model-06-build-lgb-search-lr-old-backup.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.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_feats_add = ['encoded_latlon', 'building_per_land_area'] # add feats into model\n",
    "cols_num_add = ['building_per_land_area']\n",
    "cols_cat_add = ['encoded_latlon']\n",
    "\n",
    "#cols_feats_rm = [] # TODO: implement before using it\n",
    "#['day_in_year_txn_dt', 'day_in_year_building_complete_dt', 'dt_diff_building_complete_dt_txn_dt'] # dt num feats\n",
    "#['dow_txn_dt', 'dow_building_complete_dt', 'month_txn_dt', 'month_building_complete_dt',\n",
    "# 'quarter_txn_dt', 'quarter_building_complete_dt', 'year_txn_dt', 'year_building_complete_dt'] # dt cat feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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['building_per_land_area'] = df['building_area'] / df['land_area']\n",
    " \n",
    "    #cols_feats = cols_num + cols_cat + cols_feats_add\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",
    "\n",
    "class FeatureEngineering():\n",
    "    def __init__(self, cols_num, cols_cat, cols_feats, col_target, n_ran_encode=4):\n",
    "        # cols\n",
    "        self.cols_num = cols_num\n",
    "        self.cols_cat = cols_cat\n",
    "        self.cols_feats = cols_feats\n",
    "        self.col_target = col_target\n",
    "        \n",
    "        # Numbers\n",
    "        self.n_ran_encode = n_ran_encode\n",
    "        \n",
    "        # encoders\n",
    "        #self.tar_encoder = TargetMeanEncoding(col_target = self.col_target)\n",
    "        #self.ran_encoder = MultiLabelEncoding(self.n_ran_encode)\n",
    "        self.cat_astyper = CategoricalColumnsEncoder(mode='pandas')\n",
    "        \n",
    "    def fit_transform(self, df):\n",
    "        df = df.copy()\n",
    "    \n",
    "#        encoded1 = self.tar_encoder.fit_transform(df[self.cols_cat], df[self.col_target])\n",
    "#        encoded2 = self.ran_encoder.fit_transform(df[self.cols_cat])\n",
    "        self.cat_astyper.fit_transform(df, self.cols_cat)\n",
    "    \n",
    "        return df\n",
    "#        return pd.concat([df, encoded1, encoded2], axis=1)\n",
    "    \n",
    "    def transform(self, df):\n",
    "        df = df.copy()\n",
    "#        encoded1 = self.tar_encoder.transform(df[self.cols_cat])\n",
    "#        encoded2 = self.ran_encoder.transform(df[self.cols_cat])     # TODO: add generated feats into cols_feats\n",
    "        self.cat_astyper.transform(df)\n",
    "\n",
    "        return 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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>building_id</th>\n",
       "      <th>building_material</th>\n",
       "      <th>city</th>\n",
       "      <th>txn_dt</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>building_type</th>\n",
       "      <th>building_use</th>\n",
       "      <th>building_complete_dt</th>\n",
       "      <th>parking_way</th>\n",
       "      <th>parking_area</th>\n",
       "      <th>...</th>\n",
       "      <th>XIV_500</th>\n",
       "      <th>XIV_index_500</th>\n",
       "      <th>XIV_1000</th>\n",
       "      <th>XIV_index_1000</th>\n",
       "      <th>XIV_5000</th>\n",
       "      <th>XIV_index_5000</th>\n",
       "      <th>XIV_10000</th>\n",
       "      <th>XIV_index_10000</th>\n",
       "      <th>XIV_MIN</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e3mMIMR3JJqCaXz1</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>18674</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6271</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>2483</td>\n",
       "      <td>1</td>\n",
       "      <td>6011</td>\n",
       "      <td>1</td>\n",
       "      <td>34.469802848528126</td>\n",
       "      <td>647603.7704719775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LgwzgklNvy4QCtq5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>18800</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7885</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>15872</td>\n",
       "      <td>1</td>\n",
       "      <td>32221</td>\n",
       "      <td>1</td>\n",
       "      <td>40.07357348973039</td>\n",
       "      <td>3321452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ucIR2NLLsC3T650L</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>19289</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6028</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>15760</td>\n",
       "      <td>1</td>\n",
       "      <td>32228</td>\n",
       "      <td>1</td>\n",
       "      <td>54.46208108983661</td>\n",
       "      <td>9570885.316206373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jre1pJhcQj91Kdky</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>20385</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18325</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2568</td>\n",
       "      <td>1</td>\n",
       "      <td>7271</td>\n",
       "      <td>1</td>\n",
       "      <td>99.62896580763416</td>\n",
       "      <td>14215011.127100045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rQpYpY9nRG7X5mmr</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>20657</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6880</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2587</td>\n",
       "      <td>1</td>\n",
       "      <td>7442</td>\n",
       "      <td>1</td>\n",
       "      <td>124.1312362968124</td>\n",
       "      <td>762711.9757135696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 235 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        building_id building_material city txn_dt total_floor building_type  \\\n",
       "0  e3mMIMR3JJqCaXz1                 8   21  18674           4             3   \n",
       "1  LgwzgklNvy4QCtq5                 8    7  18800           5             1   \n",
       "2  ucIR2NLLsC3T650L                 8    7  19289           4             1   \n",
       "3  jre1pJhcQj91Kdky                 8   21  20385          24             0   \n",
       "4  rQpYpY9nRG7X5mmr                 1   21  20657           2             4   \n",
       "\n",
       "  building_use building_complete_dt parking_way parking_area  \\\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:251: UserWarning: Trying to unpickle estimator KMeans from version 0.20.3 when using version 0.20.1. 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": "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": [
    "### Grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid search\n",
    "params_fix = {'task': 'train',\n",
    "              'boosting_type': 'gbdt',\n",
    "              'objective': 'mse',\n",
    "              'metric': 'mape',}\n",
    "#              'learning_rate': 0.01 }\n",
    "\n",
    "lgb_other_params = {'num_boost_round': 500000,\n",
    "                    'verbose_eval': 2000,\n",
    "                    'early_stopping_rounds': 1000 }\n",
    "\n",
    "# prvs baseline best estimator\n",
    "# ('objective', 'mse'), ('metric', 'mape'),\n",
    "# ('learning_rate', 0.015), ('num_leaves', 255),\n",
    "# ('feature_fraction', 0.5), ('min_data_in_leaf', 5),\n",
    "# ('lambda_l1', 0.1), ('lambda_l2', 0.1)),\n",
    "# 5524.863045737181,\n",
    "# [5528.862755759428, 5530.862894274216, 5514.863487177898]]\n",
    "\n",
    "#params_gsearch = {'max_depth':[-1, 8],\n",
    "#                  'num_leaves': [200, 255]}\n",
    "\n",
    "params_gsearch = {'learning_rate': [0.1, 0.07, 0.05, 0.03, 0.01, 0.007, 0.005, 0.003],\n",
    "                  'max_depth': [16],\n",
    "                  'num_leaves': [300],\n",
    "                  'feature_fraction': [0.5],\n",
    "                  'min_data_in_leaf': [10],\n",
    "                  'lambda_l1': [0.01],\n",
    "                  'lambda_l2': [0.1]\n",
    "                 }\n",
    "\n",
    "#params_gsearch = {'max_depth': [-1, 8, 16, 24],\n",
    "#                  'num_leaves': [200, 255, 300],           # may reduce in dim-reduction exp\n",
    "#                  'feature_fraction': [0.5, 0.75, 1.0],\n",
    "#                  'min_data_in_leaf': [5, 10, 20],\n",
    "#                  'lambda_l1': [0, 0.01, 0.1],\n",
    "#                  'lambda_l2': [0, 0.01, 0.1],\n",
    "#                 }\n",
    "N_fold = 3\n",
    "N_iter = 8\n",
    "score_low_bound = 5600 # if score smaller than this, skip the param set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rand_params_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Fold 1 ====\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00957238\n",
      "[4000]\tvalid_0's mape: 0.00956263\n",
      "[6000]\tvalid_0's mape: 0.00956142\n",
      "Early stopping, best iteration is:\n",
      "[5556]\tvalid_0's mape: 0.00956142\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.05, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5704.86741, 0.03940, 0.12712, 0.00955.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00980767\n",
      "[4000]\tvalid_0's mape: 0.00980586\n",
      "Early stopping, best iteration is:\n",
      "[3110]\tvalid_0's mape: 0.00980586\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.1, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5554.86431, 0.04076, 0.13040, 0.00980.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00971187\n",
      "[4000]\tvalid_0's mape: 0.00952097\n",
      "[6000]\tvalid_0's mape: 0.00947285\n",
      "[8000]\tvalid_0's mape: 0.00945633\n",
      "[10000]\tvalid_0's mape: 0.00944843\n",
      "[12000]\tvalid_0's mape: 0.00944395\n",
      "[14000]\tvalid_0's mape: 0.00944167\n",
      "[16000]\tvalid_0's mape: 0.0094401\n",
      "[18000]\tvalid_0's mape: 0.0094391\n",
      "[20000]\tvalid_0's mape: 0.00943832\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[19993]\tvalid_0's mape: 0.00943832\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.01, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5768.86921, 0.03853, 0.12550, 0.00943.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0101011\n",
      "[4000]\tvalid_0's mape: 0.00968896\n",
      "[6000]\tvalid_0's mape: 0.009555\n",
      "[8000]\tvalid_0's mape: 0.00949212\n",
      "[10000]\tvalid_0's mape: 0.00946018\n",
      "[12000]\tvalid_0's mape: 0.00944391\n",
      "[14000]\tvalid_0's mape: 0.00943264\n",
      "[16000]\tvalid_0's mape: 0.00942558\n",
      "[18000]\tvalid_0's mape: 0.00942074\n",
      "[20000]\tvalid_0's mape: 0.00941716\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[19999]\tvalid_0's mape: 0.00941715\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.005, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5752.86958, 0.03833, 0.12523, 0.00941.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0106538\n",
      "[4000]\tvalid_0's mape: 0.00995857\n",
      "[6000]\tvalid_0's mape: 0.00972948\n",
      "[8000]\tvalid_0's mape: 0.00961535\n",
      "[10000]\tvalid_0's mape: 0.00954961\n",
      "[12000]\tvalid_0's mape: 0.00950767\n",
      "[14000]\tvalid_0's mape: 0.00947989\n",
      "[16000]\tvalid_0's mape: 0.00946103\n",
      "[18000]\tvalid_0's mape: 0.00944874\n",
      "[20000]\tvalid_0's mape: 0.00943993\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20000]\tvalid_0's mape: 0.00943993\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.003, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5743.86925, 0.03824, 0.12553, 0.00944.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0098718\n",
      "[4000]\tvalid_0's mape: 0.00958185\n",
      "[6000]\tvalid_0's mape: 0.0094941\n",
      "[8000]\tvalid_0's mape: 0.00945903\n",
      "[10000]\tvalid_0's mape: 0.00944223\n",
      "[12000]\tvalid_0's mape: 0.00943385\n",
      "[14000]\tvalid_0's mape: 0.00942833\n",
      "[16000]\tvalid_0's mape: 0.00942447\n",
      "[18000]\tvalid_0's mape: 0.00942192\n",
      "[20000]\tvalid_0's mape: 0.00942007\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20000]\tvalid_0's mape: 0.00942007\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.007, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5758.86950, 0.03841, 0.12524, 0.00942.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00966466\n",
      "[4000]\tvalid_0's mape: 0.00965937\n",
      "Early stopping, best iteration is:\n",
      "[4088]\tvalid_0's mape: 0.00965933\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.07, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5625.86616, 0.03979, 0.12843, 0.00965.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00955048\n",
      "[4000]\tvalid_0's mape: 0.00952073\n",
      "[6000]\tvalid_0's mape: 0.00951583\n",
      "[8000]\tvalid_0's mape: 0.00951423\n",
      "Early stopping, best iteration is:\n",
      "[8742]\tvalid_0's mape: 0.00951397\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.03, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5729.86817, 0.03907, 0.12650, 0.00951.\n",
      "==== Fold 2 ====\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00962348\n",
      "[4000]\tvalid_0's mape: 0.00961353\n",
      "[6000]\tvalid_0's mape: 0.00961233\n",
      "Early stopping, best iteration is:\n",
      "[5504]\tvalid_0's mape: 0.00961233\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.05, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5692.86858, 0.04097, 0.12809, 0.00961.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00973492\n",
      "[4000]\tvalid_0's mape: 0.00955267\n",
      "[6000]\tvalid_0's mape: 0.00950321\n",
      "[8000]\tvalid_0's mape: 0.00948393\n",
      "[10000]\tvalid_0's mape: 0.00947474\n",
      "[12000]\tvalid_0's mape: 0.00946941\n",
      "[14000]\tvalid_0's mape: 0.00946636\n",
      "[16000]\tvalid_0's mape: 0.0094648\n",
      "[18000]\tvalid_0's mape: 0.00946362\n",
      "[20000]\tvalid_0's mape: 0.00946286\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[19996]\tvalid_0's mape: 0.00946286\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.01, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5753.87061, 0.04008, 0.12606, 0.00945.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0101032\n",
      "[4000]\tvalid_0's mape: 0.00971369\n",
      "[6000]\tvalid_0's mape: 0.00959125\n",
      "[8000]\tvalid_0's mape: 0.00953426\n",
      "[10000]\tvalid_0's mape: 0.00950368\n",
      "[12000]\tvalid_0's mape: 0.0094849\n",
      "[14000]\tvalid_0's mape: 0.00947324\n",
      "[16000]\tvalid_0's mape: 0.00946563\n",
      "[18000]\tvalid_0's mape: 0.0094609\n",
      "[20000]\tvalid_0's mape: 0.00945738\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[19999]\tvalid_0's mape: 0.00945738\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.005, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5754.87066, 0.04001, 0.12597, 0.00945.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.010594\n",
      "[4000]\tvalid_0's mape: 0.00996317\n",
      "[6000]\tvalid_0's mape: 0.00974526\n",
      "[8000]\tvalid_0's mape: 0.00963913\n",
      "[10000]\tvalid_0's mape: 0.00958073\n",
      "[12000]\tvalid_0's mape: 0.00954211\n",
      "[14000]\tvalid_0's mape: 0.00951639\n",
      "[16000]\tvalid_0's mape: 0.00949814\n",
      "[18000]\tvalid_0's mape: 0.00948465\n",
      "[20000]\tvalid_0's mape: 0.00947565\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20000]\tvalid_0's mape: 0.00947565\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.003, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5740.87034, 0.03990, 0.12621, 0.00947.\n",
      "Training until validation scores don't improve for 1000 rounds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2000]\tvalid_0's mape: 0.0098919\n",
      "[4000]\tvalid_0's mape: 0.0096155\n",
      "[6000]\tvalid_0's mape: 0.00953518\n",
      "[8000]\tvalid_0's mape: 0.00950033\n",
      "[10000]\tvalid_0's mape: 0.00948264\n",
      "[12000]\tvalid_0's mape: 0.00947268\n",
      "[14000]\tvalid_0's mape: 0.0094671\n",
      "[16000]\tvalid_0's mape: 0.00946355\n",
      "[18000]\tvalid_0's mape: 0.00946104\n",
      "[20000]\tvalid_0's mape: 0.00945913\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20000]\tvalid_0's mape: 0.00945913\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.007, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5736.87063, 0.04003, 0.12601, 0.00945.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00971804\n",
      "[4000]\tvalid_0's mape: 0.00971237\n",
      "Early stopping, best iteration is:\n",
      "[4221]\tvalid_0's mape: 0.0097123\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.07, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5616.86735, 0.04180, 0.12940, 0.00970.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00959889\n",
      "[4000]\tvalid_0's mape: 0.00956758\n",
      "[6000]\tvalid_0's mape: 0.0095615\n",
      "[8000]\tvalid_0's mape: 0.0095598\n",
      "Early stopping, best iteration is:\n",
      "[8887]\tvalid_0's mape: 0.00955955\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.03, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5694.86929, 0.04079, 0.12737, 0.00955.\n",
      "==== Fold 3 ====\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00969479\n",
      "[4000]\tvalid_0's mape: 0.00968325\n",
      "[6000]\tvalid_0's mape: 0.00968218\n",
      "Early stopping, best iteration is:\n",
      "[5515]\tvalid_0's mape: 0.00968218\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.05, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5638.86482, 0.04105, 0.12874, 0.00968.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00980517\n",
      "[4000]\tvalid_0's mape: 0.00962068\n",
      "[6000]\tvalid_0's mape: 0.00957016\n",
      "[8000]\tvalid_0's mape: 0.00955058\n",
      "[10000]\tvalid_0's mape: 0.00954035\n",
      "[12000]\tvalid_0's mape: 0.00953489\n",
      "[14000]\tvalid_0's mape: 0.00953185\n",
      "[16000]\tvalid_0's mape: 0.00953028\n",
      "[18000]\tvalid_0's mape: 0.00952923\n",
      "[20000]\tvalid_0's mape: 0.00952855\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[19986]\tvalid_0's mape: 0.00952855\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.01, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5710.86725, 0.04006, 0.12668, 0.00952.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0102113\n",
      "[4000]\tvalid_0's mape: 0.00979483\n",
      "[6000]\tvalid_0's mape: 0.00966828\n",
      "[8000]\tvalid_0's mape: 0.00960772\n",
      "[10000]\tvalid_0's mape: 0.00957706\n",
      "[12000]\tvalid_0's mape: 0.00955871\n",
      "[14000]\tvalid_0's mape: 0.00954663\n",
      "[16000]\tvalid_0's mape: 0.00953909\n",
      "[18000]\tvalid_0's mape: 0.00953378\n",
      "[20000]\tvalid_0's mape: 0.00952965\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20000]\tvalid_0's mape: 0.00952965\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.005, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5728.86715, 0.04005, 0.12668, 0.00953.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0107465\n",
      "[4000]\tvalid_0's mape: 0.0100499\n",
      "[6000]\tvalid_0's mape: 0.0098199\n",
      "[8000]\tvalid_0's mape: 0.0097116\n",
      "[10000]\tvalid_0's mape: 0.00964848\n",
      "[12000]\tvalid_0's mape: 0.00960805\n",
      "[14000]\tvalid_0's mape: 0.00958137\n",
      "[16000]\tvalid_0's mape: 0.00956251\n",
      "[18000]\tvalid_0's mape: 0.00954941\n",
      "[20000]\tvalid_0's mape: 0.00953977\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20000]\tvalid_0's mape: 0.00953977\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.003, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5738.86707, 0.03989, 0.12683, 0.00954.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00996592\n",
      "[4000]\tvalid_0's mape: 0.00968257\n",
      "[6000]\tvalid_0's mape: 0.00960197\n",
      "[8000]\tvalid_0's mape: 0.00956819\n",
      "[10000]\tvalid_0's mape: 0.00954956\n",
      "[12000]\tvalid_0's mape: 0.00953995\n",
      "[14000]\tvalid_0's mape: 0.00953436\n",
      "[16000]\tvalid_0's mape: 0.00953043\n",
      "[18000]\tvalid_0's mape: 0.0095274\n",
      "[20000]\tvalid_0's mape: 0.00952561\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[19999]\tvalid_0's mape: 0.00952561\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.007, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5723.86726, 0.04006, 0.12664, 0.00952.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.00978592\n",
      "[4000]\tvalid_0's mape: 0.00978037\n",
      "Early stopping, best iteration is:\n",
      "[4192]\tvalid_0's mape: 0.00978032\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.07, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5614.86361, 0.04145, 0.13006, 0.00978.\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[2000]\tvalid_0's mape: 0.0096626\n",
      "[4000]\tvalid_0's mape: 0.00963002\n",
      "[6000]\tvalid_0's mape: 0.00962412\n",
      "[8000]\tvalid_0's mape: 0.00962251\n",
      "Early stopping, best iteration is:\n",
      "[8695]\tvalid_0's mape: 0.0096223\n",
      "params: {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'mse', 'metric': 'mape', 'learning_rate': 0.03, 'max_depth': 16, 'num_leaves': 300, 'feature_fraction': 0.5, 'min_data_in_leaf': 10, 'lambda_l1': 0.01, 'lambda_l2': 0.1}\n",
      "score, mse, mae, mape: 5669.86588, 0.04071, 0.12787, 0.00962.\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "gsearch = {}\n",
    "folds = KFold(n_splits=N_fold, shuffle=True, random_state=123)\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)\n",
    "    df_val = feat_eng.transform(df_val)\n",
    "\n",
    "    # Construct lgb dataset\n",
    "    lgb_train = lgb.Dataset(df_train[cols_feats], df_train[col_target]).construct()\n",
    "    lgb_val = lgb.Dataset(df_val[cols_feats], df_val[col_target], reference=lgb_train).construct()\n",
    "        \n",
    "    # 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 = lgb.train(params, lgb_train, valid_sets=lgb_val, keep_training_booster=True,\n",
    "                          **lgb_other_params)\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(model.best_iteration)\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total computing time: 8949.083100795746\n"
     ]
    }
   ],
   "source": [
    "print('Total computing time:', t1-t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: add lr= 0.001 results here (see how to compare build and predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.005), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5745.53579672955 0.03946035174309782 0.12596061626189592 0.009462608022376475 [5752.869578598745, 5754.870657351918, 5728.867154237989] [0.03832685527804058, 0.04000603321012385, 0.040048166741129015] [0.12523173555017528, 0.12596626063210492, 0.1266838526034075] [0.009413391197403038, 0.009448560727400909, 0.009525872142325479]\n",
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.01), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5744.535691455677 0.039555112742465155 0.1260791649927265 0.009470989557398966 [5768.869214197509, 5753.870609569317, 5710.867250600207] [0.03853098755952153, 0.04007526749910198, 0.04005908316877197] [0.12550209268235346, 0.12605965735522268, 0.12667574494060335] [0.00943394367630861, 0.009454254190835818, 0.009524770805052473]\n",
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.003), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5741.202217355036 0.03934386895892106 0.1261904308763895 0.009479590982408635 [5743.869246128784, 5740.870340154869, 5738.867065781454] [0.03824367114495259, 0.03990276320874554, 0.039885172523065025] [0.12552987699211995, 0.1262122392143182, 0.12682917642273034] [0.009435572399732603, 0.009467031969592778, 0.009536168577900525]\n",
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.007), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5739.869129449726 0.039502641595580375 0.12596540694823558 0.00946276511696027 [5758.86950187138, 5736.870630593554, 5723.867255884243] [0.03841058098208283, 0.04003357819462729, 0.040063765610031] [0.12524425634504202, 0.12600856180912404, 0.12664340269054072] [0.009415308369676969, 0.009450430356358367, 0.009522556624845472]\n",
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.03), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5698.201113441514 0.040191575332366815 0.12724324455076158 0.009559747812040173 [5729.868167835901, 5694.869288082981, 5669.865884405659] [0.03907352158511781, 0.04078955042036631, 0.04071165399161632] [0.12649754562155535, 0.1273666820597766, 0.12786550597095275] [0.009509185550019386, 0.00955187034523361, 0.009618187540867525]\n",
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.05), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5678.8669385951325 0.04047597297328946 0.12798360239204187 0.009614113372202185 [5704.867412627905, 5692.868581317095, 5638.864821840399] [0.039403938673970865, 0.04097204433871111, 0.04105193590718641] [0.127122343383192, 0.1280897664568802, 0.1287386973360534] [0.00955475841032264, 0.009608143407998957, 0.009679438298284953]\n",
      "(('task', 'train'), ('boosting_type', 'gbdt'), ('objective', 'mse'), ('metric', 'mape'), ('learning_rate', 0.07), ('max_depth', 16), ('num_leaves', 300), ('feature_fraction', 0.5), ('min_data_in_leaf', 10), ('lambda_l1', 0.01), ('lambda_l2', 0.1)) 5619.199039959192 0.041011651972860906 0.1292976781507114 0.009712350845695716 [5625.86616177201, 5616.867352749384, 5614.863605356181] [0.03979322578730114, 0.04179586199592007, 0.04144586813536149] [0.12842956873420117, 0.12940445642916149, 0.1300590092887716] [0.009653736365579093, 0.00970470864168559, 0.009778607529822464]\n"
     ]
    }
   ],
   "source": [
    "for row in gsearch_results:\n",
    "    print(*[x for i, x in enumerate(row) if i != 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['score', 'mse', 'mae', 'mape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>learning_rate</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>num_leaves</th>\n",
       "      <th>feature_fraction</th>\n",
       "      <th>min_data_in_leaf</th>\n",
       "      <th>lambda_l1</th>\n",
       "      <th>lambda_l2</th>\n",
       "      <th>score</th>\n",
       "      <th>mse</th>\n",
       "      <th>mae</th>\n",
       "      <th>mape</th>\n",
       "      <th>best_iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5745.535797</td>\n",
       "      <td>0.039460</td>\n",
       "      <td>0.125961</td>\n",
       "      <td>0.009463</td>\n",
       "      <td>19999.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5744.535691</td>\n",
       "      <td>0.039555</td>\n",
       "      <td>0.126079</td>\n",
       "      <td>0.009471</td>\n",
       "      <td>19991.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5741.202217</td>\n",
       "      <td>0.039344</td>\n",
       "      <td>0.126190</td>\n",
       "      <td>0.009480</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.007</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5739.869129</td>\n",
       "      <td>0.039503</td>\n",
       "      <td>0.125965</td>\n",
       "      <td>0.009463</td>\n",
       "      <td>19999.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.030</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5698.201113</td>\n",
       "      <td>0.040192</td>\n",
       "      <td>0.127243</td>\n",
       "      <td>0.009560</td>\n",
       "      <td>8774.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.050</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5678.866939</td>\n",
       "      <td>0.040476</td>\n",
       "      <td>0.127984</td>\n",
       "      <td>0.009614</td>\n",
       "      <td>5525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.070</td>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5619.199040</td>\n",
       "      <td>0.041012</td>\n",
       "      <td>0.129298</td>\n",
       "      <td>0.009712</td>\n",
       "      <td>4167.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   learning_rate  max_depth  num_leaves  feature_fraction  min_data_in_leaf  \\\n",
       "0          0.005         16         300               0.5                10   \n",
       "1          0.010         16         300               0.5                10   \n",
       "2          0.003         16         300               0.5                10   \n",
       "3          0.007         16         300               0.5                10   \n",
       "4          0.030         16         300               0.5                10   \n",
       "5          0.050         16         300               0.5                10   \n",
       "6          0.070         16         300               0.5                10   \n",
       "\n",
       "   lambda_l1  lambda_l2        score       mse       mae      mape  \\\n",
       "0       0.01        0.1  5745.535797  0.039460  0.125961  0.009463   \n",
       "1       0.01        0.1  5744.535691  0.039555  0.126079  0.009471   \n",
       "2       0.01        0.1  5741.202217  0.039344  0.126190  0.009480   \n",
       "3       0.01        0.1  5739.869129  0.039503  0.125965  0.009463   \n",
       "4       0.01        0.1  5698.201113  0.040192  0.127243  0.009560   \n",
       "5       0.01        0.1  5678.866939  0.040476  0.127984  0.009614   \n",
       "6       0.01        0.1  5619.199040  0.041012  0.129298  0.009712   \n",
       "\n",
       "   best_iteration  \n",
       "0    19999.333333  \n",
       "1    19991.666667  \n",
       "2    20000.000000  \n",
       "3    19999.666667  \n",
       "4     8774.666667  \n",
       "5     5525.000000  \n",
       "6     4167.000000  "
      ]
     },
     "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(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": 24,
   "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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Fj4ikncqI89f/rOB3by+maWYj/nDRkYzSKCdlKHhEJK0s31jOLRM+o2DVVk7v14m7Rg2icxuNclKJgkdE0kLsKKdZ4wweuORIzj9Ko5xUpOARkXpv2cZybvnnHGauLuWMIzpz1wUD6aRRTspS8IhIvVUZccZ/tJzfv7OE5o0zePCSoxh5VDeNclKcgkdE6qXCDeXcMmEOs1aXclb/zvzmgoF0aq1RTn2g4BGReqUy4jzx4XLuf3cJLZpk8NCYozjvSI1y6pNGiezczEaY2WIzKzSz26pY39TMXgzWTzeznnHrc82s3MxuPlCfZvZssHyemT1pZo2D5aeYWZmZzQ5+fpm4IxaRRFq6fjujHv+Ye95cxKl9O/Luj09mpCYQ1DsJG/GYWQbwGHAmUAzMMLNJ7r4gptlVwFZ3721mY4B7gUti1t8PvFnNPp8Fvh00fQ64Gng8+P6hu59T18coIslRURlh3IfLefDdpbRsmsEjlw7hnMFdFTj1VCJPtQ0HCt19OYCZvQCMBGKDZyQwNvg8AXjUzMzd3czOB1YAO6rTp7tP3t/IzD4FchJyVCKSVEvWb+eWf85hTnEZZw/swp0jB9KxddOwy5JaSOSptmygKOZ7cbCsyjbuXgGUAe3NrBVwK3DHwfYZnGK7HHgrZvFxZjbHzN40swE1OxwRSaaKygiPvVfIOQ9/RNHWXTx22VAe//YwhU4aqPaIx8xOAPq4+1/NrCPQyt1XJKiuscAD7l5eg6H0H4EP3P3D4PtMoEfQ1zeAV4A+8RuZ2TXANQC5ubk1rVtE6sDiddu5+Z9zmFtSxjcHdeWOkQPo0EqBky6qFTxm9isgD+gL/BVoDPwdOP4rNisBusd8zwmWVdWm2MwygbbAZuAYYLSZ3QdkAREz2w0UfFWfQZ0dge/vX+bu22I+TzazP5pZB3ffFFuIu48DxgHk5eX5VxyXiCTIvsoIf35/GQ9NWUqbZo157LKhfHNw17DLkjpW3RHPBcAQoqMH3H2NmbU+wDYzgD5m1otoOIwBLotrMwm4EvgEGA1MdXcHTtzfwMzGAuXu/mgQTlX2aWZXA18HTnf3SMz2XYD1wXWj4URPL26u5nGLSJIsWreNm/85h3kl2zhncFfuOG8A7TXKSUvVDZ69wS9uBzCzlgfawN0rzOxG4G0gA3jS3eeb2Z1AvrtPAsYDz5hZIbCFaJAcdJ/B6j8Bq4BPgtNzL7n7nUQD7TozqwB2AWOCcBORFLCvMsLj/17GI1OX0rZ5Yx7/1lDOHqRRTjqz6vwODu6j6UN0GvPdwP8Az7n7I4ktLxx5eXmen58fdhkiaW/Bmm3cMmEO89ds49wju3HHeQM4pGWTsMuSGjKzAnfPO1C7ao143P33ZnYmsI3odZ5fuvu7taxRRBqoyojz6NRCHpm6lKwWjfnTt4cyYqBGOQ1FdScXtCR6/eVdM+sL9DWzxu6+L7HliUg6+vMHy3jgX0s4LxjltNMop0Gp7n08HwBNzSyb6P0xlwN/S1RRIpK+irbs5OEpS/n6gM48fOkQhU4DVN3gMXffCYwCHnf3iwDdiCkiB8Xd+cWr88gwY+x5+hXSUFU7eMzsOOBbwBvBsozElCQi6Wry3HX8e/FGfnJWX7q2bR52ORKS6gbPTcBtRKcozw/uo5mauLJEJN1s272PO16bz4BubbjyuB5hlyMhqu59PDuBCHCpmX0bMED3wohItf3h7cVsLN/DE1fkkZmR0DeySIqrbvA8C9wMzCMaQCIi1TanqJSnp63iimN7cGT3rLDLkZBVN3g2uvtrCa1ERNJSRWWE21+eS8dWTfnp1/uGXY6kgOoGz6/M7C/AFGDP/oXu/lJCqhKRtPHUJ6uYv2Ybf/zWUNo0axx2OZICqhs83wX6EX0q9f5TbQ4oeETkS60t28X97yzm1L4dOXtgl7DLkRRR3eA52t01RhaRgzJ20nwq3blz5EC9plo+V92pJR+bWf+EViIiaeXdBet5e/56bjr9cLof0iLsciSFVHfEcyww28xWEL3GY4C7++CEVSYi9daOPRX86tV59O3cmqtP7BV2OZJiqhs8IxJahYiklQf/tYQ1ZbuZeNkQGuueHYlT3dcirEp0ISKSHhas2caT/1nJpcO7M6zHIWGXIylIf4qISJ2pjDi3vzyXrOaNuXVEv7DLkRSl4BGROvPcp6uZXVTK/51zBFkt9LoDqZqCR0TqxIbtu7nvrUUc37s95x+VHXY5ksIUPCJSJ379+kL27Ivwa92zIweQ0OAxsxFmttjMCs3stirWNzWzF4P1082sZ9z6XDMrN7ObD9SnmfUK+igM+mxSnX2ISO19sGQjr81Zw/WnHsahHVuFXY6kuIQFj5llAI8BZwP9ib5SIf4m1KuAre7eG3gAuDdu/f3Am9Xs817ggaCvrUHf1dmHiNTC7n2V/N8r8zi0Q0uuO+WwsMuReiCRI57hQKG7L3f3vcALwMi4NiOBp4LPE4DTLRijm9n5wApg/oH6DLY5LeiDoM/zD7QPEam9R6cWsnrLTn5zwUCaZurFxHJgiQyebKAo5ntxsKzKNu5eAZQB7c2sFXArcEc1+2wPlAZ9xO+ryn3U+KhE5HOFG7bz5w+WMWpINl87rEPY5Ug9kaqTC8YSPW1Wnqwdmtk1ZpZvZvkbN25M1m5F6i135/aX59GiSSa3f/OIsMuReqS6j8ypiRKge8z3nGBZVW2KzSwTaAtsBo4BRpvZfUAWEDGz3UDBl/S5Gcgys8xgVBO7ry/bxxe4+zhgHEBeXp5e6y1yAP8sKObTFVu4Z9QgOrRqGnY5Uo8kcsQzA+gTzDZrAowBJsW1mQRcGXweDUz1qBPdvae79wQeBO5y90e/rE93d+C9oA+CPl/9qn3U9cGKNCRbduzl7skLyevRjovzuh94A5EYCRvxuHuFmd0IvA1kAE+6+3wzuxPId/dJwHjgGTMrBLYQDZKD7jNYfSvwgpn9BpgV9M3B7kNEDuyuyQvZvruCu0YNolEjzdWRg2P64/+/5eXleX5+fthliKSkacs3M2bcNK475TA9j02+wMwK3D3vQO1SdXKBiKSgPRWV/O/Lc+l+SHN+eFqfsMuReiqRkwtEJM2Me385yzbu4K/fPZrmTXTPjtSMRjwiUi0rN+3gkfcK+eagrpzat1PY5Ug9puARkQNyd37x6jyaZjTil+fGP/lK5OAoeETkgCbNWcOHSzdx89f70rlNs7DLkXpOwSMiX6ls1z5+/fpCBue05dvH9gi7HEkDmlwgIl/pvrcWsWXHHv723aPJ0D07Ugc04hGRL1WwaivPTl/Nd4/vxcDstmGXI2lCwSMiVdpXGeF/X55L17bN+MmZh4ddjqQRnWoTkSo9+dEKFq3bzp8vH0bLpvpVIXVHIx4R+S/FW3fy4L+WcsYRnfn6gC5hlyNpRsEjIl/g7vzq1fmYwR0jB4RdjqQhBY+IfMHb89cxZdEGfnzG4WRnNQ+7HElDCh4R+Vz5ngrGTlrAEV3b8N3je4ZdjqQpBY+IfO4P7yxm/fbd3HXBQDIz9OtBEkP/ZYkIAPNKynjq45V865hchuS2C7scSWMKHhGhMuLc/vJc2rdqyi1f18vdJLEUPCLCM5+s5LPiMn5xTn/aNm8cdjmS5hQ8Ig3curLd/P6dJZzYpwPnDu4adjnSACh4RBq4O1+fz77KCL85fyBmegioJJ6CR6QBe2/RBibPXccPTutNj/Ytwy5HGoiEBo+ZjTCzxWZWaGa3VbG+qZm9GKyfbmY9g+XDzWx28DPHzC6I2eYmM5tnZvPN7Ecxy1+M2Walmc0Olvc0s10x6/6UyGMWqS927a3kF6/Oo3enVlxz0mFhlyMNSMKe/GdmGcBjwJlAMTDDzCa5+4KYZlcBW929t5mNAe4FLgHmAXnuXmFmXYE5ZvYa0A/4HjAc2Au8ZWavu3uhu18Ss+8/AGUx+1nm7kcl6lhF6qOHpiyleOsuXrzmWJpk6uSHJE8i/2sbDhS6+3J33wu8AIyMazMSeCr4PAE43czM3Xe6e0WwvBngwecjgOkx698HRsV2aNGT1BcDz9f5EYmkicXrtvOXD5dz0bAcjjm0fdjlSAOTyODJBopivhcHy6psEwRJGdAewMyOMbP5wFzg2mD9POBEM2tvZi2AbwDd4/o8EVjv7ktjlvUys1lm9r6ZnVg3hydSP0WCe3ZaN8vk5984IuxypAFK2ZdsuPt0YICZHQE8ZWZvuvtCM7sXeAfYAcwGKuM2vZQvjnbWArnuvtnMhgGvmNkAd98Wu5GZXQNcA5Cbm5uYgxJJAS/mF1Gwaiu/Gz2YQ1o2CbscaYASOeIp4YujkZxgWZVtzCwTaAtsjm3g7guBcmBg8H28uw9z95OArcCS/W2DPkYBL8Zsv8fdNwefC4BlwH+9TtHdx7l7nrvndezYsUYHLJLqNpXv4Z43F3FMr0MYPSwn7HKkgUpk8MwA+phZLzNrAowBJsW1mQRcGXweDUx1dw+2yQQwsx5EJxWsDL53Cv6ZSzRknovp7wxgkbsX719gZh2DiQ6Y2aFAH2B5XR6oSH3x2zcWsnNvBb+9YJDu2ZHQJOxUWzAj7UbgbSADeNLd55vZnUC+u08CxgPPmFkhsIVoOAGcANxmZvuACHC9u28K1k00s/bAPuAGdy+N2e0Y/ntSwUnAnTF9XevuW+r8gEVS3H8KN/HyrBJ+cFpvendqFXY50oCZux+4VQOTl5fn+fn5YZchUmd276vk7Ic+JOLO2z86iWaNM8IuSdKQmRW4e96B2qXs5AIRqTuP/3sZKzbt4Jmrhit0JHS6a0wkzS3bWM7j/17GeUd248Q+mjgj4VPwiKQxd+d/X55L08aN+L9zdM+OpAYFj0gae2lmCdOWb+HWEf3o1LpZ2OWIAAoekbS1dcdefjt5IUNys7hsuG6KltSh4BFJU/e8uYiyXfu464JBNGqke3YkdSh4RNLQpyu28GJ+EVef0IsjurYJuxyRL1DwiKSZvRUR/vfluWRnNeemM/qEXY7If9F9PCJp5okPl7N0Qznjr8yjRRP9Ly6pRyMekTSyevNOHp6ylBEDunD6EZ3DLkekSgoekTTh7vzi1XlkNjJ+dV7/sMsR+VIKHpE08cbctby/ZCM/PasvXds2D7sckS+l4BFJA9t27+OO1xYwMLsNV36tZ9jliHwlXXkUSQO/f3sxm8v3MP7KPDJ0z46kOI14ROq52UWlPDNtFVcc15PBOVlhlyNyQAoekXqsojLC7S/NpVPrpvz0rP96o7tISlLwiNRjf/t4JQvWbuNX5w6gdbPGYZcjUi26xiOfK9u1j9lFpcxctZVZRaVs372P73ytJ+cM7qbrBiloTeku7n93Caf27cjZA7uEXY5ItSl4GqhIxFm6oZxZq7cyc/VWZq4upXBDOQCNDA7v3JqKiHPTC7N56F9LufG03px3ZDcyMzRIThVjJ80n4s6dIwdipj8MpP5Q8DQQpTv3MquolFnBaGb26lK276kAoF2LxgzJbcf5R3VjaG47BnfPolXTTCIR550F63hoSiE/+cccHp6ylBtO7c35Q7JprAAK1bsL1vPOgvXcOqIf3Q9pEXY5IgfF3D3sGlJOXl6e5+fnh11GjVVGnCXrtzNrdWkwmtnK8o07gOhopl+XNgztkcWQ7u0Y2qMdPdu3+Mq/mCMR518L1/Pw1KXMK9lG90Oac8MpvRk1NIcmmQqgZNuxp4Iz73+f1s0a8/oPT9AfAZIyzKzA3fMO1C6hIx4zGwE8BGQAf3H3e+LWNwWeBoYBm4FL3H2lmQ0Hxu1vBox195eDbW4Cvhcsf8LdHwyWjw2Wbwy2u93dJwfrfg5cBVQCP3T3txNzxOHYsmMvs4u2MnNVKbOKtjKnqIzyYDRzSMsmDM3N4sKhOQzJzeLInCxaNj24f+2NGhlnDejCmf07M3XRBh6espTbXprLI1MLuf7Uwxg9LIemmRmJODSpwoP/WsKast1MvGyIQkfqpYSNeMwsA1gCnAkUAzOAS919QUyb64HB7n6tmY0BLnD3S8ysBbDX3SvMrCswB+gG9ANeAIYDe4G3gGvdvTAInnJ3/31cHf2B54NtugH/Ag5398ovqz2VRzwVlREWx4xmZq0uZcWm6Ggmo5ESncDLAAANEklEQVRxRNfWwUgmi6G57cg95KtHMzXh7ry/ZCMPTVnKrNWldGvbjOtOOYyL8rrTrLECKJEWrNnGuY9+xMV5Odw9anDY5Yh8QSqMeIYDhe6+PCjoBWAksCCmzUhgbPB5AvComZm774xp0wzYn45HANP3rzez94FRwH1fUcdI4AV33wOsMLPCoLZPanFsSbO5fM8XQmZOcSk790Yzs0OrJgzJbcfFed0ZkpvF4Jy2SXkMvplxSt9OnHx4Rz4q3MRD/1rKL16dz6PvFXLtyYdx6fBcBVAdi0Sc6Su2cOfrC8hq3phbR/QLuySRGkvkb6lsoCjmezFwzJe1CUY3ZUB7YJOZHQM8CfQALg/WzwN+a2btgV3AN4DYocmNZnZFsOyn7r412Me0uDqy44s1s2uAawByc8N5P31FZYRF67YHM82iYbNqczSDMxsZ/bu14aJhOQzt0Y6hue3Iadc81NlMZsaJfTpyQu8OfLJsMw9OWcodry3gj/9exvdPOpRvHdOD5k0UQLWxctMOXppZzMSZJZSU7qJV00zuv/hIslo0Cbs0kRpL2Vlt7j4dGGBmRwBPmdmb7r7QzO4F3gF2ALOJXrcBeBz4NdHR0a+BPwD/cxD7G0dwXSkvLy8pMy42bt/zecjMWr2Vz4rL2LUvejgdWzdlaG4Wlw3PZUhuOwZlt03ZX+Jmxtd6d+BrvTswbflmHp6ylN+8sZA/vb+Ma046lG8f20MvJDsIZbv28cZna5k4s5iCVVtpZHB87w78bERfzurfJWX/OxCprkT+NigBusd8zwmWVdWm2MwygbZEJxl8LgibcmAgkO/u44HxAGZ2F9ERDO6+fv82ZvYE8PpB1JFw+yojLFy77QszzYq27AKgcYbRv1tbLjm6ezCaySI7K9zRTE0de2h7jj20PTNWbuHhKUu5a/Ii/vT+cq4+sRdXHNeTVgc5saGhqKiM8GHhJiYWFPPugvXsqYjQu1Mrbh3RjwuGZNOlbbOwSxSpM4mcXJBJdHLB6UR/0c8ALnP3+TFtbgAGxUwuGOXuF5tZL6AoOL3Wg+j1mMHuvsnMOrn7BjPLJTryOdbdS82sq7uvDfr9MXCMu48xswHAc/z/yQVTgD6JnlywYfvu6Cyz4NrMZyWl7N4XAaBzm6YMzY2eLhuSm8XA7LZpe02kYNVWHp6ylPeXbCSrRWOuPqEXV36tpx7vEli8bjsTZxbzyqwSNmzfQ1aLxpx3ZDcuHJrD4Jy29fKPD2m4Qp9cEITGjcDbRKdTP+nu883sTqIjl0lERy7PBBf8twBjgs1PAG4zs31ABLje3TcF6yYG13j2ATe4e2mw/D4zO4roqbaVwPeDOuab2T+ITmqoCLb50tCpjcIN5Tw0ZSkzV22lpDQ6mmmS0YgB2W24bHiPz2eadW3brMH8QhnWox1P/c9wZheV8siUpfz+nSWM+2A5V51wKN85vidtmze8ANpcvodJc9YwcWYx80q2kdkoOllj9LBsTu3XSVPTJe3pBtIq1HTEs3LTDi59YtrnI5khue0Y0K1N2o5mamJucRkPT13KuwvW07pZJt89vhf/c3zPtL9YvrciwtRFG5g4s5j3Fm2gIuIM6NaGC4fmMPKobrRv1TTsEkVqrbojHgVPFWoaPO7eYEYytTV/TRmPTCnkrfnraNU0kyu/1oOrTziUdi3TJ4DcnbklZUwsKGbSnDVs3bmPjq2bcsGQbEYNzaZflzZhlyhSpxQ8tZDKN5Cmm0XrtvHI1EImz11Li8YZXH5cT753Yq96PQJYv203L88qYWJBMUs3lNMksxFn9e/MhcNyOLF3Bz1oVdKWgqcWFDzJt2T9dh6dWshrn62hWWYGlx/Xg++deCgdW9ePANq9r5K3569j4swSPlq6kYhHr29dODSHbw7u2iCvZUnDo+CpBQVPeAo3lPPYe4W8OruEJpmNuGx4D649+VA6tUm96cTuTv6qrUwsKOaNz9ayfU8F2VnNGTU0m1FDc+jVoWXYJYoklYKnFhQ84VuxaQePvVfIy7NKyGhkXDY8l2tPPiwl7mcp2rKTl2aW8NKsYlZt3kmLJhmcPbArFw7L5the7Wmkl+ZJA6XgqQUFT+pYtXkHf3xvGRNnFtPIjEuO7s61pxxGdlbzpNZRvqeCyXPXMrGgmOkrtmAGxx3anguH5jBiYJeDfuK3SDpS8NSCgif1FG3ZyR//vYwJBdHH/12U153rTj4soS9Bq4w4Hy/bxEszS3hr3jp27aukV4eWXDg0mwuG5iQ9/ERSnYKnFhQ8qaukdBeP/7uQf8woJuLOhUNzuOHU3uS2r7sAKtxQ/vnTBNaW7aZ1s0zODZ4mMDQ3S1PmRb6EgqcWFDypb23ZLv7072U8P6OIyohzwZBsbji1d40v6Jfu3Mtrc9YwYWYJc4pKyWhknNSnAxcOy+GMIzrrJmCRalDw1IKCp/5Yv203f35/Oc9OX8W+ygjnH5XNDaf15rCOrQ647b7KCO8v3sjEmcVMWbiBvZUR+nVpHX2awJBudGod/kQGkfpEwVMLCp76Z8P23TzxwXKembaKvRURzj2yGzee2ps+nVv/V9v5a8qYWFDCpDklbCrfS/uWTTjvqOiptAHd2uhUmkgNKXhqQcFTf20q38MTHy7nmU9WsWtfJd8Y1JUfnNab9i2b8ursEiYUFLNo3XYaZxin94s+TeCUvh1prKcJiNSagqcWFDz135Ydexn/0XKe+ngV5XsqyGhkVEacI3PacuGwHM4d3C2tngsnkgoUPLWg4EkfpTv38swnq9hdUckFQ7Lp3em/T72JSN0I/X08Iqkgq0UTfnB6n7DLEJEYOrEtIiJJpeAREZGkUvCIiEhSKXhERCSpFDwiIpJUCh4REUkqBY+IiCSVgkdERJJKTy6ogpltBFbVoosOwKY6KidM6XIcoGNJRelyHKBj2a+Hu3c8UCMFTwKYWX51HhuR6tLlOEDHkorS5ThAx3KwdKpNRESSSsEjIiJJpeBJjHFhF1BH0uU4QMeSitLlOEDHclB0jUdERJJKIx4REUkqBU8dMrMRZrbYzArN7Law66kpM3vSzDaY2bywa6ktM+tuZu+Z2QIzm29mN4VdU02YWTMz+9TM5gTHcUfYNdWWmWWY2Swzez3sWmrDzFaa2Vwzm21m9fYNkmaWZWYTzGyRmS00s+MSti+daqsbZpYBLAHOBIqBGcCl7r4g1MJqwMxOAsqBp919YNj11IaZdQW6uvtMM2sNFADn17d/L2ZmQEt3LzezxsBHwE3uPi3k0mrMzH4C5AFt3P2csOupKTNbCeS5e72+j8fMngI+dPe/mFkToIW7lyZiXxrx1J3hQKG7L3f3vcALwMiQa6oRd/8A2BJ2HXXB3de6+8zg83ZgIZAdblUHz6PKg6+Ng596+1ejmeUA3wT+EnYtAmbWFjgJGA/g7nsTFTqg4KlL2UBRzPdi6uEvuHRmZj2BIcD0cCupmeDU1GxgA/Cuu9fL4wg8CPwMiIRdSB1w4B0zKzCza8IupoZ6ARuBvwanP/9iZi0TtTMFjzQIZtYKmAj8yN23hV1PTbh7pbsfBeQAw82sXp4GNbNzgA3uXhB2LXXkBHcfCpwN3BCcqq5vMoGhwOPuPgTYASTsOrWCp+6UAN1jvucEyyRkwTWRicCz7v5S2PXUVnAK5D1gRNi11NDxwHnBtZEXgNPM7O/hllRz7l4S/HMD8DLR0+71TTFQHDOKnkA0iBJCwVN3ZgB9zKxXcGFuDDAp5JoavOCi/HhgobvfH3Y9NWVmHc0sK/jcnOgklkXhVlUz7v5zd89x955E/z+Z6u7fDrmsGjGzlsGkFYJTU2cB9W42qLuvA4rMrG+w6HQgYRNwMhPVcUPj7hVmdiPwNpABPOnu80Muq0bM7HngFKCDmRUDv3L38eFWVWPHA5cDc4PrIwC3u/vkEGuqia7AU8HsyUbAP9y9Xk9DThOdgZejf9+QCTzn7m+FW1KN/QB4NvjDeTnw3UTtSNOpRUQkqXSqTUREkkrBIyIiSaXgERGRpFLwiIhIUil4REQkqRQ8ImnAzMaa2c1h1yFSHQoekRRjUfp/U9KW/uMWSQFm1jN4l9PTRO98H29m+fHv3gne/XKHmc0M3gHTr4q+vmdmbwZPOBBJOXpygUjq6ANc6e7TzOwQd98SPKlgipkNdvfPgnab3H2omV0P3Axcvb+D4OkZZxJ959CepB+BSDVoxCOSOlbFvNjtYjObCcwCBgD9Y9rtf9BpAdAzZvkVRJ+QPFqhI6lMwSOSOnYAmFkvoiOZ0919MPAG0Cym3f5QqeSLZy3mEg2inIRXKlILCh6R1NOGaAiVmVlnoqOY6pgFfB+YZGbdElWcSG0peERSjLvPIRoii4DngP8cxLYfER0tvWFmHRJToUjt6OnUIiKSVBrxiIhIUil4REQkqRQ8IiKSVAoeERFJKgWPiIgklYJHRESSSsEjIiJJpeAREZGk+n9WAXnG9MgMHQAAAABJRU5ErkJggg==\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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2IxKR5mTRhr3c8MQyWqa04KnrzmVU785hl9TkxNrj+RVwHvDF4PsPiITKiYwBCt19o7tXAnOIBEO0KcDs4PVc4KIg5KYAc9y9wt03AYXB+YYAi929zN2rgTeBy4Pjvw781N0rANx9d9R71HUuEZFjPL10K1+ZtYSu7Vvzwo3jFTpxEmvwnOvuNwLlAO6+D0g7yTE9gW1R3xcF2+psEwRJKZBxgmMLgAlmlmFm6cBkIDtoMzDYt9jM3jSz0adQB2Y23czyzSx/z549J7k0EWlKamud+19Zy23zVnFevwzmfWMc2Z3Twy6ryYp1ckFVMHTmAGaWBdTGraqP4e5rzWwm8DpwCFgOHBkySwU6A2OB0cAzZtb3FM79MPAwQF5enjdk3SLSeJVVVnPLnOW8vmYXXx7bm7s+N5TUFK2fHE+x/nT/B3ge6GpmPwH+Dtx3kmO282FvBKBXsK3ONmaWCnQAik90rLvPcvdR7n4BsA94L2hTBDznEUuIBGNmjHWISDO060A5X/jNP1iwdhd3fW4o90wZptBJgJh+wu7+e+BWImGzA7jU3Z89yWFLgQFmlmtmaURu8M8/rs184Org9VTgDXf3YPu0YNZbLjAAWAIQNWkgh8j9nSeD418ALgz2DSQyFLj3ROcSkearYHspU/53IZv2HOK3V+fxtfG5mrmWIKfyOZ504Mhw20kfOuHu1WZ2E/BacNyj7r7azO4B8t19PjALeMLMCoESIuFE0O4ZYA1QDdwYNQttnpllAFXB9v3B9keBR82sgMiMt6uDEDvRuUSkGfrTml1866l36JTekrlfH8eQ7u3DLqlZsci/zSdpZHYn8HlgHmDApcCz7n5vfMsLR15enufn54ddhog0MHfnt29t4r4/rmVEzw488pU8urRvHXZZTYaZLXP3vJO1i7XHcxWRz8eUByf/KZEb+00yeESk6amqqeXOPxTw1JJtTD6zGz/7/EjapGn5mzDEGjw7gNYE06mBVugGvYgkidKyKr7x5DIWFhZz44X9+O7EQXpwW4hiDZ5SIvdK/kTkHs9EYImZ/Q+Au38rTvWJiNTLluJDXPPYUraWlPHg1BF8Pi/75AdJXMUaPM8HX0f8teFLERFpWEs2lXD9E/k48MS15zK2b0bYJQkxBo+7zz55KxGRxuP5d4q4be4qenVqw6yvjiY3s23YJUkg1kVCBwD3A0OJ3OsBwN1jXhlARCQRamudny94j1+8Uch5fTN46Evn0DH9ZCt8SSLFOtT2O+Au4OdEPqT5NeL8SAURkVNVXlXDd59dwcsrd/KFvF7ce+mZpKXqn6rGJtbgaePufzYzc/ctwN1mtgy4M461iYjEbM8HFVz3eD4rivYz4zODuf6CvlqJoJGKNXgqzKwFsD5YjWA7cEb8yhIRid269z/gmseWUnyogoeuGsWk4d3CLklOINY+6M1Elsz5FjAK+BLwlXgVJSISq7+u280VDy2iqqaWZ68fp9BJArH2eBx4AugNtAy2PQKMiEdRIiKxmL1oMz96cTWDu7Vn1lfz6N7hpMtISiMQa/D8Hvg+sIoQnsMjIhKtuqaWH7+0htn/2MKnhnThv6edTdtWp7LmsYQp1v9Se4LVpEVEQvVBeRXffOod/rpuD/92fi63Tx5Cipa/SSqxBs9dZvZb4M9AxZGN7v5cXKoSEalD0b4yrn0sn8I9B7nvsjP5f+fmhF2SnIZYg+drwGAi93eODLU5oOARkYR4Z+s+rnt8GRXVNcz+2hjOH5AZdklymmINntHuPiiulYiIfIyXVu7gu8+soGv71syZfi79u7QLuySph1inUy8ys6FxrURE5Djuzv++sZ6bnnyHM3t24PlvjFPoNAGx9njGAsvNbBORezwGuLtrOrWIxEVFdQ23P7eK597ezmVn9+SnV5xJq1Q9uK0piDV4JsW1ChGRKCWHKrnhiWUs2VzCdyYO5Jv/0l/L3zQhsT4WYUu8CxERASjcfZBrZy9lZ2k5v/ji2XzurB5hlyQNTJ+4EpFGY2HhXr7+f8tIS23BnOljOSenU9glSRwoeESkUXhqyVZ++EIBfbPaMuvq0WR3Tg+7JIkTBY+IhKqm1pn56rs8/LeNfGJgFv/7/86mXeuWJz9QkpaCR0RCU1ZZzc1zlvOnNbu4+rze/PCSoaSm6MFtTZ2CR0RC8X5pOdfOXsranQe4+3ND+er43LBLkgRR8IhIwm0tLuPzv1nEwfJqZl09mgsHdwm7JEkgBY+IJFRtrfO9uSsoq6xh7tfHMaR7+7BLkgTTYKqIJNT/Ld7Ckk0l/PCSoQqdZkrBIyIJs62kjJ/+8V0uGJjF50f1CrscCYmCR0QSwt25bd5KWpjx08vP1BI4zVhcg8fMJpnZOjMrNLMZdexvZWZPB/sXm1mfqH23B9vXmdnFUdtvNrMCM1ttZrdEbb/bzLab2fLga3KwvY+ZHY7a/ut4XrOI1O3JJVtZtKGYOyYPoUfHNmGXIyGK2+QCM0sBfglMBIqApWY2393XRDW7Ftjn7v3NbBowE7gyeATDNGAY0ANYYGYDgSHAdcAYoBJ41cxecvfC4Hw/d/f/qKOcDe4+Mg6XKSIx2L7/MPe/8i7n98/ki2Oywy5HQhbPHs8YoNDdN7p7JTAHmHJcmynA7OD1XOAii/S/pwBz3L3C3TcBhcH5hgCL3b3M3auBN4HL43gNIlJP7s6MeSupded+DbEJ8Q2ensC2qO+Lgm11tgmCpBTIOMGxBcAEM8sws3RgMhD969NNZrbSzB41s+jVBXPN7B0ze9PMJjTAtYlIjJ7J38Zb6/dy+2cGa/01AZJscoG7ryUyHPc68CqwHKgJdj8E9ANGAjuBnwXbdwI57n428B3gSTP7yBxOM5tuZvlmlr9nz574XohIM7Gz9DD3vrSWsX07c9W5vcMuRxqJeAbPdo7tjfQKttXZxsxSgQ5A8YmOdfdZ7j7K3S8A9gHvBdt3uXuNu9cCjxAZmiMYrisOXi8DNgADjy/W3R929zx3z8vKyqrXhYtIZIjtjudWUV3rzLxiBC1aaIhNIuIZPEuBAWaWa2ZpRCYLzD+uzXzg6uD1VOANd/dg+7Rg1lsuMABYAmBmXYI/c4jc33ky+L571HkvIzIsh5llBRMdMLO+wbk2NvC1ishxnnt7O39Zt4fvXzyI3hltwy5HGpG4zWpz92ozuwl4DUgBHnX31WZ2D5Dv7vOBWcATZlYIlBAJJ4J2zwBrgGrgRnc/MqQ2z8wygKpg+/5g+wNmNhJwYDNwfbD9AuAeM6sCaoEb3L0kXtctIrD7QDk/enE1o/t04qvj+oRdjjQyFulgSLS8vDzPz88PuwyRpOTuXPf4Mt5av4dXb7mA3Ez1dpoLM1vm7nkna5dUkwtEpPGbv2IHC9bu4nufHqTQkTopeESkwez5oIK75q/m7JyOXHO+nq8jdVPwiEiDufMPBZRV1vDg1BGkaBabfAwFj4g0iJdX7uSPBe9zy6cG0L9Lu7DLkUZMwSMi9VZ8sII7/1DAiF4dmD6hb9jlSCOnJ5CKSL3dNX81B8qreHLqWFJT9PusnJj+hohIvbxa8D4vrdzJt/5lAIO6aYhNTk7BIyKnbd+hSv79hQKG9WjPDZ/sF3Y5kiQ01CYip+2el9awv6ySx68ZQ0sNsUmM9DdFRE7LgjW7eP6d7Xzjwv4M7fGRBd9FPpaCR0ROWWlZFXc8v4rB3dpx04X9wy5HkoyG2kTklP345TUUH6rk0a+OJi1Vv7/KqdHfGBE5JX9Zt5u5y4q44RN9Gd6zQ9jlSBJS8IhIzA6UV3HHc6sY0OUMvnXRgLDLkSSl4BGRmN3/ylp2HSjnwc+fRavUlLDLkSSl4BGRmPx9/V6eWrKN6yb0ZWR2x7DLkSSm4BGRkzpYUc1t81bSN6st3544MOxyJMlpVpuInNRP/7iWHaWHmXvDebRuqSE2qR/1eETkhBZt2Mv//XMr14zPZVTvzmGXI02AgkdEPlZZZTUz5q2iT0Y63/v0oLDLkSZCQ20i8rEeeHUdW0vKeHr6WNqkaYhNGoZ6PCJSpyWbSnhs0WauPq835/bNCLscaUIUPCLyEYcra7h17gqyO7fh1kmDwy5HmhgNtYnIR/zs9XVsLi7jyevOpW0r/TMhDUs9HhE5xrIt+5i1cBNXnZvDuH6ZYZcjTZCCR0SOKq+q4ftzV9CjQxtunzwk7HKkiVIfWkSO+vmC99i45xCPXzOGMzTEJnGiHo+IALB8234e+dtGrszL5oKBWWGXI02YgkdEqKiu4fvPrqBr+9b84BINsUl8qS8tIvziz4Ws332Q331tNO1btwy7HGni1OMRaeYKtpfy0JsbuOKcXlw4qEvY5UgzENfgMbNJZrbOzArNbEYd+1uZ2dPB/sVm1idq3+3B9nVmdnHU9pvNrMDMVpvZLVHb7zaz7Wa2PPiafLJziTR3ldW1fO/ZFWS0TePOS4aGXY40E3EbajOzFOCXwESgCFhqZvPdfU1Us2uBfe7e38ymATOBK81sKDANGAb0ABaY2UBgCHAdMAaoBF41s5fcvTA438/d/T+Oq6POc7l7TXyuXCR5/PIvhbz7/gc88pU8OqRriE0SI549njFAobtvdPdKYA4w5bg2U4DZweu5wEVmZsH2Oe5e4e6bgMLgfEOAxe5e5u7VwJvA5Sep4+POJdKsrdlxgF/+pZApI3swcWjXsMuRZiSewdMT2Bb1fVGwrc42QZCUAhknOLYAmGBmGWaWDkwGsqPa3WRmK83sUTPrdAp1YGbTzSzfzPL37NlzalcqkmSqamr5/twVdExvyd2fGxZ2OdLMJNXkAndfS2Q47nXgVWA5cGTI7CGgHzAS2An87BTP/bC757l7XlaWPsMgTdtv3tzA6h0HuPfS4XRqmxZ2OdLMxDN4tnNsb6RXsK3ONmaWCnQAik90rLvPcvdR7n4BsA94L9i+y91r3L0WeIQPh9NiqUOk2Vj3/gf895/X89kR3Zk0vHvY5UgzFM/gWQoMMLNcM0sjcoN//nFt5gNXB6+nAm+4uwfbpwWz3nKBAcASADPrEvyZQ+T+zpPB99H/B11GZFiOE51LpLmpDobY2rVuyT3/qiE2CUfcZrW5e7WZ3QS8BqQAj7r7ajO7B8h39/nALOAJMysESoiEE0G7Z4A1QDVwY9QstHlmlgFUBdv3B9sfMLORgAObgetjOJdIs/LIW5tYWVTKL754NhlntAq7HGmmLNLBkGh5eXmen58fdhkiDapw90Em/89bXDgoi19/aRSRCaQiDcfMlrl73snaJdXkAhE5PTW1zvfnriA9LYUfXzpcoSOh0lptIs3A7xZu4p2t+/mvK0fSpV3rsMuRZk49HpEmbtPeQzz42jo+NaQLU0b2CLscEQWPSFNWW+vcOncFrVJb8JPLztQQmzQKCh6RJmz2PzazdPM+fnjJULq21xCbNA4KHpEmakvxIR54dR2fHJTF1FG9wi5H5CgFj0gTVFvr3DZvJaktjPs0xCaNjIJHpAn6/ZKt/HNjCT/47BB6dGwTdjkix1DwiDQx20rKuP+VtUwYkMmVo7NPfoBIgulzPNJkuTvFhyrZWlLGGa1SGdDljCY/5OTu3P7cKgy4/3INsUnjpOCRpFZZXcv2/YfZUnyIbSVlbC0pY0tx5M9tJWUcqvxwWb7MM1oxvn8G4/tlMq5/Br06pYdYeXzMWbqNvxfu5ceXDm+S1ydNg4JHGjV3p/Rw1dEw2VpSxtao1ztLD1Mbtdxgq9QWZHdOp3fndMb2zaB3RjrZndIpOVTJwg17WVhYzB+W7wCgd0Y64/plcn7/TM7rl0HnJH8uzY79h/nJy2sZ27czV43JCbsckY+l4JHQVdfUsmN/+dEw2VIS6b0cCZsPyquPaZ95RityOrdhdJ9O5GT0IqdzOjmd0+mdkU7WGa1o0aLu4aUvjM7G3Xlv10EWFu5l0Ya9vLhiB08t2QrA0O7tGd8/g3H9MxnTpzNtWyXP/x5Hhthqap0HrjjrY38GIo1B8vyfJUntQHnVMT2V6J7L9v2HqYnqtrRMMbI7pZPdOZ1RvTsdDZacoPdSn0AwMwZ1a8egbu245vxcqmtqWbm9lIXr97Jww15mL9rCI29tomWKcXZ2J8b1z2B8/0xGZndZNRJaAAALMklEQVSkZUrjnYvz7LIi3nxvD3d/big5GRpik8ZNj0Wogx6LcOpqap33D5R/5F7LtpIytpSUsb+s6pj2ndJbkpPRNgiVNvTu3JbsIFy6tW9NSki/sR+urCF/SwkLC4tZWLiXgh2luEPbtBTG5HZmfP9MxvXLZHC3do2mV/F+aTkTf/4mQ7q1Z870sY2mLml+Yn0sgno8ErNDFdV13mfZWlJG0b4yqmo+/CUmtYXRs1MbcjqnM/nM7vQOei1HwqV965YhXsnHa5OWwoQBWUwYkAXA/rJK/rmxOBJEG/byl5fXApDRNo2x/TI4v38m4/tlhtbLcHd+8PwqKqtrmTl1hEJHkoKCR46qqXV2HSg/OiPsaM8leL33YOUx7du1TqV3RjpDu7fn4mHdjt5nyemcTvcOrUltxENTseqYnsak4d2ZNDzyZPWdpYdZWFjMosLI0NzLK3cC0KtTm6Oz5cb1yySrXWKe7vnC8u38+d3d/Ptnh5Cb2TYh7ylSXxpqq0NTHWo7MkNsW8nhSLjs+3DacdG+wx/ptbQw6N6hzdEwyY4KlpzO6XRMT+5ZYPXl7mzYc4hFG/by9/V7+efGYg4EEyEGd2vHuH6ZjO+fwbl9MzgjDhMVdn9QzsT//Bv9stry7A3jQhueFDki1qE2BU8dkjl4yqtq2L4/EixFJUeC5cOgOX6GWMf0lpFQCW7mZ3duc/T7Hh3bkJaa/L2WRKmpdQq2l7Jww14WFRazdHMJFdW1pLQwRmZ3ZHy/yIy5s3M60io1pV7v5e5c/8Qy/vreHl751gT6dzmjga5C5PQpeOqhMQdPba2z+4OKoz2VI4GyLQiY9w+UH9M+LbUF2cG9luygp9KrUyRgsjs33nstTUF5VQ1vb9l39PNDK4v2U+vQumULxuRmML5fZMbc0O7tT/nezPwVO/jWU+8w4zODueET/eJ0BSKnRsFTD2EHT2Q4LAiTfR/2WrbtiwyJVVbXHm1rBt3at470VjodGRL7MGhO9LkWSazSw1Us3ljMog2RGXPrdx8EIr3O8/pGekPn98+kT0b6CZe62Xuwgon/+SY5ndOZ9/VxTeJemjQNmtXWiB1Z5iX6Jn50wJQePnbqcfvWqeRkpDOoazsmDulKryMzxDq1oWenNvUetpHE6NCmJZ8e1o1PD+sGwO4D5UdDaGHhXv5Y8D4APTq0Zlz/zKPL+3Q57gFud/1hNYcqanjw82cpdCQpKXjiwN3Zc2Q4bF8ZW4sPHw2WopIydh4oJ7qjmZbSgl6dIkNfI7M7HnfPJZ0ObTQc1hR1ad+aS8/uyaVn98Td2VxcdnRFhQVrdzF3WREAA7qcEXx+KIMD5dW8vGon3/v0QAZ2bRfyFYicHg211eF0h9oKtpdyy9PL2VZSRkXUcBhA1/atPhIoR4bFurZrreEwOUZtrbNm54FIb2hDMUs3lXC4KrLg6bAe7XnhxvGNeiUFaZ401BaCTm3T6JfVlgsHZR0Nl+xO6fTq1IbWLTUcJrFr0cIY3rMDw3t24PpP9KOiuoblW/eTv2Ufl4zortCRpKYeTx3CnlwgIpKMYu3x6NcmERFJKAWPiIgklIJHREQSSsEjIiIJFdfgMbNJZrbOzArNbEYd+1uZ2dPB/sVm1idq3+3B9nVmdnHU9pvNrMDMVpvZLXWc87tm5maWGXz/STMrNbPlwded8blaERGJRdymU5tZCvBLYCJQBCw1s/nuviaq2bXAPnfvb2bTgJnAlWY2FJgGDAN6AAvMbCAwBLgOGANUAq+a2UvuXhi8ZzbwaWDrceW85e6XxOtaRUQkdvHs8YwBCt19o7tXAnOAKce1mQLMDl7PBS6yyCJVU4A57l7h7puAwuB8Q4DF7l7m7tXAm8DlUef7OXAroDniIiKNVDyDpyewLer7omBbnW2CICkFMk5wbAEwwcwyzCwdmAxkA5jZFGC7u6+oo5bzzGyFmf3RzIbV+8pEROS0JdXKBe6+1sxmAq8Dh4DlQE0QQncQGWY73ttAb3c/aGaTgReAAcc3MrPpwPTg24Nmtq4epWYCe+txfGPRVK4DdC2NUVO5DtC1HNE7lkbxDJ7tBL2RQK9gW11tiswsFegAFJ/oWHefBcwCMLP7iPSG+gG5wIpgOflewNtmNsbd3z9yEnd/xcx+ZWaZ7n7MD9bdHwYertcVB8wsP5ZP7zZ2TeU6QNfSGDWV6wBdy6mK51DbUmCAmeWaWRqRyQLzj2szH7g6eD0VeMMja/jMB6YFs95yifRQlgCYWZfgzxwi93eedPdV7t7F3fu4ex8iYXSOu79vZt2C+0aY2Zjgmovjd9kiInIicevxuHu1md0EvAakAI+6+2ozuwfId/f5RHouT5hZIVBCJJwI2j0DrAGqgRvdvSY49TwzywCqgu37T1LKVODrZlYNHAamuRaoExEJjRYJjQMzmx4M3SW1pnIdoGtpjJrKdYCu5ZTfQ8EjIiKJpCVzREQkoRQ8DehkSwQlCzN71Mx2m1lB2LXUl5llm9lfzGxNsMzSzWHXdDrMrLWZLQk+j7bazH4Udk31ZWYpZvaOmb0Udi31YWabzWxVsCRX0j7Iy8w6mtlcM3vXzNaa2Xlxey8NtTWMYImg94haIgj44nFLBCUFM7sAOAg87u7Dw66nPsysO9Dd3d82s3bAMuDSZPvvEszMbBt8Hq0l8HfgZnf/Z8ilnTYz+w6QB7RP5iWtzGwzkHf8RzSSjZnNJrK82G+DmcjpMUzeOi3q8TScWJYISgru/jciswyTnrvvdPe3g9cfAGv56AoajZ5HHAy+bRl8Je1vjWbWC/gs8NuwaxEwsw7ABQSfkXT3yniFDih4GlIsSwRJiILVz88GFodbyekJhqaWA7uBP7l7Ul5H4L+IrKtYG3YhDcCB181sWbACSjLKBfYAvwuGP39rZm3j9WYKHmkWzOwMYB5wi7sfCLue0+HuNe4+ksjKHGPMLCmHQc3sEmC3uy8Lu5YGcr67nwN8BrgxGKpONqnAOcBD7n42kSXJ4nafWsHTcGJZIkhCENwTmQf83t2fC7ue+gqGQP4CTAq7ltM0HvjX4N7IHOBfzOz/wi3p9Ln7keW8dgPPExl2TzZFQFFUL3oukSCKCwVPw4lliSBJsOCm/Cxgrbv/Z9j1nC4zyzKzjsHrNkQmsbwbblWnx91vd/dewfJW04gslfWlkMs6LWbWNpi0QjA09Wkiq+gnlWBNy21mNijYdBGRlWPiIqlWp27MPm6JoJDLOi1m9hTwSSDTzIqAu4LFWZPReODLwKrg/gjAHe7+Sog1nY7uwOxg9mQL4Bl3T+ppyE1EV+D5YDnIVCJrR74abkmn7ZvA74NfnDcCX4vXG2k6tYiIJJSG2kREJKEUPCIiklAKHhERSSgFj4iIJJSCR0REEkrBI9IEmNndZva9sOsQiYWCR6SRsQj9vylNlv5yizQCZtYneJbT40Q++T7LzPKPf/ZO8OyXH5nZ28EzYAbXca7rzOyPwQoHIo2OVi4QaTwGAFe7+z/NrLO7lwQrFfzZzEa4+8qg3V53P8fMvgF8D/i3IycIVs+YSOSZQxUJvwKRGKjHI9J4bIl6sNsXzOxt4B1gGDA0qt2RhU6XAX2itn+FyArJUxU60pgpeEQaj0MAZpZLpCdzkbuPAF4GWke1OxIqNRw7arGKSBD1inulIvWg4BFpfNoTCaFSM+tKpBcTi3eA64H5ZtYjXsWJ1JeCR6SRcfcVRELkXeBJYOEpHPt3Ir2ll80sMz4VitSPVqcWEZGEUo9HREQSSsEjIiIJpeAREZGEUvCIiEhCKXhERCShFDwiIpJQCh4REUkoBY+IiCTU/wdSJXS78TNzsAAAAABJRU5ErkJggg==\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": 25,
   "metadata": {
    "scrolled": false
   },
   "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"
    },
    {
     "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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\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"
    }
   ],
   "source": [
    "for p in params_gsearch.keys():\n",
    "    plt.scatter(df_gsearch_results[p], df_gsearch_results['score'], s=3)\n",
    "    plt.scatter(df_gsearch_results[p][:10], df_gsearch_results['score'][:10], s=3)\n",
    "    plt.xlabel(p); plt.ylabel('score'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "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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\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"
    }
   ],
   "source": [
    "for p in params_gsearch.keys():\n",
    "    plt.scatter(df_gsearch_results[p], df_gsearch_results['mae'], s=3)\n",
    "    plt.scatter(df_gsearch_results[p][:10], df_gsearch_results['mae'][:10], s=3)\n",
    "    plt.xlabel(p); plt.ylabel('mae'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['learning_rate', 'max_depth', 'num_leaves', 'feature_fraction',\n",
       "       'min_data_in_leaf', 'lambda_l1', 'lambda_l2', 'score', 'mse', 'mae',\n",
       "       'mape', 'best_iteration'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gsearch_results.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "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"
    },
    {
     "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zSR6T5HEASR4DPAe4cQnORZI0haHeRquqHUnOBDYBq4CLquqmJGcDo1W1EbgQeH+SMeAeuoREq3cZcDOwA3h1VT2Y5IeBj3R9CFgNfLCq/m7JT06S9JB0FwkaGRmp0VG/kiNJc5Fkc1XN+NbmYd9GkyTtAkw2kqTemWwkSb0z2UiSemeykST1zmQjSeqdyUaS1DuTjSSpdyYbSVLvTDaSpN6ZbCRJvTPZSJJ6Z7KRJPXOZCNJ6p3JRpLUO5ONJKl3JhtJUu9MNpKk3plsJEm9M9lIknpnspEk9c5kI0nqnclGktQ7k40kqXcmG0lS70w2kqTemWwkSb0berJJckKSLUnGkmyYZP0eSS5t669Osm5g3VmtfEuS42e7T0nS0hpqskmyCjgP+CXgcOAlSQ6fUO104N6qOhR4O3BO2/Zw4CTgCOAE4E+SrJrlPiVJS2jYVzZHAWNVdWtVfQ+4BFg/oc564OI2fzlwbJK08kuq6rtVdRsw1vY3m31KkpbQsJPNgcAdA8tbW9mkdapqB3A/sO80285mn5KkJTTsZDNUSc5IMppkdPv27cMOR5JWrGEnm23AQQPLa1vZpHWSrAb2Au6eZtvZ7BOAqrqgqkaqamTNmjULOA1J0nSGnWyuAQ5LckiS3eke+G+cUGcjcGqbPxG4sqqqlZ/UeqsdAhwGfG6W+5QkLaHVwzx4Ve1IciawCVgFXFRVNyU5Gxitqo3AhcD7k4wB99AlD1q9y4CbgR3Aq6vqQYDJ9rnU5yZJeli6iwSNjIzU6OjosMOQpB8oSTZX1chM9YZ9G02StAsw2UiSemeykST1zmQjSeqdyUaS1DuTjSSpdyYbSVLvTDaSpN6ZbCRJvTPZSJJ6Z7KRJPXOZCNJ6p3JRpLUO5ONJKl3JhtJUu9MNpKk3plsJEm9M9lIknpnspEk9c5kI0nqnclGktQ7k40kqXcmG0lS70w2kqTemWwkSb0z2UiSemeykST1bmjJJsk+Sa5I8pX2+YQp6p3a6nwlyakD5U9PckOSsSTvSJJW/pYk25Jc16bnLtU5SZImN8wrmw3AJ6vqMOCTbXknSfYB3gz8LHAU8OaBpPRu4NeAw9p0wsCmb6+qI9v0Nz2egyRpFoaZbNYDF7f5i4EXTlLneOCKqrqnqu4FrgBOSHIA8Piq+mxVFfC+KbaXJC0Dw0w2P1xVd7b5/wv88CR1DgTuGFje2soObPMTy8edmeT6JBdNdXtOkrR0ek02ST6R5MZJpvWD9drVSS3SYd8N/BhwJHAn8EfTxHdGktEko9u3b1+kw0uSJlrd586r6henWpfk60kOqKo7222xuyaptg04ZmB5LXBVK187oXxbO+bXB47xZ8DHp4nvAuCCVnd7kq/OcEqD9gO+MYf6S2W5xgXLN7blGhcs39iWa1ywfGNbqXH9yGwq9ZpsZrAROBX4g/b5sUnqbAL+58CtsOcAZ1XVPUm+meRo4GrgFOCdAOMJrNX/FeDG2QRTVWvmEnyS0aoamcs2S2G5xgXLN7blGhcs39iWa1ywfGPb1eMaZrL5A+CyJKcDXwX+E0CSEeCVVfWKllR+D7imbXN2Vd3T5l8FvBfYE/jbNgG8NcmRdLflbgd+fQnORZI0jaElm6q6Gzh2kvJR4BUDyxcBF01R78mTlL90cSOVJC2UIwjM3wXDDmAKyzUuWL6xLde4YPnGtlzjguUb2y4dV7qOYJIk9ccrG0lS70w2EyRZleTaJB9vy4ckubqNwXZpkt1b+R5teaytXzewj7Na+ZYkxy9CTHsnuTzJl5J8MckzphpbLp13tONfn+RpA/uZdJy5Bcb22iQ3te9P/WWSRw+rzdqXeO9KcuNA2aK1U6YYj2+ecZ3b/j2vT/KRJHvP1BZJTmhlY0k2DJRP2t7zjW1g3X9NUkn2Ww5t1spf09rtpiRvXeo2m+Lf8sgkn003FuNokqOWur3atgcl+VSSm1v7/GYrH/rvAABV5TQwAa8DPgh8vC1fBpzU5s8HfqPNvwo4v82fBFza5g8HvgDsARwC3AKsWmBMFwOvaPO7A3sDbwU2tLINwDlt/rl0PfMCHA1c3cr3AW5tn09o809YYFwHArcBew601WnDajPgPwBPA24cKFu0dgI+1+qmbftLC4jrOcDqNn/OQFyTtkWbbgF+tP0MfAE4fLqf0fnG1soPovvqwVeB/ZZJm/0C8Algj7a8/1K32RRx/f34ebU2umqp26ttewDwtDb/OODLrW2G/jtQVSabCf9Ya+kGBX023ZdBQ/dlp/E/Cs8ANrX5TcAz2vzqVi/AWXTfBWJivXnGtBfdH/RMKN8CHDDwQ7alzf8p8JKJ9YCXAH86UL5TvXnGNj6c0D6tDT5ON57d0NoMWDfhD8GitFNb96WB8p3qzTWuCet+BfhAm5+0LQbbcbDedD+jC4kNuBz4abqvD4wnm6G2GV2C+MVJ6i1pm00S1ybgxQPn+MFhtNckcX4MOG65/A54G21nfwz8NvBvbXlf4L6q2tGWB8dge2jctrb+/lZ/qvHc5usQYDvw5+lu770nyWOYemy56caTW8y4qKptwB8C/0I3NND9wGaG32aDFqudZhqPbyFezsPfE5trXNP9jM5LuuGktlXVFyasGnab/TjwrHb769NJfmaecS12m/0WcG6SO+h+H86aZ1yL1l7pblE/le5L78vid8Bk0yR5HnBXVW0ediwTrKa7bH93VT0V+A4TXsdQ3X8zlrxbYbv3u54uIT4ReAw7v+phWRlWO00nyRuAHcAHhh0LQJIfAv4b8KZhxzKJ1XRX0UcDr6f7Uvjsnxn05zeA11bVQcBrgQuHGUySxwJ/BfxWVX1zcN0wfwdMNg/798ALktwOXEJ3K+1/AXsnGf/y60NjsLXPgwDa+r2AuwfLJ9lmPrYCW6vq6rZ8OV3y+Xq6MeXIzmPLTXX8xY4L4BeB26pqe1V9H/gwXTsOu80GLVY7TTke33wlOQ14HnBy+yMwn7juZur2no8fo/vPwxfa78Ja4PNJ/t08YlvsNtsKfLg6n6O7A7HfPOJa7DY7le5nH+BDdO/eYh5xLbi9kuxGl2g+UFXjMS2P34H53g9cyRPd4J/jHQQ+xM4PEl/V5l/Nzg+7L2vzR7Dzw8pbWXgHgX8EntTm3wKc26bBh35vbfO/zM4P/T7Xyvehe/bzhDbdBuyzwLh+FrgJ+KF2vIuB1wyzzXjk/fRFayce+XD0uQuI6wTgZmDNhHqTtgXd/+pvbWXjD7uPmO5ndL6xTVh3Ow8/sxl2m72Sbsgq6G6p3dH2u6RtNklcXwSOafPHApuH1F6he7fXH08oXx6/A/P5hV7pEzsnmx9tDTzWfkDHe8I8ui2PtfU/OrD9G+h6wWxhDr01ponnSGAUuB74aPsB2JeuM8NX6HrojP8wBDivHf8GYGRgPy9v8Y4BL1uktvpd4Et0A56+v/3CD6XNgL+ke3b0fbr/BZ++mO0EjLTzvAV4FxM6bcwxrjG6P5bXten8mdqCrvfQl9u6NwyUT9re841twvrbeTjZDLvNdgf+ou3v88Czl7rNpojrmXTPKr9A94zk6UvdXm3bZ9LdIrt+4OfquSyD34GqcgQBSVL/fGYjSeqdyUaS1DuTjSSpdyYbSVLvTDaSpN6ZbCRJvTPZSNNIsm7iMPfz2McxSX5uhjqvTHJKmz8tyRMXcszpjj94LGmprJ65iqQFOgb4NvDPU1WoqvMHFk+j++Lc12Z7gCSr6+GBJac9/oRjSUvCL3VK02ij5/4d3TfEn0Y3PM8pwE8CbwMeSzdc/WlVdWeS/0I3rMoOuuFoNgCfBR6kG737NVX1j5Mc5y10CeF24L10Y049QDf8/eFTHOsqum+JP5Pum+1fBv473Tft7wZOBvaceHy6IVW+XVV/mORIuuFafojuW+Evr6p7276vpnuHzN50Iws8Im5ptryNJs3sScCfVNVPAt+kG+PtncCJVfV04CLg91vdDcBTq+qngFdW1e10f8zfXlVHzvQHu6oupxua6OSqOpIuaU11LIDdq2qkqv4I+Cfg6OpGB78E+O1ZHP99wO+0eG8A3jywbnVVHUU3hP6bkRbA22jSzO6oqs+0+b+gG4L/ycAVbYT7VXTjZUE3LtUHknyUbhy7hXrSNMcCuHRgfi1waRvZd3e6ARSnlGQvYO+q+nQruphunLBx46MGb6YbfFKaN5ONNLOJ95q/BdxUVc+YpO4v0706+PnAG5I8ZYHHzjTHgu79RuPeCbytqjYmOYZuhPCF+G77fBD/VmiBvI0mzezgJON/7P8z3TOQNeNlSXZLckSSRwEHVdWngN+he1/PY+mS0+PmcLzB+lsmO9YU2+3Fw+8XOXWK/T2kqu4H7k3yrFb0UuDTE+tJi8FkI81sC/DqJF+ke73DO4ETgXOSfIHuIf3P0d3i+oskNwDXAu+oqvuA/w38SpLrBv6wT+e9wPlJrmv7nOxYk3kL8KEkm+k6Eoyb7vin0r3S+Hq6V1mcPYv4pDmzN5okqXde2UiSeudDP2kJJXkD8KsTij9UVb8/WX1ppfA2miSpd95GkyT1zmQjSeqdyUaS1DuTjSSpdyYbSVLv/j8Qh0LKGDg16QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IQa+kHc0n4M3MrEU4TMzMLJvDxMzMsjlMzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLJvDxMzMsjlMzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLJvDxMzMsjlMzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLJvDxMzMsjlMzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLJvDxMzMspUeJpLmS9okqVPS0j7m7ynpxjR/laQZqX2CpGskbZC0TtK8qmUOT+2dki6XpLJfh5mZ9a/UMJE0DrgCOAGYDZwqaXZNtzOB5yPiTcCXgItT+ycAIuJQ4Fjg7yX11vuVNH9Weswv83WYmdnAyv5kMhfojIjNEfEScAOwoKbPAuDaNH0LcEz6pDEbuBsgIrYB24GKpAOBN0TE/RERwDeAk0p+HWZmNoCyw2Qq8HTV867U1mefiOgBfgEcAKwDTpTUJmkmcDgwPfXvGmSdZmY2jNpGuoABXA28FegAngTuA15pZAWSlgBLAA4++OBm12dmZknZn0y2Unya6DUttfXZR1IbsC/wbET0RMQ5ETEnIhYAk4BHU/9pg6wTgIhYHhGViKi0t7c35QWZmdmuyg6T1cAsSTMlTQAWAitq+qwAFqXpk4G7IyIk7SVpbwBJxwI9EfFwRDwD/FLSkencyhnAbSW/DjMzG0Cph7kiokfSWcBKYBxwdURslLQM6IiIFcDXgG9K6gSeowgcgCnASkmvUnzyOL1q1Z8Cvg5MBL6XHmZmNkJUXBC1+6tUKtHR0THSZZiZtRRJayKiMlg/3wFvZmbZHCZmZpbNYWJmZtkcJmZmls1hYmZm2RwmZmaWzWFiZmbZHCZmZpbNYWJmZtkcJmZmls1hYmZm2RwmZmaWzWFiZmbZHCZmZpbNYWJmZtkcJmZmls1hYmZm2RwmZmaWzWFiZmbZHCZmZpbNYWJmZtkcJmZmls1hYmZm2RwmZmaWzWFiZmbZHCZmZpbNYWJmZtlKDxNJ8yVtktQpaWkf8/eUdGOav0rSjNQ+XtK1kjZIekTS+VXLbEntayV1lP0azMxsYKWGiaRxwBXACcBs4FRJs2u6nQk8HxFvAr4EXJzaTwH2jIhDgcOBP+kNmuS9ETEnIiolvgQzM6tD2Z9M5gKdEbE5Il4CbgAW1PRZAFybpm8BjpEkIIC9JbUBE4GXgF+WXK+ZmQ1B2WEyFXi66nlXauuzT0T0AL8ADqAIll8DzwBPAV+MiOfSMgH8q6Q1kpaUV76ZmdWjbaQLGMBc4BXgIGA/4F5J34+IzcC7I2KrpCnAnZJ+GhH/VruCFDRLAA4++OBhLN3MbGwpO0y2AtOrnk9LbX316UqHtPYFngU+CtwRES8D2yT9CKgAmyNiK0BEbJP0bYrg2SVMImI5sBxAUrekJxuofTLw8wb6D5fRWheM3tpGa10wemtzXY0brbXl1vXb9XQqO0xWA7MkzaQIjYUUIVFtBbAI+DFwMnB3RISkp4D3Ad+UtDdwJHBZmt4jIl5I08cBywYrJCLaGylcUsdoPLk/WuuC0VvbaK0LRm9trqtxo7W24aqr1DCJiB5JZwErgXHA1RGxUdIyoCMiVgBfowiMTuA5isCB4iqwayRtBARcExHrJb0R+HZxjp424PqIuKPM12FmZgMr/ZxJRHwX+G5N22erpv8fxWXAtcv9qp/2zcDbm1+pmZkNle+A79/ykS6gH6O1Lhi9tY3WumD01ua6GjdaaxuWuhQRw7EdMzPbjfmTiZmZZRtzYSJpnKQHJd2ens9MY4J1pjHCJqT2PscMS/POT+2bJB3fhJomSbpF0k/TOGRHSdpf0p2SHks/90t9JenytP31kt5RtZ5Fqf9jkhbl1pXWeY6kjZIekvRPkl43UvtM0tWStkl6qKqtaftJ0uFpzLfOtKwy6rok/Xuul/RtSZMG2xfqZxy7/vb3UOqqmveXkkLS5OHeXwPVJunstN82SvrCaNhnkuZIul9pLEBJc4d7n0maLukHkh5O++bPU/uIv/93iIgx9QDOBa4Hbk/PbwIWpukrgT9N058CrkzTC4Eb0/RsYB2wJzATeBwYl1nTtcAfp+kJwCTgC8DS1LYUuDhNvx/4HsUVbkcCq1L7/sDm9HO/NL1fZl1TgSeAiVX7avFI7TPgaOAdwENVbU3bT8BPUl+lZU/IqOs4oC1NX1xVV5/7Ij0eB96Y3gPrgNkDvUeHUldqn05xheWTwOTh3l8D7LP3At+nGJMPYMpo2GfAv/a+trSf7hmB99iBwDvS9OuBR9N+GfH3/44ac/7YtNqD4qbJuyjuX7k97bSf89ov/VHAyjS9EjgqTbelfgLOB86vWueOfkOsaV+KP9iqad8EHFj1RtqUpq8CTq3tB5wKXFXVvlO/IdbWO9TN/mkf3A4cP5L7DJhR84velP2U5v20qn2nfo3WVTPvD4Dr0nSf+6J6P1b3G+g9OtS6KIYqejuwhdfCZFj3Vz//ljcBv99HvxHdZ2l7H6l6ndeP1D6rWvY24NjR8v6PiDF3mOsy4K+BV9PzA4DtUYwJBjuPHdbfmGH1jDfWiJlAN8U9NQ9K+qqKmzF/KyKeSX3+A/it2rpqtt/suohipIEvUoyN9gzFPljDyO+zas3aT1PTdBk1fpzif3pDqWug92jDJC0AtkbEuppZo2F/vRl4Tzo89UNJRwyxtqbuM+AvgEskPU3x+9D7dRgjss9UHD4+DFjFKHr/j5kwkfRBYFtErBnpWmq0UXys/kpEHEYxuOVO3/sSxX8Vhv2yu3T8dQFF4B0E7A3MH+466jVS+2kgki4AeoDrRkEtewH/A/jsYH1HSBvFp+AjgfOAmxo+bl+OPwXOiYjpwDkUN1qPCEn7AP8M/EVE7DSK+ki//8dMmAC/C5woaQvFUPjvA/4XMEnFmGCw89hhO8YV085jhtUz3lgjuoCuiFiVnt9CES7/KenAtP0DgW21ddVsv9l1Afw+8EREdEcxRtqtFPtxpPdZtWbtp61pumk1SloMfBA4Lf2iD6WuZ+l/fzfqv1L8x2Bd+j2YBjwg6b8Moa6m7y+K34Vbo/ATiiMIk4dQWzP3GRTDPd2apm+mGAuQIdSVtc8kjacIkusioree0fP+H8rxulZ/APN47QT8zex8ou5TafrP2Plk8k1p+m3sfDJwM/kn4O8FDknTnwMuSY/qE2tfSNMfYOcTaz9J7ftTnHvZLz2eAPbPrOudwEZgr7S9a4GzR3Kfsevx7KbtJ3Y9Afn+jLrmAw8D7TX9+twXFP8r35zaek8mv22g9+hQ6qqZt4XXzpkM6/7qZ599EliWpt9McThGI73PgEeAeWn6GGDNcO+z1P8bwGU17aPi/R8xxk7AV/0DzOO1MHlj2omd6Q3YeyXJ69LzzjT/jVXLX0BxFckmGrzioZ965gAdwHrgO+kf+QCKiwUeo7jCpfcfXBTjlj0ObAAqVev5eKq3E/hYk/bV3wI/BR4Cvpl+oUdknwH/RHHu5mWK/8We2cz9RDEq9UNpmf9NzUURDdbVSfHHcG16XDnYvqC4AufRNO+CqvY+9/dQ6qqZv4XXwmTY9tcA+2wC8K20zgeA942GfQa8m+Jc4TqK8xSHj8B77N0Uh7DWV72n3s8oeP/3PnwHvJmZZRtL50zMzKwkDhMzM8vmMDEzs2wOEzMzy+YwMTOzbA4TMzPL5jCxMU3SjNph0IewjnmS3jVIn09KOiNNL5Z0UM42B9p+9bbMhkvp3wFvNgbMA34F3Ndfh4i4surpYoqbw35W7wYktcVrAxcOuP2abZkNC9+0aGNaGoH1Doo7nN9BMXzMGcBbgUuBfSiGM18cEc9I+u8Uw370UAyXshS4H3iFYvTnsyPi3j628zmKP/hbgK9TjHv0IsXw6LP72dY9FHc6v5vizuxHgb+huFP8WeA0YGLt9imG/PhVRHxR0hyK4UT2oriz+eMR8Xxa9yqK7xCZRHF3/C51m9XLh7nM4BDgHyLircAvKcYY+zJwckQcDlwNfD71XQocFhG/A3wyIrZQ/LH+UkTMGewPckTcQjF0zmkRMYcilPrbFsCEiKhExN8D/w4cGcXo0jcAf13H9r8BfDrVuwG4sGpeW0TMpRhi/ULMMvgwlxk8HRE/StPfohim/b8Bd6YR0MdRjNcExdhI10n6DsU4arkOGWBbADdWTU8Dbkyjw06gGKSvX5L2BSZFxA9T07UU41T16h15dg3F4IZmQ+YwMdv1OyBeADZGxFF99P0AxVe7fgi4QNKhmdvWANuC4vtten0ZuDQiVkiaRzHCdI7fpJ+v4L8FlsmHuczgYEm9f8w/SnEOor23TdJ4SW+TtAcwPSJ+AHya4vta9qEIn9c3sL3q/pv62lY/y+3La98xsaif9e0QEb8Anpf0ntR0OvDD2n5mzeAwMSv+oP+ZpEcohv//MnAycLGkdRQnwd9FcQjqW5I2AA8Cl0fEduBfgD+QtLbqD/dAvg5cKWltWmdf2+rL54CbJa2hOFHfa6DtL6L4ytn1FF91sKyO+swa5qu5zMwsmz+ZmJlZNp90M2siSRcAp9Q03xwRn++rv9nuwoe5zMwsmw9zmZlZNoeJmZllc5iYmVk2h4mZmWVzmJiZWbb/D2pQskMkmYwlAAAAAElFTkSuQmCC\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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iIDEzsywOEjMzy+IgMTOzLA4SMzPL4iAxM7MspQaJpIWStkrqk7S8zvzJklan+esldaX2BZI2pcfdks5pdJ1mZja2SgsSSROAa4G3AfOA8yTNq+l2IfBkRBwDXAOsTO33ApWImA8sBD4vqaPBdZqZ2Rgqc49kAdAXEdsi4nngFmBRTZ9FwKo0vQY4Q5Ii4tmI2JPaDwBiBOs0M7MxVGaQzAAernq+I7XV7ZOC42lgOoCkkyRtATYDF6X5jazTzMzGUNuebI+I9RFxHPAG4DJJB4xkeUnLJPVI6tm5c2c5RZqZWalB0g/Mqno+M7XV7SOpA5gK7KruEBH3A88Ar2twnQPLXR8RlYiodHZ2ZrwMMzMbSplBsgGYK2mOpEnAEqC7pk83sDRNLwbWRUSkZToAJL0KeA2wvcF1mpnZGOooa8URsUfSJcBaYAJwY0RskbQC6ImIbuAG4CZJfcATFMEAcCqwXNILwIvAxRHxOEC9dZb1GszMbHiKiOF77ecqlUr09PS0ugwzs/2KpN6IqAzXr21PtpuZ2f7BQWJmZlkcJGZmlsVBYmZmWRwkZmaWxUFiZmZZHCRmZpbFQWJmZlkcJGZmlsVBYmZmWRwkZmaWxUFiZmZZHCRmZpbFQWJmZlkcJGZmlsVBYmZmWRwkZmaWxUFiZmZZHCRmZpbFQWJmZlkcJGZmlsVBYmZmWRwkZmaWxUFiZmZZHCRmZpal1CCRtFDSVkl9kpbXmT9Z0uo0f72krtR+pqReSZvTz7dULfP9tM5N6XFEma/BzMyG1lHWiiVNAK4FzgR2ABskdUfEfVXdLgSejIhjJC0BVgLnAo8DvxsRj0h6HbAWmFG13PkR0VNW7WZm1rgy90gWAH0RsS0ingduARbV9FkErErTa4AzJCkiNkbEI6l9CzBF0uQSazUzs1EqM0hmAA9XPd/BvnsV+/SJiD3A08D0mj6/B/w4Ip6ravtiOqz155LU3LLNzGwk2vpku6TjKA53/deq5vMj4njgtPR4zyDLLpPUI6ln586d5RdrZjZOlRkk/cCsquczU1vdPpI6gKnArvR8JnA78N6IeHBggYjoTz9/AdxMcQjtJSLi+oioRESls7OzKS/IzMxeqswg2QDMlTRH0iRgCdBd06cbWJqmFwPrIiIkTQO+BSyPiH8d6CypQ9LhaXoi8A7g3hJfg5mZDaO0IEnnPC6h+MTV/cCtEbFF0gpJ70zdbgCmS+oDPgQMfET4EuAY4GM1H/OdDKyVdA+wiWKP5gtlvQYzMxueIqLVNZSuUqlET48/LWxmNhKSeiOiMly/tj7ZbmZm7W9c7JFI2gn8bASLHE7xpch20651QfvW5rpGrl1ra9e6oH1ry63rVREx7KeVxkWQjJSknkZ258Zau9YF7Vub6xq5dq2tXeuC9q1trOryoS0zM8viIDEzsywOkvqub3UBg2jXuqB9a3NdI9eutbVrXdC+tY1JXT5HYmZmWbxHYmZmWcZVkEiaIGmjpG+m53PSDbX60g22JqX2ujfcSvMuS+1bJZ3VhJqmSVoj6QFJ90s6RdJhkr4r6afp56GpryR9Om3/Hkmvr1rP0tT/p5KWDr7FEdX2QUlbJN0r6e8lHdCKMZN0o6THJN1b1da0MZJ0ooqbqPWlZRu+ovQgtV2V/j3vkXR7uuTPkGOhQW4CN9h4j6auqnn/XVJo7+WGWj5mqf39ady2SLqyHcZM0nxJP1RxdY0eSQvGeswkzZL0z5LuS2Pzp6m9Ld4DAETEuHlQXIblZuCb6fmtwJI0fR3wx2n6YuC6NL0EWJ2m5wF3U1yqZQ7wIDAhs6ZVwH9J05OAacCVFNcZg+KyMSvT9NuBOwEBJwPrU/thwLb089A0fWhmXTOAh4ApVWN1QSvGDHgT8Hrg3qq2po0R8KPUV2nZt2XW9lagI02vrKqt7likx4PA0el34G5g3lC/o6OpK7XPorhs0c+Aw9tozN4MfA+YnJ4f0Q5jBnxn4LWlcfr+WI8ZcCTw+jR9CPCTNC5t8R6IiPETJBRXH/4n4C3AN9OAPc7eN/wpwNo0vRY4JU13pH4CLgMuq1rnr/uNsqapFH+sVdO+FTiy6pdoa5r+PHBebT/gPODzVe379BtlbQP3ijksjcE3gbNaNWZAV80bvCljlOY9UNW+T7/R1FYz7xzgq2m67lhUj2N1v6F+R0dbF8UN5P4TsJ29QdLyMaP44//bdfq1dMzS9s6tep03t2rMqpb9B4o7z7bNe2A8Hdr6FPBnwIvp+XTgqSguLgn73nhrsBtuNXKzrpGYA+ykuFHXRkl/J+kg4JUR8Wjq83+BV9bWVbP9ZtdFFJfr/yTwf4BHKcagl9aP2YBmjdGMNN3s+ga8j+J/eKOpbajf0RGTtAjoj4i7a2a1w5i9GjgtHZK6S9IbRllbU8cM+ABwlaSHKd4Pl42yrqaMmYpDxicA62mj98C4CBJJ7wAei4jeVtdSo4NiV/pzEXEC8O/svQIyAFH8F2HMP1qXjrcuogi7o4CDgIVjXUcjWjVGw5F0ObAH+Gob1HIg8FHgY62uZRAdFHu/JwOXAreO+Dh9Of4Y+GBEzAI+SHHF8paQdDDwdeADEfHz6nmtfg+MiyAB3gi8U9J2invHvwX4n8A0FTfUgn1vvDXYDbcauVnXSOwAdkTE+vR8DUWw/D9JR6btHwk8VltXzfabXRfAbwMPRcTOiHgBuI1iHFs9ZgOaNUb9abqp9Um6gOJ+OeenN/loatvF4OM9Uv+R4j8Fd6f3wUzgx5L+wyjqKmPMdgC3ReFHFEcODh9Fbc0cMyjul3Rbmv4ae2+kN6ZjpuL+S1+nOEw6UE/7vAdGc4xuf34Ap7P3ZPvX2Pek3MVp+k/Y98TxrWn6OPY98beN/JPt/wIcm6avAK5Kj+qTaFem6d9h35NoP0rth1Gcazk0PR4CDsus6yRgC3Bg2t4q4P2tGjNeeuy6aWPES080vj2ztoXAfUBnTb+6Y0Hxv/FtqW3gxPFxQ/2Ojqaumnnb2XuOpB3G7CJgRZp+NcUhGLV6zCjupXR6mj4D6B3rMUv9vwx8qqa9fd4DI31D7+8P9g2So9MA9qVfvoFPjByQnvel+UdXLX85xadFtjLCTzYMUs98oAe4B7gj/QNPp/hgwE8pPsky8I8t4Nq0/c1ApWo970v19gF/2KSx+gTwAMVdKG9Kb+YxHzPg7ynO07xA8T/XC5s5RkAlvcYHgc9S8+GHUdTWR/GHcFN6XDfcWFB80uYnad7lVe11x3s0ddXM387eIGmHMZsEfCWt88fAW9phzIBTKc4N3k1xXuLEsR6zVENQ/I0Y+J16O23yHogIf7PdzMzyjJdzJGZmVhIHiZmZZXGQmJlZFgeJmZllcZCYmVkWB4mZmWVxkNi4Jamr9lLmo1jH6ZJ+a5g+F0l6b5q+QNJROdscavvV2zIbKx3DdzGzIZwOPAP822AdIuK6qqcXUHzx65FGNyCpI/ZehHDI7ddsy2xM+AuJNm6lK6l+m+Kby6+nuCTMe4HXAlcDB1NckvyCiHhU0n+juJTHHopLoCwHfgj8iuIqzu+PiH+ps50rKP7Ybwe+RHEdo90UlzifN8i2vk/xDeZTKb5x/RPgf1B8A3wXcD4wpXb7FJfxeCYiPilpPsUlQg6k+Mby+yLiybTu9RT3AJlG8a33l9Rt1igf2rLx7ljgbyPitcDPKa4Z9hlgcUScCNwI/EXquxw4ISJ+E7goIrZT/KG+JiLmD/fHOCLWUFwO5/yImE8RSINtC2BSRFQi4m+A/wWcHMVVom8B/qyB7X8Z+EiqdzPw8ap5HRGxgOIy6R/HLIMPbdl493BE/Gua/grFpdZfB3w3XcV8AsX1l6C41tFXJd1BcV20XMcOsS2A1VXTM4HV6SqvkyguuDcoSVOBaRFxV2paRXHdqQEDV5DtpbhQodmoOUhsvKs9tvsLYEtEnFKn7+9Q3I71d4HLJR2fuW0NsS0o7k8z4DPA1RHRLel0iitF53gu/fwV/jtgmXxoy8a72ZIG/pD/AcU5h86BNkkTJR0n6RXArIj4Z+AjFPdbOZgieA4Zwfaq+2+tt61BlpvK3ntELB1kfb8WEU8DT0o6LTW9B7irtp9ZMzhIbLzbCvyJpPspLuH/GWAxsFLS3RQnvH+L4rDTVyRtBjYCn46Ip4BvAOdI2lT1R3soXwKuk7QprbPetuq5AviapF6Kk/IDhtr+UorbxN5DcbuCFQ3UZzZi/tSWmZll8R6JmZll8Uk2syaRdDnw7prmr0XEX9Trb/Zy4UNbZmaWxYe2zMwsi4PEzMyyOEjMzCyLg8TMzLI4SMzMLMv/BzZJt2mSHd6TAAAAAElFTkSuQmCC\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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "booster must be Booster or LGBMModel.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-9312abb43a65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgsearch_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 050, 051, 052\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/lightgbm/plotting.py\u001b[0m in \u001b[0;36mplot_importance\u001b[0;34m(booster, ax, height, xlim, ylim, title, xlabel, ylabel, importance_type, max_num_features, ignore_zero, figsize, grid, **kwargs)\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mbooster\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbooster_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbooster\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBooster\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'booster must be Booster or LGBMModel.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0mimportance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportance_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimportance_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: booster must be Booster or LGBMModel."
     ]
    }
   ],
   "source": [
    "lgb.plot_importance(gsearch_results[0][5][0], figsize=(12, 30), height=0.5) # 050, 051, 052\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "booster must be Booster or LGBMModel.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-d98df6a9b2c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgsearch_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimportance_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gain'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 050, 051, 052\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/lightgbm/plotting.py\u001b[0m in \u001b[0;36mplot_importance\u001b[0;34m(booster, ax, height, xlim, ylim, title, xlabel, ylabel, importance_type, max_num_features, ignore_zero, figsize, grid, **kwargs)\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mbooster\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbooster_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbooster\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBooster\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'booster must be Booster or LGBMModel.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0mimportance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbooster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportance_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimportance_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: booster must be Booster or LGBMModel."
     ]
    }
   ],
   "source": [
    "lgb.plot_importance(gsearch_results[0][5][0], figsize=(12, 30), height=0.5, importance_type='gain') # 050, 051, 052\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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