optuna_ver2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
       ".datatable .bool    { background: #DDDD99; }\n",
       ".datatable .object  { background: #565656; }\n",
       ".datatable .int     { background: #5D9E5D; }\n",
       ".datatable .float   { background: #4040CC; }\n",
       ".datatable .str     { background: #CC4040; }\n",
       ".datatable .time    { background: #40CC40; }\n",
       ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
       ".datatable .frame tbody td { text-align: left; }\n",
       ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
       ".datatable th:nth-child(2) { padding-left: 12px; }\n",
       ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
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       ".datatable .sp {  opacity: 0.25;}\n",
       ".datatable .footer { font-size: 9px; }\n",
       ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
       "</style>\n"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "# 日本語フォントの指定\n",
    "mpl.rcParams['font.family'] = 'IPAexGothic' # ここにインストールしたフォント名を入れる\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GroupKFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from nyaggle.feature.category_encoder import TargetEncoder\n",
    "\n",
    "from nyaggle.experiment import run_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import warnings\n",
    "from typing import Dict, Iterable, Optional, Union\n",
    "\n",
    "import optuna.integration.lightgbm as optuna_lgb\n",
    "import pandas as pd\n",
    "import sklearn.utils.multiclass as multiclass\n",
    "from sklearn.model_selection import BaseCrossValidator\n",
    "\n",
    "from nyaggle.validation.split import check_cv\n",
    "from nyaggle.experiment.hyperparameter_tuner import find_best_lgbm_parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ver1 = pd.read_csv('yukiCup2023Autumn-atmaCup_dataset/yukiCup 2023 Autumn x atmaCup/train_feature_ver1.csv')\n",
    "test_ver1 = pd.read_csv('yukiCup2023Autumn-atmaCup_dataset/yukiCup 2023 Autumn x atmaCup/test_feature_ver1.csv')\n",
    "\n",
    "train_ver2 = pd.read_csv('yukiCup2023Autumn-atmaCup_dataset/yukiCup 2023 Autumn x atmaCup/train_feature_ver2.csv')\n",
    "test_ver2 = pd.read_csv('yukiCup2023Autumn-atmaCup_dataset/yukiCup 2023 Autumn x atmaCup/test_feature_ver2.csv')\n",
    "\n",
    "era = pd.read_csv('yukiCup2023Autumn-atmaCup_dataset/yukiCup 2023 Autumn x atmaCup/era.csv')\n",
    "sample_submission = pd.read_csv('yukiCup2023Autumn-atmaCup_dataset/yukiCup 2023 Autumn x atmaCup/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train_ver1, train_ver2.iloc[:, 17:]], axis=1)\n",
    "test = pd.concat([test_ver1, test_ver2.iloc[:, 16:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['棟名', '種別2', '国', '時代', '都道府県 ※美工品は「所有者住所（所在都道府県）」', '所在地', '保管施設の名称', '所有者名', '管理団体又は責任者']\n",
      "['緯度', '経度', '開始', '終了', '緯度*経度', '終了-開始', 'label_棟名', 'label_種別2', 'label_国', 'label_時代', 'label_都道府県 ※美工品は「所有者住所（所在都道府県）」', 'label_所在地', 'label_保管施設の名称', 'label_所有者名', 'label_管理団体又は責任者', 'count_棟名', 'count_種別2', 'count_国', 'count_時代', 'count_都道府県 ※美工品は「所有者住所（所在都道府県）」', 'count_所在地', 'count_保管施設の名称', 'count_所有者名', 'count_管理団体又は責任者', '棟名_緯度_min', '棟名_緯度_max', '棟名_緯度_mean', '棟名_緯度_std', '棟名_経度_min', '棟名_経度_max', '棟名_経度_mean', '棟名_経度_std', '棟名_開始_min', '棟名_開始_max', '棟名_開始_mean', '棟名_開始_std', '棟名_終了_min', '棟名_終了_max', '棟名_終了_mean', '棟名_終了_std', '棟名_緯度*経度_min', '棟名_緯度*経度_max', '棟名_緯度*経度_mean', '棟名_緯度*経度_std', '棟名_終了-開始_min', '棟名_終了-開始_max', '棟名_終了-開始_mean', '棟名_終了-開始_std', '種別2_緯度_min', '種別2_緯度_max', '種別2_緯度_mean', '種別2_緯度_std', '種別2_経度_min', '種別2_経度_max', '種別2_経度_mean', '種別2_経度_std', '種別2_開始_min', '種別2_開始_max', '種別2_開始_mean', '種別2_開始_std', '種別2_終了_min', '種別2_終了_max', '種別2_終了_mean', '種別2_終了_std', '種別2_緯度*経度_min', '種別2_緯度*経度_max', '種別2_緯度*経度_mean', '種別2_緯度*経度_std', '種別2_終了-開始_min', '種別2_終了-開始_max', '種別2_終了-開始_mean', '種別2_終了-開始_std', '国_緯度_min', '国_緯度_max', '国_緯度_mean', '国_緯度_std', '国_経度_min', '国_経度_max', '国_経度_mean', '国_経度_std', '国_開始_min', '国_開始_max', '国_開始_mean', '国_開始_std', '国_終了_min', '国_終了_max', '国_終了_mean', '国_終了_std', '国_緯度*経度_min', '国_緯度*経度_max', '国_緯度*経度_mean', '国_緯度*経度_std', '国_終了-開始_min', '国_終了-開始_max', '国_終了-開始_mean', '国_終了-開始_std', '時代_緯度_min', '時代_緯度_max', '時代_緯度_mean', '時代_緯度_std', '時代_経度_min', '時代_経度_max', '時代_経度_mean', '時代_経度_std', '時代_開始_min', '時代_開始_max', '時代_開始_mean', '時代_開始_std', '時代_終了_min', '時代_終了_max', '時代_終了_mean', '時代_終了_std', '時代_緯度*経度_min', '時代_緯度*経度_max', '時代_緯度*経度_mean', '時代_緯度*経度_std', '時代_終了-開始_min', '時代_終了-開始_max', '時代_終了-開始_mean', '時代_終了-開始_std', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度_min', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度_max', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度_mean', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度_std', '都道府県 ※美工品は「所有者住所（所在都道府県）」_経度_min', '都道府県 ※美工品は「所有者住所（所在都道府県）」_経度_max', '都道府県 ※美工品は「所有者住所（所在都道府県）」_経度_mean', '都道府県 ※美工品は「所有者住所（所在都道府県）」_経度_std', '都道府県 ※美工品は「所有者住所（所在都道府県）」_開始_min', '都道府県 ※美工品は「所有者住所（所在都道府県）」_開始_max', '都道府県 ※美工品は「所有者住所（所在都道府県）」_開始_mean', '都道府県 ※美工品は「所有者住所（所在都道府県）」_開始_std', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了_min', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了_max', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了_mean', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了_std', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度*経度_min', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度*経度_max', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度*経度_mean', '都道府県 ※美工品は「所有者住所（所在都道府県）」_緯度*経度_std', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了-開始_min', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了-開始_max', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了-開始_mean', '都道府県 ※美工品は「所有者住所（所在都道府県）」_終了-開始_std', '所在地_緯度_min', '所在地_緯度_max', '所在地_緯度_mean', '所在地_緯度_std', '所在地_経度_min', '所在地_経度_max', '所在地_経度_mean', '所在地_経度_std', '所在地_開始_min', '所在地_開始_max', '所在地_開始_mean', '所在地_開始_std', '所在地_終了_min', '所在地_終了_max', '所在地_終了_mean', '所在地_終了_std', '所在地_緯度*経度_min', '所在地_緯度*経度_max', '所在地_緯度*経度_mean', '所在地_緯度*経度_std', '所在地_終了-開始_min', '所在地_終了-開始_max', '所在地_終了-開始_mean', '所在地_終了-開始_std', '保管施設の名称_緯度_min', '保管施設の名称_緯度_max', '保管施設の名称_緯度_mean', '保管施設の名称_緯度_std', '保管施設の名称_経度_min', '保管施設の名称_経度_max', '保管施設の名称_経度_mean', '保管施設の名称_経度_std', '保管施設の名称_開始_min', '保管施設の名称_開始_max', '保管施設の名称_開始_mean', '保管施設の名称_開始_std', '保管施設の名称_終了_min', '保管施設の名称_終了_max', '保管施設の名称_終了_mean', '保管施設の名称_終了_std', '保管施設の名称_緯度*経度_min', '保管施設の名称_緯度*経度_max', '保管施設の名称_緯度*経度_mean', '保管施設の名称_緯度*経度_std', '保管施設の名称_終了-開始_min', '保管施設の名称_終了-開始_max', '保管施設の名称_終了-開始_mean', '保管施設の名称_終了-開始_std', '所有者名_緯度_min', '所有者名_緯度_max', '所有者名_緯度_mean', '所有者名_緯度_std', '所有者名_経度_min', '所有者名_経度_max', '所有者名_経度_mean', '所有者名_経度_std', '所有者名_開始_min', '所有者名_開始_max', '所有者名_開始_mean', '所有者名_開始_std', '所有者名_終了_min', '所有者名_終了_max', '所有者名_終了_mean', '所有者名_終了_std', '所有者名_緯度*経度_min', '所有者名_緯度*経度_max', '所有者名_緯度*経度_mean', '所有者名_緯度*経度_std', '所有者名_終了-開始_min', '所有者名_終了-開始_max', '所有者名_終了-開始_mean', '所有者名_終了-開始_std', '管理団体又は責任者_緯度_min', '管理団体又は責任者_緯度_max', '管理団体又は責任者_緯度_mean', '管理団体又は責任者_緯度_std', '管理団体又は責任者_経度_min', '管理団体又は責任者_経度_max', '管理団体又は責任者_経度_mean', '管理団体又は責任者_経度_std', '管理団体又は責任者_開始_min', '管理団体又は責任者_開始_max', '管理団体又は責任者_開始_mean', '管理団体又は責任者_開始_std', '管理団体又は責任者_終了_min', '管理団体又は責任者_終了_max', '管理団体又は責任者_終了_mean', '管理団体又は責任者_終了_std', '管理団体又は責任者_緯度*経度_min', '管理団体又は責任者_緯度*経度_max', '管理団体又は責任者_緯度*経度_mean', '管理団体又は責任者_緯度*経度_std', '管理団体又は責任者_終了-開始_min', '管理団体又は責任者_終了-開始_max', '管理団体又は責任者_終了-開始_mean', '管理団体又は責任者_終了-開始_std', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', 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      "is_kokuhou\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#categorical変数抽出\n",
    "cat_cols = [c for c in test.columns if train[c].dtype == np.object]\n",
    "#cat_cols.remove('string') # これは取り除かないといけない\n",
    "\n",
    "# numerical変数抽出\n",
    "num_cols = [c for c in test.columns if train[c].dtype != np.object]\n",
    "\n",
    "#目的変数抽出\n",
    "target_col = 'is_kokuhou'\n",
    "\n",
    "print(cat_cols)\n",
    "print(num_cols)\n",
    "print(target_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# target encoding\n",
    "\n",
    "# kf = KFold(4)\n",
    "# kf = KFold(4, groups=train.categorical)　#GroupKFold\n",
    "kf = StratifiedKFold(5, shuffle=True, random_state=42531) #StratifiedKFold\n",
    "\n",
    "# Target encoding with K-fold\n",
    "# te = TargetEncoder(kf.split(train))\n",
    "# te = TargetEncoder(kf.split(train)) #GroupKFold\n",
    "te = TargetEncoder(kf.split(train, train[target_col])) #StratifiedKFold\n",
    "\n",
    "# use fit/fit_transform to train data, then apply transform to test data\n",
    "train.loc[:, cat_cols] = te.fit_transform(train[cat_cols], train[target_col])\n",
    "test.loc[:, cat_cols] = te.transform(test[cat_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "760\n"
     ]
    }
   ],
   "source": [
    "print(len(cat_cols) + len(num_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'boosting': \"dart\", # dart, gbdt, goss, rf\n",
    "    'objective': \"binary\", # binary, multiclass, regression\n",
    "    'metric': \"binary_logloss\", # mae, mse, auc, binary_logloss, multi_logloss, rmse, average_precision\n",
    "    'learning_rate':0.05, #0.05\n",
    "    'n_estimators': 210,\n",
    "    'max_depth': -1,\n",
    "    'random_state' : 42531\n",
    "    #'num_leaves' : 100, #カーディナリティが高い場合は大きくする\n",
    "    #'max_bin' : 500,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-11-25 06:06:51,807]\u001b[0m A new study created in memory with name: no-name-2dea8111-916d-4e60-b8fb-8d70d0454ea6\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/callback.py:223: UserWarning: Early stopping is not available in dart mode\n",
      "  _log_warning('Early stopping is not available in dart mode')\n",
      "\u001b[32m[I 2023-11-25 06:06:54,656]\u001b[0m Trial 0 finished with value: 0.221978816289628 and parameters: {'feature_fraction': 0.5}. Best is trial 0 with value: 0.221978816289628.\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/callback.py:223: UserWarning: Early stopping is not available in dart mode\n",
      "  _log_warning('Early stopping is not available in dart mode')\n",
      "\u001b[32m[I 2023-11-25 06:06:58,106]\u001b[0m Trial 1 finished with value: 0.22607797094115273 and parameters: {'feature_fraction': 0.8}. Best is trial 0 with value: 0.221978816289628.\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/callback.py:223: UserWarning: Early stopping is not available in dart mode\n",
      "  _log_warning('Early stopping is not available in dart mode')\n",
      "\u001b[32m[I 2023-11-25 06:07:00,213]\u001b[0m Trial 2 finished with value: 0.22209400507421037 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 0.221978816289628.\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/callback.py:223: UserWarning: Early stopping is not available in dart mode\n",
      "  _log_warning('Early stopping is not available in dart mode')\n",
      "\u001b[32m[I 2023-11-25 06:07:03,223]\u001b[0m Trial 3 finished with value: 0.2260101191645586 and parameters: {'feature_fraction': 0.7}. Best is trial 0 with value: 0.221978816289628.\u001b[0m\n",
      "feature_fraction, val_score: 0.221979:  57%|#####7    | 4/7 [00:11<00:08,  2.85s/it]\n",
      "  0%|          | 0/20 [00:00<?, ?it/s]\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\n",
      "  0%|          | 0/6 [00:00<?, ?it/s]\n",
      "  0%|          | 0/20 [00:00<?, ?it/s]\n",
      "  0%|          | 0/5 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "optuna_params = find_best_lgbm_parameter(base_param = params,\n",
    "                         X=train.drop(['is_kokuhou', '名称'], axis=1), \n",
    "                         y=train['is_kokuhou'],\n",
    "                         cv=StratifiedKFold(5, shuffle=True, random_state=42531),\n",
    "                         time_budget=10,\n",
    "                         type_of_target='auto'\n",
    "                         ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'boosting': 'dart', 'objective': 'binary', 'metric': 'binary_logloss', 'learning_rate': 0.05, 'max_depth': -1, 'random_state': 42531, 'feature_pre_filter': False, 'verbosity': -1, 'lambda_l1': 0.0, 'lambda_l2': 0.0, 'num_leaves': 31, 'feature_fraction': 0.5, 'bagging_fraction': 1.0, 'bagging_freq': 0, 'min_child_samples': 20, 'num_iterations': 210, 'early_stopping_round': 100}\n"
     ]
    }
   ],
   "source": [
    "print(optuna_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is []\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.261728\n",
      "[2]\tvalid_0's binary_logloss: 0.258115\n",
      "[3]\tvalid_0's binary_logloss: 0.254906\n",
      "[4]\tvalid_0's binary_logloss: 0.252307\n",
      "[5]\tvalid_0's binary_logloss: 0.250502\n",
      "[6]\tvalid_0's binary_logloss: 0.248555\n",
      "[7]\tvalid_0's binary_logloss: 0.24619\n",
      "[8]\tvalid_0's binary_logloss: 0.243803\n",
      "[9]\tvalid_0's binary_logloss: 0.241937\n",
      "[10]\tvalid_0's binary_logloss: 0.240466\n",
      "[11]\tvalid_0's binary_logloss: 0.23842\n",
      "[12]\tvalid_0's binary_logloss: 0.237259\n",
      "[13]\tvalid_0's binary_logloss: 0.236164\n",
      "[14]\tvalid_0's binary_logloss: 0.234643\n",
      "[15]\tvalid_0's binary_logloss: 0.233511\n",
      "[16]\tvalid_0's binary_logloss: 0.232575\n",
      "[17]\tvalid_0's binary_logloss: 0.231893\n",
      "[18]\tvalid_0's binary_logloss: 0.231092\n",
      "[19]\tvalid_0's binary_logloss: 0.230128\n",
      "[20]\tvalid_0's binary_logloss: 0.229462\n",
      "[21]\tvalid_0's binary_logloss: 0.228912\n",
      "[22]\tvalid_0's binary_logloss: 0.228481\n",
      "[23]\tvalid_0's binary_logloss: 0.227357\n",
      "[24]\tvalid_0's binary_logloss: 0.226657\n",
      "[25]\tvalid_0's binary_logloss: 0.226811\n",
      "[26]\tvalid_0's binary_logloss: 0.226384\n",
      "[27]\tvalid_0's binary_logloss: 0.225943\n",
      "[28]\tvalid_0's binary_logloss: 0.225842\n",
      "[29]\tvalid_0's binary_logloss: 0.225526\n",
      "[30]\tvalid_0's binary_logloss: 0.225144\n",
      "[31]\tvalid_0's binary_logloss: 0.224739\n",
      "[32]\tvalid_0's binary_logloss: 0.223804\n",
      "[33]\tvalid_0's binary_logloss: 0.223716\n",
      "[34]\tvalid_0's binary_logloss: 0.223325\n",
      "[35]\tvalid_0's binary_logloss: 0.223434\n",
      "[36]\tvalid_0's binary_logloss: 0.223676\n",
      "[37]\tvalid_0's binary_logloss: 0.223566\n",
      "[38]\tvalid_0's binary_logloss: 0.223554\n",
      "[39]\tvalid_0's binary_logloss: 0.223443\n",
      "[40]\tvalid_0's binary_logloss: 0.223558\n",
      "[41]\tvalid_0's binary_logloss: 0.2235\n",
      "[42]\tvalid_0's binary_logloss: 0.223761\n",
      "[43]\tvalid_0's binary_logloss: 0.223264\n",
      "[44]\tvalid_0's binary_logloss: 0.223243\n",
      "[45]\tvalid_0's binary_logloss: 0.223126\n",
      "[46]\tvalid_0's binary_logloss: 0.222782\n",
      "[47]\tvalid_0's binary_logloss: 0.22323\n",
      "[48]\tvalid_0's binary_logloss: 0.223045\n",
      "[49]\tvalid_0's binary_logloss: 0.222358\n",
      "[50]\tvalid_0's binary_logloss: 0.222483\n",
      "[51]\tvalid_0's binary_logloss: 0.222334\n",
      "[52]\tvalid_0's binary_logloss: 0.22238\n",
      "[53]\tvalid_0's binary_logloss: 0.222657\n",
      "[54]\tvalid_0's binary_logloss: 0.2223\n",
      "[55]\tvalid_0's binary_logloss: 0.221955\n",
      "[56]\tvalid_0's binary_logloss: 0.221662\n",
      "[57]\tvalid_0's binary_logloss: 0.221507\n",
      "[58]\tvalid_0's binary_logloss: 0.221398\n",
      "[59]\tvalid_0's binary_logloss: 0.221725\n",
      "[60]\tvalid_0's binary_logloss: 0.221281\n",
      "[61]\tvalid_0's binary_logloss: 0.22132\n",
      "[62]\tvalid_0's binary_logloss: 0.220611\n",
      "[63]\tvalid_0's binary_logloss: 0.220549\n",
      "[64]\tvalid_0's binary_logloss: 0.220434\n",
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      "[66]\tvalid_0's binary_logloss: 0.22085\n",
      "[67]\tvalid_0's binary_logloss: 0.221152\n",
      "[68]\tvalid_0's binary_logloss: 0.221261\n",
      "[69]\tvalid_0's binary_logloss: 0.22127\n",
      "[70]\tvalid_0's binary_logloss: 0.221711\n",
      "[71]\tvalid_0's binary_logloss: 0.221855\n",
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      "[86]\tvalid_0's binary_logloss: 0.2272\n",
      "[87]\tvalid_0's binary_logloss: 0.227281\n",
      "[88]\tvalid_0's binary_logloss: 0.227484\n",
      "[89]\tvalid_0's binary_logloss: 0.227535\n",
      "[90]\tvalid_0's binary_logloss: 0.227867\n",
      "[91]\tvalid_0's binary_logloss: 0.227721\n",
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      "[93]\tvalid_0's binary_logloss: 0.227878\n",
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      "[95]\tvalid_0's binary_logloss: 0.228001\n",
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      "[106]\tvalid_0's binary_logloss: 0.229813\n",
      "[107]\tvalid_0's binary_logloss: 0.230221\n",
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      "[112]\tvalid_0's binary_logloss: 0.230224\n",
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      "[115]\tvalid_0's binary_logloss: 0.230876\n",
      "[116]\tvalid_0's binary_logloss: 0.230596\n",
      "[117]\tvalid_0's binary_logloss: 0.230781\n",
      "[118]\tvalid_0's binary_logloss: 0.231127\n",
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      "[123]\tvalid_0's binary_logloss: 0.232104\n",
      "[124]\tvalid_0's binary_logloss: 0.232424\n",
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      "[129]\tvalid_0's binary_logloss: 0.232041\n",
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      "[135]\tvalid_0's binary_logloss: 0.233228\n",
      "[136]\tvalid_0's binary_logloss: 0.233366\n",
      "[137]\tvalid_0's binary_logloss: 0.233615\n",
      "[138]\tvalid_0's binary_logloss: 0.233961\n",
      "[139]\tvalid_0's binary_logloss: 0.233852\n",
      "[140]\tvalid_0's binary_logloss: 0.233901\n",
      "[141]\tvalid_0's binary_logloss: 0.234084\n",
      "[142]\tvalid_0's binary_logloss: 0.234452\n",
      "[143]\tvalid_0's binary_logloss: 0.234994\n",
      "[144]\tvalid_0's binary_logloss: 0.235238\n",
      "[145]\tvalid_0's binary_logloss: 0.235539\n",
      "[146]\tvalid_0's binary_logloss: 0.235809\n",
      "[147]\tvalid_0's binary_logloss: 0.23618\n",
      "[148]\tvalid_0's binary_logloss: 0.236553\n",
      "[149]\tvalid_0's binary_logloss: 0.237061\n",
      "[150]\tvalid_0's binary_logloss: 0.236965\n",
      "[151]\tvalid_0's binary_logloss: 0.237183\n",
      "[152]\tvalid_0's binary_logloss: 0.237176\n",
      "[153]\tvalid_0's binary_logloss: 0.237107\n",
      "[154]\tvalid_0's binary_logloss: 0.237118\n",
      "[155]\tvalid_0's binary_logloss: 0.237223\n",
      "[156]\tvalid_0's binary_logloss: 0.237364\n",
      "[157]\tvalid_0's binary_logloss: 0.237053\n",
      "[158]\tvalid_0's binary_logloss: 0.23699\n",
      "[159]\tvalid_0's binary_logloss: 0.237145\n",
      "[160]\tvalid_0's binary_logloss: 0.237472\n",
      "[161]\tvalid_0's binary_logloss: 0.237416\n",
      "[162]\tvalid_0's binary_logloss: 0.237201\n",
      "[163]\tvalid_0's binary_logloss: 0.237076\n",
      "[164]\tvalid_0's binary_logloss: 0.237288\n",
      "[165]\tvalid_0's binary_logloss: 0.237395\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.6457369549922142, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6457369549922142\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.0002736286090580672, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002736286090580672\n",
      "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n",
      "[LightGBM] [Warning] lambda_l2 is set=0.0014099660582306847, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014099660582306847\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[1]\tvalid_0's binary_logloss: 0.260068\n",
      "[2]\tvalid_0's binary_logloss: 0.256054\n",
      "[3]\tvalid_0's binary_logloss: 0.253298\n",
      "[4]\tvalid_0's binary_logloss: 0.249808\n",
      "[5]\tvalid_0's binary_logloss: 0.247715\n",
      "[6]\tvalid_0's binary_logloss: 0.245895\n",
      "[7]\tvalid_0's binary_logloss: 0.243875\n",
      "[8]\tvalid_0's binary_logloss: 0.242116\n",
      "[9]\tvalid_0's binary_logloss: 0.240684\n",
      "[10]\tvalid_0's binary_logloss: 0.238191\n",
      "[11]\tvalid_0's binary_logloss: 0.236507\n",
      "[12]\tvalid_0's binary_logloss: 0.234673\n",
      "[13]\tvalid_0's binary_logloss: 0.234286\n",
      "[14]\tvalid_0's binary_logloss: 0.233766\n",
      "[15]\tvalid_0's binary_logloss: 0.233336\n",
      "[16]\tvalid_0's binary_logloss: 0.232619\n",
      "[17]\tvalid_0's binary_logloss: 0.23192\n",
      "[18]\tvalid_0's binary_logloss: 0.231264\n",
      "[19]\tvalid_0's binary_logloss: 0.23021"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is []\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[20]\tvalid_0's binary_logloss: 0.229672\n",
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      "[LightGBM] [Warning] bagging_fraction is set=0.6457369549922142, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6457369549922142\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.0002736286090580672, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002736286090580672\n",
      "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n",
      "[LightGBM] [Warning] lambda_l2 is set=0.0014099660582306847, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014099660582306847\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n",
      "[1]\tvalid_0's binary_logloss: 0.25978\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is []\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16]\tvalid_0's binary_logloss: 0.219898\n",
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      "[LightGBM] [Warning] bagging_fraction is set=0.6457369549922142, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6457369549922142\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.0002736286090580672, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002736286090580672\n",
      "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n",
      "[LightGBM] [Warning] lambda_l2 is set=0.0014099660582306847, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014099660582306847\n",
      "[LightGBM] [Warning] bagging_freq is set=6, subsample_freq=0 will be ignored. Current value: bagging_freq=6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is []\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.259221\n",
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      "[LightGBM] [Warning] bagging_fraction is set=0.6457369549922142, subsample=1.0 will be ignored. Current value: bagging_fraction=0.6457369549922142\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.0002736286090580672, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.0002736286090580672\n",
      "[LightGBM] [Warning] feature_fraction is set=0.4, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.4\n",
      "[LightGBM] [Warning] lambda_l2 is set=0.0014099660582306847, reg_lambda=0.0 will be ignored. Current value: lambda_l2=0.0014099660582306847\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.\n",
      "  _log_warning('Using categorical_feature in Dataset.')\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is []\n",
      "  _log_warning('categorical_feature in Dataset is overridden.\\n'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4]\tvalid_0's binary_logloss: 0.248242\n",
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      "[119]\tvalid_0's binary_logloss: 0.210221\n",
      "[120]\tvalid_0's binary_logloss: 0.210346\n",
      "[121]\tvalid_0's binary_logloss: 0.210398\n",
      "[122]\tvalid_0's binary_logloss: 0.210303\n",
      "[123]\tvalid_0's binary_logloss: 0.210078\n",
      "[124]\tvalid_0's binary_logloss: 0.209673\n",
      "[125]\tvalid_0's binary_logloss: 0.209741\n",
      "[126]\tvalid_0's binary_logloss: 0.209917\n",
      "[127]\tvalid_0's binary_logloss: 0.209996\n",
      "[128]\tvalid_0's binary_logloss: 0.209883\n",
      "[129]\tvalid_0's binary_logloss: 0.209998\n",
      "[130]\tvalid_0's binary_logloss: 0.21029\n",
      "[131]\tvalid_0's binary_logloss: 0.210389\n",
      "[132]\tvalid_0's binary_logloss: 0.210615\n",
      "[133]\tvalid_0's binary_logloss: 0.210492\n",
      "[134]\tvalid_0's binary_logloss: 0.211148\n",
      "[135]\tvalid_0's binary_logloss: 0.2113\n",
      "[136]\tvalid_0's binary_logloss: 0.211465\n",
      "[137]\tvalid_0's binary_logloss: 0.211955\n",
      "[138]\tvalid_0's binary_logloss: 0.212218\n",
      "[139]\tvalid_0's binary_logloss: 0.212394\n",
      "[140]\tvalid_0's binary_logloss: 0.212434\n",
      "[141]\tvalid_0's binary_logloss: 0.212704\n",
      "[142]\tvalid_0's binary_logloss: 0.212511\n",
      "[143]\tvalid_0's binary_logloss: 0.212793\n",
      "[144]\tvalid_0's binary_logloss: 0.212889\n",
      "[145]\tvalid_0's binary_logloss: 0.213005\n",
      "[146]\tvalid_0's binary_logloss: 0.213326\n",
      "[147]\tvalid_0's binary_logloss: 0.21346\n",
      "[148]\tvalid_0's binary_logloss: 0.213962\n",
      "[149]\tvalid_0's binary_logloss: 0.213973\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 26178 (\\N{CJK UNIFIED IDEOGRAPH-6642}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 20195 (\\N{CJK UNIFIED IDEOGRAPH-4EE3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 25152 (\\N{CJK UNIFIED IDEOGRAPH-6240}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 26377 (\\N{CJK UNIFIED IDEOGRAPH-6709}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 32773 (\\N{CJK UNIFIED IDEOGRAPH-8005}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 21517 (\\N{CJK UNIFIED IDEOGRAPH-540D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 31278 (\\N{CJK UNIFIED IDEOGRAPH-7A2E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 21029 (\\N{CJK UNIFIED IDEOGRAPH-5225}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 32239 (\\N{CJK UNIFIED IDEOGRAPH-7DEF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 32066 (\\N{CJK UNIFIED IDEOGRAPH-7D42}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 20102 (\\N{CJK UNIFIED IDEOGRAPH-4E86}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 32076 (\\N{CJK UNIFIED IDEOGRAPH-7D4C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 38283 (\\N{CJK UNIFIED IDEOGRAPH-958B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 37117 (\\N{CJK UNIFIED IDEOGRAPH-90FD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 36947 (\\N{CJK UNIFIED IDEOGRAPH-9053}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 24220 (\\N{CJK UNIFIED IDEOGRAPH-5E9C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 30476 (\\N{CJK UNIFIED IDEOGRAPH-770C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 32654 (\\N{CJK UNIFIED IDEOGRAPH-7F8E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 24037 (\\N{CJK UNIFIED IDEOGRAPH-5DE5}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 21697 (\\N{CJK UNIFIED IDEOGRAPH-54C1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 12399 (\\N{HIRAGANA LETTER HA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 12300 (\\N{LEFT CORNER BRACKET}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 20303 (\\N{CJK UNIFIED IDEOGRAPH-4F4F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 22312 (\\N{CJK UNIFIED IDEOGRAPH-5728}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 12301 (\\N{RIGHT CORNER BRACKET}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 22320 (\\N{CJK UNIFIED IDEOGRAPH-5730}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 20445 (\\N{CJK UNIFIED IDEOGRAPH-4FDD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 31649 (\\N{CJK UNIFIED IDEOGRAPH-7BA1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 26045 (\\N{CJK UNIFIED IDEOGRAPH-65BD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 35373 (\\N{CJK UNIFIED IDEOGRAPH-8A2D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 12398 (\\N{HIRAGANA LETTER NO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 31216 (\\N{CJK UNIFIED IDEOGRAPH-79F0}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 26847 (\\N{CJK UNIFIED IDEOGRAPH-68DF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 29702 (\\N{CJK UNIFIED IDEOGRAPH-7406}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 22243 (\\N{CJK UNIFIED IDEOGRAPH-56E3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 21448 (\\N{CJK UNIFIED IDEOGRAPH-53C8}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 36012 (\\N{CJK UNIFIED IDEOGRAPH-8CAC}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 20219 (\\N{CJK UNIFIED IDEOGRAPH-4EFB}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:57: UserWarning: Glyph 22269 (\\N{CJK UNIFIED IDEOGRAPH-56FD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 26178 (\\N{CJK UNIFIED IDEOGRAPH-6642}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 20195 (\\N{CJK UNIFIED IDEOGRAPH-4EE3}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 25152 (\\N{CJK UNIFIED IDEOGRAPH-6240}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 26377 (\\N{CJK UNIFIED IDEOGRAPH-6709}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 32773 (\\N{CJK UNIFIED IDEOGRAPH-8005}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 21517 (\\N{CJK UNIFIED IDEOGRAPH-540D}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 31278 (\\N{CJK UNIFIED IDEOGRAPH-7A2E}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 21029 (\\N{CJK UNIFIED IDEOGRAPH-5225}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 32239 (\\N{CJK UNIFIED IDEOGRAPH-7DEF}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 32066 (\\N{CJK UNIFIED IDEOGRAPH-7D42}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 20102 (\\N{CJK UNIFIED IDEOGRAPH-4E86}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 32076 (\\N{CJK UNIFIED IDEOGRAPH-7D4C}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 38283 (\\N{CJK UNIFIED IDEOGRAPH-958B}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 37117 (\\N{CJK UNIFIED IDEOGRAPH-90FD}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 36947 (\\N{CJK UNIFIED IDEOGRAPH-9053}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 24220 (\\N{CJK UNIFIED IDEOGRAPH-5E9C}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 30476 (\\N{CJK UNIFIED IDEOGRAPH-770C}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 32654 (\\N{CJK UNIFIED IDEOGRAPH-7F8E}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 24037 (\\N{CJK UNIFIED IDEOGRAPH-5DE5}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 21697 (\\N{CJK UNIFIED IDEOGRAPH-54C1}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 12399 (\\N{HIRAGANA LETTER HA}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 12300 (\\N{LEFT CORNER BRACKET}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 20303 (\\N{CJK UNIFIED IDEOGRAPH-4F4F}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 22312 (\\N{CJK UNIFIED IDEOGRAPH-5728}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 12301 (\\N{RIGHT CORNER BRACKET}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 22320 (\\N{CJK UNIFIED IDEOGRAPH-5730}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 20445 (\\N{CJK UNIFIED IDEOGRAPH-4FDD}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 31649 (\\N{CJK UNIFIED IDEOGRAPH-7BA1}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 26045 (\\N{CJK UNIFIED IDEOGRAPH-65BD}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 35373 (\\N{CJK UNIFIED IDEOGRAPH-8A2D}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 12398 (\\N{HIRAGANA LETTER NO}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 31216 (\\N{CJK UNIFIED IDEOGRAPH-79F0}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 26847 (\\N{CJK UNIFIED IDEOGRAPH-68DF}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 29702 (\\N{CJK UNIFIED IDEOGRAPH-7406}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 22243 (\\N{CJK UNIFIED IDEOGRAPH-56E3}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 21448 (\\N{CJK UNIFIED IDEOGRAPH-53C8}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 36012 (\\N{CJK UNIFIED IDEOGRAPH-8CAC}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 20219 (\\N{CJK UNIFIED IDEOGRAPH-4EFB}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/nyaggle/util/plot_importance.py:59: UserWarning: Glyph 22269 (\\N{CJK UNIFIED IDEOGRAPH-56FD}) missing from current font.\n",
      "  plt.savefig(path)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26178 (\\N{CJK UNIFIED IDEOGRAPH-6642}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20195 (\\N{CJK UNIFIED IDEOGRAPH-4EE3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 25152 (\\N{CJK UNIFIED IDEOGRAPH-6240}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26377 (\\N{CJK UNIFIED IDEOGRAPH-6709}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32773 (\\N{CJK UNIFIED IDEOGRAPH-8005}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21517 (\\N{CJK UNIFIED IDEOGRAPH-540D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31278 (\\N{CJK UNIFIED IDEOGRAPH-7A2E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21029 (\\N{CJK UNIFIED IDEOGRAPH-5225}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32239 (\\N{CJK UNIFIED IDEOGRAPH-7DEF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32066 (\\N{CJK UNIFIED IDEOGRAPH-7D42}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20102 (\\N{CJK UNIFIED IDEOGRAPH-4E86}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32076 (\\N{CJK UNIFIED IDEOGRAPH-7D4C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 38283 (\\N{CJK UNIFIED IDEOGRAPH-958B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22987 (\\N{CJK UNIFIED IDEOGRAPH-59CB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 37117 (\\N{CJK UNIFIED IDEOGRAPH-90FD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36947 (\\N{CJK UNIFIED IDEOGRAPH-9053}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 24220 (\\N{CJK UNIFIED IDEOGRAPH-5E9C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30476 (\\N{CJK UNIFIED IDEOGRAPH-770C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32654 (\\N{CJK UNIFIED IDEOGRAPH-7F8E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 24037 (\\N{CJK UNIFIED IDEOGRAPH-5DE5}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21697 (\\N{CJK UNIFIED IDEOGRAPH-54C1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 12399 (\\N{HIRAGANA LETTER HA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 12300 (\\N{LEFT CORNER BRACKET}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20303 (\\N{CJK UNIFIED IDEOGRAPH-4F4F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22312 (\\N{CJK UNIFIED IDEOGRAPH-5728}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 12301 (\\N{RIGHT CORNER BRACKET}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22320 (\\N{CJK UNIFIED IDEOGRAPH-5730}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20445 (\\N{CJK UNIFIED IDEOGRAPH-4FDD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31649 (\\N{CJK UNIFIED IDEOGRAPH-7BA1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26045 (\\N{CJK UNIFIED IDEOGRAPH-65BD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 35373 (\\N{CJK UNIFIED IDEOGRAPH-8A2D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 12398 (\\N{HIRAGANA LETTER NO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31216 (\\N{CJK UNIFIED IDEOGRAPH-79F0}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26847 (\\N{CJK UNIFIED IDEOGRAPH-68DF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 29702 (\\N{CJK UNIFIED IDEOGRAPH-7406}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22243 (\\N{CJK UNIFIED IDEOGRAPH-56E3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21448 (\\N{CJK UNIFIED IDEOGRAPH-53C8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36012 (\\N{CJK UNIFIED IDEOGRAPH-8CAC}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20219 (\\N{CJK UNIFIED IDEOGRAPH-4EFB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22269 (\\N{CJK UNIFIED IDEOGRAPH-56FD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 再度LightGBMで回す\n",
    "\n",
    "result = run_experiment(optuna_params,\n",
    "                        train[num_cols + cat_cols],\n",
    "                        train[target_col],\n",
    "                        test[num_cols + cat_cols],\n",
    "                        cv=StratifiedKFold(5, shuffle=True, random_state=42531), #cv=4,\n",
    "                        #groups=train.group\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.819061996779388, 0.8029307568438003, 0.8495410628019323, 0.8191103059581321, 0.8451185166864792, 0.8252112299800464]\n"
     ]
    }
   ],
   "source": [
    "print(result.metrics) # Evalulation metrics for each fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                       feature  importance\n",
       " 0                           緯度   88.289520\n",
       " 1                           経度   62.729690\n",
       " 2                           開始  173.593210\n",
       " 3                           終了  106.372989\n",
       " 4                        緯度*経度   86.241551\n",
       " ..                         ...         ...\n",
       " 244  都道府県 ※美工品は「所有者住所（所在都道府県）」  208.560219\n",
       " 245                        所在地  218.019808\n",
       " 246                    保管施設の名称  136.096169\n",
       " 247                       所有者名  725.456172\n",
       " 248                  管理団体又は責任者   90.217170\n",
       " \n",
       " [249 rows x 2 columns],\n",
       "                        feature  importance\n",
       " 0                           緯度   71.962060\n",
       " 1                           経度   85.899500\n",
       " 2                           開始  159.160159\n",
       " 3                           終了  120.026139\n",
       " 4                        緯度*経度   81.772959\n",
       " ..                         ...         ...\n",
       " 244  都道府県 ※美工品は「所有者住所（所在都道府県）」  191.598289\n",
       " 245                        所在地  187.986960\n",
       " 246                    保管施設の名称   57.038719\n",
       " 247                       所有者名  608.680617\n",
       " 248                  管理団体又は責任者   25.751510\n",
       " \n",
       " [249 rows x 2 columns],\n",
       "                        feature  importance\n",
       " 0                           緯度  101.844301\n",
       " 1                           経度   85.429840\n",
       " 2                           開始  214.076401\n",
       " 3                           終了  117.452120\n",
       " 4                        緯度*経度   85.638920\n",
       " ..                         ...         ...\n",
       " 244  都道府県 ※美工品は「所有者住所（所在都道府県）」  319.724361\n",
       " 245                        所在地  258.764987\n",
       " 246                    保管施設の名称  201.772869\n",
       " 247                       所有者名  752.376107\n",
       " 248                  管理団体又は責任者   98.159789\n",
       " \n",
       " [249 rows x 2 columns],\n",
       "                        feature  importance\n",
       " 0                           緯度   17.949930\n",
       " 1                           経度   56.345479\n",
       " 2                           開始  115.249249\n",
       " 3                           終了   64.994961\n",
       " 4                        緯度*経度   63.177761\n",
       " ..                         ...         ...\n",
       " 244  都道府県 ※美工品は「所有者住所（所在都道府県）」  205.605741\n",
       " 245                        所在地   64.761060\n",
       " 246                    保管施設の名称  121.019431\n",
       " 247                       所有者名  591.716581\n",
       " 248                  管理団体又は責任者   22.628910\n",
       " \n",
       " [249 rows x 2 columns],\n",
       "                        feature  importance\n",
       " 0                           緯度    3.168600\n",
       " 1                           経度   83.444090\n",
       " 2                           開始  168.784751\n",
       " 3                           終了   91.497641\n",
       " 4                        緯度*経度   45.987130\n",
       " ..                         ...         ...\n",
       " 244  都道府県 ※美工品は「所有者住所（所在都道府県）」  162.476090\n",
       " 245                        所在地  149.346529\n",
       " 246                    保管施設の名称   93.616669\n",
       " 247                       所有者名  566.737148\n",
       " 248                  管理団体又は責任者  100.324929\n",
       " \n",
       " [249 rows x 2 columns]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.importance     # Feature importance for each fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01529058 0.2473737  0.29955036 ... 0.09977635 0.02522443 0.01409199]\n",
      "[0.01474522 0.14593166 0.22391101 ... 0.04026878 0.3509301  0.0232702 ]\n"
     ]
    }
   ],
   "source": [
    "print(result.test_prediction)  # Test prediction in numpy array\n",
    "print(result.oof_prediction)   # Out-of-fold prediction in numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[target_col].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best threshold: 0.18181818181818182\n",
      "Best F1 Score: 0.37979094076655057\n"
     ]
    }
   ],
   "source": [
    "# データ全体のF1スコアを計算 & 閾値の最適化\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "def find_optimal_threshold(y_true, y_scores):\n",
    "    thresholds = np.linspace(0, 1, 100)\n",
    "    best_threshold = 0\n",
    "    best_score = 0\n",
    "\n",
    "    for threshold in thresholds:\n",
    "        y_pred = (y_scores > threshold).astype(int)\n",
    "        score = f1_score(y_true, y_pred)\n",
    "\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_threshold = threshold\n",
    "\n",
    "    return best_threshold, best_score\n",
    "\n",
    "# 例として使用するデータ（実際にはモデルからの予測値と実際のラベルを使用）\n",
    "y_true = train[target_col].values\n",
    "y_scores = result.oof_prediction\n",
    "\n",
    "best_threshold, best_f1_score = find_optimal_threshold(y_true, y_scores)\n",
    "print(f\"Best threshold: {best_threshold}\")\n",
    "print(f\"Best F1 Score: {best_f1_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 ... 0 0 0]\n",
      "(10065,)\n"
     ]
    }
   ],
   "source": [
    "# 閾値を超える確率を持つ要素を1に、それ以外を0に変換\n",
    "y_pred = (result.test_prediction > best_threshold).astype(int)\n",
    "print(y_pred)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>is_kokuhou</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10060</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10061</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10062</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10063</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10064</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10065 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_kokuhou\n",
       "0               0\n",
       "1               1\n",
       "2               1\n",
       "3               0\n",
       "4               0\n",
       "...           ...\n",
       "10060           0\n",
       "10061           0\n",
       "10062           0\n",
       "10063           0\n",
       "10064           0\n",
       "\n",
       "[10065 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission['is_kokuhou'] = y_pred\n",
    "sample_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9084\n",
       "1     981\n",
       "Name: is_kokuhou, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission[\"is_kokuhou\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission.to_csv('optuna_ver1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "vmax = .02\n",
    "bins = np.linspace(0, 10, 100)\n",
    "ax.hist(result.test_prediction, bins=bins, density=True, alpha=.5, label=\"Test\")\n",
    "ax.hist(result.oof_prediction, bins=bins, density=True, alpha=.5, label=\"OutOfFold\")\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "# ax.set_title(\"テストと学習時の予測傾向差分\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}