model-11-build-rf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/lightgbm/__init__.py:45: FutureWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS will be built by the Apple Clang compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you won't need to install the gcc compiler anymore.\n",
      "Instead of that, you'll need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "  \"You can install the OpenMP library by the following command: ``brew install libomp``.\", FutureWarning)\n"
     ]
    }
   ],
   "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.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import StratifiedKFold\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 cal_mape\n",
    "\n",
    "from utilities import cal_score, convert_types, create_dt_feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_log_target = True\n",
    "is_per_area_target = True\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_num_add = ['land_per_building_area']\n",
    "cols_cat_add = ['encoded_latlon']\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": 3,
   "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['land_per_building_area'] = df['land_area'] / df['building_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",
    "    return df  #, cols_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_engineering import CategoricalColumnsEncoder\n",
    "from feature_engineering import TargetMeanEncoding\n",
    "from feature_engineering import MultiLabelEncoding\n",
    "from feature_engineering import Imputation\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",
    "        # imputer\n",
    "        self.imputer = Imputation()\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",
    "        encoded1 = encoded1.rename(columns={'encoded_'+col: col for col in self.cols_cat})\n",
    "        df.loc[:,self.cols_cat] = encoded1[self.cols_cat]\n",
    "\n",
    "        imputed = self.imputer.fit_transform(df, cols=self.cols_num)\n",
    "        df.loc[:,self.cols_num] = imputed[self.cols_num]\n",
    "        \n",
    "        return df\n",
    "\n",
    "#        encoded2 = self.ran_encoder.fit_transform(df[self.cols_cat])\n",
    "#        self.cat_astyper.fit_transform(df, self.cols_cat)    \n",
    "#        return pd.concat([df, encoded1, encoded2], axis=1)\n",
    "    \n",
    "    def transform(self, df):\n",
    "        df = df.copy()\n",
    "        \n",
    "        encoded1 = self.tar_encoder.transform(df[self.cols_cat])\n",
    "        encoded1 = encoded1.rename(columns={'encoded_'+col: col for col in self.cols_cat})\n",
    "        df.loc[:,self.cols_cat] = encoded1[self.cols_cat]\n",
    "\n",
    "        imputed = self.imputer.transform(df, cols=self.cols_num)\n",
    "        df.loc[:,self.cols_num] = imputed[self.cols_num]\n",
    "        \n",
    "        return df\n",
    "\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 pd.concat([df, encoded1, encoded2], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
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       "  </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  \\\n",
       "0            2                 6271           2          NaN   \n",
       "1            2                 7885           2          NaN   \n",
       "2            2                 6028           2          NaN   \n",
       "3            2                18325           0          NaN   \n",
       "4            2                 6880           2          NaN   \n",
       "\n",
       "          ...         XIV_500 XIV_index_500 XIV_1000 XIV_index_1000 XIV_5000  \\\n",
       "0         ...              58             1      157              1     2483   \n",
       "1         ...              28             1      115              1    15872   \n",
       "2         ...              78             1      212              1    15760   \n",
       "3         ...              20             1      125              1     2568   \n",
       "4         ...              18             1       47              1     2587   \n",
       "\n",
       "  XIV_index_5000 XIV_10000 XIV_index_10000             XIV_MIN  \\\n",
       "0              1      6011               1  34.469802848528126   \n",
       "1              1     32221               1   40.07357348973039   \n",
       "2              1     32228               1   54.46208108983661   \n",
       "3              1      7271               1   99.62896580763416   \n",
       "4              1      7442               1   124.1312362968124   \n",
       "\n",
       "          total_price  \n",
       "0   647603.7704719775  \n",
       "1           3321452.0  \n",
       "2   9570885.316206373  \n",
       "3  14215011.127100045  \n",
       "4   762711.9757135696  \n",
       "\n",
       "[5 rows x 235 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset/train.csv', dtype=object)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/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": [
    "df = pre_processing(df, cols_num, cols_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct feats cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 feature shuffle results (exp-l04-per-area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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",
      "75\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', 'X_index_50', 'IV_10', 'XIII_index_10000', 'XII_10', 'III_10']\n",
      "N feats (before): 213\n",
      "N feats (after): 138\n",
      "['land_area', 'X_5000', 'III_250', 'XIII_1000', 'VIII_MIN', 'junior_rate', 'VIII_100', 'total_floor', 'V_index_50', 'XI_10000', 'encoded_latlon', 'X_10000', 'XIV_MIN', 'V_MIN', 'X_50', 'XIV_50', 'XIII_500', 'VIII_10', 'VIII_250', 'I_5000', 'I_MIN', 'XIV_5000', 'master_rate', 'IV_10000', 'IX_500', 'VI_MIN', 'V_50', 'V_5000', 'X_250', 'building_use', 'V_250', 'X_MIN', 'village_income_median', 'XII_MIN', 'VII_10000', 'XII_1000', 'jobschool_rate', 'XIII_MIN', 'III_500', 'IX_250', 'XIII_250', 'VIII_1000', 'III_5000', 'VII_5000', 'III_100', 'II_250', 'III_index_500', 'I_1000', 'XIV_10000', 'IV_5000', 'X_500', 'VII_MIN', 'IX_10000', 'building_material', 'IV_250', 'XIV_1000', 'II_10000', 'N_50', 'elementary_rate', 'II_1000', 'IV_500', 'II_500', 'doc_rate', 'XII_10000', 'building_area', 'parking_price', 'XII_500', 'N_10000', 'IV_index_500', 'II_MIN', 'N_1000', 'born_rate', 'XIII_5000', 'IX_1000', 'XI_5000', 'VIII_50', 'XII_250', 'town', 'marriage_rate', 'III_10000', 'XIII_50', 'IV_MIN', 'land_per_building_area', 'VII_500', 'town_area', 'III_50', 'III_1000', 'VII_250', 'XII_5000', 'building_type', 'XI_250', 'IV_100', 'I_10000', 'XI_500', 'V_10000', 'XIII_100', 'V_500', 'X_1000', 'XIV_250', 'VIII_500', 'VIII_10000', 'IX_MIN', 'town_population_density', 'VII_1000', 'VIII_5000', 'parking_way', 'II_100', 'building_complete_dt', 'V_1000', 'city', 'txn_floor', 'bachelor_rate', 'IV_1000', 'VI_5000', 'XI_1000', 'txn_dt', 'village', 'XI_50', 'lon', 'II_5000', 'VII_50', 'XI_MIN', 'IX_5000', 'III_MIN', 'IX_100', 'XII_100', 'parking_area', 'XIV_100', 'lat', 'VI_10000', 'XIV_500', 'XIII_10000', 'VIII_index_50', 'town_population', 'highschool_rate', 'VII_100', 'death_rate', 'XII_50']\n",
      "N num feats (before): 156\n",
      "N num feats (after): 126\n",
      "['land_area', 'X_5000', 'III_250', 'XIII_1000', 'VIII_MIN', 'junior_rate', 'VIII_100', 'total_floor', 'XI_10000', 'X_10000', 'XIV_MIN', 'V_MIN', 'X_50', 'XIV_50', 'XIII_500', 'VIII_10', 'VIII_250', 'I_5000', 'I_MIN', 'XIV_5000', 'master_rate', 'IV_10000', 'IX_500', 'VI_MIN', 'V_50', 'V_5000', 'X_250', 'V_250', 'X_MIN', 'village_income_median', 'XII_MIN', 'VII_10000', 'XII_1000', 'jobschool_rate', 'XIII_MIN', 'III_500', 'IX_250', 'XIII_250', 'VIII_1000', 'III_5000', 'VII_5000', 'III_100', 'II_250', 'I_1000', 'XIV_10000', 'IV_5000', 'X_500', 'VII_MIN', 'IX_10000', 'IV_250', 'XIV_1000', 'II_10000', 'N_50', 'elementary_rate', 'II_1000', 'IV_500', 'II_500', 'doc_rate', 'XII_10000', 'building_area', 'parking_price', 'XII_500', 'N_10000', 'II_MIN', 'N_1000', 'born_rate', 'XIII_5000', 'IX_1000', 'XI_5000', 'VIII_50', 'XII_250', 'marriage_rate', 'III_10000', 'XIII_50', 'IV_MIN', 'land_per_building_area', 'VII_500', 'town_area', 'III_50', 'III_1000', 'VII_250', 'XII_5000', 'XI_250', 'IV_100', 'I_10000', 'XI_500', 'V_10000', 'XIII_100', 'V_500', 'X_1000', 'XIV_250', 'VIII_500', 'VIII_10000', 'IX_MIN', 'town_population_density', 'VII_1000', 'VIII_5000', 'II_100', 'building_complete_dt', 'V_1000', 'txn_floor', 'bachelor_rate', 'IV_1000', 'VI_5000', 'XI_1000', 'txn_dt', 'XI_50', 'lon', 'II_5000', 'VII_50', 'XI_MIN', 'IX_5000', 'III_MIN', 'IX_100', 'XII_100', 'parking_area', 'XIV_100', 'lat', 'VI_10000', 'XIV_500', 'XIII_10000', 'town_population', 'highschool_rate', 'VII_100', 'death_rate', 'XII_50']\n",
      "N cat feats (before): 57\n",
      "N cat feats (after): 12\n",
      "['building_type', 'encoded_latlon', 'building_material', 'IV_index_500', 'town', 'city', 'village', 'III_index_500', 'VIII_index_50', 'V_index_50', 'building_use', 'parking_way']\n"
     ]
    }
   ],
   "source": [
    "# PARS #\n",
    "N_feats_rm = 75\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)\n",
    "\n",
    "print('N num feats (before):', len(cols_num))\n",
    "cols_num = list(set(cols_num) - set(feats_rm))\n",
    "print('N num feats (after):', len(cols_num))\n",
    "print(cols_num)\n",
    "\n",
    "print('N cat feats (before):', len(cols_cat))\n",
    "cols_cat = list(set(cols_cat) - set(feats_rm))\n",
    "print('N cat feats (after):', len(cols_cat))\n",
    "print(cols_cat)\n",
    "\n",
    "check1 = set(cols_feats) - set(cols_cat) - set(cols_num)\n",
    "assert len(check1)==0, 'cols_feats - cols_cat - cols_num: {}'.format(check1)\n",
    "check2 = (set(cols_cat) | set(cols_num)) - set(cols_feats)\n",
    "assert len(check2)==0, 'cols_cat + cols_num - cols_feats: {}'.format(check2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_fix = {'n_jobs': -1}\n",
    "\n",
    "#params_gsearch = {\n",
    "#    'max_depth': [None, 8, 16, 20, 24],\n",
    "#    'max_features': [None, 'log2', 0.5, 0.8],\n",
    "#    'min_samples_split': [2, 10, 20, 50]\n",
    "#}\n",
    "#N_iter = 5 * 4 * 4\n",
    "\n",
    "\n",
    "params_gsearch = {\n",
    "    'n_estimators': [10, 100, 500, 1000, 2000, 5000, 10000, 20000]\n",
    "}\n",
    "N_iter = 8\n",
    "\n",
    "N_fold = 3\n",
    "score_low_bound = 0 # if score smaller than this, skip the param set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round 1\n",
    "rand_params_set = []\n",
    "for i in range(N_iter):\n",
    "    params_rand = {key: np.random.choice(params_gsearch[key]) for key in params_gsearch}\n",
    "    while params_rand in rand_params_set:\n",
    "        params_rand = {key: np.random.choice(params_gsearch[key]) for key in params_gsearch}\n",
    "    rand_params_set.append(params_rand)\n",
    "gsearch = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Fold 1 ====\n",
      "params: {'n_jobs': -1, 'n_estimators': 500}\n",
      "score, mse, mae, mape: 4830.81825, 0.06753, 0.16529, 0.01248.\n",
      "params: {'n_jobs': -1, 'n_estimators': 5000}\n",
      "score, mse, mae, mape: 4840.81839, 0.06746, 0.16512, 0.01247.\n",
      "params: {'n_jobs': -1, 'n_estimators': 10000}\n",
      "score, mse, mae, mape: 4826.81849, 0.06739, 0.16508, 0.01246.\n",
      "params: {'n_jobs': -1, 'n_estimators': 1000}\n",
      "score, mse, mae, mape: 4824.81835, 0.06742, 0.16531, 0.01248.\n",
      "params: {'n_jobs': -1, 'n_estimators': 10}\n",
      "score, mse, mae, mape: 4511.80516, 0.07531, 0.17762, 0.01340.\n",
      "params: {'n_jobs': -1, 'n_estimators': 2000}\n",
      "score, mse, mae, mape: 4831.81822, 0.06754, 0.16523, 0.01247.\n",
      "params: {'n_jobs': -1, 'n_estimators': 20000}\n",
      "score, mse, mae, mape: 4830.81853, 0.06738, 0.16507, 0.01246.\n",
      "params: {'n_jobs': -1, 'n_estimators': 100}\n",
      "score, mse, mae, mape: 4780.81621, 0.06871, 0.16657, 0.01258.\n",
      "==== Fold 2 ====\n",
      "params: {'n_jobs': -1, 'n_estimators': 500}\n",
      "score, mse, mae, mape: 4836.82396, 0.06547, 0.16416, 0.01237.\n",
      "params: {'n_jobs': -1, 'n_estimators': 5000}\n",
      "score, mse, mae, mape: 4863.82479, 0.06511, 0.16374, 0.01233.\n",
      "params: {'n_jobs': -1, 'n_estimators': 10000}\n",
      "score, mse, mae, mape: 4850.82485, 0.06507, 0.16372, 0.01233.\n",
      "params: {'n_jobs': -1, 'n_estimators': 1000}\n",
      "score, mse, mae, mape: 4850.82453, 0.06524, 0.16394, 0.01235.\n",
      "params: {'n_jobs': -1, 'n_estimators': 10}\n",
      "score, mse, mae, mape: 4564.81249, 0.07296, 0.17553, 0.01321.\n",
      "params: {'n_jobs': -1, 'n_estimators': 2000}\n",
      "score, mse, mae, mape: 4850.82468, 0.06516, 0.16382, 0.01234.\n",
      "params: {'n_jobs': -1, 'n_estimators': 20000}\n",
      "score, mse, mae, mape: 4845.82482, 0.06506, 0.16372, 0.01233.\n",
      "params: {'n_jobs': -1, 'n_estimators': 100}\n",
      "score, mse, mae, mape: 4845.82304, 0.06600, 0.16497, 0.01243.\n",
      "==== Fold 3 ====\n",
      "params: {'n_jobs': -1, 'n_estimators': 500}\n",
      "score, mse, mae, mape: 4782.81517, 0.07040, 0.16787, 0.01268.\n",
      "params: {'n_jobs': -1, 'n_estimators': 5000}\n",
      "score, mse, mae, mape: 4792.81562, 0.07017, 0.16751, 0.01265.\n",
      "params: {'n_jobs': -1, 'n_estimators': 10000}\n",
      "score, mse, mae, mape: 4794.81559, 0.07018, 0.16753, 0.01265.\n",
      "params: {'n_jobs': -1, 'n_estimators': 1000}\n",
      "score, mse, mae, mape: 4771.81505, 0.07058, 0.16780, 0.01267.\n",
      "params: {'n_jobs': -1, 'n_estimators': 10}\n",
      "score, mse, mae, mape: 4450.80094, 0.07874, 0.18060, 0.01363.\n",
      "params: {'n_jobs': -1, 'n_estimators': 2000}\n",
      "score, mse, mae, mape: 4793.81552, 0.07018, 0.16753, 0.01265.\n",
      "params: {'n_jobs': -1, 'n_estimators': 20000}\n",
      "score, mse, mae, mape: 4795.81567, 0.07015, 0.16747, 0.01265.\n",
      "params: {'n_jobs': -1, 'n_estimators': 100}\n",
      "score, mse, mae, mape: 4752.81384, 0.07122, 0.16939, 0.01278.\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "gsearch = {}\n",
    "\n",
    "### stratified\n",
    "id_check = df['building_id'].copy()\n",
    "df_tmp = df[['building_id','total_price']].copy()\n",
    "df_tmp = df_tmp.sort_values(by='total_price').reset_index(drop=True)\n",
    "df_tmp['total_price_level'] = df_tmp['total_price'].index // (df_tmp.shape[0] // 6 )\n",
    "df = pd.merge(df, df_tmp.drop('total_price', axis=1), on='building_id', how='left')\n",
    "print(df['total_price_level'].value_counts())\n",
    "print(df.index)\n",
    "assert (id_check == df['building_id']).all(), 'building_id err'\n",
    "###\n",
    "\n",
    "#folds = KFold(n_splits=N_fold, shuffle=True, random_state=123)\n",
    "folds = StratifiedKFold(n_splits=N_fold, shuffle=True, random_state=123)\n",
    "for i_fold, (itrain, ival) in enumerate(folds.split(df, df['total_price_level'])): # stratified-kfold\n",
    "#for i_fold, (itrain, ival) in enumerate(folds.split(df)): # kfold\n",
    "    print('==== Fold', i_fold+1, '====')\n",
    "    \n",
    "    # split train, val\n",
    "    df_train = df.loc[itrain]\n",
    "    df_val = df.loc[ival]\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).copy()\n",
    "    df_val = feat_eng.transform(df_val).copy()\n",
    "    \n",
    "    # random sample - grid search\n",
    "    rand_params_next = []\n",
    "    for params_rand in rand_params_set:\n",
    "        params = params_fix.copy()\n",
    "        params.update(params_rand)\n",
    "        \n",
    "        model = RandomForestRegressor(**params)\n",
    "        model.fit(df_train[cols_feats], df_train[col_target])\n",
    "\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",
    "\n",
    "        print('params:', params)\n",
    "        print('score, mse, mae, mape: %.5f, %.5f, %.5f, %.5f.' % (score, mse, mae, mape))\n",
    "\n",
    "        if score < score_low_bound:\n",
    "            continue\n",
    "\n",
    "        rand_params_next.append(params_rand)\n",
    "        tuple_params = tuple(params.items())\n",
    "        if tuple_params not in gsearch:\n",
    "            gsearch[tuple_params] = [[], [], [], [], []]\n",
    "        gsearch[tuple_params][0].append('nothing')\n",
    "        gsearch[tuple_params][1].append(score)\n",
    "        gsearch[tuple_params][2].append(mse)\n",
    "        gsearch[tuple_params][3].append(mae)\n",
    "        gsearch[tuple_params][4].append(mape)\n",
    "        \n",
    "    rand_params_set = rand_params_next\n",
    "# aggregate, sort gsearch results\n",
    "gsearch_results = [[key, *[np.mean(val_i) for val_i in val[1:]], *val] for key, val in gsearch.items()]\n",
    "gsearch_results.sort(key= lambda x: x[1], reverse=True)\n",
    "t1 = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total computing time: 43303.15902709961\n"
     ]
    }
   ],
   "source": [
    "print('Total computing time:', t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('n_jobs', -1), ('n_estimators', 5000)) 4832.486268829904 0.06757912064872906 0.16545538839521134 0.012482092841694102 [4840.818388928506, 4863.824794892606, 4792.815622668599] [0.06745986986344224, 0.06510733101213627, 0.0701701610706087] [0.16511800174234867, 0.1637388620395527, 0.16750930140373266] [0.012465604798426138, 0.012332767038040211, 0.012647906688615956]\n",
      "(('n_jobs', -1), ('n_estimators', 2000)) 4825.486141399552 0.06762394465107273 0.16552630475372518 0.012487606209893962 [4831.818217824603, 4850.824682335902, 4793.815524038149] [0.06753604830234233, 0.06516011153532805, 0.0701756741155478] [0.16523135244536083, 0.16381620369509178, 0.16753135812072292] [0.01247470793276424, 0.012338345968453967, 0.012649764728463683]\n",
      "(('n_jobs', -1), ('n_estimators', 20000)) 4824.153006698362 0.06752984412774199 0.16541974641584448 0.01247937268419678 [4830.818531720081, 4845.824818947847, 4795.815669427161] [0.06737660158650972, 0.06506386547419878, 0.07014906532251745] [0.1650683224611554, 0.163718735154447, 0.167472181631931] [0.012461620704027572, 0.012331247085524176, 0.012645250263038596]\n",
      "(('n_jobs', -1), ('n_estimators', 10000)) 4824.152975446153 0.06754949709750802 0.16544194543388638 0.012481139166493382 [4826.818493676101, 4850.824845972595, 4794.815586689763] [0.06739489972750576, 0.06507283065712888, 0.07018076090788943] [0.16507502943826954, 0.1637243539518222, 0.16752645291156737] [0.01246246008144376, 0.012331519141216643, 0.012649438276819736]\n",
      "(('n_jobs', -1), ('n_estimators', 500)) 4816.819122923951 0.0678006891529641 0.1657738764509159 0.012506932264949297 [4830.8182465076725, 4836.823956923815, 4782.815165340366] [0.06752618655695894, 0.06547363516713675, 0.07040224573479659] [0.16528724221644, 0.16416446187279088, 0.16786992526351688] [0.012477929197840715, 0.012367722185690013, 0.012675145411317163]\n",
      "(('n_jobs', -1), ('n_estimators', 1000)) 4815.819314484471 0.06774731635570848 0.16568076018757402 0.012499278912083835 [4824.818354532808, 4850.824533974904, 4771.8150549457005] [0.06742279789321454, 0.06524056895755388, 0.07057858221635702] [0.16531049900007747, 0.16393646719893845, 0.1677953143637061] [0.012479515564692999, 0.012348336314163193, 0.01266998485739531]\n",
      "(('n_jobs', -1), ('n_estimators', 100)) 4793.151029414806 0.06864639983812197 0.16697528236059656 0.012596553391904118 [4780.816213541032, 4845.823038255739, 4752.813836447644] [0.06871227527035544, 0.06600425326144886, 0.07122267098256159] [0.16657220442126294, 0.16496790385560844, 0.1693857388049183] [0.012577066996422004, 0.012427845505735186, 0.01278474767355517]\n",
      "(('n_jobs', -1), ('n_estimators', 10)) 4509.139531140959 0.0756690320407266 0.17791860622313885 0.013412019939488208 [4511.805163919615, 4564.812485392896, 4450.800944110369] [0.07530595519589535, 0.07296494215944302, 0.07873619876684143] [0.17762358280896914, 0.17552967021504745, 0.18060256564540003] [0.013397028734987288, 0.0132115462585239, 0.01362748482495344]\n"
     ]
    }
   ],
   "source": [
    "for row in gsearch_results:\n",
    "    print(*[x for i, x in enumerate(row) if i != 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(('n_jobs', -1), ('n_estimators', 5000)),\n",
       "  4832.486268829904,\n",
       "  0.06757912064872906,\n",
       "  0.16545538839521134,\n",
       "  0.012482092841694102,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4840.818388928506, 4863.824794892606, 4792.815622668599],\n",
       "  [0.06745986986344224, 0.06510733101213627, 0.0701701610706087],\n",
       "  [0.16511800174234867, 0.1637388620395527, 0.16750930140373266],\n",
       "  [0.012465604798426138, 0.012332767038040211, 0.012647906688615956]],\n",
       " [(('n_jobs', -1), ('n_estimators', 2000)),\n",
       "  4825.486141399552,\n",
       "  0.06762394465107273,\n",
       "  0.16552630475372518,\n",
       "  0.012487606209893962,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4831.818217824603, 4850.824682335902, 4793.815524038149],\n",
       "  [0.06753604830234233, 0.06516011153532805, 0.0701756741155478],\n",
       "  [0.16523135244536083, 0.16381620369509178, 0.16753135812072292],\n",
       "  [0.01247470793276424, 0.012338345968453967, 0.012649764728463683]],\n",
       " [(('n_jobs', -1), ('n_estimators', 20000)),\n",
       "  4824.153006698362,\n",
       "  0.06752984412774199,\n",
       "  0.16541974641584448,\n",
       "  0.01247937268419678,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4830.818531720081, 4845.824818947847, 4795.815669427161],\n",
       "  [0.06737660158650972, 0.06506386547419878, 0.07014906532251745],\n",
       "  [0.1650683224611554, 0.163718735154447, 0.167472181631931],\n",
       "  [0.012461620704027572, 0.012331247085524176, 0.012645250263038596]],\n",
       " [(('n_jobs', -1), ('n_estimators', 10000)),\n",
       "  4824.152975446153,\n",
       "  0.06754949709750802,\n",
       "  0.16544194543388638,\n",
       "  0.012481139166493382,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4826.818493676101, 4850.824845972595, 4794.815586689763],\n",
       "  [0.06739489972750576, 0.06507283065712888, 0.07018076090788943],\n",
       "  [0.16507502943826954, 0.1637243539518222, 0.16752645291156737],\n",
       "  [0.01246246008144376, 0.012331519141216643, 0.012649438276819736]],\n",
       " [(('n_jobs', -1), ('n_estimators', 500)),\n",
       "  4816.819122923951,\n",
       "  0.0678006891529641,\n",
       "  0.1657738764509159,\n",
       "  0.012506932264949297,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4830.8182465076725, 4836.823956923815, 4782.815165340366],\n",
       "  [0.06752618655695894, 0.06547363516713675, 0.07040224573479659],\n",
       "  [0.16528724221644, 0.16416446187279088, 0.16786992526351688],\n",
       "  [0.012477929197840715, 0.012367722185690013, 0.012675145411317163]],\n",
       " [(('n_jobs', -1), ('n_estimators', 1000)),\n",
       "  4815.819314484471,\n",
       "  0.06774731635570848,\n",
       "  0.16568076018757402,\n",
       "  0.012499278912083835,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4824.818354532808, 4850.824533974904, 4771.8150549457005],\n",
       "  [0.06742279789321454, 0.06524056895755388, 0.07057858221635702],\n",
       "  [0.16531049900007747, 0.16393646719893845, 0.1677953143637061],\n",
       "  [0.012479515564692999, 0.012348336314163193, 0.01266998485739531]],\n",
       " [(('n_jobs', -1), ('n_estimators', 100)),\n",
       "  4793.151029414806,\n",
       "  0.06864639983812197,\n",
       "  0.16697528236059656,\n",
       "  0.012596553391904118,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4780.816213541032, 4845.823038255739, 4752.813836447644],\n",
       "  [0.06871227527035544, 0.06600425326144886, 0.07122267098256159],\n",
       "  [0.16657220442126294, 0.16496790385560844, 0.1693857388049183],\n",
       "  [0.012577066996422004, 0.012427845505735186, 0.01278474767355517]],\n",
       " [(('n_jobs', -1), ('n_estimators', 10)),\n",
       "  4509.139531140959,\n",
       "  0.0756690320407266,\n",
       "  0.17791860622313885,\n",
       "  0.013412019939488208,\n",
       "  ['nothing', 'nothing', 'nothing'],\n",
       "  [4511.805163919615, 4564.812485392896, 4450.800944110369],\n",
       "  [0.07530595519589535, 0.07296494215944302, 0.07873619876684143],\n",
       "  [0.17762358280896914, 0.17552967021504745, 0.18060256564540003],\n",
       "  [0.013397028734987288, 0.0132115462585239, 0.01362748482495344]]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aaa= {}\n",
    "#for g in gsearch_results:\n",
    "#    nrm = g[0][11][1]\n",
    "#    score = g[1]\n",
    "#    if nrm not in aaa:\n",
    "#        aaa[nrm] = score\n",
    "#    elif score > aaa[nrm]:\n",
    "#            aaa[nrm] = score\n",
    "#print(aaa)\n",
    "#bbb = np.array(sorted(aaa.items(), key= lambda x: x[0]))\n",
    "#plt.plot(bbb[:,0], bbb[:,1], '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['score', 'mse', 'mae', 'mape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>n_estimators</th>\n",
       "      <th>score</th>\n",
       "      <th>mse</th>\n",
       "      <th>mae</th>\n",
       "      <th>mape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>4832.486269</td>\n",
       "      <td>0.067579</td>\n",
       "      <td>0.165455</td>\n",
       "      <td>0.012482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>4825.486141</td>\n",
       "      <td>0.067624</td>\n",
       "      <td>0.165526</td>\n",
       "      <td>0.012488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000</td>\n",
       "      <td>4824.153007</td>\n",
       "      <td>0.067530</td>\n",
       "      <td>0.165420</td>\n",
       "      <td>0.012479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>4824.152975</td>\n",
       "      <td>0.067549</td>\n",
       "      <td>0.165442</td>\n",
       "      <td>0.012481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>4816.819123</td>\n",
       "      <td>0.067801</td>\n",
       "      <td>0.165774</td>\n",
       "      <td>0.012507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000</td>\n",
       "      <td>4815.819314</td>\n",
       "      <td>0.067747</td>\n",
       "      <td>0.165681</td>\n",
       "      <td>0.012499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>4793.151029</td>\n",
       "      <td>0.068646</td>\n",
       "      <td>0.166975</td>\n",
       "      <td>0.012597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>4509.139531</td>\n",
       "      <td>0.075669</td>\n",
       "      <td>0.177919</td>\n",
       "      <td>0.013412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_estimators        score       mse       mae      mape\n",
       "0          5000  4832.486269  0.067579  0.165455  0.012482\n",
       "1          2000  4825.486141  0.067624  0.165526  0.012488\n",
       "2         20000  4824.153007  0.067530  0.165420  0.012479\n",
       "3         10000  4824.152975  0.067549  0.165442  0.012481\n",
       "4           500  4816.819123  0.067801  0.165774  0.012507\n",
       "5          1000  4815.819314  0.067747  0.165681  0.012499\n",
       "6           100  4793.151029  0.068646  0.166975  0.012597\n",
       "7            10  4509.139531  0.075669  0.177919  0.013412"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gsearch_results = []\n",
    "for row in gsearch_results:\n",
    "    result = dict(row[0])\n",
    "    result['score'] = row[1]\n",
    "    result['mse'] = row[2]\n",
    "    result['mae'] = row[3]\n",
    "    result['mape'] = row[4]\n",
    "#    result['best_iteration'] = np.mean([m.best_iteration for m in row[5]])\n",
    "    df_gsearch_results.append(result)\n",
    "df_gsearch_results = pd.DataFrame(df_gsearch_results)\n",
    "df_gsearch_results = df_gsearch_results[list(params_gsearch.keys())+metrics] # +['best_iteration']\n",
    "display(df_gsearch_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZdJPJ9jJnZhOr6qT/AuUHzNeAPwGe5cTfA2NmZicpk82xZvlCGhtU6VImTbkBsyf5/hYzM5tkr+4bZOvrh/jAmnMqXcqkKjdg/rukLwKPAIcLgxHxz6lUZWY2jWzYmu+/1Mv8l4JyA+Y3gQvJ918Kp8gCcMCYmZ2mrp4cc2c28cYl9dN/gfID5vKIuCDVSszMpqlMNscVyxfVVf8Fyr9M+d8lrUq1EjOzaeiVfQNsy/XXxe35S5V7BLMWeErSVvI9GAEREb5M2czsNBS+/6WeJlgWlBsw16ZahZnZNJXp6WVe6wzeeFZ99V+g/Nv1b0+7EDOz6agrmf/SUGf9Fyi/B2NmZpNs194BXurtr7vLkwscMGZmFZLpSe4/VocNfnDAmJlVTCabY37bDC44c06lS0mFA8bMrEIyW+u3/wIOGDOzitjZ18+O3gGurNP+CzhgzMwqIpPtBWBtnfZfwAFjZlYRXT05Fs5q5g1n1Gf/BRwwZmYVkanj+S8FDhgzsym2o7efXXsH6nb+S4EDxsxsinVl63v+S4EDxsxsimWyORbNamblGbMrXUqqUg0YSddKekHSFkl3jrO+RdI3kvUbJHUm44skrZd0UNJdRdu3SfqOpM2SNkn6ZNG6c5J9fizpGUnvTvO9mZmdiogg05Nj7YpFSPXbf4EUA0ZSI/BZ4DpgFXDLON8pcxvQFxHnA58GPpWMDwIfAe4Y56n/NiIuBC4F3ibpumT8L4FvRsSlwM3A3ZP5fszMJsOO3gFe3jdYl7fnL5XmEcwVwJaIyEbEEPAAcEPJNjcA9yfLDwJXS1JEHIqIx8gHzRER0R8R65PlIeBJoKOwGijc73oe8PJkvyEzs9N19Ptf6rv/AukGzFJgR9HjncnYuNtExAiwDyjrT13SfOC9wCPJ0MeAD0raCTwE/MGpFm5mlpaubI7Fs5s5v877L1CjTX5JTcDXgc9ERDYZvgX43xHRAbwb+Iqkn3l/kj4kqVtS9549e6auaDOb9iIiP/9lGvRfIN2A2QUsK3rckYyNu00SGvOAXBnPfQ/wYkT8XdHYbcA3ASKiC5gJLC7dMSLuiYjVEbG6vb29zLdiZnb6tuf6eWXfYF3ff6xYmgHzBLBS0nJJzeQb7+tKtlkH3Jos3wT8ICLieE8q6RPkg+jDJateAq5Otnkj+YDxIYqZVY3p1H+BMr8y+VRExIik24GHgUbgvojYJOnjQHdErAPuJX8qawvQSz6EAJC0jXzTvlnSjcA1wH7gL4DNwJPJIeZdEfFF4I+BL0j6r+Qb/r9xorAyM5tKmWyO9jktnNc+q9KlTInUAgYgIh4i33AvHvto0fIg8L4J9u2c4GnHPXEZEc8BbzulQs3MUhYRdGWnx/yXgpps8puZ1ZptuX5e2394Wsx/KXDAmJlNga6e5P5j06T/Ag4YM7MpkcnmOGNOC8sXT4/+CzhgzMxSV5j/cuV506f/Ag4YM7PUZV8/xO4Dh6fN5ckFDhgzs5RNt/kvBQ4YM7OUdfXkOGvuTDoXtVW6lCnlgDEzS1G+/9LL2hULp1X/BRwwZmap6tlzkNcPHq77r0cejwPGzCxFXdleYPr1X8ABY2aWqkw2x5J5Mzln4fTqv4ADxswsNRHBhmyOK6fR/ceKOWDMzFKyZfdBXj84NC1Pj4EDxswsNV3J/Jfp2OAHB4yZWWoy2RxL57fSsaC10qVUhAPGzCwFhfkva6bh/JcCB4yZWQp++tpBeg8NTavb85dywJiZpWC63n+smAPGzCwFXT05Oha0smwazn8pcMCYmU2ysbFgw9bctD56AQeMmdmk++nuA/T1DztgKl2AmVm96eop9F8WVriSynLAmJlNskw2x7KFrXQsmL79F3DAmJlNqnz/pXdaX55c4IAxM5tEm189wF73XwAHjJnZpPL8l6McMGZmk6grm+PcRW2cPX963n+smAPGzGySjI0Fj2/tZe1yH72AA8bMbNI898p+9g0MT9vb85dywJiZTZJC/2XNNJ//UuCAMTObJJlsL52L2lgyz/0XcMCYmU2K0eT+Yz49dpQDxsxsEjz/yn4ODI748uQiDhgzs0lw9P5jDpiCVANG0rWSXpC0RdKd46xvkfSNZP0GSZ3J+CJJ6yUdlHRX0fZtkr4jabOkTZI+WfJ875f0XLLuH9J8b2ZmxTLZHCsWz+LMuTMrXUrVSC1gJDUCnwWuA1YBt0haVbLZbUBfRJwPfBr4VDI+CHwEuGOcp/7biLgQuBR4m6TrktdbCfwZ8LaIeBPw4Ul+S2Zm4xpN5r+s8dHLMdI8grkC2BIR2YgYAh4AbijZ5gbg/mT5QeBqSYqIQxHxGPmgOSIi+iNifbI8BDwJdCSrfxv4bET0Jet3p/GmzMxKbXp5HwcOj7jBXyLNgFkK7Ch6vDMZG3ebiBgB9gFl/ReSNB94L/BIMvQG4A2SfiQpI+naCfb7kKRuSd179uwp+82YmY2nf2iErz/+EgBrl3v+S7GmShdwKiQ1AV8HPhMR2WS4CVgJvJP8Uc0PJb05IvYW7xsR9wD3AKxevTqmrGgzqyv7+of5ctc2vvTv2+g9NMR7LzmbM9x/OUaaAbMLWFb0uCMZG2+bnUlozANyZTz3PcCLEfF3RWM7gQ0RMQxslfRT8oHzxCnWb2b2M3YfGOTex7bytcxLHDw8wlUXnsHvvfM8Vnf66KVUmgHzBLBS0nLyQXIz8Gsl26wDbgW6gJuAH0TEcY8qJH2CfBD9Vsmq/wPcAnxJ0mLyp8yymJlNgpdy/fz9D3v4x407GRkd4/qLz+Z333Eeq86eW+nSqlZqARMRI5JuBx4GGoH7ImKTpI8D3RGxDrgX+IqkLUAv+RACQNI2YC7QLOlG4BpgP/AXwGbgSUkAd0XEF5PXuUbSc8Ao8CcRUc7RkJnZhF549QCfe3QL//LMKzRK/OpbO/idt6+gc/GsSpdW9XSCA4a6tnr16uju7q50GWZWhZ58qY+71/fw/55/jbbmRj6w5hx+6+dXeJ4LIGljRKw+0XY12eQ3M0tDRPDYlte5e30PXdkc89tm8OH/uJJbr+xkwazmSpdXcxwwZjbtjY0F33vuVe5+tIdndu7jzLkt/OX1b+SWK85hVos/Jk+V/+TMbNoaHh3j20+9zOce3ULPnkOcu6iNT/7Km/nly5bS0tRY6fJqngPGzKadgaFRvtm9g3t+mGXX3gEuPGsOn7nlUt590Vk0NfoewJPFAWNm08a+gWG+mtnOfY9tJXdoiNXnLuATN17EOy9oJ7kq1SaRA8bM6t6eA4e570db+WrXdg4cHuGdF7Tze+88nyt8a5dUOWDMrG7t6O3nC/+W5RtP7GBodIx3v3kJv/uO87ho6bxKlzYtOGDMrO68+NoBPvdoD99++mUaBL9yaQe/844VrGifXenSphUHjJnVjad27OXu9Vv43nOv0TqjkVuv7OS3376cJfNaK13atOSAMbOaFhH8e0+Oux/dwo+25Jg7s4k/vHolv/EfOlnoyZEV5YAxs5o0NhZ8//nXuPvRHp7esZcz5rTw5+++kF9bcy6zPTmyKvi/gpnVlOHRMf7l6Zf53KM9vLj7IOcsbOOvf/kifvWyDmbO8OTIauKAMbOaMDg8yj927+Dvf5hlZ98AF5w5h/9181u4/s1LPDmySjlgzOpQRPD6wSF29vWzs2+AnX0D7OjrZ1//MC1NDcxsbmRmUyOtzQ20zmhkZtFP/nEyfmS7orEZjbQ0NUzZxMQDg8N8NfMS9z62ldcPHuayc+bzsfe+iasuPIOGBk+OrGYOGLMaFBH0HhpiR99AUYjkf+/o7WfX3gEGh8eO2WdB2wwWzGrm8PAYh0dGGRgaZWB4lLFT+MYOCVqa8oFzbEA15MOoqfG0Q2xkLPj6hpe4v2sbBwZH+PmVi/n9X7iUNcsXetZ9jXDAmFWhiKCvf/iY0Nh5TJgMMDA8esw+89tm0LGglZVnzOGqC8+gY0EbHQta6VjQxtIFreM2viOC4dFgcGSUwaFRBofHGBgeZXA4Hz4Dw6McTn4PDo8xMDR6dNuR5HHR+sLy3v7hZN9jn+9kv35KgusuOovffcf5vLnDkyNrjQPGrAIigr39wyWh0X/kVNbOvgH6h44NkHmt+QBZ0T6Lt7+h/Uh4LFvYytL5rcyZOeOk65BEc5Nobmpg7insfzIigqHRMQaHxhhMjqCO/C4Kp8Lv4ZExfm5lO+ef4cmRtcoBU2VGx4KhkbH8z2jyM1L0MzrK4ZExhkfjmLHC8uFkv+GRODI+PBo0NSg5BdFIW3P+FEXp4yPLRY+n8lx7PYkI9g+MHAmL0hDZ2TfAwcMjx+wzp6WJjoVtdC6axc+dXwiQfIh0LGxNPQDSJomWpkZamhqZR22/FyuPA+YU/GTXPjZu7zsaAiPjBcEEY6XLJWOjp3JCfAKNDaK5sYEZjWJ0LOg/hVMUDeJIGBWCpzSMCufUjwRVcyNtM0rXNdHa3JDs13T0eZobaG5ML8QigggYjWAsWR6LYCzyYR7J8liyfmzs6HIk2xS2j6L9CutHxsbYfeDwz/ZA+gY4UBIgs1uajgTG2hWLWLaw7ZgQmdfqD12rLw6YU/CjLa/zN/+6+ZixGY35D/PmpqM/MxrzH54tyeOZMxqYO7MpWd/IjEbl1xXv19hYtKwJxkt+l4y3JK/dWHKFTURweOToqYj+odFjzqH3F5aHknXDRY+H8+fdj+w3PEru0BADfUX7neJ59sYGHQmt1iRwgvxEusKHfyEYRseKP+yT9UUhMBbHLp9sLadjVnPjkdBYu2LRMeGxbEEbc1ubfDRo04oD5hR8cO253PTWDmYUPtQbG2ricklJR67imZ/SaxRCbGCoJLSKgmu8QCt+PDQyhgQNEo0NOrLcUPjdULSs/PrGZLx420YJJds0iGS/o/tK+XArjBW2bWzg2P1+5jXz6/OvCe2zZ9KxoJX5bTMcIGZFHDCnYFZLk7+newLFIbag0sWYWUV5+quZmaXCAWNmZqlwwJiZWSocMGZmlgoHjJmZpcIBY2ZmqXDAmJlZKhwwZmaWCsVU3kujykjaA2w/xd0XA69PYjlpq6V6a6lWqK16a6lWqK16a6lWOL16z42I9hNtNK0D5nRI6o6I1ZWuo1y1VG8t1Qq1VW8t1Qq1VW8t1QpTU69PkZmZWSocMGZmlgoHzKm7p9IFnKRaqreWaoXaqreWaoXaqreWaoUpqNc9GDMzS4WPYMzMLBUOmFMg6VpJL0jaIunOStdzPJLuk7Rb0k8qXcuJSFomab2k5yRtkvRHla5pIpJmSnpc0tNJrX9V6ZrKIalR0o8l/d9K13I8krZJelbSU5K6K13PiUiaL+lBSZslPS/pykrXNB5JFyR/poWf/ZI+nNrr+RTZyZHUCPwUeBewE3gCuCUinqtoYROQ9HbgIPDliLio0vUcj6QlwJKIeFLSHGAjcGM1/tkq/9WVsyLioKQZwGPAH0VEpsKlHZek/wasBuZGxHsqXc9EJG0DVkdETcwrkXQ/8G8R8UVJzUBbROytdF3Hk3yW7QLWRMSpzgc8Lh/BnLwrgC0RkY2IIeAB4IYK1zShiPgh0FvpOsoREa9ExJPJ8gHgeWBpZasaX+QdTB7OSH6q+l9rkjqA64EvVrqWeiJpHvB24F6AiBiq9nBJXA30pBUu4IA5FUuBHUWPd1KlH4K1TFIncCmwobKVTCw53fQUsBv4fkRUba2JvwP+FBirdCFlCOB7kjZK+lClizmB5cAe4EvJ6ccvSppV6aLKcDPw9TRfwAFjVUfSbOCfgA9HxP5K1zORiBiNiLcAHcAVkqr2FKSk9wC7I2JjpWsp089FxGXAdcDvJ6d6q1UTcBnwuYi4FDgEVHtvthn4JeAf03wdB8zJ2wUsK3rckYzZJEj6Gf8EfC0i/rnS9ZQjOR2yHri20rUcx9uAX0p6Gw8AV0n6amVLmlhE7Ep+7wa+Rf7UdLXaCewsOoJ9kHzgVLPrgCcj4rU0X8QBc/KeAFZKWp78K+BmYF2Fa6oLSeP8XuD5iPifla7neCS1S5qfLLeSv+hjc2WrmlhE/FlEdEREJ/m/sz+IiA9WuKxxSZqVXORBcqrpGqBqr4KMiFeBHZIuSIauBqruwpQSt5Dy6THIH9rZSYj82NU4AAAB1UlEQVSIEUm3Aw8DjcB9EbGpwmVNSNLXgXcCiyXtBP57RNxb2aom9DbgPwHPJr0NgD+PiIcqWNNElgD3J1fiNADfjIiqvvS3hpwJfCv/7w2agH+IiO9WtqQT+gPga8k/OrPAb1a4ngklof0u4HdSfy1fpmxmZmnwKTIzM0uFA8bMzFLhgDEzs1Q4YMzMLBUOGDMzS4UDxqyGSPqYpDsqXYdZORwwZhWiPP8/aHXLf7nNppCkzuS7hL5Mfnb6vZK6S79TJvk+lL+S9GTyvSgXjvNcvy3pX5M7CZhVHc/kN5t6K4FbIyIjaWFE9CZ3BHhE0sUR8Uyy3esRcZmk3wPuAH6r8ATJ3STeRf77cg5P+TswK4OPYMym3vaiLyZ7v6QngR8DbwJWFW1XuNnnRqCzaPzXyd+s8CaHi1UzB4zZ1DsEIGk5+SOTqyPiYuA7wMyi7QrhMcqxZxueJR84HalXanYaHDBmlTOXfNjsk3Qm+aOScvyY/I0K10k6O63izE6XA8asQiLiafJhsRn4B+BHJ7HvY+SPfr4jaXE6FZqdHt9N2czMUuEjGDMzS4UDxszMUuGAMTOzVDhgzMwsFQ4YMzNLhQPGzMxS4YAxM7NUOGDMzCwV/x8/dCnVv3Q87AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in metrics:\n",
    "    plt.plot(df_gsearch_results[name])\n",
    "    plt.xlabel('rank'); plt.ylabel(name); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p in params_gsearch.keys():\n",
    "    plt.scatter(df_gsearch_results[p], df_gsearch_results['score'], s=7)\n",
    "    plt.scatter(df_gsearch_results[p][:10], df_gsearch_results['score'][:10], s=7)\n",
    "    plt.xlabel(p); plt.ylabel('score'); plt.xscale('log'); plt.grid(); plt.show()\n",
    "for p in params_gsearch.keys():\n",
    "    plt.scatter(df_gsearch_results[p], df_gsearch_results['score'], s=10)\n",
    "    plt.scatter(df_gsearch_results[p][:10], df_gsearch_results['score'][:10], s=10)\n",
    "    plt.xlabel(p); plt.ylabel('score'); plt.xscale('log'); \n",
    "    plt.ylim(df_gsearch_results['score'][:10].max()-100, df_gsearch_results['score'][:10].max()+10); plt.grid(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p in params_gsearch.keys():\n",
    "    plt.scatter(df_gsearch_results[p], df_gsearch_results['mae'], s=10)\n",
    "    plt.scatter(df_gsearch_results[p][:10], df_gsearch_results['mae'][:10], s=10)\n",
    "    plt.xlabel(p); plt.ylabel('mae'); plt.xscale('log'); plt.grid(); plt.show()\n",
    "for p in params_gsearch.keys():\n",
    "    plt.scatter(df_gsearch_results[p], df_gsearch_results['mae'], s=10)\n",
    "    plt.scatter(df_gsearch_results[p][:10], df_gsearch_results['mae'][:10], s=10)\n",
    "    plt.xlabel(p); plt.ylabel('mae'); plt.xscale('log'); \n",
    "    plt.ylim(df_gsearch_results['mae'].min()-0.0005, df_gsearch_results['mae'].min()+0.002); plt.grid(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['n_estimators', 'score', 'mse', 'mae', 'mape'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gsearch_results.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#best iteration\n",
    "for c in df_gsearch_results.columns:\n",
    "    if c != 'best_iteration':\n",
    "        plt.scatter(df_gsearch_results['best_iteration'], df_gsearch_results[c])\n",
    "        plt.ylabel(c)\n",
    "    else:\n",
    "        plt.hist(df_gsearch_results['best_iteration'], bins=30)\n",
    "        plt.ylabel('N')        \n",
    "    plt.xlabel('best_iteration')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### correlation for num feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['building_area' 0.004409716998422031 0.004409716998422031]\n",
      " ['XIV_50' 0.028219079605206927 0.028219079605206927]\n",
      " ['land_area' -0.04240772536374149 0.04240772536374149]\n",
      " ['txn_floor' -0.043790374542577164 0.043790374542577164]\n",
      " ['XIV_100' 0.04581863693587386 0.04581863693587386]\n",
      " ['land_per_building_area' -0.07214458078836318 0.07214458078836318]\n",
      " ['XIV_250' 0.07511520319308908 0.07511520319308908]\n",
      " ['parking_area' 0.07946155517973283 0.07946155517973283]\n",
      " ['XIV_MIN' -0.08731459849477233 0.08731459849477233]\n",
      " ['building_complete_dt' -0.0880369935695401 0.0880369935695401]\n",
      " ['IV_100' 0.08901320517895964 0.08901320517895964]\n",
      " ['XIII_50' 0.09176742868072367 0.09176742868072367]\n",
      " ['XIV_500' 0.09773694981373081 0.09773694981373081]\n",
      " ['txn_dt' 0.10211860321027197 0.10211860321027197]\n",
      " ['X_50' 0.11403570756235858 0.11403570756235858]\n",
      " ['XIV_1000' 0.1148232436078084 0.1148232436078084]\n",
      " ['VIII_10' 0.125024222989481 0.125024222989481]\n",
      " ['N_10000' 0.12872651499964394 0.12872651499964394]\n",
      " ['XIII_100' 0.13638248799092156 0.13638248799092156]\n",
      " ['IV_250' 0.13865799271762816 0.13865799271762816]\n",
      " ['XI_50' 0.14104211767933636 0.14104211767933636]\n",
      " ['total_floor' 0.1415038045547608 0.1415038045547608]\n",
      " ['IV_500' 0.1640572794983922 0.1640572794983922]\n",
      " ['V_50' 0.19281288659669918 0.19281288659669918]\n",
      " ['IV_1000' 0.2104394258687408 0.2104394258687408]\n",
      " ['III_50' 0.21055597408954224 0.21055597408954224]\n",
      " ['town_area' -0.2201128108521767 0.2201128108521767]\n",
      " ['II_100' 0.22207403707501883 0.22207403707501883]\n",
      " ['XII_MIN' -0.23022019485878034 0.23022019485878034]\n",
      " ['marriage_rate' 0.23433651760008942 0.23433651760008942]\n",
      " ['VIII_50' 0.23577197636110636 0.23577197636110636]\n",
      " ['XIII_250' 0.2437804430832531 0.2437804430832531]\n",
      " ['II_MIN' -0.2544099987056559 0.2544099987056559]\n",
      " ['I_MIN' -0.26279082030804635 0.26279082030804635]\n",
      " ['XII_50' 0.2744845411987913 0.2744845411987913]\n",
      " ['VIII_MIN' -0.27589661575318253 0.27589661575318253]\n",
      " ['IV_MIN' -0.28505214618967606 0.28505214618967606]\n",
      " ['X_MIN' -0.28711233822276544 0.28711233822276544]\n",
      " ['XIV_5000' 0.2879586956861745 0.2879586956861745]\n",
      " ['born_rate' 0.28840486844402907 0.28840486844402907]\n",
      " ['IX_100' 0.29121879972714476 0.29121879972714476]\n",
      " ['V_MIN' -0.2913231334669996 0.2913231334669996]\n",
      " ['IX_MIN' -0.2959869366045791 0.2959869366045791]\n",
      " ['III_MIN' -0.30381450649571173 0.30381450649571173]\n",
      " ['III_100' 0.3047771309280052 0.3047771309280052]\n",
      " ['XI_MIN' -0.3228227861130784 0.3228227861130784]\n",
      " ['VII_MIN' -0.3258753889332602 0.3258753889332602]\n",
      " ['VIII_100' 0.32664994130672187 0.32664994130672187]\n",
      " ['VI_MIN' -0.33722497053070716 0.33722497053070716]\n",
      " ['II_250' 0.3395071373677238 0.3395071373677238]\n",
      " ['VII_50' 0.3487290414605125 0.3487290414605125]\n",
      " ['XIII_MIN' -0.3490744291392444 0.3490744291392444]\n",
      " ['N_50' 0.3625496216068983 0.3625496216068983]\n",
      " ['XIII_500' 0.363814754410607 0.363814754410607]\n",
      " ['N_1000' 0.36507458449518904 0.36507458449518904]\n",
      " ['village_income_median' 0.3676621900361126 0.3676621900361126]\n",
      " ['X_250' 0.38313789376038315 0.38313789376038315]\n",
      " ['town_population' 0.38822463993071427 0.38822463993071427]\n",
      " ['XII_100' 0.40941459860366747 0.40941459860366747]\n",
      " ['IX_250' 0.419290943275123 0.419290943275123]\n",
      " ['VII_100' 0.4457468807299726 0.4457468807299726]\n",
      " ['III_250' 0.4464557939325784 0.4464557939325784]\n",
      " ['II_500' 0.45417335798758196 0.45417335798758196]\n",
      " ['I_1000' 0.459427009766884 0.459427009766884]\n",
      " ['XI_250' 0.4594540723550346 0.4594540723550346]\n",
      " ['IV_5000' 0.46209211091578556 0.46209211091578556]\n",
      " ['junior_rate' -0.46240985282908725 0.46240985282908725]\n",
      " ['VIII_250' 0.463765596897221 0.463765596897221]\n",
      " ['death_rate' -0.46528520532402595 0.46528520532402595]\n",
      " ['V_250' 0.4824215543807676 0.4824215543807676]\n",
      " ['town_population_density' 0.49344862665835765 0.49344862665835765]\n",
      " ['IX_500' 0.5085241944482107 0.5085241944482107]\n",
      " ['X_500' 0.5097994123069889 0.5097994123069889]\n",
      " ['XIV_10000' 0.5159808093740824 0.5159808093740824]\n",
      " ['VII_250' 0.5278154268768384 0.5278154268768384]\n",
      " ['XIII_1000' 0.5297384995848007 0.5297384995848007]\n",
      " ['XII_250' 0.5302803012400251 0.5302803012400251]\n",
      " ['lat' 0.5306257795938323 0.5306257795938323]\n",
      " ['III_500' 0.5346955677014844 0.5346955677014844]\n",
      " ['VIII_500' 0.5480356873516268 0.5480356873516268]\n",
      " ['IX_1000' 0.5496435273681856 0.5496435273681856]\n",
      " ['II_1000' 0.5515770439511702 0.5515770439511702]\n",
      " ['XI_500' 0.5637266686732141 0.5637266686732141]\n",
      " ['X_1000' 0.5688738626853873 0.5688738626853873]\n",
      " ['VII_500' 0.5703576658993038 0.5703576658993038]\n",
      " ['XII_500' 0.5721748428749592 0.5721748428749592]\n",
      " ['III_1000' 0.5800972370919922 0.5800972370919922]\n",
      " ['I_5000' 0.5808684087913216 0.5808684087913216]\n",
      " ['V_500' 0.5835249852640986 0.5835249852640986]\n",
      " ['XII_1000' 0.586028075846866 0.586028075846866]\n",
      " ['VIII_1000' 0.5861852279753952 0.5861852279753952]\n",
      " ['VII_1000' 0.5891506834145492 0.5891506834145492]\n",
      " ['VI_5000' 0.5930605012299428 0.5930605012299428]\n",
      " ['XI_1000' 0.5980342900998319 0.5980342900998319]\n",
      " ['doc_rate' 0.6062718271855618 0.6062718271855618]\n",
      " ['V_1000' 0.6194926576658641 0.6194926576658641]\n",
      " ['lon' 0.6314234738755085 0.6314234738755085]\n",
      " ['XI_5000' 0.6434085123116627 0.6434085123116627]\n",
      " ['II_5000' 0.6451760471588682 0.6451760471588682]\n",
      " ['IX_5000' 0.6469751032917737 0.6469751032917737]\n",
      " ['III_5000' 0.6489122906541039 0.6489122906541039]\n",
      " ['X_5000' 0.6527716112599078 0.6527716112599078]\n",
      " ['master_rate' 0.6539124060797235 0.6539124060797235]\n",
      " ['XII_5000' 0.6572773984995479 0.6572773984995479]\n",
      " ['VIII_5000' 0.6671752773979914 0.6671752773979914]\n",
      " ['highschool_rate' -0.6821100384532618 0.6821100384532618]\n",
      " ['VII_5000' 0.6853788174543339 0.6853788174543339]\n",
      " ['bachelor_rate' 0.6885701328851653 0.6885701328851653]\n",
      " ['V_5000' 0.7006755012617919 0.7006755012617919]\n",
      " ['elementary_rate' -0.7089054964445821 0.7089054964445821]\n",
      " ['IV_10000' 0.7100802705931318 0.7100802705931318]\n",
      " ['I_10000' 0.7110075626673027 0.7110075626673027]\n",
      " ['jobschool_rate' 0.7404113998225503 0.7404113998225503]\n",
      " ['XI_10000' 0.7419100918015863 0.7419100918015863]\n",
      " ['II_10000' 0.7455577346696095 0.7455577346696095]\n",
      " ['VI_10000' 0.7455891637960778 0.7455891637960778]\n",
      " ['XII_10000' 0.7473917087752799 0.7473917087752799]\n",
      " ['X_10000' 0.7546829394430955 0.7546829394430955]\n",
      " ['III_10000' 0.7582978767989192 0.7582978767989192]\n",
      " ['parking_price' 0.7617685215538561 0.7617685215538561]\n",
      " ['VIII_10000' 0.7668069066015559 0.7668069066015559]\n",
      " ['IX_10000' 0.7706838793423205 0.7706838793423205]\n",
      " ['XIII_5000' 0.7750506292412358 0.7750506292412358]\n",
      " ['V_10000' 0.7778768023404692 0.7778768023404692]\n",
      " ['VII_10000' 0.7836898762894066 0.7836898762894066]\n",
      " ['XIII_10000' 0.787644819271357 0.787644819271357]\n",
      " ['target' 1.0 1.0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c96bd30>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = pd.DataFrame({'corr': df[cols_num+['target']].corr()['target']})\n",
    "corr['abs_corr'] = np.abs(corr['corr'])\n",
    "corr = corr.sort_values(by='abs_corr')\n",
    "print(corr.reset_index().values)\n",
    "corr['cum_count'] = range(corr.shape[0], 0, -1)\n",
    "plt.plot(corr['abs_corr'], corr['cum_count'], '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other parameter results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.5), ('min_samples_split', 2)) 4894.494214273407 0.06265746939525367 0.16028405906807822 0.012085187770723897 [4895.830016675878, 4885.828324269542, 4901.824301874801] [0.06024280947473378, 0.064221472360651, 0.06350812635037621] [0.15843030636390684, 0.16058703706897995, 0.16183483377134783] [0.011956074029166317, 0.012095821802066047, 0.012203667480939324]  \n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.5), ('min_samples_split', 2)) 4876.160085271735 0.06308521423338094 0.16073573369304103 0.012120703853966195 [4890.829930419136, 4867.826984194412, 4869.823341201656] [0.060108032404545944, 0.06504224602957091, 0.06410536426602595] [0.15858820401279888, 0.1611535938751923, 0.1624654031911319] [0.011967245019534735, 0.012141935878537657, 0.01225293066382619]  \n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.8), ('min_samples_split', 2)) 4859.821791323055 0.06670492039929045 0.1634845294652394 0.012339044318081147 [4865.824723560141, 4858.821506113914, 4854.819144295109] [0.06381348866412308, 0.06913113622410681, 0.06717013630964147] [0.16170115433994123, 0.1639093448019447, 0.16484308925383226] [0.0122140309674413, 0.012362699295706351, 0.012440402691095791]  \n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.8), ('min_samples_split', 2)) 4851.488183432914 0.06676639751322253 0.1637180453188293 0.012356681138586475 [4858.824306583111, 4855.8211531809075, 4839.819090534726] [0.06392131646253335, 0.06922062617771226, 0.06715724989942198] [0.1618491840103487, 0.1641379531125099, 0.16516699883362929] [0.01222614553554275, 0.012380081454909172, 0.012463816425307498]  \n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', None), ('min_samples_split', 2)) 4815.151885689237 0.06884730413310751 0.16569492134834576 0.012506830216372307 [4806.821621313692, 4825.817423828317, 4812.816611925702] [0.06590365906941706, 0.0717768667308858, 0.06886138659901965] [0.16405559348284468, 0.1663457299430262, 0.1666834406191664] [0.012390755085657415, 0.012551006671834413, 0.0125787288916251]  \n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.5), ('min_samples_split', 2)) 4808.492070843775 0.06344013535346599 0.1620625637309551 0.012218930105771832 [4802.82789529089, 4808.826069461986, 4813.822247778448] [0.06094251415813592, 0.0650746248372235, 0.06430326706503857] [0.16026314862558702, 0.16233840620833073, 0.1635861363589476] [0.012092863798400163, 0.012228670571349895, 0.01233525594756544]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', None), ('min_samples_split', 2)) 4801.818132550052 0.06899712244060341 0.16603551984056947 0.012532099815613283 [4793.820954312809, 4818.816641960497, 4792.81680137685] [0.06613146477441396, 0.07221384712555973, 0.06864605542183655] [0.1645388433374626, 0.1666781135471317, 0.1668896026371141] [0.012427616431827555, 0.01257748745480008, 0.012591195560212215]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.8), ('min_samples_split', 2)) 4793.820150837365 0.06713520787841128 0.16500052263848983 0.01245178364007792 [4777.8232407392, 4808.81942886031, 4794.817782912585] [0.06411104212726428, 0.0697903318376494, 0.06750424967032016] [0.1630876433014977, 0.16551479900968635, 0.1663991256042854] [0.01231631758237285, 0.012483297678538964, 0.012555735659321944]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', None), ('min_samples_split', 2)) 4736.4833366374 0.06938223264432701 0.16738381714674702 0.012632339006567989 [4716.820022098716, 4752.8148244235645, 4739.815163389919] [0.06619312255266271, 0.07286022058533198, 0.06909335479498632] [0.16569917356582783, 0.16810325740314216, 0.16834902047127107] [0.012512200114018934, 0.012683969831774226, 0.012700847073910802]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.5), ('min_samples_split', 10)) 4734.82304341886 0.06455264580748156 0.16448116144294805 0.012397550161997497 [4735.825259395929, 4740.824279223627, 4727.819591637025] [0.062277589759409495, 0.06568966427436472, 0.06569068338867047] [0.16292668494704704, 0.164295381106129, 0.1662214182756681] [0.012290443200840684, 0.012371743178934332, 0.012530464106217476]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.5), ('min_samples_split', 10)) 4724.156427600588 0.064468543707852 0.1645050840827522 0.012399064920990967 [4727.825477725621, 4726.82407891989, 4717.819726156255] [0.06205944820205, 0.06584466616779285, 0.06550151675371316] [0.16279058444300926, 0.16451460764003611, 0.16621006016521114] [0.01227900795521076, 0.01238903980840477, 0.012529146999357374]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.8), ('min_samples_split', 10)) 4722.151127396878 0.06823530162620285 0.16729815421961755 0.012621498860495755 [4720.820549328047, 4733.817436401448, 4711.815396461138] [0.06544278373178498, 0.07057652185459587, 0.06868659929222772] [0.16560218181465408, 0.1676566945772915, 0.16863558626690708] [0.012503021085507481, 0.012640742447674918, 0.012720733048304863]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.8), ('min_samples_split', 10)) 4718.150877554969 0.06845432704036207 0.16739366375830764 0.012630208438540425 [4719.820214900478, 4736.817445310021, 4697.814972454407] [0.06566954166640657, 0.0706835542840868, 0.06900988517059281] [0.16581632737156315, 0.1675039643783978, 0.1688606995249619] [0.012521561790061852, 0.012630164047400678, 0.012738899478158745]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', None), ('min_samples_split', 10)) 4693.147726768011 0.0705869687660237 0.16946810066867338 0.012787160090912434 [4681.817493006387, 4704.813257468176, 4692.81242982947] [0.0676130242391015, 0.07337530134404512, 0.0707725807149245] [0.1679037687171008, 0.16991930041558623, 0.17058123287333318] [0.012676401888544348, 0.012817211128381752, 0.012867867255811205]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.5), ('min_samples_split', 10)) 4679.488661189756 0.06481794379830781 0.1654252965698392 0.012468973713355654 [4679.824731783258, 4677.822296339893, 4680.818955446118] [0.062176228812749215, 0.06658169021700029, 0.06569591236517391] [0.16362747725631527, 0.16564349623708718, 0.16700491621611516] [0.0123414191446277, 0.01247700779281269, 0.01258849420262657]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', None), ('min_samples_split', 10)) 4673.814261743043 0.07058144099502774 0.1696256209148542 0.012798454262698433 [4663.81725059525, 4691.812958811786, 4665.812575822094] [0.0676415690216829, 0.073512581072221, 0.07059017289117929] [0.16817655027124515, 0.16996613861265294, 0.17073417386066453] [0.012696116736766318, 0.012820655031254175, 0.012878591020074803]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.8), ('min_samples_split', 10)) 4660.483206534533 0.06863316759006416 0.16846563967922148 0.012709333496440581 [4649.819091882276, 4684.816422227974, 4646.814105493347] [0.06597774086892225, 0.07082305772771048, 0.06909870417355973] [0.1669724574368845, 0.16851818360796492, 0.16990627799281513] [0.012606540401985796, 0.012705521797790184, 0.012815938289545767]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', None), ('min_samples_split', 10)) 4619.146527432433 0.07089189233669772 0.17073271148129965 0.012880498175981683 [4598.816067186805, 4636.812278112417, 4621.811236998078] [0.0680330455238713, 0.07354786507844083, 0.07109476640778106] [0.16930663139664093, 0.1709660242493251, 0.1719254787979329] [0.012780348894002307, 0.012894407387682848, 0.012966738246259891]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.5), ('min_samples_split', 20)) 4547.817115884131 0.06730053278329712 0.16998752414931748 0.012808697883645236 [4553.819760754908, 4553.817707057688, 4535.813879839797] [0.06462429680584478, 0.06897539113785273, 0.06830191040619384] [0.16830038819423185, 0.1698525745458452, 0.1718096097078753] [0.012689380781344078, 0.01279026289976823, 0.0129464499698234]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.5), ('min_samples_split', 2)) 4544.486051147253 0.06553323466073922 0.16790042971120836 0.01265759303894235 [4537.822041114304, 4553.820208806315, 4541.815903521141] [0.06286467811894535, 0.06711239650614347, 0.06662262935712883] [0.16610736040262625, 0.16801126647539022, 0.1695826622556086] [0.012529759182441924, 0.012655605344022764, 0.012787414590362358]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.5), ('min_samples_split', 20)) 4538.483689883499 0.0674229621036436 0.17003495478387812 0.012812616853237068 [4543.819490733324, 4535.818154927193, 4535.813423989981] [0.06487074794528541, 0.06867924175028599, 0.06871889661535938] [0.16854334488076173, 0.16964264736100304, 0.1719188721098696] [0.012708681804115668, 0.012773782380484367, 0.01295538637511117]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.8), ('min_samples_split', 20)) 4536.145298349221 0.07097650906333437 0.17281032507204439 0.013033058312526264 [4537.814717462754, 4540.812000713763, 4529.809176871146] [0.06814529401433932, 0.07308203260461793, 0.07170220057104588] [0.17124904560002258, 0.17277473083181852, 0.17440719878429198] [0.012924227448482815, 0.01302268797464458, 0.0131522595144514]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.8), ('min_samples_split', 20)) 4527.478569967554 0.07102580663761072 0.17290140178502936 0.01303948953711312 [4525.81446019134, 4548.811682810341, 4507.80956690098] [0.06825613009272559, 0.07342231224703256, 0.07139897757307401] [0.17150359497849357, 0.17286098940878264, 0.1743396209678119] [0.01294281078551875, 0.01302972548549196, 0.013145932340328646]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.8), ('min_samples_split', 2)) 4513.480387687672 0.06939254512540786 0.1712737717516322 0.012920533847512418 [4505.816283969034, 4524.813554210526, 4509.811324883456] [0.06651613240109781, 0.0718192397182698, 0.069842263256856] [0.16971972663169604, 0.17152819230539446, 0.17257339631780613] [0.012812281760896148, 0.012932118535781072, 0.013017201245860035]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.5), ('min_samples_split', 20)) 4512.816853738083 0.0672568162679577 0.1705648195996385 0.012849915202618467 [4512.819112071632, 4518.818192427893, 4506.813256714724] [0.06485257764811009, 0.06841921725176564, 0.06849865390399736] [0.1689677679538338, 0.1701963753712075, 0.17253031547387424] [0.01273858064376309, 0.012811570913411688, 0.01299959405068062]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', None), ('min_samples_split', 20)) 4502.808697398165 0.07319961055954115 0.17504366349381825 0.013201604395971003 [4482.811327138111, 4524.807798253931, 4500.806966802455] [0.07045996428677455, 0.07596900323504811, 0.07316986415680077] [0.1738097847384919, 0.17519835647925514, 0.17612284926370775] [0.013115739255780832, 0.013209551023594453, 0.013279522908537723]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', None), ('min_samples_split', 20)) 4502.475286029516 0.07319770293014116 0.17511385259986975 0.01320628503598572 [4492.811408365316, 4511.807340542847, 4502.807109180384] [0.07035052301215398, 0.07620235844600987, 0.07304022733225961] [0.1738223893588512, 0.17534454038830355, 0.17617462805245443] [0.013115872146113696, 0.013221328318028963, 0.013281654643814499]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.8), ('min_samples_split', 20)) 4494.145016712325 0.07091190496111978 0.1734519177448299 0.013079138270370117 [4510.814020656738, 4500.811842624629, 4470.809186855609] [0.06825269992441067, 0.07299245858579255, 0.0714905563731561] [0.17213076508901642, 0.17329180898296712, 0.17493317916250617] [0.012989453100345083, 0.013058592996053206, 0.01318936871471206]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.5), ('min_samples_split', 10)) 4475.816875245216 0.06671676598297772 0.17034077379992793 0.012837030240526251 [4461.819089000489, 4483.818046699343, 4481.813490035815] [0.06432699092580749, 0.06791091322182659, 0.06791239380129908] [0.16891952606131747, 0.17005472685399506, 0.1720480684844712] [0.012737381051417475, 0.012805810717959719, 0.012967898952201561]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', None), ('min_samples_split', 20)) 4465.47490122206 0.07315996934835246 0.17579495376064389 0.013255575323577457 [4450.810572452055, 4479.8074240029855, 4465.80670721114] [0.07058398120601371, 0.07581061302874284, 0.07308531381030085] [0.17472079943308244, 0.17585686749103951, 0.17680719435780964] [0.013182449072962921, 0.013257176855642198, 0.013327100042127251]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', None), ('min_samples_split', 2)) 4463.143303326953 0.07175546667994602 0.1737721264168427 0.013108053716606642 [4432.812743200847, 4489.808971552751, 4466.808195227261] [0.06887971077868187, 0.07465117171599503, 0.07173551754516118] [0.17250348446585742, 0.17401328617525283, 0.17479960860941784] [0.013017861766390129, 0.013124182815410649, 0.013182116568019145]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.8), ('min_samples_split', 10)) 4448.144696730095 0.07052877192105024 0.1734893765491119 0.013083745452947063 [4442.813799695802, 4461.811133098365, 4439.80915739612] [0.06784110689883259, 0.07284984572754014, 0.07089536313677802] [0.172118400498201, 0.17358304281275042, 0.1747666863363843] [0.012989155882145243, 0.013083897521558389, 0.013178182955137552]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', None), ('min_samples_split', 10)) 4404.474208299493 0.07295817839262748 0.17616729661847694 0.01328435330621721 [4388.810417384603, 4425.806352160572, 4398.805855353303] [0.06997090353669903, 0.07589718527735526, 0.07300644636382819] [0.1748894977294898, 0.17640597232212457, 0.17720641980381646] [0.013193010427752271, 0.013300776267892023, 0.013359273223007334]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.5), ('min_samples_split', 20)) 4351.479570994291 0.06878129308056695 0.1742375031317441 0.013125775158272535 [4354.815100883041, 4365.814550223583, 4333.809061876246] [0.06646216622665437, 0.06963052559427846, 0.07025118742076801] [0.17292233603179716, 0.17352615637001045, 0.1762640169934247] [0.013033413530025172, 0.01306241248748147, 0.013281499457310962]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.8), ('min_samples_split', 20)) 4336.473808712922 0.07271244463036687 0.17754325006138374 0.013384927583131543 [4344.809189944347, 4350.807120802968, 4313.80511539145] [0.07019486131313943, 0.07496519562494743, 0.07297727695301376] [0.17633430892409838, 0.177440087997769, 0.1788553532622838] [0.013304208960357188, 0.013370034009898819, 0.013480539779138623]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', None), ('min_samples_split', 20)) 4285.1370018912685 0.07494536474243878 0.1801200353457462 0.013576816352380469 [4265.806138905946, 4312.803067077897, 4276.801799689964] [0.07218293411233906, 0.0775840792083933, 0.07506908090658396] [0.17911276530476486, 0.18002995353724693, 0.18121738719522681] [0.013507147780405961, 0.013567095042990997, 0.013656206233744445]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.5), ('min_samples_split', 50)) 4194.803816565755 0.07456831545600907 0.1827552031143452 0.0137589512659011 [4190.806240227417, 4190.804847272697, 4202.800362197153] [0.07191434942484044, 0.0759623687673295, 0.07582822817585731] [0.1813969502388945, 0.1820740706696032, 0.18479458843453783] [0.013663565927169132, 0.013701186948108745, 0.013912100922425426]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.5), ('min_samples_split', 50)) 4191.80391908657 0.07447553150665248 0.18266936461812935 0.01375292463859236 [4196.806040135361, 4184.806001027194, 4193.7997160971545] [0.0720440569159931, 0.07523540111249791, 0.07614713649146641] [0.18139357365875625, 0.18162731608991597, 0.18498720410571587] [0.013664327174925526, 0.01366466494697223, 0.013929781793879326]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.5), ('min_samples_split', 50)) 4176.470072989029 0.07473605397211408 0.18311675649041637 0.013786424295269559 [4169.805593586293, 4179.805052723931, 4179.799572656862] [0.07234369355708199, 0.07568156884018845, 0.07618289951907183] [0.18186388547031337, 0.18223433443910617, 0.18525204956182956] [0.013699366273785594, 0.013711544769927086, 0.013948361842096]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 0.8), ('min_samples_split', 50)) 4174.13198387297 0.07825498551034092 0.18572294540796896 0.013992886831912263 [4171.800726332072, 4177.798580609441, 4172.796644677395] [0.07570728962525122, 0.08057979420369966, 0.07847787270207186] [0.1846550377384129, 0.18539573208920487, 0.18711806639628908] [0.013921140002249456, 0.01396301630688736, 0.014094504186599969]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 'log2'), ('min_samples_split', 2)) 4167.80897808088 0.07284771928510893 0.18311669833215083 0.013749247717238242 [4140.808734926889, 4194.812657415615, 4167.805541900133] [0.07179218055709431, 0.07215992602102923, 0.07459105127720325] [0.18227632163824117, 0.18210761219363042, 0.18496616116458087] [0.013707761499568427, 0.013655341508312352, 0.013884640143833952]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 0.8), ('min_samples_split', 50)) 4166.798742740707 0.07818236719561981 0.18570921465776546 0.013991669224924888 [4160.800421131708, 4176.799603011146, 4162.796204079266] [0.07584749210116044, 0.07997224315021514, 0.07872736633548386] [0.18485742396634627, 0.18501569227024645, 0.1872545277367037] [0.013936949003693792, 0.013931817550348842, 0.014106241120732027]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 0.8), ('min_samples_split', 50)) 4154.798284368474 0.07837595031881366 0.186143842982424 0.01402441729655302 [4142.800108050256, 4159.798997760461, 4161.795747294704] [0.07587828139723381, 0.08026286323996097, 0.07898670631924619] [0.1851746529994719, 0.18551514862499477, 0.18774172732280534] [0.013960561815123212, 0.013970379826034742, 0.014142310248501107]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', None), ('min_samples_split', 50)) 4133.1290937842705 0.08021471130296567 0.188060208183182 0.014167351684860325 [4114.797117760757, 4131.795894721767, 4152.794268870288] [0.07812235590555912, 0.08245480367736567, 0.08006697432597223] [0.18756098188721881, 0.1876429653492908, 0.1889766773130363] [0.014137387007279838, 0.014132439825297292, 0.014232228222003849]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', None), ('min_samples_split', 50)) 4130.795780935784 0.08022647407210998 0.18806684838334056 0.014167837444853953 [4120.796774594284, 4143.796178561688, 4127.794389651379] [0.07837071210254633, 0.08232379876142933, 0.07998491135235428] [0.18771879023702412, 0.18750899673126203, 0.18897275818173553] [0.014150210171346274, 0.01412140892771739, 0.014231893235498191]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 'log2'), ('min_samples_split', 2)) 4125.474641305308 0.07316988864037667 0.18408465947493094 0.01382311257046328 [4110.807611813296, 4147.811653713874, 4117.804658388755] [0.07223252372508808, 0.0725108939356927, 0.07476624826034926] [0.18343016989604138, 0.1831191894492972, 0.18570461907945426] [0.013794755069833017, 0.013733040390192134, 0.013941542251364687]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', None), ('min_samples_split', 50)) 4117.462138044804 0.08032222337501599 0.18844611350044224 0.014195159689017557 [4098.796921278073, 4117.795802222177, 4135.793690634165] [0.07819432991943216, 0.08242629525406847, 0.08034604495154733] [0.18795043411147944, 0.18794229043004926, 0.18944561595979803] [0.014164836510441058, 0.014152300875593011, 0.014268341681018601]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.5), ('min_samples_split', 50)) 4104.8015831295525 0.07546679508470337 0.18494882626820677 0.013922705999465192 [4093.803106074608, 4120.803564710647, 4099.798078603401] [0.0734689494454999, 0.07621056884410907, 0.07672086696450114] [0.1840199083442951, 0.1838015391793553, 0.18702503128096998] [0.013861670171381905, 0.01382774374376369, 0.01407870408324998]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 0.8), ('min_samples_split', 50)) 4065.796292672032 0.07912506578643276 0.18816194226912827 0.014173003161173944 [4063.7984076144826, 4073.796718515203, 4059.793751886411] [0.07657532909613833, 0.0811330149097974, 0.07966685335336254] [0.18708769148572757, 0.18776002670627992, 0.18963810861537736] [0.014100297354839633, 0.014136371507411436, 0.014282340621270758]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', None), ('min_samples_split', 50)) 4025.459894710424 0.08115253529171175 0.19072754905788214 0.014362754485905174 [3992.794217077921, 4048.7937530362638, 4034.7917140170885] [0.07929487585568665, 0.08314829839956552, 0.08101443161988309] [0.1904441380538871, 0.190081757490419, 0.19165675162934032] [0.01434981404628252, 0.014310472775087112, 0.014427976636345886]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 'log2'), ('min_samples_split', 2)) 4005.804456956573 0.0745670707620859 0.18738179750118636 0.014073849223784518 [3986.8042586059014, 4025.8082266509973, 4004.8008856128195] [0.07345093028569011, 0.07383298637067012, 0.07641729562989745] [0.1865647157634792, 0.18630833114082346, 0.1892723455992564] [0.014031094133005597, 0.013975626674805605, 0.01421482686354235]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 'log2'), ('min_samples_split', 10)) 3908.800886779351 0.07652093948759148 0.19083741345995195 0.014326750290770648 [3871.800421499062, 3921.804488959337, 3932.797749879654] [0.07566100997015168, 0.0755252924830855, 0.07837651600953725] [0.19032650757320188, 0.189641433048239, 0.19254429975841492] [0.014309373267324932, 0.014221414607005023, 0.014449462997981985]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 'log2'), ('min_samples_split', 10)) 3888.4672614751958 0.0766222508093808 0.1911483814520855 0.01434950810806153 [3857.7998647257027, 3904.804700054095, 3902.797219645789] [0.07592765116646596, 0.07537646671264397, 0.07856263454903248] [0.19083295176513718, 0.18967621899735254, 0.19293597359376685] [0.014346168416341428, 0.014222549692027452, 0.014479806215815716]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 'log2'), ('min_samples_split', 10)) 3820.7982507886095 0.07769871226181246 0.19339297522690926 0.014519722023347935 [3797.7980378820744, 3825.802025477579, 3838.794689006174] [0.07677425753537721, 0.07658646194633523, 0.07973541730372495] [0.19268336011924547, 0.19216577099449988, 0.1953297945669825] [0.014485215745153632, 0.01441140612914858, 0.014662544195741598]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 'log2'), ('min_samples_split', 20)) 3723.7933530839723 0.08080862523972948 0.1981802882456256 0.014873435281262724 [3688.792576005505, 3738.797513550464, 3743.7899696959475] [0.08014493143236662, 0.07931337470505197, 0.08296756958176989] [0.19791186521251772, 0.19659763515890027, 0.2000313643654589] [0.014874075170760186, 0.014738427103996194, 0.015007803569031792]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 'log2'), ('min_samples_split', 20)) 3708.4594984043943 0.08108912185460035 0.19870384525911913 0.01491294060584275 [3668.7919987735277, 3734.7970535695176, 3721.7894428701384] [0.08047882258003514, 0.07955900455102437, 0.08322953843274153] [0.19846686826310406, 0.19705192346849934, 0.20059274404575395] [0.014915491245440442, 0.014772807863999605, 0.0150505227080882]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 'log2'), ('min_samples_split', 2)) 3690.1274470682342 0.07947236889361982 0.1973269627195471 0.01482366294701383 [3664.7940442495906, 3718.797510530333, 3686.790786424778] [0.07837756305785715, 0.07868890166965686, 0.08135064195334543] [0.19653972490859156, 0.19641479569155132, 0.19902636755849845] [0.014780480153720753, 0.01473996601861464, 0.014950542668706096]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 'log2'), ('min_samples_split', 20)) 3670.7914484297266 0.08176997358341585 0.2000447060820452 0.015013603809427428 [3625.7905605309934, 3695.7955509104727, 3690.7882338477148] [0.08122865121511132, 0.08035081609281415, 0.08373045344232208] [0.19983102062908165, 0.1985469499930709, 0.20175614762398308] [0.015017981348294749, 0.014885225719186355, 0.015137604360801183]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 'log2'), ('min_samples_split', 10)) 3619.123214143841 0.0821070326740005 0.20141356009007935 0.015125629608708018 [3571.789176905095, 3644.7938499643255, 3640.786615562101] [0.08134373736665032, 0.08083946461296208, 0.08413789604238911] [0.2011107042203688, 0.2000335722341417, 0.20309640381572752] [0.015122396054078804, 0.015004949919536728, 0.015249542852508522]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 'log2'), ('min_samples_split', 20)) 3517.1179333101986 0.08553269265463463 0.20653649991606784 0.015504060676589071 [3470.7839071760527, 3538.788909298538, 3541.7809834560053] [0.0848510685934774, 0.08389970408918933, 0.08784730528123713] [0.20621879234918744, 0.2048534609823467, 0.2085372464166695] [0.015499299430287633, 0.015362099376654236, 0.01565078322282535]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 24), ('max_features', 'log2'), ('min_samples_split', 50)) 3412.7779541360665 0.09084615438649286 0.21313210978428007 0.01598793669890847 [3355.776947289925, 3432.7819320312296, 3449.774983087044] [0.09030863887036486, 0.08923364778226857, 0.09299617650684515] [0.21303969515902124, 0.21167024872354467, 0.21468638547027433] [0.016003187102044323, 0.015862324022070346, 0.01609829897261075]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', None), ('max_features', 'log2'), ('min_samples_split', 50)) 3412.444606168368 0.09084463752856808 0.2131302400188314 0.015988455267356497 [3355.7770315039056, 3427.78181907939, 3453.7749679218077] [0.09019331814363732, 0.0892635132836707, 0.09307708115839625] [0.2129837048641281, 0.21166235998125174, 0.2147446552111144] [0.015999297456984137, 0.015863486000968412, 0.01610258234411694]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 20), ('max_features', 'log2'), ('min_samples_split', 50)) 3398.777216092383 0.09127022861287491 0.2138535439512048 0.016042340701932868 [3353.7763219219164, 3406.781202670189, 3435.7741236850434] [0.09059447874793197, 0.08967786679484305, 0.0935383402958497] [0.2136277071388267, 0.2123367565792477, 0.21559616813553995] [0.016047219945955028, 0.015913368697747437, 0.01616643346209614]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 16), ('max_features', 'log2'), ('min_samples_split', 50)) 3316.7731733027626 0.0936528345489432 0.21766870835005128 0.01633040684198564 [3249.772136725984, 3325.7771933886866, 3374.770189793617]/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",
    " [0.0930376416729889, 0.09200271097575835, 0.09591815099808236] [0.2175750231119982, 0.2161498281291891, 0.21928127380896653] [0.016344572035596083, 0.016201046809501244, 0.01644560168085959]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.5), ('min_samples_split', 10)) 3087.749437283624 0.10653101074988151 0.23370917045286674 0.017591625838600796 [3022.7507190907368, 3109.7512258067486, 3130.746366953386] [0.1045980397871366, 0.10744183279662234, 0.10755315966588555] [0.2334460173854761, 0.23282312299760075, 0.2348583709755233] [0.017575177493404724, 0.01751764714946285, 0.01768205287293481]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.5), ('min_samples_split', 2)) 3085.415936137367 0.10663610122821847 0.23380638587339872 0.017600173014860585 [3035.750590193472, 3104.752118907831, 3115.745099310798] [0.1046644368593814, 0.10679360000963097, 0.108450266815643] [0.2333973643223156, 0.23235140176497054, 0.23567039153291006] [0.017574458510359766, 0.017480948808943002, 0.01774511172527899]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.5), ('min_samples_split', 20)) 3084.416094305298 0.106638778439665 0.23373771635707227 0.017592278455374092 [3015.750297512043, 3107.7509503687784, 3129.747035035073] [0.10489543989615449, 0.10777385487721167, 0.10724704054562885] [0.23367261356120536, 0.23294040669454463, 0.23460012881546677] [0.017592116575073063, 0.017525722012783972, 0.017658996778265244]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.5), ('min_samples_split', 50)) 3084.4150762208124 0.10772592600744772 0.2347015305357537 0.017661078349354414 [3013.749579814951, 3119.750636660153, 3119.745012187334] [0.10568076892854447, 0.10835660472351182, 0.10914040437028691] [0.23435162385061728, 0.23348998526712397, 0.2362629824895199] [0.017640188851536967, 0.017562944495432824, 0.017780101701093454]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.8), ('min_samples_split', 20)) 3029.4058215131113 0.11363913266730334 0.24045156818129518 0.018104820549955405 [3016.7415789746287, 3047.739046003316, 3023.736839561388] [0.1106142765867086, 0.11593757326129311, 0.11436554815390834] [0.23969889378719283, 0.24003632912719172, 0.24161948162950092] [0.01805190026028035, 0.01807186344863065, 0.018190697940955215]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.8), ('min_samples_split', 10)) 3025.7396786125714 0.11321025309009652 0.24004399824070574 0.01807593576283594 [3017.7421058531927, 3028.7391062739453, 3030.7378237105754] [0.11024586537144844, 0.11576554002978233, 0.11361935386905879] [0.23924146751746317, 0.23986125034401823, 0.24102927686063577] [0.018019078856769392, 0.018062259533722477, 0.01814646889801596]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.8), ('min_samples_split', 2)) 3023.4063236727798 0.11319388055946851 0.24006772320802652 0.01807824145819675 [3006.7411246681795, 3031.739778903167, 3031.7380674469928] [0.11089997749858191, 0.11532389513511877, 0.11335776904470482] [0.23999614232132846, 0.23948604933683432, 0.2407209779659169] [0.018076679204919486, 0.018032966426500863, 0.0181250787431699]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 0.8), ('min_samples_split', 50)) 3015.738876358901 0.11434479910164345 0.2412688693257989 0.018159353713285063 [3010.7396169873587, 3022.7390084915787, 3013.7380035977662] [0.11237895474118409, 0.11659494307540157, 0.1140604994883447] [0.24127583577807057, 0.2409325979515953, 0.2415981742477308] [0.01816675642717297, 0.018132570098499345, 0.01817873461418287]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', None), ('min_samples_split', 10)) 2966.3993877707635 0.11832873799369004 0.24554877721267365 0.018481370881997302 [2942.735102406546, 3001.730932666881, 2954.7321282388634] [0.1154332202702556, 0.12164341906828142, 0.11790957464253307] [0.2451673584588492, 0.2455410480461601, 0.24593792513301166] [0.01845352544569022, 0.018488375709366244, 0.018502211490935436]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', None), ('min_samples_split', 2)) 2965.7327685838877 0.11827061804424803 0.24563556698804498 0.01848814090213559 [2939.7350384549222, 2994.731556014219, 2962.7317112825203] [0.11546440771783274, 0.12129441192503676, 0.11805303448987461] [0.24528117006578617, 0.24543238202730394, 0.2461931488710448] [0.01846292290230373, 0.01847934318892684, 0.018522156615176193]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', None), ('min_samples_split', 20)) 2958.065672307468 0.11870269239814617 0.24587567716922587 0.018504596770898222 [2930.7342912644103, 2986.7309548854155, 2956.731770772577] [0.11602471306166805, 0.12183001760868045, 0.11825334652408998] [0.24574316284255268, 0.24570269637249711, 0.24618117229262781] [0.01849588478034348, 0.018498477052194227, 0.018519428480156957]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', None), ('min_samples_split', 50)) 2952.7320758345218 0.11930296718529243 0.24671250589891902 0.018560182327501114 [2922.732850314354, 2986.7316039908155, 2948.7317731983967] [0.1173277751402826, 0.12191941548493486, 0.1186617109306598] [0.2468762554748252, 0.24630698224101402, 0.2469542799809178] [0.018577746608044815, 0.018534178496075308, 0.018568621878383224]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 'log2'), ('min_samples_split', 2)) 2565.7166942977174 0.13622785880646351 0.2709910070505972 0.02034204949602918 [2515.7144605058443, 2582.721629258382, 2598.7139931289257] [0.13611802859060715, 0.1337409705520793, 0.1388245772767041] [0.2717365615821821, 0.2687577475612246, 0.27247871200838497] [0.02041692948398496, 0.020160958814194756, 0.020448260189907832]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 'log2'), ('min_samples_split', 20)) 2555.3827889013114 0.13687133212512673 0.2714959922997649 0.02037838890609432 [2489.713911856819, 2577.7203905947117, 2598.714064252403] [0.13692405339612054, 0.13480325588471662, 0.138886687094543] [0.2723702892076784, 0.26985752888029507, 0.2722601588113212] [0.020460930323350304, 0.02024425043062018, 0.020429985964312468]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 'log2'), ('min_samples_split', 50)) 2552.7156389313045 0.13744238059043257 0.27188948981272126 0.020406936921519417 [2502.7130968733118, 2573.720282028884, 2581.7135378917183] [0.13747518872629994, 0.13502428872017946, 0.13982766432481833] [0.2729113394147673, 0.2698740272914068, 0.2728831027319895] [0.020502027871710876, 0.020243341875818527, 0.020475441017028848]\n",
    "(('n_jobs', -1), ('n_estimators', 1000), ('max_depth', 8), ('max_features', 'log2'), ('min_samples_split', 10)) 2551.383683512157 0.1360257620620486 0.2707641435961847 0.02032318696741047 [2499.7147537840365, 2573.721317769449, 2580.7149789829846] [0.13597905922058734, 0.13400659611771304, 0.13809163084784545] [0.2715198228397349, 0.26910493316406786, 0.27166767478475146] [0.02039834319427346, 0.020187432135278124, 0.02038378557267983]\n"
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