape: 0.00908525\n",
      "[50000]\tvalid_0's mape: 0.00907866\n",
      "[52000]\tvalid_0's mape: 0.00907243\n",
      "[54000]\tvalid_0's mape: 0.00906697\n",
      "[56000]\tvalid_0's mape: 0.00906218\n",
      "[58000]\tvalid_0's mape: 0.00905777\n",
      "[60000]\tvalid_0's mape: 0.00905345\n",
      "[62000]\tvalid_0's mape: 0.0090491\n",
      "[64000]\tvalid_0's mape: 0.00904546\n",
      "[66000]\tvalid_0's mape: 0.00904202\n",
      "[68000]\tvalid_0's mape: 0.00903887\n",
      "[70000]\tvalid_0's mape: 0.00903602\n",
      "[72000]\tvalid_0's mape: 0.00903323\n",
      "[74000]\tvalid_0's mape: 0.00903086\n",
      "[76000]\tvalid_0's mape: 0.00902854\n",
      "[78000]\tvalid_0's mape: 0.00902632\n",
      "[80000]\tvalid_0's mape: 0.00902428\n",
      "[82000]\tvalid_0's mape: 0.00902247\n",
      "[84000]\tvalid_0's mape: 0.00902066\n",
      "[86000]\tvalid_0's mape: 0.00901899\n",
      "[88000]\tvalid_0's mape: 0.00901731\n",
      "[90000]\tvalid_0's mape: 0.00901586\n",
      "[92000]\tvalid_0's mape: 0.00901452\n",
      "[94000]\tvalid_0's mape: 0.00901314\n",
      "[96000]\tvalid_0's mape: 0.00901191\n",
      "[98000]\tvalid_0's mape: 0.00901073\n",
      "[100000]\tvalid_0's mape: 0.00900956\n",
      "[102000]\tvalid_0's mape: 0.00900848\n",
      "[104000]\tvalid_0's mape: 0.00900744\n",
      "[106000]\tvalid_0's mape: 0.00900646\n",
      "[108000]\tvalid_0's mape: 0.00900553\n",
      "[110000]\tvalid_0's mape: 0.00900466\n",
      "[112000]\tvalid_0's mape: 0.00900387\n",
      "[114000]\tvalid_0's mape: 0.00900311\n",
      "[116000]\tvalid_0's mape: 0.00900237\n",
      "[118000]\tvalid_0's mape: 0.00900163\n",
      "[120000]\tvalid_0's mape: 0.00900099\n",
      "[122000]\tvalid_0's mape: 0.00900031\n",
      "[124000]\tvalid_0's mape: 0.0089997\n",
      "[126000]\tvalid_0's mape: 0.00899907\n",
      "[128000]\tvalid_0's mape: 0.00899854\n",
      "[130000]\tvalid_0's mape: 0.008998\n",
      "[132000]\tvalid_0's mape: 0.00899748\n",
      "[134000]\tvalid_0's mape: 0.00899699\n",
      "[136000]\tvalid_0's mape: 0.00899653\n",
      "[138000]\tvalid_0's mape: 0.00899605\n",
      "[140000]\tvalid_0's mape: 0.00899557\n",
      "[142000]\tvalid_0's mape: 0.00899518\n",
      "[144000]\tvalid_0's mape: 0.00899473\n",
      "[146000]\tvalid_0's mape: 0.00899434\n",
      "[148000]\tvalid_0's mape: 0.00899398\n",
      "[150000]\tvalid_0's mape: 0.00899365\n",
      "[152000]\tvalid_0's mape: 0.00899326\n",
      "[154000]\tvalid_0's mape: 0.00899294\n",
      "[156000]\tvalid_0's mape: 0.00899259\n",
      "[158000]\tvalid_0's mape: 0.00899225\n",
      "[160000]\tvalid_0's mape: 0.00899195\n",
      "[162000]\tvalid_0's mape: 0.00899165\n",
      "[164000]\tvalid_0's mape: 0.00899143\n",
      "[166000]\tvalid_0's mape: 0.00899116\n",
      "[168000]\tvalid_0's mape: 0.00899089\n",
      "[170000]\tvalid_0's mape: 0.00899062\n",
      "[172000]\tvalid_0's mape: 0.00899035\n",
      "[174000]\tvalid_0's mape: 0.00899009\n",
      "[176000]\tvalid_0's mape: 0.00898986\n",
      "[178000]\tvalid_0's mape: 0.00898966\n",
      "[180000]\tvalid_0's mape: 0.00898943\n",
      "[182000]\tvalid_0's mape: 0.00898922\n",
      "[184000]\tvalid_0's mape: 0.00898901\n",
      "[186000]\tvalid_0's mape: 0.00898884\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[188000]\tvalid_0's mape: 0.00898864\n",
      "[190000]\tvalid_0's mape: 0.00898848\n",
      "[192000]\tvalid_0's mape: 0.00898827\n",
      "[194000]\tvalid_0's mape: 0.00898808\n",
      "[196000]\tvalid_0's mape: 0.0089879\n",
      "[198000]\tvalid_0's mape: 0.00898775\n",
      "[200000]\tvalid_0's mape: 0.00898758\n",
      "[202000]\tvalid_0's mape: 0.00898743\n",
      "[204000]\tvalid_0's mape: 0.00898729\n",
      "[206000]\tvalid_0's mape: 0.00898713\n",
      "[208000]\tvalid_0's mape: 0.00898701\n",
      "[210000]\tvalid_0's mape: 0.00898687\n",
      "[212000]\tvalid_0's mape: 0.00898674\n",
      "[214000]\tvalid_0's mape: 0.00898661\n",
      "[216000]\tvalid_0's mape: 0.0089865\n",
      "[218000]\tvalid_0's mape: 0.00898639\n",
      "[220000]\tvalid_0's mape: 0.00898625\n",
      "[222000]\tvalid_0's mape: 0.00898615\n",
      "[224000]\tvalid_0's mape: 0.00898603\n",
      "[226000]\tvalid_0's mape: 0.00898593\n",
      "[228000]\tvalid_0's mape: 0.00898583\n",
      "[230000]\tvalid_0's mape: 0.00898572\n",
      "[232000]\tvalid_0's mape: 0.00898563\n",
      "[234000]\tvalid_0's mape: 0.0089855\n",
      "[236000]\tvalid_0's mape: 0.0089854\n",
      "[238000]\tvalid_0's mape: 0.00898532\n",
      "[240000]\tvalid_0's mape: 0.00898525\n",
      "[242000]\tvalid_0's mape: 0.00898515\n",
      "[244000]\tvalid_0's mape: 0.00898505\n",
      "[246000]\tvalid_0's mape: 0.00898499\n",
      "[248000]\tvalid_0's mape: 0.00898491\n",
      "[250000]\tvalid_0's mape: 0.00898482\n",
      "[252000]\tvalid_0's mape: 0.00898473\n",
      "[254000]\tvalid_0's mape: 0.00898464\n",
      "[256000]\tvalid_0's mape: 0.00898459\n",
      "[258000]\tvalid_0's mape: 0.00898451\n",
      "[260000]\tvalid_0's mape: 0.00898444\n",
      "[262000]\tvalid_0's mape: 0.00898436\n",
      "[264000]\tvalid_0's mape: 0.0089843\n",
      "[266000]\tvalid_0's mape: 0.00898423\n",
      "[268000]\tvalid_0's mape: 0.00898417\n",
      "[270000]\tvalid_0's mape: 0.00898408\n",
      "[272000]\tvalid_0's mape: 0.008984\n",
      "[274000]\tvalid_0's mape: 0.00898394\n",
      "[276000]\tvalid_0's mape: 0.00898389\n",
      "[278000]\tvalid_0's mape: 0.00898381\n",
      "[280000]\tvalid_0's mape: 0.00898377\n",
      "[282000]\tvalid_0's mape: 0.00898373\n",
      "[284000]\tvalid_0's mape: 0.00898369\n",
      "[286000]\tvalid_0's mape: 0.00898365\n",
      "[288000]\tvalid_0's mape: 0.00898359\n",
      "[290000]\tvalid_0's mape: 0.00898353\n",
      "[292000]\tvalid_0's mape: 0.00898346\n",
      "[294000]\tvalid_0's mape: 0.00898341\n",
      "[296000]\tvalid_0's mape: 0.00898336\n",
      "[298000]\tvalid_0's mape: 0.00898332\n",
      "[300000]\tvalid_0's mape: 0.00898328\n",
      "[302000]\tvalid_0's mape: 0.00898323\n",
      "[304000]\tvalid_0's mape: 0.00898318\n",
      "[306000]\tvalid_0's mape: 0.00898315\n",
      "[308000]\tvalid_0's mape: 0.0089831\n",
      "[310000]\tvalid_0's mape: 0.00898305\n",
      "[312000]\tvalid_0's mape: 0.00898301\n",
      "[314000]\tvalid_0's mape: 0.00898295\n",
      "[316000]\tvalid_0's mape: 0.00898292\n",
      "[318000]\tvalid_0's mape: 0.00898288\n",
      "[320000]\tvalid_0's mape: 0.00898284\n",
      "[322000]\tvalid_0's mape: 0.00898281\n",
      "[324000]\tvalid_0's mape: 0.00898277\n",
      "[326000]\tvalid_0's mape: 0.00898272\n",
      "[328000]\tvalid_0's mape: 0.00898269\n",
      "[330000]\tvalid_0's mape: 0.00898263\n",
      "[332000]\tvalid_0's mape: 0.0089826\n",
      "[334000]\tvalid_0's mape: 0.00898257\n",
      "[336000]\tvalid_0's mape: 0.00898254\n",
      "[338000]\tvalid_0's mape: 0.00898252\n",
      "[340000]\tvalid_0's mape: 0.00898249\n",
      "[342000]\tvalid_0's mape: 0.00898246\n",
      "[344000]\tvalid_0's mape: 0.00898242\n",
      "[346000]\tvalid_0's mape: 0.00898239\n",
      "[348000]\tvalid_0's mape: 0.00898236\n",
      "[350000]\tvalid_0's mape: 0.00898234\n",
      "[352000]\tvalid_0's mape: 0.0089823\n",
      "[354000]\tvalid_0's mape: 0.00898228\n",
      "[356000]\tvalid_0's mape: 0.00898224\n",
      "[358000]\tvalid_0's mape: 0.00898221\n",
      "[360000]\tvalid_0's mape: 0.00898217\n",
      "[362000]\tvalid_0's mape: 0.00898215\n",
      "[364000]\tvalid_0's mape: 0.00898212\n",
      "[366000]\tvalid_0's mape: 0.0089821\n",
      "[368000]\tvalid_0's mape: 0.00898206\n",
      "[370000]\tvalid_0's mape: 0.00898204\n",
      "[372000]\tvalid_0's mape: 0.00898202\n",
      "[374000]\tvalid_0's mape: 0.008982\n",
      "[376000]\tvalid_0's mape: 0.00898196\n",
      "[378000]\tvalid_0's mape: 0.00898194\n",
      "[380000]\tvalid_0's mape: 0.00898191\n",
      "[382000]\tvalid_0's mape: 0.00898187\n",
      "[384000]\tvalid_0's mape: 0.00898184\n",
      "[386000]\tvalid_0's mape: 0.00898182\n",
      "[388000]\tvalid_0's mape: 0.00898179\n",
      "[390000]\tvalid_0's mape: 0.00898177\n",
      "[392000]\tvalid_0's mape: 0.00898174\n",
      "[394000]\tvalid_0's mape: 0.00898173\n",
      "[396000]\tvalid_0's mape: 0.00898171\n",
      "[398000]\tvalid_0's mape: 0.00898168\n",
      "[400000]\tvalid_0's mape: 0.00898165\n",
      "[402000]\tvalid_0's mape: 0.00898163\n",
      "[404000]\tvalid_0's mape: 0.00898161\n",
      "[406000]\tvalid_0's mape: 0.00898159\n",
      "[408000]\tvalid_0's mape: 0.00898157\n",
      "[410000]\tvalid_0's mape: 0.00898154\n",
      "[412000]\tvalid_0's mape: 0.00898153\n",
      "[414000]\tvalid_0's mape: 0.00898151\n",
      "[416000]\tvalid_0's mape: 0.0089815\n",
      "[418000]\tvalid_0's mape: 0.00898146\n",
      "[420000]\tvalid_0's mape: 0.00898146\n",
      "[422000]\tvalid_0's mape: 0.00898143\n",
      "[424000]\tvalid_0's mape: 0.00898141\n",
      "[426000]\tvalid_0's mape: 0.0089814\n",
      "[428000]\tvalid_0's mape: 0.00898139\n",
      "Early stopping, best iteration is:\n",
      "[428481]\tvalid_0's mape: 0.00898139\n",
      "(Fold 2) score, mse, mae, mape: 6012.87415, 0.03769, 0.11944, 0.00898.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0156416\n",
      "[4000]\tvalid_0's mape: 0.0114295\n",
      "[6000]\tvalid_0's mape: 0.0105908\n",
      "[8000]\tvalid_0's mape: 0.0102047\n",
      "[10000]\tvalid_0's mape: 0.00996557\n",
      "[12000]\tvalid_0's mape: 0.00980359\n",
      "[14000]\tvalid_0's mape: 0.0096828\n",
      "[16000]\tvalid_0's mape: 0.00959096\n",
      "[18000]\tvalid_0's mape: 0.00952038\n",
      "[20000]\tvalid_0's mape: 0.00946444\n",
      "[22000]\tvalid_0's mape: 0.0094177\n",
      "[24000]\tvalid_0's mape: 0.00937871\n",
      "[26000]\tvalid_0's mape: 0.00934534\n",
      "[28000]\tvalid_0's mape: 0.00931646\n",
      "[30000]\tvalid_0's mape: 0.00929154\n",
      "[32000]\tvalid_0's mape: 0.0092697\n",
      "[34000]\tvalid_0's mape: 0.00925076\n",
      "[36000]\tvalid_0's mape: 0.00923359\n",
      "[38000]\tvalid_0's mape: 0.00921799\n",
      "[40000]\tvalid_0's mape: 0.00920444\n",
      "[42000]\tvalid_0's mape: 0.00919203\n",
      "[44000]\tvalid_0's mape: 0.00918128\n",
      "[46000]\tvalid_0's mape: 0.00917109\n",
      "[48000]\tvalid_0's mape: 0.00916217\n",
      "[50000]\tvalid_0's mape: 0.00915374\n",
      "[52000]\tvalid_0's mape: 0.00914603\n",
      "[54000]\tvalid_0's mape: 0.00913921\n",
      "[56000]\tvalid_0's mape: 0.00913294\n",
      "[58000]\tvalid_0's mape: 0.00912719\n",
      "[60000]\tvalid_0's mape: 0.00912172\n",
      "[62000]\tvalid_0's mape: 0.00911668\n",
      "[64000]\tvalid_0's mape: 0.00911197\n",
      "[66000]\tvalid_0's mape: 0.00910766\n",
      "[68000]\tvalid_0's mape: 0.00910359\n",
      "[70000]\tvalid_0's mape: 0.00909977\n",
      "[72000]\tvalid_0's mape: 0.00909642\n",
      "[74000]\tvalid_0's mape: 0.00909305\n",
      "[76000]\tvalid_0's mape: 0.00909001\n",
      "[78000]\tvalid_0's mape: 0.00908709\n",
      "[80000]\tvalid_0's mape: 0.00908446\n",
      "[82000]\tvalid_0's mape: 0.00908188\n",
      "[84000]\tvalid_0's mape: 0.00907945\n",
      "[86000]\tvalid_0's mape: 0.00907726\n",
      "[88000]\tvalid_0's mape: 0.00907521\n",
      "[90000]\tvalid_0's mape: 0.00907312\n",
      "[92000]\tvalid_0's mape: 0.00907132\n",
      "[94000]\tvalid_0's mape: 0.00906953\n",
      "[96000]\tvalid_0's mape: 0.00906788\n",
      "[98000]\tvalid_0's mape: 0.00906639\n",
      "[100000]\tvalid_0's mape: 0.00906488\n",
      "[102000]\tvalid_0's mape: 0.00906349\n",
      "[104000]\tvalid_0's mape: 0.00906215\n",
      "[106000]\tvalid_0's mape: 0.00906093\n",
      "[108000]\tvalid_0's mape: 0.00905971\n",
      "[110000]\tvalid_0's mape: 0.00905858\n",
      "[112000]\tvalid_0's mape: 0.00905757\n",
      "[114000]\tvalid_0's mape: 0.0090566\n",
      "[116000]\tvalid_0's mape: 0.00905565\n",
      "[118000]\tvalid_0's mape: 0.00905475\n",
      "[120000]\tvalid_0's mape: 0.00905388\n",
      "[122000]\tvalid_0's mape: 0.00905305\n",
      "[124000]\tvalid_0's mape: 0.00905229\n",
      "[126000]\tvalid_0's mape: 0.00905152\n",
      "[128000]\tvalid_0's mape: 0.0090508\n",
      "[130000]\tvalid_0's mape: 0.0090501\n",
      "[132000]\tvalid_0's mape: 0.00904939\n",
      "[134000]\tvalid_0's mape: 0.00904877\n",
      "[136000]\tvalid_0's mape: 0.00904816\n",
      "[138000]\tvalid_0's mape: 0.00904757\n",
      "[140000]\tvalid_0's mape: 0.00904701\n",
      "[142000]\tvalid_0's mape: 0.00904648\n",
      "[144000]\tvalid_0's mape: 0.00904594\n",
      "[146000]\tvalid_0's mape: 0.00904547\n",
      "[148000]\tvalid_0's mape: 0.00904499\n",
      "[150000]\tvalid_0's mape: 0.00904456\n",
      "[152000]\tvalid_0's mape: 0.00904407\n",
      "[154000]\tvalid_0's mape: 0.00904364\n",
      "[156000]\tvalid_0's mape: 0.00904323\n",
      "[158000]\tvalid_0's mape: 0.00904285\n",
      "[160000]\tvalid_0's mape: 0.00904248\n",
      "[162000]\tvalid_0's mape: 0.00904213\n",
      "[164000]\tvalid_0's mape: 0.00904178\n",
      "[166000]\tvalid_0's mape: 0.00904143\n",
      "[168000]\tvalid_0's mape: 0.00904106\n",
      "[170000]\tvalid_0's mape: 0.00904077\n",
      "[172000]\tvalid_0's mape: 0.00904043\n",
      "[174000]\tvalid_0's mape: 0.0090401\n",
      "[176000]\tvalid_0's mape: 0.0090398\n",
      "[178000]\tvalid_0's mape: 0.00903952\n",
      "[180000]\tvalid_0's mape: 0.00903924\n",
      "[182000]\tvalid_0's mape: 0.00903893\n",
      "[184000]\tvalid_0's mape: 0.00903873\n",
      "[186000]\tvalid_0's mape: 0.00903848\n",
      "[188000]\tvalid_0's mape: 0.00903825\n",
      "[190000]\tvalid_0's mape: 0.00903801\n",
      "[192000]\tvalid_0's mape: 0.00903776\n",
      "[194000]\tvalid_0's mape: 0.00903749\n",
      "[196000]\tvalid_0's mape: 0.00903727\n",
      "[198000]\tvalid_0's mape: 0.00903708\n",
      "[200000]\tvalid_0's mape: 0.00903687\n",
      "[202000]\tvalid_0's mape: 0.00903668\n",
      "[204000]\tvalid_0's mape: 0.00903643\n",
      "[206000]\tvalid_0's mape: 0.00903625\n",
      "[208000]\tvalid_0's mape: 0.00903605\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[210000]\tvalid_0's mape: 0.00903591\n",
      "[212000]\tvalid_0's mape: 0.00903573\n",
      "[214000]\tvalid_0's mape: 0.00903558\n",
      "[216000]\tvalid_0's mape: 0.00903539\n",
      "[218000]\tvalid_0's mape: 0.00903525\n",
      "[220000]\tvalid_0's mape: 0.0090351\n",
      "[222000]\tvalid_0's mape: 0.00903494\n",
      "[224000]\tvalid_0's mape: 0.0090348\n",
      "[226000]\tvalid_0's mape: 0.00903466\n",
      "[228000]\tvalid_0's mape: 0.00903453\n",
      "[230000]\tvalid_0's mape: 0.00903434\n",
      "[232000]\tvalid_0's mape: 0.00903423\n",
      "[234000]\tvalid_0's mape: 0.00903412\n",
      "[236000]\tvalid_0's mape: 0.00903397\n",
      "[238000]\tvalid_0's mape: 0.00903388\n",
      "[240000]\tvalid_0's mape: 0.00903374\n",
      "[242000]\tvalid_0's mape: 0.00903364\n",
      "[244000]\tvalid_0's mape: 0.0090335\n",
      "[246000]\tvalid_0's mape: 0.0090334\n",
      "[248000]\tvalid_0's mape: 0.00903328\n",
      "[250000]\tvalid_0's mape: 0.00903318\n",
      "[252000]\tvalid_0's mape: 0.00903308\n",
      "[254000]\tvalid_0's mape: 0.00903298\n",
      "[256000]\tvalid_0's mape: 0.00903286\n",
      "[258000]\tvalid_0's mape: 0.00903275\n",
      "[260000]\tvalid_0's mape: 0.00903265\n",
      "[262000]\tvalid_0's mape: 0.00903256\n",
      "[264000]\tvalid_0's mape: 0.00903248\n",
      "[266000]\tvalid_0's mape: 0.00903238\n",
      "[268000]\tvalid_0's mape: 0.00903231\n",
      "[270000]\tvalid_0's mape: 0.00903222\n",
      "[272000]\tvalid_0's mape: 0.00903214\n",
      "[274000]\tvalid_0's mape: 0.00903206\n",
      "[276000]\tvalid_0's mape: 0.00903198\n",
      "[278000]\tvalid_0's mape: 0.00903191\n",
      "[280000]\tvalid_0's mape: 0.00903185\n",
      "[282000]\tvalid_0's mape: 0.00903175\n",
      "[284000]\tvalid_0's mape: 0.00903168\n",
      "[286000]\tvalid_0's mape: 0.00903158\n",
      "[288000]\tvalid_0's mape: 0.00903152\n",
      "[290000]\tvalid_0's mape: 0.00903143\n",
      "[292000]\tvalid_0's mape: 0.00903138\n",
      "[294000]\tvalid_0's mape: 0.00903131\n",
      "[296000]\tvalid_0's mape: 0.00903124\n",
      "[298000]\tvalid_0's mape: 0.00903118\n",
      "[300000]\tvalid_0's mape: 0.00903112\n",
      "[302000]\tvalid_0's mape: 0.00903107\n",
      "[304000]\tvalid_0's mape: 0.009031\n",
      "[306000]\tvalid_0's mape: 0.00903094\n",
      "[308000]\tvalid_0's mape: 0.0090309\n",
      "[310000]\tvalid_0's mape: 0.00903084\n",
      "[312000]\tvalid_0's mape: 0.00903078\n",
      "[314000]\tvalid_0's mape: 0.00903072\n",
      "[316000]\tvalid_0's mape: 0.00903067\n",
      "[318000]\tvalid_0's mape: 0.00903062\n",
      "[320000]\tvalid_0's mape: 0.00903058\n",
      "[322000]\tvalid_0's mape: 0.00903052\n",
      "[324000]\tvalid_0's mape: 0.00903048\n",
      "[326000]\tvalid_0's mape: 0.00903044\n",
      "[328000]\tvalid_0's mape: 0.00903039\n",
      "[330000]\tvalid_0's mape: 0.00903035\n",
      "[332000]\tvalid_0's mape: 0.0090303\n",
      "[334000]\tvalid_0's mape: 0.00903026\n",
      "[336000]\tvalid_0's mape: 0.00903022\n",
      "[338000]\tvalid_0's mape: 0.00903018\n",
      "[340000]\tvalid_0's mape: 0.00903014\n",
      "[342000]\tvalid_0's mape: 0.00903009\n",
      "[344000]\tvalid_0's mape: 0.00903005\n",
      "[346000]\tvalid_0's mape: 0.00903002\n",
      "[348000]\tvalid_0's mape: 0.00902998\n",
      "[350000]\tvalid_0's mape: 0.00902994\n",
      "[352000]\tvalid_0's mape: 0.0090299\n",
      "[354000]\tvalid_0's mape: 0.00902985\n",
      "[356000]\tvalid_0's mape: 0.00902982\n",
      "[358000]\tvalid_0's mape: 0.00902978\n",
      "[360000]\tvalid_0's mape: 0.00902974\n",
      "[362000]\tvalid_0's mape: 0.00902969\n",
      "[364000]\tvalid_0's mape: 0.00902967\n",
      "[366000]\tvalid_0's mape: 0.00902963\n",
      "[368000]\tvalid_0's mape: 0.00902959\n",
      "[370000]\tvalid_0's mape: 0.00902958\n",
      "[372000]\tvalid_0's mape: 0.00902953\n",
      "[374000]\tvalid_0's mape: 0.00902952\n",
      "[376000]\tvalid_0's mape: 0.00902949\n",
      "[378000]\tvalid_0's mape: 0.00902946\n",
      "[380000]\tvalid_0's mape: 0.00902942\n",
      "[382000]\tvalid_0's mape: 0.00902939\n",
      "[384000]\tvalid_0's mape: 0.00902937\n",
      "[386000]\tvalid_0's mape: 0.00902932\n",
      "[388000]\tvalid_0's mape: 0.00902929\n",
      "[390000]\tvalid_0's mape: 0.00902925\n",
      "[392000]\tvalid_0's mape: 0.00902923\n",
      "[394000]\tvalid_0's mape: 0.00902921\n",
      "[396000]\tvalid_0's mape: 0.00902918\n",
      "[398000]\tvalid_0's mape: 0.00902916\n",
      "[400000]\tvalid_0's mape: 0.00902913\n",
      "[402000]\tvalid_0's mape: 0.00902909\n",
      "[404000]\tvalid_0's mape: 0.00902906\n",
      "[406000]\tvalid_0's mape: 0.00902904\n",
      "[408000]\tvalid_0's mape: 0.009029\n",
      "[410000]\tvalid_0's mape: 0.00902898\n",
      "[412000]\tvalid_0's mape: 0.00902896\n",
      "[414000]\tvalid_0's mape: 0.00902894\n",
      "[416000]\tvalid_0's mape: 0.00902891\n",
      "[418000]\tvalid_0's mape: 0.00902888\n",
      "[420000]\tvalid_0's mape: 0.00902886\n",
      "[422000]\tvalid_0's mape: 0.00902884\n",
      "[424000]\tvalid_0's mape: 0.00902883\n",
      "[426000]\tvalid_0's mape: 0.00902881\n",
      "[428000]\tvalid_0's mape: 0.00902879\n",
      "[430000]\tvalid_0's mape: 0.00902879\n",
      "Early stopping, best iteration is:\n",
      "[429000]\tvalid_0's mape: 0.00902878\n",
      "(Fold 3) score, mse, mae, mape: 5987.87644, 0.03733, 0.12022, 0.00903.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0159336\n",
      "[4000]\tvalid_0's mape: 0.0114422\n",
      "[6000]\tvalid_0's mape: 0.0105104\n",
      "[8000]\tvalid_0's mape: 0.0100922\n",
      "[10000]\tvalid_0's mape: 0.00984221\n",
      "[12000]\tvalid_0's mape: 0.00967563\n",
      "[14000]\tvalid_0's mape: 0.00955679\n",
      "[16000]\tvalid_0's mape: 0.0094668\n",
      "[18000]\tvalid_0's mape: 0.00939802\n",
      "[20000]\tvalid_0's mape: 0.00934216\n",
      "[22000]\tvalid_0's mape: 0.00929626\n",
      "[24000]\tvalid_0's mape: 0.00925863\n",
      "[26000]\tvalid_0's mape: 0.00922643\n",
      "[28000]\tvalid_0's mape: 0.00919922\n",
      "[30000]\tvalid_0's mape: 0.00917585\n",
      "[32000]\tvalid_0's mape: 0.00915512\n",
      "[34000]\tvalid_0's mape: 0.00913723\n",
      "[36000]\tvalid_0's mape: 0.00912158\n",
      "[38000]\tvalid_0's mape: 0.0091075\n",
      "[40000]\tvalid_0's mape: 0.00909493\n",
      "[42000]\tvalid_0's mape: 0.00908397\n",
      "[44000]\tvalid_0's mape: 0.0090741\n",
      "[46000]\tvalid_0's mape: 0.00906541\n",
      "[48000]\tvalid_0's mape: 0.00905717\n",
      "[50000]\tvalid_0's mape: 0.00905022\n",
      "[52000]\tvalid_0's mape: 0.00904365\n",
      "[54000]\tvalid_0's mape: 0.00903783\n",
      "[56000]\tvalid_0's mape: 0.00903225\n",
      "[58000]\tvalid_0's mape: 0.00902726\n",
      "[60000]\tvalid_0's mape: 0.0090228\n",
      "[62000]\tvalid_0's mape: 0.00901878\n",
      "[64000]\tvalid_0's mape: 0.00901524\n",
      "[66000]\tvalid_0's mape: 0.00901166\n",
      "[68000]\tvalid_0's mape: 0.00900833\n",
      "[70000]\tvalid_0's mape: 0.00900521\n",
      "[72000]\tvalid_0's mape: 0.00900248\n",
      "[74000]\tvalid_0's mape: 0.00899997\n",
      "[76000]\tvalid_0's mape: 0.00899755\n",
      "[78000]\tvalid_0's mape: 0.00899514\n",
      "[80000]\tvalid_0's mape: 0.00899308\n",
      "[82000]\tvalid_0's mape: 0.00899123\n",
      "[84000]\tvalid_0's mape: 0.0089895\n",
      "[86000]\tvalid_0's mape: 0.00898788\n",
      "[88000]\tvalid_0's mape: 0.00898636\n",
      "[90000]\tvalid_0's mape: 0.00898481\n",
      "[92000]\tvalid_0's mape: 0.00898346\n",
      "[94000]\tvalid_0's mape: 0.00898214\n",
      "[96000]\tvalid_0's mape: 0.00898093\n",
      "[98000]\tvalid_0's mape: 0.00897973\n",
      "[100000]\tvalid_0's mape: 0.00897859\n",
      "[102000]\tvalid_0's mape: 0.0089775\n",
      "[104000]\tvalid_0's mape: 0.00897641\n",
      "[106000]\tvalid_0's mape: 0.00897548\n",
      "[108000]\tvalid_0's mape: 0.00897462\n",
      "[110000]\tvalid_0's mape: 0.00897376\n",
      "[112000]\tvalid_0's mape: 0.00897288\n",
      "[114000]\tvalid_0's mape: 0.00897198\n",
      "[116000]\tvalid_0's mape: 0.00897127\n",
      "[118000]\tvalid_0's mape: 0.00897057\n",
      "[120000]\tvalid_0's mape: 0.00896995\n",
      "[122000]\tvalid_0's mape: 0.00896929\n",
      "[124000]\tvalid_0's mape: 0.00896877\n",
      "[126000]\tvalid_0's mape: 0.00896813\n",
      "[128000]\tvalid_0's mape: 0.00896754\n",
      "[130000]\tvalid_0's mape: 0.00896707\n",
      "[132000]\tvalid_0's mape: 0.00896655\n",
      "[134000]\tvalid_0's mape: 0.00896606\n",
      "[136000]\tvalid_0's mape: 0.00896562\n",
      "[138000]\tvalid_0's mape: 0.00896518\n",
      "[140000]\tvalid_0's mape: 0.00896475\n",
      "[142000]\tvalid_0's mape: 0.00896434\n",
      "[144000]\tvalid_0's mape: 0.00896401\n",
      "[146000]\tvalid_0's mape: 0.00896364\n",
      "[148000]\tvalid_0's mape: 0.00896331\n",
      "[150000]\tvalid_0's mape: 0.00896296\n",
      "[152000]\tvalid_0's mape: 0.00896266\n",
      "[154000]\tvalid_0's mape: 0.0089624\n",
      "[156000]\tvalid_0's mape: 0.00896209\n",
      "[158000]\tvalid_0's mape: 0.00896184\n",
      "[160000]\tvalid_0's mape: 0.00896155\n",
      "[162000]\tvalid_0's mape: 0.00896127\n",
      "[164000]\tvalid_0's mape: 0.00896102\n",
      "[166000]\tvalid_0's mape: 0.00896077\n",
      "[168000]\tvalid_0's mape: 0.00896055\n",
      "[170000]\tvalid_0's mape: 0.00896035\n",
      "[172000]\tvalid_0's mape: 0.00896015\n",
      "[174000]\tvalid_0's mape: 0.00895993\n",
      "[176000]\tvalid_0's mape: 0.00895975\n",
      "[178000]\tvalid_0's mape: 0.00895957\n",
      "[180000]\tvalid_0's mape: 0.00895936\n",
      "[182000]\tvalid_0's mape: 0.00895921\n",
      "[184000]\tvalid_0's mape: 0.00895902\n",
      "[186000]\tvalid_0's mape: 0.0089588\n",
      "[188000]\tvalid_0's mape: 0.0089586\n",
      "[190000]\tvalid_0's mape: 0.00895844\n",
      "[192000]\tvalid_0's mape: 0.00895828\n",
      "[194000]\tvalid_0's mape: 0.00895813\n",
      "[196000]\tvalid_0's mape: 0.00895799\n",
      "[198000]\tvalid_0's mape: 0.00895786\n",
      "[200000]\tvalid_0's mape: 0.00895771\n",
      "[202000]\tvalid_0's mape: 0.00895759\n",
      "[204000]\tvalid_0's mape: 0.00895748\n",
      "[206000]\tvalid_0's mape: 0.00895737\n",
      "[208000]\tvalid_0's mape: 0.00895724\n",
      "[210000]\tvalid_0's mape: 0.00895714\n",
      "[212000]\tvalid_0's mape: 0.00895702\n",
      "[214000]\tvalid_0's mape: 0.00895689\n",
      "[216000]\tvalid_0's mape: 0.00895676\n",
      "[218000]\tvalid_0's mape: 0.00895666\n",
      "[220000]\tvalid_0's mape: 0.00895655\n",
      "[222000]\tvalid_0's mape: 0.00895649\n",
      "[224000]\tvalid_0's mape: 0.00895641\n",
      "[226000]\tvalid_0's mape: 0.00895632\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[228000]\tvalid_0's mape: 0.00895622\n",
      "[230000]\tvalid_0's mape: 0.00895615\n",
      "[232000]\tvalid_0's mape: 0.00895605\n",
      "[234000]\tvalid_0's mape: 0.00895595\n",
      "[236000]\tvalid_0's mape: 0.00895586\n",
      "[238000]\tvalid_0's mape: 0.00895577\n",
      "[240000]\tvalid_0's mape: 0.00895569\n",
      "[242000]\tvalid_0's mape: 0.00895562\n",
      "[244000]\tvalid_0's mape: 0.00895554\n",
      "[246000]\tvalid_0's mape: 0.00895544\n",
      "[248000]\tvalid_0's mape: 0.00895537\n",
      "[250000]\tvalid_0's mape: 0.00895529\n",
      "[252000]\tvalid_0's mape: 0.00895523\n",
      "[254000]\tvalid_0's mape: 0.00895518\n",
      "[256000]\tvalid_0's mape: 0.00895512\n",
      "[258000]\tvalid_0's mape: 0.00895504\n",
      "[260000]\tvalid_0's mape: 0.00895497\n",
      "[262000]\tvalid_0's mape: 0.00895492\n",
      "[264000]\tvalid_0's mape: 0.00895484\n",
      "[266000]\tvalid_0's mape: 0.00895479\n",
      "[268000]\tvalid_0's mape: 0.00895474\n",
      "[270000]\tvalid_0's mape: 0.0089547\n",
      "[272000]\tvalid_0's mape: 0.00895463\n",
      "[274000]\tvalid_0's mape: 0.00895457\n",
      "[276000]\tvalid_0's mape: 0.00895453\n",
      "[278000]\tvalid_0's mape: 0.00895447\n",
      "[280000]\tvalid_0's mape: 0.00895443\n",
      "[282000]\tvalid_0's mape: 0.00895439\n",
      "[284000]\tvalid_0's mape: 0.00895434\n",
      "[286000]\tvalid_0's mape: 0.0089543\n",
      "[288000]\tvalid_0's mape: 0.00895426\n",
      "[290000]\tvalid_0's mape: 0.0089542\n",
      "[292000]\tvalid_0's mape: 0.00895415\n",
      "[294000]\tvalid_0's mape: 0.00895412\n",
      "[296000]\tvalid_0's mape: 0.00895407\n",
      "[298000]\tvalid_0's mape: 0.00895402\n",
      "[300000]\tvalid_0's mape: 0.00895397\n",
      "[302000]\tvalid_0's mape: 0.00895393\n",
      "[304000]\tvalid_0's mape: 0.00895391\n",
      "[306000]\tvalid_0's mape: 0.00895387\n",
      "[308000]\tvalid_0's mape: 0.00895383\n",
      "[310000]\tvalid_0's mape: 0.0089538\n",
      "[312000]\tvalid_0's mape: 0.00895377\n",
      "[314000]\tvalid_0's mape: 0.00895375\n",
      "[316000]\tvalid_0's mape: 0.0089537\n",
      "[318000]\tvalid_0's mape: 0.00895367\n",
      "[320000]\tvalid_0's mape: 0.00895364\n",
      "[322000]\tvalid_0's mape: 0.00895362\n",
      "[324000]\tvalid_0's mape: 0.00895358\n",
      "[326000]\tvalid_0's mape: 0.00895355\n",
      "[328000]\tvalid_0's mape: 0.00895351\n",
      "[330000]\tvalid_0's mape: 0.00895349\n",
      "[332000]\tvalid_0's mape: 0.00895346\n",
      "[334000]\tvalid_0's mape: 0.00895343\n",
      "[336000]\tvalid_0's mape: 0.0089534\n",
      "[338000]\tvalid_0's mape: 0.00895338\n",
      "[340000]\tvalid_0's mape: 0.00895335\n",
      "[342000]\tvalid_0's mape: 0.00895332\n",
      "[344000]\tvalid_0's mape: 0.00895331\n",
      "[346000]\tvalid_0's mape: 0.00895328\n",
      "[348000]\tvalid_0's mape: 0.00895325\n",
      "[350000]\tvalid_0's mape: 0.00895323\n",
      "[352000]\tvalid_0's mape: 0.00895321\n",
      "[354000]\tvalid_0's mape: 0.00895318\n",
      "[356000]\tvalid_0's mape: 0.00895316\n",
      "[358000]\tvalid_0's mape: 0.00895314\n",
      "[360000]\tvalid_0's mape: 0.00895312\n",
      "[362000]\tvalid_0's mape: 0.0089531\n",
      "[364000]\tvalid_0's mape: 0.00895308\n",
      "[366000]\tvalid_0's mape: 0.00895304\n",
      "Early stopping, best iteration is:\n",
      "[366200]\tvalid_0's mape: 0.00895303\n",
      "(Fold 4) score, mse, mae, mape: 5972.87500, 0.03692, 0.11914, 0.00895.\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "\n",
    "predicts_val = []\n",
    "predicts_test = test[['building_id']].copy()\n",
    "best_iterations = []\n",
    "scores = []\n",
    "for i, id_fold in enumerate(id_cv):\n",
    "    #id_fold = id_fold['building_id']\n",
    "    # split train, val\n",
    "    df_train = train.loc[~train['building_id'].isin(id_fold)].copy()\n",
    "    df_val = train.loc[train['building_id'].isin(id_fold)].copy()\n",
    "    df_test = test.copy()\n",
    "    \n",
    "    # feat eng\n",
    "    feat_eng = FeatureEngineering(cols_num, cols_cat, cols_feats, col_target)\n",
    "    df_train = feat_eng.fit_transform(df_train)\n",
    "    df_val = feat_eng.transform(df_val)\n",
    "    df_test = feat_eng.transform(df_test)\n",
    "\n",
    "    if rm_outlier:\n",
    "        df_train = df_train.merge(training_buildings, on='building_id', how='inner').reset_index()\n",
    "\n",
    "    # Construct lgb dataset\n",
    "    lgb_train = lgb.Dataset(df_train[cols_feats], df_train[col_target]).construct()\n",
    "    lgb_val = lgb.Dataset(df_val[cols_feats], df_val[col_target], reference=lgb_train).construct()\n",
    "        \n",
    "    # train model    \n",
    "    model = lgb.train(params, lgb_train, valid_sets=lgb_val, **lgb_other_params, keep_training_booster=True)\n",
    "    best_iterations.append(model.best_iteration)\n",
    "\n",
    "    # Predict on val set\n",
    "    y_pred = model.predict(df_val[cols_feats])\n",
    "    mse = mean_squared_error(df_val[col_target], y_pred)\n",
    "    mae = mean_absolute_error(df_val[col_target], y_pred)\n",
    "    mape = cal_mape(df_val[col_target], y_pred)\n",
    "    \n",
    "    y_pred_final = post_processing(y_pred, df_val)\n",
    "    score = cal_score(df_val['total_price'], y_pred_final)\n",
    "    scores.append(score)\n",
    "\n",
    "    print('(Fold %d) score, mse, mae, mape: %.5f, %.5f, %.5f, %.5f.' % (i, score, mse, mae, mape))\n",
    "    predicts_val.append(pd.DataFrame({'building_id': df_val['building_id'],\n",
    "                                      'total_price': df_val['total_price'],\n",
    "                                      'total_price_predict': y_pred_final}))\n",
    "\n",
    "    # Predict on test set\n",
    "    y_pred_test = model.predict(df_test[cols_feats])\n",
    "    predicts_test['target_{}'.format(i)] = y_pred_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "48s per 2000 - ETA (all including one): 16.6 hrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total run time: 48665.24586009979\n"
     ]
    }
   ],
   "source": [
    "print('Total run time:', time.time() - t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kfold cv score (mean, stdev): 5983.276109811884 37.45122433871052\n"
     ]
    }
   ],
   "source": [
    "print('Kfold cv score (mean, stdev, all):', np.mean(scores), np.std(scores), scores) # 5868.87314"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>target_0</th>\n",
       "      <th>target_1</th>\n",
       "      <th>target_2</th>\n",
       "      <th>target_3</th>\n",
       "      <th>target_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X5gsdTWGS3W7JJQB</td>\n",
       "      <td>15.108238</td>\n",
       "      <td>15.144833</td>\n",
       "      <td>15.133459</td>\n",
       "      <td>15.191798</td>\n",
       "      <td>15.078641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTshNOJyKHnT2YIT</td>\n",
       "      <td>13.137452</td>\n",
       "      <td>13.111892</td>\n",
       "      <td>13.136005</td>\n",
       "      <td>13.137316</td>\n",
       "      <td>13.131938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dhdymr0lV8N5kZOT</td>\n",
       "      <td>13.644638</td>\n",
       "      <td>13.660838</td>\n",
       "      <td>13.668330</td>\n",
       "      <td>13.652730</td>\n",
       "      <td>13.660801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VEwyGGMcD56w5BOc</td>\n",
       "      <td>14.824193</td>\n",
       "      <td>14.819720</td>\n",
       "      <td>14.783903</td>\n",
       "      <td>14.825478</td>\n",
       "      <td>14.784292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>wmUeMoJZfsqaSX9b</td>\n",
       "      <td>12.152215</td>\n",
       "      <td>12.126819</td>\n",
       "      <td>12.069087</td>\n",
       "      <td>12.180481</td>\n",
       "      <td>12.175286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        building_id   target_0   target_1   target_2   target_3   target_4\n",
       "0  X5gsdTWGS3W7JJQB  15.108238  15.144833  15.133459  15.191798  15.078641\n",
       "1  BTshNOJyKHnT2YIT  13.137452  13.111892  13.136005  13.137316  13.131938\n",
       "2  dhdymr0lV8N5kZOT  13.644638  13.660838  13.668330  13.652730  13.660801\n",
       "3  VEwyGGMcD56w5BOc  14.824193  14.819720  14.783903  14.825478  14.784292\n",
       "4  wmUeMoJZfsqaSX9b  12.152215  12.126819  12.069087  12.180481  12.175286"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicts_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat(predicts_val, axis=0).to_csv('output/'+model_name+'-cv.csv', index=False)\n",
    "\n",
    "predicts_test['target_mean'] = \\\n",
    "    predicts_test[['target_'+str(i) for i in range(len(id_cv))]].mean(axis=1)\n",
    "predicts_test['total_price'] = post_processing(predicts_test['target_mean'].values, df_test)\n",
    "predicts_test[['building_id', 'total_price']].to_csv('output/'+model_name+'-test-kfold.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best iterations: [372876, 339811, 428481, 429000, 366200]\n",
      "Mean best iterations: 387273\n"
     ]
    }
   ],
   "source": [
    "print('Best iterations:', best_iterations)\n",
    "print('Mean best iterations:', int(np.mean(best_iterations)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-fold test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_boost_round': 464728, 'verbose_eval': 2000}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb_other_params_one = {'num_boost_round': int(np.mean(best_iterations)*1.2),\n",
    "                        'verbose_eval': 2000,}\n",
    "lgb_other_params_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'task': 'train',\n",
       " 'boosting_type': 'gbdt',\n",
       " 'objective': 'mse',\n",
       " 'metric': 'mape',\n",
       " 'learning_rate': 0.001,\n",
       " 'max_depth': 24,\n",
       " 'num_leaves': 279,\n",
       " 'feature_fraction': 0.45,\n",
       " 'min_data_in_leaf': 8,\n",
       " 'lambda_l1': 0.07,\n",
       " 'lambda_l2': 0.01,\n",
       " 'seed': 666,\n",
       " 'bagging_freq': 10,\n",
       " 'bagging_fraction': 0.9}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2000]\ttraining's mape: 0.0135386\n",
      "[4000]\ttraining's mape: 0.00826514\n",
      "[6000]\ttraining's mape: 0.00681398\n",
      "[8000]\ttraining's mape: 0.00596918\n",
      "[10000]\ttraining's mape: 0.00536002\n",
      "[12000]\ttraining's mape: 0.00488617\n",
      "[14000]\ttraining's mape: 0.00449979\n",
      "[16000]\ttraining's mape: 0.00417601\n",
      "[18000]\ttraining's mape: 0.00389735\n",
      "[20000]\ttraining's mape: 0.00365297\n",
      "[22000]\ttraining's mape: 0.00343615\n",
      "[24000]\ttraining's mape: 0.00324172\n",
      "[26000]\ttraining's mape: 0.00306515\n",
      "[28000]\ttraining's mape: 0.00290394\n",
      "[30000]\ttraining's mape: 0.00275664\n",
      "[32000]\ttraining's mape: 0.00262226\n",
      "[34000]\ttraining's mape: 0.0024977\n",
      "[36000]\ttraining's mape: 0.00238198\n",
      "[38000]\ttraining's mape: 0.00227376\n",
      "[40000]\ttraining's mape: 0.00217395\n",
      "[42000]\ttraining's mape: 0.00208054\n",
      "[44000]\ttraining's mape: 0.00199383\n",
      "[46000]\ttraining's mape: 0.00191195\n",
      "[48000]\ttraining's mape: 0.00183495\n",
      "[50000]\ttraining's mape: 0.00176216\n",
      "[52000]\ttraining's mape: 0.00169433\n",
      "[54000]\ttraining's mape: 0.00162997\n",
      "[56000]\ttraining's mape: 0.00156979\n",
      "[58000]\ttraining's mape: 0.00151247\n",
      "[60000]\ttraining's mape: 0.00145765\n",
      "[62000]\ttraining's mape: 0.00140595\n",
      "[64000]\ttraining's mape: 0.00135782\n",
      "[66000]\ttraining's mape: 0.00131146\n",
      "[68000]\ttraining's mape: 0.00126739\n",
      "[70000]\ttraining's mape: 0.00122574\n",
      "[72000]\ttraining's mape: 0.00118651\n",
      "[74000]\ttraining's mape: 0.00114902\n",
      "[76000]\ttraining's mape: 0.00111311\n",
      "[78000]\ttraining's mape: 0.00107922\n",
      "[80000]\ttraining's mape: 0.00104696\n",
      "[82000]\ttraining's mape: 0.00101585\n",
      "[84000]\ttraining's mape: 0.000985901\n",
      "[86000]\ttraining's mape: 0.000957694\n",
      "[88000]\ttraining's mape: 0.000931058\n",
      "[90000]\ttraining's mape: 0.00090497\n",
      "[92000]\ttraining's mape: 0.000880307\n",
      "[94000]\ttraining's mape: 0.000856431\n",
      "[96000]\ttraining's mape: 0.000833559\n",
      "[98000]\ttraining's mape: 0.000811489\n",
      "[100000]\ttraining's mape: 0.000790271\n",
      "[102000]\ttraining's mape: 0.000770384\n",
      "[104000]\ttraining's mape: 0.000751113\n",
      "[106000]\ttraining's mape: 0.000732811\n",
      "[108000]\ttraining's mape: 0.000715195\n",
      "[110000]\ttraining's mape: 0.000698008\n",
      "[112000]\ttraining's mape: 0.000681607\n",
      "[114000]\ttraining's mape: 0.00066586\n",
      "[116000]\ttraining's mape: 0.000650753\n",
      "[118000]\ttraining's mape: 0.000636088\n",
      "[120000]\ttraining's mape: 0.000622249\n",
      "[122000]\ttraining's mape: 0.000608683\n",
      "[124000]\ttraining's mape: 0.000595629\n",
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      "[128000]\ttraining's mape: 0.000570869\n",
      "[130000]\ttraining's mape: 0.000559067\n",
      "[132000]\ttraining's mape: 0.000547889\n",
      "[134000]\ttraining's mape: 0.000537025\n",
      "[136000]\ttraining's mape: 0.000526301\n",
      "[138000]\ttraining's mape: 0.000516197\n",
      "[140000]\ttraining's mape: 0.000506316\n",
      "[142000]\ttraining's mape: 0.000496912\n",
      "[144000]\ttraining's mape: 0.000487654\n",
      "[146000]\ttraining's mape: 0.000478861\n",
      "[148000]\ttraining's mape: 0.000470083\n",
      "[150000]\ttraining's mape: 0.000461749\n",
      "[152000]\ttraining's mape: 0.000453666\n",
      "[154000]\ttraining's mape: 0.000445805\n",
      "[156000]\ttraining's mape: 0.000438223\n",
      "[158000]\ttraining's mape: 0.000430798\n",
      "[160000]\ttraining's mape: 0.000423551\n",
      "[162000]\ttraining's mape: 0.000416727\n",
      "[164000]\ttraining's mape: 0.000409967\n",
      "[166000]\ttraining's mape: 0.000403252\n",
      "[168000]\ttraining's mape: 0.000396783\n",
      "[170000]\ttraining's mape: 0.000390711\n",
      "[172000]\ttraining's mape: 0.000384653\n",
      "[174000]\ttraining's mape: 0.000378759\n",
      "[176000]\ttraining's mape: 0.000373024\n",
      "[178000]\ttraining's mape: 0.000367368\n",
      "[180000]\ttraining's mape: 0.000362082\n",
      "[182000]\ttraining's mape: 0.000356847\n",
      "[184000]\ttraining's mape: 0.000351795\n",
      "[186000]\ttraining's mape: 0.000346821\n",
      "[188000]\ttraining's mape: 0.000341974\n",
      "[190000]\ttraining's mape: 0.000337207\n",
      "[192000]\ttraining's mape: 0.000332477\n",
      "[194000]\ttraining's mape: 0.000328025\n",
      "[196000]\ttraining's mape: 0.000323577\n",
      "[198000]\ttraining's mape: 0.000319215\n",
      "[200000]\ttraining's mape: 0.000315092\n",
      "[202000]\ttraining's mape: 0.000311017\n",
      "[204000]\ttraining's mape: 0.00030704\n",
      "[206000]\ttraining's mape: 0.000303179\n",
      "[208000]\ttraining's mape: 0.000299298\n",
      "[210000]\ttraining's mape: 0.000295644\n",
      "[212000]\ttraining's mape: 0.000292057\n",
      "[214000]\ttraining's mape: 0.000288509\n",
      "[216000]\ttraining's mape: 0.000285066\n",
      "[218000]\ttraining's mape: 0.000281723\n",
      "[220000]\ttraining's mape: 0.000278507\n",
      "[222000]\ttraining's mape: 0.000275264\n",
      "[224000]\ttraining's mape: 0.000272134\n",
      "[226000]\ttraining's mape: 0.00026899\n",
      "[228000]\ttraining's mape: 0.000265973\n",
      "[230000]\ttraining's mape: 0.000263058\n",
      "[232000]\ttraining's mape: 0.00026021\n",
      "[234000]\ttraining's mape: 0.000257357\n",
      "[236000]\ttraining's mape: 0.000254533\n",
      "[238000]\ttraining's mape: 0.000251828\n",
      "[240000]\ttraining's mape: 0.000249202\n",
      "[242000]\ttraining's mape: 0.000246619\n",
      "[244000]\ttraining's mape: 0.000244111\n",
      "[246000]\ttraining's mape: 0.000241663\n",
      "[248000]\ttraining's mape: 0.000239188\n",
      "[250000]\ttraining's mape: 0.000236756\n",
      "[252000]\ttraining's mape: 0.000234364\n",
      "[254000]\ttraining's mape: 0.000231989\n",
      "[256000]\ttraining's mape: 0.000229677\n",
      "[258000]\ttraining's mape: 0.000227433\n",
      "[260000]\ttraining's mape: 0.000225202\n",
      "[262000]\ttraining's mape: 0.000223073\n",
      "[264000]\ttraining's mape: 0.000221025\n",
      "[266000]\ttraining's mape: 0.000218929\n",
      "[268000]\ttraining's mape: 0.000216931\n",
      "[270000]\ttraining's mape: 0.000214967\n",
      "[272000]\ttraining's mape: 0.000213008\n",
      "[274000]\ttraining's mape: 0.0002111\n",
      "[276000]\ttraining's mape: 0.000209259\n",
      "[278000]\ttraining's mape: 0.000207401\n",
      "[280000]\ttraining's mape: 0.000205591\n",
      "[282000]\ttraining's mape: 0.000203806\n",
      "[284000]\ttraining's mape: 0.000202078\n",
      "[286000]\ttraining's mape: 0.000200403\n",
      "[288000]\ttraining's mape: 0.000198721\n",
      "[290000]\ttraining's mape: 0.000197048\n",
      "[292000]\ttraining's mape: 0.000195411\n",
      "[294000]\ttraining's mape: 0.000193814\n",
      "[296000]\ttraining's mape: 0.000192181\n",
      "[298000]\ttraining's mape: 0.000190583\n",
      "[300000]\ttraining's mape: 0.000189067\n",
      "[302000]\ttraining's mape: 0.000187547\n",
      "[304000]\ttraining's mape: 0.000186037\n",
      "[306000]\ttraining's mape: 0.000184615\n",
      "[308000]\ttraining's mape: 0.000183183\n",
      "[310000]\ttraining's mape: 0.00018177\n",
      "[312000]\ttraining's mape: 0.000180378\n",
      "[314000]\ttraining's mape: 0.00017899\n",
      "[316000]\ttraining's mape: 0.000177638\n",
      "[318000]\ttraining's mape: 0.000176291\n",
      "[320000]\ttraining's mape: 0.000174989\n",
      "[322000]\ttraining's mape: 0.000173708\n",
      "[324000]\ttraining's mape: 0.000172448\n",
      "[326000]\ttraining's mape: 0.000171209\n",
      "[328000]\ttraining's mape: 0.000169993\n",
      "[330000]\ttraining's mape: 0.000168773\n",
      "[332000]\ttraining's mape: 0.000167607\n",
      "[334000]\ttraining's mape: 0.000166425\n",
      "[336000]\ttraining's mape: 0.000165226\n",
      "[338000]\ttraining's mape: 0.000164094\n",
      "[340000]\ttraining's mape: 0.000162955\n",
      "[342000]\ttraining's mape: 0.00016186\n",
      "[344000]\ttraining's mape: 0.000160774\n",
      "[346000]\ttraining's mape: 0.00015969\n",
      "[348000]\ttraining's mape: 0.000158606\n",
      "[350000]\ttraining's mape: 0.000157549\n",
      "[352000]\ttraining's mape: 0.000156554\n",
      "[354000]\ttraining's mape: 0.000155539\n",
      "[356000]\ttraining's mape: 0.000154526\n",
      "[358000]\ttraining's mape: 0.000153528\n",
      "[360000]\ttraining's mape: 0.000152556\n",
      "[362000]\ttraining's mape: 0.000151553\n",
      "[364000]\ttraining's mape: 0.000150622\n",
      "[366000]\ttraining's mape: 0.000149678\n",
      "[368000]\ttraining's mape: 0.000148753\n",
      "[370000]\ttraining's mape: 0.000147872\n",
      "[372000]\ttraining's mape: 0.000146966\n",
      "[374000]\ttraining's mape: 0.000146098\n",
      "[376000]\ttraining's mape: 0.000145224\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-c7babcd95678>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# train model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlgb_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_sets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlgb_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mlgb_other_params_one\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_training_booster\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols_feats\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/lightgbm/engine.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)\u001b[0m\n\u001b[1;32m    216\u001b[0m                                     evaluation_result_list=None))\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m         \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mevaluation_result_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/lightgbm/basic.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, train_set, fobj)\u001b[0m\n\u001b[1;32m   1800\u001b[0m             _safe_call(_LIB.LGBM_BoosterUpdateOneIter(\n\u001b[1;32m   1801\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1802\u001b[0;31m                 ctypes.byref(is_finished)))\n\u001b[0m\u001b[1;32m   1803\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__is_predicted_cur_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__num_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1804\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mis_finished\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "t10 = time.time()\n",
    "\n",
    "df_train = train.copy()\n",
    "df_test = test.copy()\n",
    "\n",
    "# feat eng\n",
    "feat_eng = FeatureEngineering(cols_num, cols_cat, cols_feats, col_target)\n",
    "df_train = feat_eng.fit_transform(df_train)\n",
    "df_test = feat_eng.transform(df_test)\n",
    "\n",
    "if rm_outlier:\n",
    "    df_train = df_train.merge(training_buildings, on='building_id', how='inner').reset_index()\n",
    "\n",
    "# Construct lgb dataset\n",
    "lgb_train = lgb.Dataset(df_train[cols_feats], df_train[col_target]).construct()\n",
    "\n",
    "# train model    \n",
    "model = lgb.train(params, lgb_train, valid_sets=lgb_train, **lgb_other_params_one, keep_training_booster=True)\n",
    "\n",
    "y_pred = model.predict(df_test[cols_feats])\n",
    "y_pred_final = post_processing(y_pred, df_test)\n",
    "\n",
    "df_test_pred = pd.DataFrame({'building_id': df_test['building_id'], \n",
    "                             'total_price': y_pred_final})\n",
    "\n",
    "t11 = time.time()\n",
    "\n",
    "display(df_test_pred.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_pred.to_csv('output/'+model_name+'-test-one.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print('Total run time:', t11 - t10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb.plot_importance(model, figsize=(12, 30), height=0.5) # 050, 051, 052\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb.plot_importance(model, figsize=(12, 30), height=0.5, importance_type='gain') # 050, 051, 052\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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