model-03-build-lgb-corr-feats-selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/lightgbm/__init__.py:46: UserWarning: Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you 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``.\", UserWarning)\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_log_target = True\n",
    "is_per_area_target = True\n",
    "\n",
    "map_target = {\n",
    "    (True, True):   'log_per_area_total_price',\n",
    "    (True, False):  'log_total_price',\n",
    "    (False, True):  'per_area_total_price',\n",
    "    (False, False): 'total_price'\n",
    "}\n",
    "col_target = map_target[(is_log_target, is_per_area_target)]\n",
    "\n",
    "cols_num_add = ['building_per_land_area']\n",
    "cols_cat_add = ['encoded_latlon']\n",
    "cols_feats_rm = [] # TODO: implement before using it\n",
    "#['day_in_year_txn_dt', 'day_in_year_building_complete_dt', 'dt_diff_building_complete_dt_txn_dt'] # dt num feats\n",
    "#['dow_txn_dt', 'dow_building_complete_dt', 'month_txn_dt', 'month_building_complete_dt',\n",
    "# 'quarter_txn_dt', 'quarter_building_complete_dt', 'year_txn_dt', 'year_building_complete_dt'] # dt cat feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def info_value_woe(feature, target):\n",
    "    ''' calculate information value and weight of evidence \n",
    "    read: https://www.slideshare.net/0xdata/feature-engineering-83511751\n",
    "    notice that it's implemented as non-event/event; if opposite, woe changes sign\n",
    "    '''\n",
    "    nT = np.sum(target)\n",
    "    nF = np.sum(~target)\n",
    "    \n",
    "    df = pd.concat([feature,target],axis=1)\n",
    "    df.columns = ['feature','target']\n",
    "    \n",
    "    group = df.groupby('feature')['target']\n",
    "    cal_woe = lambda y: np.log( ((np.sum(~y)+0.5)/nF) / ((np.sum(y)+0.5)/nT) )\n",
    "    woe = group.agg(cal_woe).rename(columns={'target':'woe'})\n",
    "    cal_diff = lambda y: np.sum(~y)/nF - np.sum(y)/nT\n",
    "    diff_pct = group.agg(cal_diff).rename(columns={'target':'diff'})\n",
    "    info_value = np.sum(woe * diff_pct)\n",
    "    \n",
    "    return woe, info_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "    df['total_price'] = df['total_price'].astype('float32')\n",
    "    \n",
    "    # generate target\n",
    "    if 'total_price' in df:\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",
    "    return df, cols_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def post_processing(y_pred, df):\n",
    "    y_pred_final = pd.Series(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'].reset_index(drop=True)\n",
    "        \n",
    "    return y_pred_final.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cols_feats = pre_processing(df, cols_num, cols_cat, cols_feats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct feats cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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_origin = cols_num + cols_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove feats based on feature shuffle results (exp-l04-per-area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from vars_03 import score_base_per_area, shuffle_results_per_area\n",
    "\n",
    "feat_imp = pd.DataFrame({'feats': shuffle_results_per_area[:,0],'avg':shuffle_results_per_area[:,1]})\n",
    "feat_imp['diff'] = feat_imp['avg'] - score_base_per_area\n",
    "feat_imp = feat_imp.sort_values(by='diff', ascending=False)\n",
    "print(feat_imp.reset_index().values)\n",
    "\n",
    "feat_imp = feat_imp[feat_imp['feats'] != 'none']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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': 20000,\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': [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, 16, 24],\n",
    "                  'num_leaves': [300],           # may reduce in dim-reduction exp\n",
    "                  'feature_fraction': [0.5],\n",
    "                  'min_data_in_leaf': [10],\n",
    "                  'lambda_l1': [0, 0.01, 0.1],\n",
    "                  'lambda_l2': [0, 0.01, 0.1],\n",
    "                  'n_feats_rm': [25, 50, 75, 100, 125, 150, 175]\n",
    "                 }\n",
    "N_fold = 3\n",
    "N_iter = 189\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": null,
   "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": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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_origin, col_target)\n",
    "    df_train = feat_eng.fit_transform(df_train)\n",
    "    df_val = feat_eng.transform(df_val)\n",
    "\n",
    "    # random sample - grid search\n",
    "    rand_params_next = []\n",
    "    for params_rand in rand_params_set:\n",
    "        n_feats_rm = params_rand['n_feats_rm']\n",
    "        params = params_fix.copy()\n",
    "        params.update( params_rand )\n",
    "\n",
    "        params_lgb = params.copy()\n",
    "        del params_lgb['n_feats_rm']\n",
    "        \n",
    "        feats_rm = list(feat_imp.iloc[:n_feats_rm]['feats'].values)\n",
    "        cols_feats = list(set(cols_feats_origin) - set(feats_rm))\n",
    "        print('N feats (after):', len(cols_feats))\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",
    "        model = lgb.train(params_lgb, lgb_train, valid_sets=lgb_val, **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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Total computing time:', t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in gsearch_results:\n",
    "    print(*[x for i, x in enumerate(row) if i != 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsearch_results = []\n",
    "with open('log-model-03', 'r') as ifile:\n",
    "    for line in ifile:\n",
    "        line = line.strip()\n",
    "        line = line.replace(']', '],')\n",
    "        line = line.replace(' ', ', ')\n",
    "        line = line.replace(',,', ',')\n",
    "        line = '[' + line + ']'\n",
    "        gsearch_results.append(eval(line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "   ('max_depth', 24),\n",
       "   ('num_leaves', 300),\n",
       "   ('feature_fraction', 0.5),\n",
       "   ('min_data_in_leaf', 10),\n",
       "   ('lambda_l1', 0.1),\n",
       "   ('lambda_l2', 0.0),\n",
       "   ('n_feats_rm', 75)),\n",
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       " [(('task', 'train'),\n",
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       " [(('task', 'train'),\n",
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       " [(('task', 'train'),\n",
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       "  [0.009588532782287763, 0.009643664811785172, 0.009677205232416115]]]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{75: 5753.869022347792, 100: 5753.202443287537, 50: 5753.202391619515, 25: 5751.869081832374, 150: 5751.535151544664, 125: 5736.202164753733, 175: 5709.533153966925}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1207f6a20>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aaa= {}\n",
    "for g in gsearch_results:\n",
    "    nrm = g[0][11][1]\n",
    "    score = g[1]\n",
    "    if nrm not in aaa:\n",
    "        aaa[nrm] = score\n",
    "    elif score > aaa[nrm]:\n",
    "            aaa[nrm] = score\n",
    "print(aaa)\n",
    "bbb = np.array(sorted(aaa.items(), key= lambda x: x[0]))\n",
    "plt.plot(bbb[:,0], bbb[:,1], '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = ['score', 'mse', 'mae', 'mape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>n_feats_rm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>16</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-1</td>\n",
       "      <td>300</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     max_depth  num_leaves  feature_fraction  min_data_in_leaf  lambda_l1  \\\n",
       "0           24         300               0.5                10       0.10   \n",
       "1           -1         300               0.5                10       0.01   \n",
       "2           -1         300               0.5                10       0.10   \n",
       "3           24         300               0.5                10       0.01   \n",
       "4           -1         300               0.5                10       0.00   \n",
       "5           24         300               0.5                10       0.10   \n",
       "6           24         300               0.5                10       0.10   \n",
       "7           16         300               0.5                10       0.10   \n",
       "8           24         300               0.5                10       0.10   \n",
       "9           16         300               0.5                10       0.00   \n",
       "10          16         300               0.5                10       0.01   \n",
       "11          24         300               0.5                10       0.10   \n",
       "12          16         300               0.5                10       0.10   \n",
       "13          -1         300               0.5                10       0.10   \n",
       "14          -1         300               0.5                10       0.00   \n",
       "15          16         300               0.5                10       0.01   \n",
       "16          16         300               0.5                10       0.10   \n",
       "17          24         300               0.5                10       0.01   \n",
       "18          -1         300               0.5                10       0.00   \n",
       "19          -1         300               0.5                10       0.01   \n",
       "20          -1         300               0.5                10       0.01   \n",
       "21          16         300               0.5                10       0.01   \n",
       "22          16         300               0.5                10       0.10   \n",
       "23          16         300               0.5                10       0.00   \n",
       "24          16         300               0.5                10       0.00   \n",
       "25          -1         300               0.5                10       0.01   \n",
       "26          -1         300               0.5                10       0.10   \n",
       "27          -1         300               0.5                10       0.10   \n",
       "28          16         300               0.5                10       0.00   \n",
       "29          16         300               0.5                10       0.10   \n",
       "..         ...         ...               ...               ...        ...   \n",
       "158         -1         300               0.5                10       0.01   \n",
       "159         -1         300               0.5                10       0.00   \n",
       "160         24         300               0.5                10       0.01   \n",
       "161         -1         300               0.5                10       0.01   \n",
       "162         -1         300               0.5                10       0.01   \n",
       "163         -1         300               0.5                10       0.00   \n",
       "164         24         300               0.5                10       0.01   \n",
       "165         -1         300               0.5                10       0.00   \n",
       "166         16         300               0.5                10       0.00   \n",
       "167         16         300               0.5                10       0.10   \n",
       "168         -1         300               0.5                10       0.10   \n",
       "169         24         300               0.5                10       0.10   \n",
       "170         16         300               0.5                10       0.01   \n",
       "171         24         300               0.5                10       0.01   \n",
       "172         -1         300               0.5                10       0.01   \n",
       "173         24         300               0.5                10       0.10   \n",
       "174         16         300               0.5                10       0.00   \n",
       "175         -1         300               0.5                10       0.10   \n",
       "176         16         300               0.5                10       0.10   \n",
       "177         24         300               0.5                10       0.00   \n",
       "178         24         300               0.5                10       0.00   \n",
       "179         24         300               0.5                10       0.10   \n",
       "180         16         300               0.5                10       0.01   \n",
       "181         -1         300               0.5                10       0.00   \n",
       "182         24         300               0.5                10       0.00   \n",
       "183         16         300               0.5                10       0.10   \n",
       "184         16         300               0.5                10       0.01   \n",
       "185         -1         300               0.5                10       0.10   \n",
       "186         16         300               0.5                10       0.00   \n",
       "187         -1         300               0.5                10       0.01   \n",
       "\n",
       "     lambda_l2  n_feats_rm  \n",
       "0         0.00          75  \n",
       "1         0.10         100  \n",
       "2         0.01          50  \n",
       "3         0.10          75  \n",
       "4         0.01          25  \n",
       "5         0.00          50  \n",
       "6         0.10          50  \n",
       "7         0.10         150  \n",
       "8         0.01          75  \n",
       "9         0.01          75  \n",
       "10        0.10          50  \n",
       "11        0.10          25  \n",
       "12        0.01          25  \n",
       "13        0.01          25  \n",
       "14        0.10          50  \n",
       "15        0.00         100  \n",
       "16        0.10          50  \n",
       "17        0.01          75  \n",
       "18        0.01         100  \n",
       "19        0.01          75  \n",
       "20        0.00         150  \n",
       "21        0.01          75  \n",
       "22        0.10          75  \n",
       "23        0.10          75  \n",
       "24        0.01          50  \n",
       "25        0.00         100  \n",
       "26        0.00          50  \n",
       "27        0.10         100  \n",
       "28        0.00         100  \n",
       "29        0.01          50  \n",
       "..         ...         ...  \n",
       "158       0.00         125  \n",
       "159       0.01         125  \n",
       "160       0.00         125  \n",
       "161       0.01         125  \n",
       "162       0.00         175  \n",
       "163       0.10         175  \n",
       "164       0.00         175  \n",
       "165       0.00         175  \n",
       "166       0.01         175  \n",
       "167       0.01         175  \n",
       "168       0.00         175  \n",
       "169       0.01         175  \n",
       "170       0.10         175  \n",
       "171       0.01         175  \n",
       "172       0.10         175  \n",
       "173       0.00         175  \n",
       "174       0.00         175  \n",
       "175       0.01         175  \n",
       "176       0.00         175  \n",
       "177       0.10         175  \n",
       "178       0.00         175  \n",
       "179       0.10         175  \n",
       "180       0.00         175  \n",
       "181       0.01         175  \n",
       "182       0.01         175  \n",
       "183       0.10         175  \n",
       "184       0.01         175  \n",
       "185       0.10         175  \n",
       "186       0.10         175  \n",
       "187       0.01         175  \n",
       "\n",
       "[188 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gsearch_results = []\n",
    "for row in gsearch_results:\n",
    "    result = dict(row[0])\n",
    "    result['score'] = row[1]\n",
    "    result['mse'] = row[2]\n",
    "    result['mae'] = row[3]\n",
    "    result['mape'] = row[4]\n",
    "#    result['best_iteration'] = np.mean([m.best_iteration for m in row[5]])\n",
    "    df_gsearch_results.append(result)\n",
    "df_gsearch_results = pd.DataFrame(df_gsearch_results)\n",
    "df_gsearch_results = df_gsearch_results[list(params_gsearch.keys())] #+metrics+['best_iteration']]\n",
    "display(df_gsearch_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'score'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'score'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-f5b588de675b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_gsearch_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rank'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'score'"
     ]
    }
   ],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gsearch_results.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### correlation for num feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = pd.DataFrame({'corr': df[cols_num+['target']].corr()['target']})\n",
    "corr['abs_corr'] = np.abs(corr['corr'])\n",
    "corr = corr.sort_values(by='abs_corr')\n",
    "print(corr.reset_index().values)\n",
    "corr['cum_count'] = range(corr.shape[0], 0, -1)\n",
    "plt.plot(corr['abs_corr'], corr['cum_count'], '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### info values for cat feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bin_target'] = ( df['target'] > np.median(df['target']) )\n",
    "\n",
    "info_vals = []\n",
    "for c in cols_cat:\n",
    "    woe, iv = info_value_woe(df[c], df['bin_target'])\n",
    "    info_vals.append( (c, iv) )\n",
    "info_vals.sort(key = lambda x: x[1])\n",
    "display(info_vals)\n",
    "\n",
    "info_vals = [(c, iv, len(info_vals) - i) for i, (c, iv) in enumerate(info_vals)]\n",
    "info_vals = pd.DataFrame(info_vals)\n",
    "info_vals.columns = ['feats', 'iv', 'counts']\n",
    "plt.xscale('log')\n",
    "plt.plot(info_vals['iv'], info_vals['counts'], '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.0),\n",
    "   ('n_feats_rm', 75)),\n",
    "  5753.869022347792,\n",
    "  0.03947951308202,\n",
    "  0.12606062674597365,\n",
    "  0.009469859565460013,\n",
    "  [5764.869135997275, 5748.870629152345, 5747.867301893753],\n",
    "  [0.03841400633849459, 0.039980429005792815, 0.0400441039017726],\n",
    "  [0.12560034373045953, 0.12604532305465926, 0.12653621345280222],\n",
    "  [0.009442015471698841, 0.009453773843400992, 0.009513789381280207]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', -1),\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_feats_rm', 100)),\n",
    "  5753.202443287537,\n",
    "  0.039507136554980594,\n",
    "  0.12599606027360089,\n",
    "  0.00946456486469243,\n",
    "  [5770.869493115472, 5753.870582752263, 5734.867253994875],\n",
    "  [0.03846211165831417, 0.039978342598186466, 0.04008095540844115],\n",
    "  [0.125230607043014, 0.12610206201976779, 0.1266555117580209],\n",
    "  [0.009412676391810737, 0.009457764417932593, 0.00952325378433396]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', -1),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 50)),\n",
    "  5753.202391619515,\n",
    "  0.03943681600759935,\n",
    "  0.12598104718987738,\n",
    "  0.009463258056231353,\n",
    "  [5749.86911153187, 5774.870779205181, 5734.867284121492],\n",
    "  [0.03853937390571893, 0.039857376134319104, 0.03991369798276],\n",
    "  [0.12559119676548472, 0.125863100109272, 0.1264888446948754],\n",
    "  [0.00944091223467301, 0.00943924836977937, 0.009509613564241683]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\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_feats_rm', 75)),\n",
    "  5753.20230605263,\n",
    "  0.03956406275813724,\n",
    "  0.12608150560953504,\n",
    "  0.009471944905229462,\n",
    "  [5777.869162889716, 5740.870606992368, 5740.867148275806],\n",
    "  [0.03851842833058906, 0.0400350894860593, 0.040138670457763356],\n",
    "  [0.1255898777206347, 0.12606705471252022, 0.12658758439545023],\n",
    "  [0.009441421374372155, 0.009455778395222623, 0.009518634946093604]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', -1),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.0),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 25)),\n",
    "  5751.869081832374,\n",
    "  0.03948273885472799,\n",
    "  0.12602331245362466,\n",
    "  0.009466714082202532,\n",
    "  [5755.869540456651, 5760.870519785739, 5738.867185254734],\n",
    "  [0.03831925594103244, 0.040093275189239316, 0.040035685433912206],\n",
    "  [0.12522630602593182, 0.12607541114055032, 0.1267682201943919],\n",
    "  [0.009413451965821834, 0.009455765662129257, 0.009530924618656506]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.0),\n",
    "   ('n_feats_rm', 50)),\n",
    "  5751.868893362126,\n",
    "  0.03951027292835887,\n",
    "  0.12610732078703193,\n",
    "  0.009473010332742317,\n",
    "  [5758.869063338008, 5773.870440429546, 5722.867176318821],\n",
    "  [0.038527999748031484, 0.04002234797316286, 0.03998047106388224],\n",
    "  [0.1255612228310466, 0.12614325839766447, 0.12661748113238477],\n",
    "  [0.009438813058391756, 0.009460824373072576, 0.009519393566762617]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.1),\n",
    "   ('n_feats_rm', 50)),\n",
    "  5751.53575403136,\n",
    "  0.0394488368788101,\n",
    "  0.12600088735629902,\n",
    "  0.00946397273120988,\n",
    "  [5764.869292637707, 5748.870587218214, 5740.867382238158],\n",
    "  [0.03837398337568929, 0.04005572480660774, 0.03991680245413329],\n",
    "  [0.12542362054669606, 0.1260761138983883, 0.12650292762381277],\n",
    "  [0.009427799204546086, 0.0094545350895574, 0.009509583899526155]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 16),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.1),\n",
    "   ('n_feats_rm', 150)),\n",
    "  5751.535151544664,\n",
    "  0.039642158768187406,\n",
    "  0.1263999239335986,\n",
    "  0.009495682919677615,\n",
    "  [5762.868823179015, 5736.869751424829, 5754.866880030148],\n",
    "  [0.03857024112063313, 0.040386961060237585, 0.039969274123691494],\n",
    "  [0.1257866152667855, 0.12678583845878255, 0.1266273180752278],\n",
    "  [0.009455579269722014, 0.009508976644918324, 0.009522492844392508]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 75)),\n",
    "  5751.202274026527,\n",
    "  0.03951059301574985,\n",
    "  0.1260924236622651,\n",
    "  0.009472935734821398,\n",
    "  [5760.869108103025, 5743.870581492748, 5748.86713248381],\n",
    "  [0.03842048468642275, 0.04004670587863782, 0.040064588482188974],\n",
    "  [0.12559086619972262, 0.12605348300342262, 0.1266329217836501],\n",
    "  [0.009442738968965948, 0.009454512391038564, 0.009521555844459682]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 16),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.0),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 75)),\n",
    "  5750.869004414458,\n",
    "  0.03957692383376678,\n",
    "  0.12612002818809476,\n",
    "  0.009474272237328175,\n",
    "  [5760.869296522382, 5748.870643505594, 5742.867073215397],\n",
    "  [0.038460124184024655, 0.04008421263040513, 0.04018643468687054],\n",
    "  [0.12549949886581344, 0.1260487150740168, 0.12681187062445404],\n",
    "  [0.009434618959620502, 0.009454066722946323, 0.009534131029417697]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\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_feats_rm', 50)),\n",
    "  5749.869176991005,\n",
    "  0.039463840215595646,\n",
    "  0.1258704416075245,\n",
    "  0.009454459552627838,\n",
    "  [5745.869446152027, 5772.870846008391, 5730.867238812594],\n",
    "  [0.038353738069290946, 0.03996248325894752, 0.04007529931854846],\n",
    "  [0.12527674926772925, 0.12582108525376384, 0.1265134903010804],\n",
    "  [0.00941669337262466, 0.009435339537530556, 0.009511345747728299]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.1),\n",
    "   ('n_feats_rm', 25)),\n",
    "  5749.868996998136,\n",
    "  0.03956298885959874,\n",
    "  0.12607078282356335,\n",
    "  0.009470786966902733,\n",
    "  [5770.869435689308, 5739.870356133999, 5738.867199171102],\n",
    "  [0.03843913140094592, 0.04016493828801014, 0.04008489688984015],\n",
    "  [0.12528632697693864, 0.12622833388912705, 0.12669768760462435],\n",
    "  [0.009417344692257034, 0.009468293925632254, 0.009526722282818914]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 16),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 25)),\n",
    "  5749.202361109196,\n",
    "  0.03950969457616503,\n",
    "  0.1260480448808202,\n",
    "  0.009468972104225808,\n",
    "  [5761.869364571608, 5750.870504055989, 5734.867214699991],\n",
    "  [0.03831858923212821, 0.040158015395236536, 0.04005247910113034],\n",
    "  [0.12534697045504659, 0.1261591738855774, 0.12663799030183653],\n",
    "  [0.009422853213608945, 0.009462311573872052, 0.009521751525196426]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', -1),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 25)),\n",
    "  5748.869072149585,\n",
    "  0.039487409985805015,\n",
    "  0.12602580984962308,\n",
    "  0.00946750737081488,\n",
    "  [5772.869440625416, 5736.870402972087, 5736.867372851252],\n",
    "  [0.03838061690936287, 0.04011776441567246, 0.03996384863237971],\n",
    "  [0.12526058939279652, 0.12619208693932046, 0.1266247532167523],\n",
    "  [0.009416610988070288, 0.009465245305611514, 0.009520665818762836]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', -1),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.0),\n",
    "   ('lambda_l2', 0.1),\n",
    "   ('n_feats_rm', 50)),\n",
    "  5748.869053830861,\n",
    "  0.03950842541558149,\n",
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    "  0.009463629963107007,\n",
    "  [5750.869240882312, 5756.870641882529, 5738.867278727745],\n",
    "  [0.03850222262027878, 0.0400611137362557, 0.039961939890209984],\n",
    "  [0.12545921725497056, 0.12601307984911622, 0.12649392250385932],\n",
    "  [0.009430836397722376, 0.009449434475032966, 0.00951061901656568]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\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', 0.0),\n",
    "   ('n_feats_rm', 100)),\n",
    "  5748.202501134959,\n",
    "  0.03946832112334197,\n",
    "  0.12597912507406014,\n",
    "  0.009464234027356444,\n",
    "  [5742.869417907698, 5760.870751287774, 5740.8673342094025],\n",
    "  [0.038337513505959625, 0.03996593545281853, 0.04010151441124774],\n",
    "  [0.12529402473691914, 0.12598066878862843, 0.1266626816966329],\n",
    "  [0.00941853261149451, 0.00944912302839623, 0.009525046442178586]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 16),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.1),\n",
    "   ('lambda_l2', 0.1),\n",
    "   ('n_feats_rm', 50)),\n",
    "  5748.2023266877295,\n",
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    "  0.0094685468737908,\n",
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    "  [0.1254641481111428, 0.12603574224543362, 0.12667557219038517],\n",
    "  [0.00943032639505514, 0.009451472598737043, 0.009523841627580217]],\n",
    " [(('task', 'train'),\n",
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    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', 24),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.01),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 75)),\n",
    "  5747.868934189038,\n",
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    "  [0.12565614605881933, 0.12603191021160123, 0.12673303984236636],\n",
    "  [0.009447293423508796, 0.009452631675907316, 0.009528682656569296]],\n",
    " [(('task', 'train'),\n",
    "   ('boosting_type', 'gbdt'),\n",
    "   ('objective', 'mse'),\n",
    "   ('metric', 'mape'),\n",
    "   ('learning_rate', 0.01),\n",
    "   ('max_depth', -1),\n",
    "   ('num_leaves', 300),\n",
    "   ('feature_fraction', 0.5),\n",
    "   ('min_data_in_leaf', 10),\n",
    "   ('lambda_l1', 0.0),\n",
    "   ('lambda_l2', 0.01),\n",
    "   ('n_feats_rm', 100)),\n",
    "  5747.535737178038,\n",
    "  0.03952142481621102,\n",
    "  0.1260117057566894,\n",
    "  0.00946675158025521,\n",
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