train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Eduardus Tjitrahardja | @edutjie | 2022*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ristek Datathon 2022: Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
    "\n",
    "from transformers import pipeline\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications.xception import Xception\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from keras.models import Sequential\n",
    "from keras import layers\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 7))\n",
    "\n",
    "    data = history.history\n",
    "    y1 = data['loss']\n",
    "    y2 = data['val_loss']\n",
    "    x = [i for i in range(len(y1))]\n",
    "    ax[0].plot(x, y1, 'g-', label='Train Loss')\n",
    "    ax[0].plot(x, y2, 'r--', label='Val Loss')\n",
    "    ax[0].set_title('Training Loss vs Validation Loss')\n",
    "    ax[0].set_xlabel(\"Epochs\")\n",
    "    ax[0].set_ylabel(\"Loss\")\n",
    "    ax[0].legend()\n",
    "\n",
    "    y1 = data['accuracy']\n",
    "    y2 = data['val_accuracy']\n",
    "    x = [i for i in range(len(y1))]\n",
    "    ax[1].plot(x, y1, 'g-', label='Train Accuracy')\n",
    "    ax[1].plot(x, y2, 'r--', label='Val Accuracy')\n",
    "    ax[1].set_title('Training Accuracy vs Validation Accuracy')\n",
    "    ax[1].set_xlabel(\"Epochs\")\n",
    "    ax[1].set_ylabel(\"Accuracy\")\n",
    "    ax[1].legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1518, 8), (1011, 8), (9936, 8), (12465, 8))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df = pd.read_csv('dataset/processed_combined.csv')\n",
    "\n",
    "train_df = combined_df[combined_df['label'].notnull()]\n",
    "test_df = combined_df[combined_df['label'].isnull() & combined_df['media'].notnull()]\n",
    "unlabeled_df = combined_df[combined_df['label'].isnull() & combined_df['media'].isnull()]\n",
    "\n",
    "train_df.shape, test_df.shape, unlabeled_df.shape, combined_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_name</th>\n",
       "      <th>url</th>\n",
       "      <th>text</th>\n",
       "      <th>media</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-23 04:06:39+00:00</td>\n",
       "      <td>1231430140824973313</td>\n",
       "      <td>133931409</td>\n",
       "      <td>r0b1 sur1a (黄玉春)</td>\n",
       "      <td>https://twitter.com/R0b1Sur1a/status/123143014...</td>\n",
       "      <td>akhir juga terobos banjir</td>\n",
       "      <td>ERbrKgFU4AAnADb.jpg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-05 01:46:46+00:00</td>\n",
       "      <td>1213637932411580417</td>\n",
       "      <td>253063316</td>\n",
       "      <td>Beradaptasi di Era Pandemi ☀️</td>\n",
       "      <td>https://twitter.com/MarikaRahman_/status/12136...</td>\n",
       "      <td>kemekes ri idi banten ibi banten ppni banten t...</td>\n",
       "      <td>ENe1HUVUEAANLFT.jpg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-18 06:22:09+00:00</td>\n",
       "      <td>1218418277946396673</td>\n",
       "      <td>64318803</td>\n",
       "      <td>rywyu</td>\n",
       "      <td>https://twitter.com/rywyu/status/1218418277946...</td>\n",
       "      <td>cikini rada banjir tdi pagi hingga gk tau apa ...</td>\n",
       "      <td>EOiw80RU0AcJ4CI.jpg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-22 23:38:00+00:00</td>\n",
       "      <td>1231362534717837313</td>\n",
       "      <td>17383917</td>\n",
       "      <td>ICALIZERS</td>\n",
       "      <td>https://twitter.com/icalizers/status/123136253...</td>\n",
       "      <td>sperma tt kala warga jakarta sedang prihatin b...</td>\n",
       "      <td>ERatrt9UYAAtLMI.jpg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-17 10:54:31+00:00</td>\n",
       "      <td>1206890412574568449</td>\n",
       "      <td>3102973556</td>\n",
       "      <td>AN</td>\n",
       "      <td>https://twitter.com/lokbin103/status/120689041...</td>\n",
       "      <td>kuis jakarta banjir parah hari tanya siapa yg ...</td>\n",
       "      <td>EL-8Z-vUcAIiIlV.jpg</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  created_at                   id     user_id  \\\n",
       "0  2020-02-23 04:06:39+00:00  1231430140824973313   133931409   \n",
       "1  2020-01-05 01:46:46+00:00  1213637932411580417   253063316   \n",
       "2  2020-01-18 06:22:09+00:00  1218418277946396673    64318803   \n",
       "3  2020-02-22 23:38:00+00:00  1231362534717837313    17383917   \n",
       "4  2019-12-17 10:54:31+00:00  1206890412574568449  3102973556   \n",
       "\n",
       "                       user_name  \\\n",
       "0               r0b1 sur1a (黄玉春)   \n",
       "1  Beradaptasi di Era Pandemi ☀️   \n",
       "2                          rywyu   \n",
       "3                      ICALIZERS   \n",
       "4                             AN   \n",
       "\n",
       "                                                 url  \\\n",
       "0  https://twitter.com/R0b1Sur1a/status/123143014...   \n",
       "1  https://twitter.com/MarikaRahman_/status/12136...   \n",
       "2  https://twitter.com/rywyu/status/1218418277946...   \n",
       "3  https://twitter.com/icalizers/status/123136253...   \n",
       "4  https://twitter.com/lokbin103/status/120689041...   \n",
       "\n",
       "                                                text                media  \\\n",
       "0                          akhir juga terobos banjir  ERbrKgFU4AAnADb.jpg   \n",
       "1  kemekes ri idi banten ibi banten ppni banten t...  ENe1HUVUEAANLFT.jpg   \n",
       "2  cikini rada banjir tdi pagi hingga gk tau apa ...  EOiw80RU0AcJ4CI.jpg   \n",
       "3  sperma tt kala warga jakarta sedang prihatin b...  ERatrt9UYAAtLMI.jpg   \n",
       "4  kuis jakarta banjir parah hari tanya siapa yg ...  EL-8Z-vUcAIiIlV.jpg   \n",
       "\n",
       "   label  \n",
       "0    0.0  \n",
       "1    0.0  \n",
       "2    0.0  \n",
       "3    0.0  \n",
       "4    0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Image and Textual Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1518, 256), (1011, 256), (1518, 300), (1011, 300), (9936, 300))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_features = pd.read_csv('dataset/image_features/densenet_train_image_features.csv')\n",
    "test_image_features = pd.read_csv('dataset/image_features/densenet_test_image_features.csv')\n",
    "\n",
    "train_text_features = pd.read_csv('dataset/text_features/train_text_features.csv')\n",
    "test_text_features = pd.read_csv('dataset/text_features/test_text_features.csv')\n",
    "unlabeled_text_features = pd.read_csv('dataset/text_features/unlabeled_text_features.csv')\n",
    "\n",
    "train_image_features.shape, test_image_features.shape, train_text_features.shape, test_text_features.shape, unlabeled_text_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_0</th>\n",
       "      <th>image_1</th>\n",
       "      <th>image_2</th>\n",
       "      <th>image_3</th>\n",
       "      <th>image_4</th>\n",
       "      <th>image_5</th>\n",
       "      <th>image_6</th>\n",
       "      <th>image_7</th>\n",
       "      <th>image_8</th>\n",
       "      <th>image_9</th>\n",
       "      <th>...</th>\n",
       "      <th>image_246</th>\n",
       "      <th>image_247</th>\n",
       "      <th>image_248</th>\n",
       "      <th>image_249</th>\n",
       "      <th>image_250</th>\n",
       "      <th>image_251</th>\n",
       "      <th>image_252</th>\n",
       "      <th>image_253</th>\n",
       "      <th>image_254</th>\n",
       "      <th>image_255</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002412</td>\n",
       "      <td>0.255406</td>\n",
       "      <td>0.073089</td>\n",
       "      <td>0.020818</td>\n",
       "      <td>0.021223</td>\n",
       "      <td>0.001798</td>\n",
       "      <td>0.008892</td>\n",
       "      <td>0.010197</td>\n",
       "      <td>0.070118</td>\n",
       "      <td>0.018849</td>\n",
       "      <td>...</td>\n",
       "      <td>0.599071</td>\n",
       "      <td>0.667941</td>\n",
       "      <td>0.591719</td>\n",
       "      <td>0.772846</td>\n",
       "      <td>1.053931</td>\n",
       "      <td>0.563559</td>\n",
       "      <td>0.788448</td>\n",
       "      <td>0.933903</td>\n",
       "      <td>0.325151</td>\n",
       "      <td>0.782075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003262</td>\n",
       "      <td>0.209445</td>\n",
       "      <td>0.076465</td>\n",
       "      <td>0.020601</td>\n",
       "      <td>0.013247</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.016001</td>\n",
       "      <td>0.010862</td>\n",
       "      <td>0.054241</td>\n",
       "      <td>0.016908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.857704</td>\n",
       "      <td>0.663097</td>\n",
       "      <td>0.489337</td>\n",
       "      <td>0.604398</td>\n",
       "      <td>1.065173</td>\n",
       "      <td>1.656941</td>\n",
       "      <td>0.733056</td>\n",
       "      <td>0.339387</td>\n",
       "      <td>1.417239</td>\n",
       "      <td>2.256201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001464</td>\n",
       "      <td>0.083154</td>\n",
       "      <td>0.018514</td>\n",
       "      <td>0.024711</td>\n",
       "      <td>0.028696</td>\n",
       "      <td>0.002615</td>\n",
       "      <td>0.012801</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>0.061622</td>\n",
       "      <td>0.019426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253258</td>\n",
       "      <td>0.468199</td>\n",
       "      <td>2.212710</td>\n",
       "      <td>0.125560</td>\n",
       "      <td>0.116755</td>\n",
       "      <td>0.070009</td>\n",
       "      <td>0.830658</td>\n",
       "      <td>0.214892</td>\n",
       "      <td>0.245031</td>\n",
       "      <td>0.456718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001842</td>\n",
       "      <td>0.070153</td>\n",
       "      <td>0.027252</td>\n",
       "      <td>0.012801</td>\n",
       "      <td>0.121898</td>\n",
       "      <td>0.001826</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.007936</td>\n",
       "      <td>0.151572</td>\n",
       "      <td>0.014932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.292904</td>\n",
       "      <td>0.090261</td>\n",
       "      <td>0.205571</td>\n",
       "      <td>0.167410</td>\n",
       "      <td>0.276909</td>\n",
       "      <td>0.267048</td>\n",
       "      <td>0.239819</td>\n",
       "      <td>0.328360</td>\n",
       "      <td>0.129048</td>\n",
       "      <td>0.646416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003214</td>\n",
       "      <td>0.142908</td>\n",
       "      <td>0.028323</td>\n",
       "      <td>0.022380</td>\n",
       "      <td>0.038958</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.010106</td>\n",
       "      <td>0.006874</td>\n",
       "      <td>0.074566</td>\n",
       "      <td>0.024453</td>\n",
       "      <td>...</td>\n",
       "      <td>1.003480</td>\n",
       "      <td>0.561325</td>\n",
       "      <td>0.628901</td>\n",
       "      <td>0.472665</td>\n",
       "      <td>1.572514</td>\n",
       "      <td>1.373152</td>\n",
       "      <td>0.611522</td>\n",
       "      <td>0.283688</td>\n",
       "      <td>0.721310</td>\n",
       "      <td>1.036716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 256 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    image_0   image_1   image_2   image_3   image_4   image_5   image_6  \\\n",
       "0  0.002412  0.255406  0.073089  0.020818  0.021223  0.001798  0.008892   \n",
       "1  0.003262  0.209445  0.076465  0.020601  0.013247  0.001746  0.016001   \n",
       "2  0.001464  0.083154  0.018514  0.024711  0.028696  0.002615  0.012801   \n",
       "3  0.001842  0.070153  0.027252  0.012801  0.121898  0.001826  0.005749   \n",
       "4  0.003214  0.142908  0.028323  0.022380  0.038958  0.001857  0.010106   \n",
       "\n",
       "    image_7   image_8   image_9  ...  image_246  image_247  image_248  \\\n",
       "0  0.010197  0.070118  0.018849  ...   0.599071   0.667941   0.591719   \n",
       "1  0.010862  0.054241  0.016908  ...   0.857704   0.663097   0.489337   \n",
       "2  0.007457  0.061622  0.019426  ...   0.253258   0.468199   2.212710   \n",
       "3  0.007936  0.151572  0.014932  ...   0.292904   0.090261   0.205571   \n",
       "4  0.006874  0.074566  0.024453  ...   1.003480   0.561325   0.628901   \n",
       "\n",
       "   image_249  image_250  image_251  image_252  image_253  image_254  image_255  \n",
       "0   0.772846   1.053931   0.563559   0.788448   0.933903   0.325151   0.782075  \n",
       "1   0.604398   1.065173   1.656941   0.733056   0.339387   1.417239   2.256201  \n",
       "2   0.125560   0.116755   0.070009   0.830658   0.214892   0.245031   0.456718  \n",
       "3   0.167410   0.276909   0.267048   0.239819   0.328360   0.129048   0.646416  \n",
       "4   0.472665   1.572514   1.373152   0.611522   0.283688   0.721310   1.036716  \n",
       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add prefix to column names\n",
    "train_image_features.columns = ['image_' + col for col in train_image_features.columns]\n",
    "test_image_features.columns = ['image_' + col for col in test_image_features.columns]\n",
    "train_image_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text_0</th>\n",
       "      <th>text_1</th>\n",
       "      <th>text_2</th>\n",
       "      <th>text_3</th>\n",
       "      <th>text_4</th>\n",
       "      <th>text_5</th>\n",
       "      <th>text_6</th>\n",
       "      <th>text_7</th>\n",
       "      <th>text_8</th>\n",
       "      <th>text_9</th>\n",
       "      <th>...</th>\n",
       "      <th>text_290</th>\n",
       "      <th>text_291</th>\n",
       "      <th>text_292</th>\n",
       "      <th>text_293</th>\n",
       "      <th>text_294</th>\n",
       "      <th>text_295</th>\n",
       "      <th>text_296</th>\n",
       "      <th>text_297</th>\n",
       "      <th>text_298</th>\n",
       "      <th>text_299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009883</td>\n",
       "      <td>-0.021286</td>\n",
       "      <td>0.025969</td>\n",
       "      <td>0.092511</td>\n",
       "      <td>0.028668</td>\n",
       "      <td>-0.028770</td>\n",
       "      <td>0.016315</td>\n",
       "      <td>-0.010510</td>\n",
       "      <td>0.008982</td>\n",
       "      <td>-0.090800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.013171</td>\n",
       "      <td>0.015966</td>\n",
       "      <td>-0.002016</td>\n",
       "      <td>0.032844</td>\n",
       "      <td>-0.006217</td>\n",
       "      <td>0.022417</td>\n",
       "      <td>-0.057426</td>\n",
       "      <td>0.017508</td>\n",
       "      <td>0.033850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.034512</td>\n",
       "      <td>0.009235</td>\n",
       "      <td>0.018049</td>\n",
       "      <td>0.045457</td>\n",
       "      <td>0.006226</td>\n",
       "      <td>-0.075077</td>\n",
       "      <td>0.004485</td>\n",
       "      <td>-0.026857</td>\n",
       "      <td>-0.048104</td>\n",
       "      <td>-0.050542</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040443</td>\n",
       "      <td>-0.033196</td>\n",
       "      <td>-0.006029</td>\n",
       "      <td>0.014043</td>\n",
       "      <td>-0.015839</td>\n",
       "      <td>0.013577</td>\n",
       "      <td>0.005713</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>-0.003548</td>\n",
       "      <td>0.020258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.037173</td>\n",
       "      <td>-0.050290</td>\n",
       "      <td>0.011853</td>\n",
       "      <td>0.118136</td>\n",
       "      <td>-0.018496</td>\n",
       "      <td>-0.046303</td>\n",
       "      <td>0.029109</td>\n",
       "      <td>0.011791</td>\n",
       "      <td>-0.015547</td>\n",
       "      <td>-0.080154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032494</td>\n",
       "      <td>-0.030817</td>\n",
       "      <td>-0.034458</td>\n",
       "      <td>0.015121</td>\n",
       "      <td>-0.031749</td>\n",
       "      <td>0.027591</td>\n",
       "      <td>-0.008889</td>\n",
       "      <td>-0.035597</td>\n",
       "      <td>-0.008871</td>\n",
       "      <td>0.061395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000976</td>\n",
       "      <td>-0.008979</td>\n",
       "      <td>0.025069</td>\n",
       "      <td>0.051203</td>\n",
       "      <td>0.021625</td>\n",
       "      <td>-0.046954</td>\n",
       "      <td>0.021365</td>\n",
       "      <td>0.008321</td>\n",
       "      <td>-0.029315</td>\n",
       "      <td>-0.015738</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010753</td>\n",
       "      <td>0.018024</td>\n",
       "      <td>-0.011522</td>\n",
       "      <td>-0.014646</td>\n",
       "      <td>0.006166</td>\n",
       "      <td>0.028416</td>\n",
       "      <td>0.047759</td>\n",
       "      <td>0.010205</td>\n",
       "      <td>-0.007385</td>\n",
       "      <td>0.051342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.029130</td>\n",
       "      <td>0.045310</td>\n",
       "      <td>0.027381</td>\n",
       "      <td>0.040282</td>\n",
       "      <td>-0.047682</td>\n",
       "      <td>0.001377</td>\n",
       "      <td>0.017470</td>\n",
       "      <td>-0.008542</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>-0.097448</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014846</td>\n",
       "      <td>-0.011152</td>\n",
       "      <td>-0.055743</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>-0.014798</td>\n",
       "      <td>0.048946</td>\n",
       "      <td>0.032517</td>\n",
       "      <td>-0.016963</td>\n",
       "      <td>-0.012313</td>\n",
       "      <td>0.021128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     text_0    text_1    text_2    text_3    text_4    text_5    text_6  \\\n",
       "0  0.009883 -0.021286  0.025969  0.092511  0.028668 -0.028770  0.016315   \n",
       "1  0.034512  0.009235  0.018049  0.045457  0.006226 -0.075077  0.004485   \n",
       "2  0.037173 -0.050290  0.011853  0.118136 -0.018496 -0.046303  0.029109   \n",
       "3  0.000976 -0.008979  0.025069  0.051203  0.021625 -0.046954  0.021365   \n",
       "4  0.029130  0.045310  0.027381  0.040282 -0.047682  0.001377  0.017470   \n",
       "\n",
       "     text_7    text_8    text_9  ...  text_290  text_291  text_292  text_293  \\\n",
       "0 -0.010510  0.008982 -0.090800  ...  0.007333  0.013171  0.015966 -0.002016   \n",
       "1 -0.026857 -0.048104 -0.050542  ... -0.040443 -0.033196 -0.006029  0.014043   \n",
       "2  0.011791 -0.015547 -0.080154  ... -0.032494 -0.030817 -0.034458  0.015121   \n",
       "3  0.008321 -0.029315 -0.015738  ... -0.010753  0.018024 -0.011522 -0.014646   \n",
       "4 -0.008542  0.011416 -0.097448  ... -0.014846 -0.011152 -0.055743  0.017050   \n",
       "\n",
       "   text_294  text_295  text_296  text_297  text_298  text_299  \n",
       "0  0.032844 -0.006217  0.022417 -0.057426  0.017508  0.033850  \n",
       "1 -0.015839  0.013577  0.005713  0.001407 -0.003548  0.020258  \n",
       "2 -0.031749  0.027591 -0.008889 -0.035597 -0.008871  0.061395  \n",
       "3  0.006166  0.028416  0.047759  0.010205 -0.007385  0.051342  \n",
       "4 -0.014798  0.048946  0.032517 -0.016963 -0.012313  0.021128  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add prefix to column names\n",
    "train_text_features.columns = ['text_' + col for col in train_text_features.columns]\n",
    "test_text_features.columns = ['text_' + col for col in test_text_features.columns]\n",
    "unlabeled_text_features.columns = ['text_' + col for col in unlabeled_text_features.columns]\n",
    "train_text_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>image_0</th>\n",
       "      <th>image_1</th>\n",
       "      <th>image_2</th>\n",
       "      <th>image_3</th>\n",
       "      <th>image_4</th>\n",
       "      <th>image_5</th>\n",
       "      <th>image_6</th>\n",
       "      <th>image_7</th>\n",
       "      <th>image_8</th>\n",
       "      <th>image_9</th>\n",
       "      <th>...</th>\n",
       "      <th>text_290</th>\n",
       "      <th>text_291</th>\n",
       "      <th>text_292</th>\n",
       "      <th>text_293</th>\n",
       "      <th>text_294</th>\n",
       "      <th>text_295</th>\n",
       "      <th>text_296</th>\n",
       "      <th>text_297</th>\n",
       "      <th>text_298</th>\n",
       "      <th>text_299</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002412</td>\n",
       "      <td>0.255406</td>\n",
       "      <td>0.073089</td>\n",
       "      <td>0.020818</td>\n",
       "      <td>0.021223</td>\n",
       "      <td>0.001798</td>\n",
       "      <td>0.008892</td>\n",
       "      <td>0.010197</td>\n",
       "      <td>0.070118</td>\n",
       "      <td>0.018849</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.013171</td>\n",
       "      <td>0.015966</td>\n",
       "      <td>-0.002016</td>\n",
       "      <td>0.032844</td>\n",
       "      <td>-0.006217</td>\n",
       "      <td>0.022417</td>\n",
       "      <td>-0.057426</td>\n",
       "      <td>0.017508</td>\n",
       "      <td>0.033850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003262</td>\n",
       "      <td>0.209445</td>\n",
       "      <td>0.076465</td>\n",
       "      <td>0.020601</td>\n",
       "      <td>0.013247</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.016001</td>\n",
       "      <td>0.010862</td>\n",
       "      <td>0.054241</td>\n",
       "      <td>0.016908</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040443</td>\n",
       "      <td>-0.033196</td>\n",
       "      <td>-0.006029</td>\n",
       "      <td>0.014043</td>\n",
       "      <td>-0.015839</td>\n",
       "      <td>0.013577</td>\n",
       "      <td>0.005713</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>-0.003548</td>\n",
       "      <td>0.020258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001464</td>\n",
       "      <td>0.083154</td>\n",
       "      <td>0.018514</td>\n",
       "      <td>0.024711</td>\n",
       "      <td>0.028696</td>\n",
       "      <td>0.002615</td>\n",
       "      <td>0.012801</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>0.061622</td>\n",
       "      <td>0.019426</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032494</td>\n",
       "      <td>-0.030817</td>\n",
       "      <td>-0.034458</td>\n",
       "      <td>0.015121</td>\n",
       "      <td>-0.031749</td>\n",
       "      <td>0.027591</td>\n",
       "      <td>-0.008889</td>\n",
       "      <td>-0.035597</td>\n",
       "      <td>-0.008871</td>\n",
       "      <td>0.061395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001842</td>\n",
       "      <td>0.070153</td>\n",
       "      <td>0.027252</td>\n",
       "      <td>0.012801</td>\n",
       "      <td>0.121898</td>\n",
       "      <td>0.001826</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.007936</td>\n",
       "      <td>0.151572</td>\n",
       "      <td>0.014932</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010753</td>\n",
       "      <td>0.018024</td>\n",
       "      <td>-0.011522</td>\n",
       "      <td>-0.014646</td>\n",
       "      <td>0.006166</td>\n",
       "      <td>0.028416</td>\n",
       "      <td>0.047759</td>\n",
       "      <td>0.010205</td>\n",
       "      <td>-0.007385</td>\n",
       "      <td>0.051342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003214</td>\n",
       "      <td>0.142908</td>\n",
       "      <td>0.028323</td>\n",
       "      <td>0.022380</td>\n",
       "      <td>0.038958</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.010106</td>\n",
       "      <td>0.006874</td>\n",
       "      <td>0.074566</td>\n",
       "      <td>0.024453</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014846</td>\n",
       "      <td>-0.011152</td>\n",
       "      <td>-0.055743</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>-0.014798</td>\n",
       "      <td>0.048946</td>\n",
       "      <td>0.032517</td>\n",
       "      <td>-0.016963</td>\n",
       "      <td>-0.012313</td>\n",
       "      <td>0.021128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 556 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    image_0   image_1   image_2   image_3   image_4   image_5   image_6  \\\n",
       "0  0.002412  0.255406  0.073089  0.020818  0.021223  0.001798  0.008892   \n",
       "1  0.003262  0.209445  0.076465  0.020601  0.013247  0.001746  0.016001   \n",
       "2  0.001464  0.083154  0.018514  0.024711  0.028696  0.002615  0.012801   \n",
       "3  0.001842  0.070153  0.027252  0.012801  0.121898  0.001826  0.005749   \n",
       "4  0.003214  0.142908  0.028323  0.022380  0.038958  0.001857  0.010106   \n",
       "\n",
       "    image_7   image_8   image_9  ...  text_290  text_291  text_292  text_293  \\\n",
       "0  0.010197  0.070118  0.018849  ...  0.007333  0.013171  0.015966 -0.002016   \n",
       "1  0.010862  0.054241  0.016908  ... -0.040443 -0.033196 -0.006029  0.014043   \n",
       "2  0.007457  0.061622  0.019426  ... -0.032494 -0.030817 -0.034458  0.015121   \n",
       "3  0.007936  0.151572  0.014932  ... -0.010753  0.018024 -0.011522 -0.014646   \n",
       "4  0.006874  0.074566  0.024453  ... -0.014846 -0.011152 -0.055743  0.017050   \n",
       "\n",
       "   text_294  text_295  text_296  text_297  text_298  text_299  \n",
       "0  0.032844 -0.006217  0.022417 -0.057426  0.017508  0.033850  \n",
       "1 -0.015839  0.013577  0.005713  0.001407 -0.003548  0.020258  \n",
       "2 -0.031749  0.027591 -0.008889 -0.035597 -0.008871  0.061395  \n",
       "3  0.006166  0.028416  0.047759  0.010205 -0.007385  0.051342  \n",
       "4 -0.014798  0.048946  0.032517 -0.016963 -0.012313  0.021128  \n",
       "\n",
       "[5 rows x 556 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine image features and text features\n",
    "X = pd.concat([train_image_features, train_text_features], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv('dataset/densenet_features.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv('dataset/densenet_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train_df['label']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_uncleaned = pd.read_csv('dataset/train.csv')\n",
    "test_df_uncleaned = pd.read_csv('dataset/test.csv')\n",
    "unlabeled_df_uncleaned = pd.read_csv('dataset/unlabeled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_name = \"w11wo/indonesian-roberta-base-indonli\"\n",
    "\n",
    "roberta_indonli = pipeline(\n",
    "    \"sentiment-analysis\",\n",
    "    model=pretrained_name,\n",
    "    tokenizer=pretrained_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RIBUT SOAL BANJIR\\n\\nNih gua orang betawi, shohibul beit nya jakarta,\\nGua bilangin ame elu, yang baru engeh ame jakarta, \\nJakarta mah emang kampung aer, ade rawa sari, rawa bunga, rawa bokor, ame rawa rawa laen nye dah, \\nYang nama nye rawa mah emang tempat aer, https://t.co/VFGMsBkKmk'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_uncleaned['text'][11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'entailment', 'score': 0.7383661866188049}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roberta_indonli(train_df_uncleaned['text'][11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'neutral', 'score': 0.6506001353263855}]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roberta_indonli(train_df['text'][11])"
   ]
  },
  {
   "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>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sentiment\n",
       "0   neutral\n",
       "1   neutral\n",
       "2   neutral\n",
       "3   neutral\n",
       "4   neutral"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_df = pd.DataFrame(columns=['sentiment'])\n",
    "sentiment_df['sentiment'] = train_df_uncleaned['text'].apply(lambda x: roberta_indonli(x)[0]['label'])\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentiment_df['sentiment'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of them are neutral so it won't have any effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save sentiment_df\n",
    "sentiment_df.to_csv('dataset/sentiment.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_df = pd.read_csv('dataset/sentiment.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>sentiment_contradiction</th>\n",
       "      <th>sentiment_entailment</th>\n",
       "      <th>sentiment_neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiment_contradiction  sentiment_entailment  sentiment_neutral\n",
       "0                        0                     0                  1\n",
       "1                        0                     0                  1\n",
       "2                        0                     0                  1\n",
       "3                        0                     0                  1\n",
       "4                        0                     0                  1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encode sentiment\n",
    "sentiment_df = pd.get_dummies(sentiment_df, columns=['sentiment'])\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emotion Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "5f282e1dfdc0483b840c9ed78b916170",
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       "version_minor": 0
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      "text/plain": [
       "Downloading:   0%|          | 0.00/988 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda3\\envs\\tf\\lib\\site-packages\\huggingface_hub\\file_download.py:127: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\edutjie\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
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     "metadata": {},
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    },
    {
     "data": {
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       "unit_divisor": 1000,
       "unit_scale": true
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db1de9291d74459c979ab82f91131a4f",
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     "metadata": {},
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   ],
   "source": [
    "# https://huggingface.co/StevenLimcorn/indonesian-roberta-base-emotion-classifier?text=Hal-hal+baik+akan+datang.\n",
    "pretrained_name = \"StevenLimcorn/indonesian-roberta-base-emotion-classifier\"\n",
    "\n",
    "roberta_emotion = pipeline(\n",
    "    \"sentiment-analysis\",\n",
    "    model=pretrained_name,\n",
    "    tokenizer=pretrained_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'happy', 'score': 0.851107120513916}]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roberta_emotion(train_df_uncleaned['text'][11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>happy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emotion\n",
       "0    happy\n",
       "1  sadness\n",
       "2    happy\n",
       "3  sadness\n",
       "4    happy"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_df = pd.DataFrame(columns=['emotion'])\n",
    "emotion_df['emotion'] = train_df_uncleaned['text'].apply(lambda x: roberta_emotion(x)[0]['label'])\n",
    "emotion_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emotion_df['emotion'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save emotion_df\n",
    "emotion_df.to_csv('dataset/emotion.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_df = pd.read_csv('dataset/emotion.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>emotion</th>\n",
       "      <th>anger</th>\n",
       "      <th>fear</th>\n",
       "      <th>happy</th>\n",
       "      <th>love</th>\n",
       "      <th>sadness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>646</td>\n",
       "      <td>2</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "emotion  anger  fear  happy  love  sadness\n",
       "label                                     \n",
       "0.0         87    51    646     2      406\n",
       "1.0          8    11     65     1      241"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a df where train_df[label] is the index and column is the emotion\n",
    "emotion_df_pivot = emotion_df.pivot_table(index=train_df['label'], columns='emotion', aggfunc=len, fill_value=0)\n",
    "emotion_df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>emotion_anger</th>\n",
       "      <th>emotion_fear</th>\n",
       "      <th>emotion_happy</th>\n",
       "      <th>emotion_love</th>\n",
       "      <th>emotion_sadness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emotion_anger  emotion_fear  emotion_happy  emotion_love  emotion_sadness\n",
       "0              0             0              1             0                0\n",
       "1              0             0              0             0                1\n",
       "2              0             0              1             0                0\n",
       "3              0             0              0             0                1\n",
       "4              0             0              1             0                0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_df = pd.get_dummies(emotion_df, columns=['emotion'])\n",
    "emotion_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    image_0   image_1   image_2   image_3   image_4   image_5   image_6  \\\n",
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       "2  0.001464  0.083154  0.018514  0.024711  0.028696  0.002615  0.012801   \n",
       "3  0.001842  0.070153  0.027252  0.012801  0.121898  0.001826  0.005749   \n",
       "4  0.003214  0.142908  0.028323  0.022380  0.038958  0.001857  0.010106   \n",
       "\n",
       "    image_7   image_8   image_9  ...  text_295  text_296  text_297  text_298  \\\n",
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       "3  0.007936  0.151572  0.014932  ...  0.028416  0.047759  0.010205 -0.007385   \n",
       "4  0.006874  0.074566  0.024453  ...  0.048946  0.032517 -0.016963 -0.012313   \n",
       "\n",
       "   text_299  emotion_anger  emotion_fear  emotion_happy  emotion_love  \\\n",
       "0  0.033850              0             0              1             0   \n",
       "1  0.020258              0             0              0             0   \n",
       "2  0.061395              0             0              1             0   \n",
       "3  0.051342              0             0              0             0   \n",
       "4  0.021128              0             0              1             0   \n",
       "\n",
       "   emotion_sadness  \n",
       "0                0  \n",
       "1                1  \n",
       "2                0  \n",
       "3                1  \n",
       "4                0  \n",
       "\n",
       "[5 rows x 561 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([X, emotion_df], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>emotion_anger</th>\n",
       "      <th>emotion_fear</th>\n",
       "      <th>emotion_happy</th>\n",
       "      <th>emotion_love</th>\n",
       "      <th>emotion_sadness</th>\n",
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      ],
      "text/plain": [
       "   emotion_anger  emotion_fear  emotion_happy  emotion_love  emotion_sadness\n",
       "0              0             0              1             0                0\n",
       "1              0             0              1             0                0\n",
       "2              0             0              1             0                0\n",
       "3              0             0              1             0                0\n",
       "4              0             0              1             0                0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_emotion_df = pd.DataFrame(columns=['emotion'])\n",
    "test_emotion_df['emotion'] = test_df_uncleaned['text'].apply(lambda x: roberta_emotion(x)[0]['label'])\n",
    "test_emotion_df = pd.get_dummies(test_emotion_df, columns=['emotion'])\n",
    "test_emotion_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save test_emotion_df\n",
    "test_emotion_df.to_csv('dataset/test_emotion.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_emotion_df = pd.read_csv('dataset/test_emotion.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unlabeled_emotion_df = pd.DataFrame(columns=['emotion'])\n",
    "# unlabeled_emotion_df['emotion'] = unlabeled_df_uncleaned['text'].apply(lambda x: roberta_emotion(x)[0]['label'])\n",
    "# unlabeled_emotion_df = pd.get_dummies(unlabeled_emotion_df, columns=['emotion'])\n",
    "# unlabeled_emotion_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is_Banjir Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "4    True\n",
       "Name: text, dtype: bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_banjir_df = pd.DataFrame(columns=['is_banjir'])\n",
    "is_banjir_df = train_df['text'].apply(lambda x: 'banjir' in x)\n",
    "is_banjir_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_banjir_df.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of them is True so it not going to have any effect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Prediction Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_train_path = \"dataset/media/Image\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_uncleaned[\"label\"] = train_df_uncleaned[\"label\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1443 validated image filenames belonging to 2 classes.\n",
      "Found 75 validated image filenames belonging to 2 classes.\n",
      "Found 1011 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "batchsize = 16\n",
    "\n",
    "train_gen = ImageDataGenerator(\n",
    "    rescale=1 / 255.0,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    validation_split=0.05,\n",
    "    horizontal_flip=True,\n",
    ")\n",
    "test_gen = ImageDataGenerator(rescale=1 / 255.0, horizontal_flip=True)\n",
    "\n",
    "train_data_generator = train_gen.flow_from_dataframe(\n",
    "    dataframe=train_df_uncleaned,\n",
    "    directory=image_train_path,\n",
    "    x_col=\"media\",\n",
    "    y_col=\"label\",\n",
    "    subset=\"training\",\n",
    "    batch_size=batchsize,\n",
    "    target_size=(256, 256),\n",
    "    class_mode=\"binary\",\n",
    "    shuffle=True,\n",
    "    seed=7,\n",
    ")\n",
    "\n",
    "valid_data_generator = train_gen.flow_from_dataframe(\n",
    "    dataframe=train_df_uncleaned,\n",
    "    directory=image_train_path,\n",
    "    x_col=\"media\",\n",
    "    y_col=\"label\",\n",
    "    subset=\"validation\",\n",
    "    batch_size=batchsize,\n",
    "    target_size=(256, 256),\n",
    "    class_mode=\"binary\",\n",
    "    shuffle=True,\n",
    "    seed=7,\n",
    ")\n",
    "\n",
    "test_data_generator = test_gen.flow_from_dataframe(\n",
    "    dataframe=test_df_uncleaned,\n",
    "    directory=image_train_path,\n",
    "    x_col=\"media\",\n",
    "    target_size=(256, 256),\n",
    "    batch_size=1,\n",
    "    class_mode=None,\n",
    "    shuffle=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "91/91 [==============================] - 48s 410ms/step - loss: 0.4247 - accuracy: 0.7990 - val_loss: 0.4732 - val_accuracy: 0.8533 - lr: 1.0000e-04\n",
      "Epoch 2/25\n",
      "91/91 [==============================] - 32s 350ms/step - loss: 0.3548 - accuracy: 0.8351 - val_loss: 0.3208 - val_accuracy: 0.8533 - lr: 1.0000e-04\n",
      "Epoch 3/25\n",
      "91/91 [==============================] - 32s 350ms/step - loss: 0.2791 - accuracy: 0.8822 - val_loss: 0.3268 - val_accuracy: 0.8533 - lr: 1.0000e-04\n",
      "Epoch 4/25\n",
      "91/91 [==============================] - 33s 355ms/step - loss: 0.2228 - accuracy: 0.9044 - val_loss: 0.3394 - val_accuracy: 0.8533 - lr: 1.0000e-05\n",
      "Epoch 5/25\n",
      "91/91 [==============================] - 34s 366ms/step - loss: 0.2177 - accuracy: 0.9092 - val_loss: 0.2726 - val_accuracy: 0.8667 - lr: 1.0000e-06\n",
      "Epoch 6/25\n",
      "91/91 [==============================] - 36s 391ms/step - loss: 0.2198 - accuracy: 0.9016 - val_loss: 0.3189 - val_accuracy: 0.8667 - lr: 1.0000e-06\n",
      "Epoch 7/25\n",
      "91/91 [==============================] - 33s 357ms/step - loss: 0.2172 - accuracy: 0.9099 - val_loss: 0.2875 - val_accuracy: 0.8800 - lr: 1.0000e-07\n",
      "Epoch 8/25\n",
      "91/91 [==============================] - 34s 374ms/step - loss: 0.2150 - accuracy: 0.9099 - val_loss: 0.3793 - val_accuracy: 0.8667 - lr: 1.0000e-08\n",
      "Epoch 9/25\n",
      "91/91 [==============================] - 34s 374ms/step - loss: 0.2166 - accuracy: 0.9002 - val_loss: 0.3423 - val_accuracy: 0.8267 - lr: 1.0000e-09\n",
      "Epoch 10/25\n",
      "91/91 [==============================] - 34s 372ms/step - loss: 0.2150 - accuracy: 0.9051 - val_loss: 0.3308 - val_accuracy: 0.8533 - lr: 1.0000e-10\n",
      "Epoch 10: early stopping\n"
     ]
    }
   ],
   "source": [
    "base_xception_model = Xception(\n",
    "    include_top=False,\n",
    "    weights=\"imagenet\",\n",
    "    input_shape=(256, 256, 3),\n",
    "    pooling=None,\n",
    "    classes=2,\n",
    "    classifier_activation=\"sigmoid\",\n",
    ")\n",
    "base_xception_model.trainable = True\n",
    "\n",
    "xception_model = Sequential()\n",
    "xception_model.add(base_xception_model)\n",
    "xception_model.add(layers.GlobalAveragePooling2D())\n",
    "xception_model.add(layers.Dense(1, activation=\"sigmoid\"))\n",
    "xception_model.compile(\n",
    "    optimizer=Adam(learning_rate=0.0001),\n",
    "    loss=\"binary_crossentropy\",\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "\n",
    "reduce_lr_callback = ReduceLROnPlateau(\n",
    "    monitor=\"val_loss\",\n",
    "    factor=0.1,\n",
    "    patience=1,\n",
    "    verbose=0,\n",
    "    mode=\"min\",\n",
    "    cooldown=0,\n",
    "    min_lr=0,\n",
    ")\n",
    "early_stop_callback = EarlyStopping(monitor=\"val_loss\", patience=5, verbose=1)\n",
    "\n",
    "xception_model_history = xception_model.fit(\n",
    "    train_data_generator, validation_data=valid_data_generator, epochs=25, callbacks=[reduce_lr_callback, early_stop_callback]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(xception_model_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 2s 302ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.82      0.80        60\n",
      "           1       0.15      0.13      0.14        15\n",
      "\n",
      "    accuracy                           0.68        75\n",
      "   macro avg       0.47      0.47      0.47        75\n",
      "weighted avg       0.66      0.68      0.67        75\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(valid_data_generator.classes, xception_model.predict(valid_data_generator).round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split train and validation set\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE to solve imbalance data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of X_train before SMOTE:  (1214, 561)\n",
      "Size of X_train after SMOTE:  (1896, 561)\n"
     ]
    }
   ],
   "source": [
    "smt = SMOTE()\n",
    "print(\"Size of X_train before SMOTE: \", X_train.shape)\n",
    "X_train, y_train = smt.fit_resample(X_train, y_train)\n",
    "print(\"Size of X_train after SMOTE: \", X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_model = Sequential()\n",
    "dl_model.add(layers.Dense(16, input_dim=X_train.shape[1], activation='relu'))\n",
    "dl_model.add(layers.Dropout(0.2))\n",
    "dl_model.add(layers.Dense(32, activation='relu'))\n",
    "dl_model.add(layers.Dropout(0.2))\n",
    "dl_model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "# compile the model\n",
    "dl_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "119/119 [==============================] - 1s 6ms/step - loss: 0.5915 - accuracy: 0.6857 - val_loss: 0.4587 - val_accuracy: 0.7928\n",
      "Epoch 2/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.4473 - accuracy: 0.8012 - val_loss: 0.3231 - val_accuracy: 0.8388\n",
      "Epoch 3/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.3998 - accuracy: 0.8254 - val_loss: 0.4204 - val_accuracy: 0.7895\n",
      "Epoch 4/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.3699 - accuracy: 0.8581 - val_loss: 0.3664 - val_accuracy: 0.8224\n",
      "Epoch 5/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.3324 - accuracy: 0.8581 - val_loss: 0.3533 - val_accuracy: 0.8322\n",
      "Epoch 6/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.3241 - accuracy: 0.8729 - val_loss: 0.3471 - val_accuracy: 0.8454\n",
      "Epoch 7/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.3030 - accuracy: 0.8855 - val_loss: 0.5025 - val_accuracy: 0.7664\n",
      "Epoch 8/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.2933 - accuracy: 0.8898 - val_loss: 0.3836 - val_accuracy: 0.8289\n",
      "Epoch 9/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.2781 - accuracy: 0.8956 - val_loss: 0.3166 - val_accuracy: 0.8520\n",
      "Epoch 10/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.2613 - accuracy: 0.9024 - val_loss: 0.3902 - val_accuracy: 0.8355\n",
      "Epoch 11/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.2622 - accuracy: 0.9030 - val_loss: 0.3005 - val_accuracy: 0.8783\n",
      "Epoch 12/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.2516 - accuracy: 0.9093 - val_loss: 0.3093 - val_accuracy: 0.8651\n",
      "Epoch 13/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.2438 - accuracy: 0.9082 - val_loss: 0.3374 - val_accuracy: 0.8487\n",
      "Epoch 14/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.2287 - accuracy: 0.9151 - val_loss: 0.3283 - val_accuracy: 0.8684\n",
      "Epoch 15/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.2150 - accuracy: 0.9167 - val_loss: 0.3684 - val_accuracy: 0.8586\n",
      "Epoch 16/25\n",
      "119/119 [==============================] - 0s 4ms/step - loss: 0.2169 - accuracy: 0.9188 - val_loss: 0.3469 - val_accuracy: 0.8618\n",
      "Epoch 17/25\n",
      "119/119 [==============================] - 0s 4ms/step - loss: 0.2082 - accuracy: 0.9272 - val_loss: 0.3217 - val_accuracy: 0.8684\n",
      "Epoch 18/25\n",
      "119/119 [==============================] - 0s 4ms/step - loss: 0.2043 - accuracy: 0.9235 - val_loss: 0.3140 - val_accuracy: 0.8816\n",
      "Epoch 19/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.1951 - accuracy: 0.9251 - val_loss: 0.3109 - val_accuracy: 0.8947\n",
      "Epoch 20/25\n",
      "119/119 [==============================] - 0s 4ms/step - loss: 0.1952 - accuracy: 0.9256 - val_loss: 0.3978 - val_accuracy: 0.8553\n",
      "Epoch 21/25\n",
      "119/119 [==============================] - 1s 4ms/step - loss: 0.1875 - accuracy: 0.9256 - val_loss: 0.3211 - val_accuracy: 0.8816\n",
      "Epoch 22/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.1871 - accuracy: 0.9299 - val_loss: 0.3307 - val_accuracy: 0.8914\n",
      "Epoch 23/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.1690 - accuracy: 0.9388 - val_loss: 0.3370 - val_accuracy: 0.8914\n",
      "Epoch 24/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.1730 - accuracy: 0.9362 - val_loss: 0.3051 - val_accuracy: 0.9046\n",
      "Epoch 25/25\n",
      "119/119 [==============================] - 1s 5ms/step - loss: 0.1751 - accuracy: 0.9320 - val_loss: 0.3978 - val_accuracy: 0.8520\n"
     ]
    }
   ],
   "source": [
    "dl_model_history = dl_model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=25, batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(dl_model_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 2ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       244\n",
      "           1       0.75      0.63      0.68        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.83      0.79      0.81       304\n",
      "weighted avg       0.88      0.88      0.88       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, dl_model.predict(X_val).round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 3ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.86      0.90       244\n",
      "           1       0.59      0.82      0.69        60\n",
      "\n",
      "    accuracy                           0.85       304\n",
      "   macro avg       0.77      0.84      0.79       304\n",
      "weighted avg       0.88      0.85      0.86       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SMOTE public submission: 0.80054 (HIGHEST)\n",
    "print(classification_report(y_val, dl_model.predict(X_val).round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost (worse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.011192\n",
      "0:\tlearn: 0.6844600\ttotal: 807ms\tremaining: 13m 26s\n",
      "1:\tlearn: 0.6763522\ttotal: 1.41s\tremaining: 11m 45s\n",
      "2:\tlearn: 0.6679989\ttotal: 2.13s\tremaining: 11m 47s\n",
      "3:\tlearn: 0.6602577\ttotal: 2.73s\tremaining: 11m 18s\n",
      "4:\tlearn: 0.6522008\ttotal: 3.26s\tremaining: 10m 49s\n",
      "5:\tlearn: 0.6458969\ttotal: 3.86s\tremaining: 10m 39s\n",
      "6:\tlearn: 0.6380849\ttotal: 4.4s\tremaining: 10m 24s\n",
      "7:\tlearn: 0.6308302\ttotal: 5.07s\tremaining: 10m 29s\n",
      "8:\tlearn: 0.6248208\ttotal: 5.7s\tremaining: 10m 27s\n",
      "9:\tlearn: 0.6185348\ttotal: 6.35s\tremaining: 10m 28s\n",
      "10:\tlearn: 0.6113217\ttotal: 7.02s\tremaining: 10m 31s\n",
      "11:\tlearn: 0.6048992\ttotal: 7.63s\tremaining: 10m 28s\n",
      "12:\tlearn: 0.5978653\ttotal: 8.26s\tremaining: 10m 27s\n",
      "13:\tlearn: 0.5915119\ttotal: 8.83s\tremaining: 10m 22s\n",
      "14:\tlearn: 0.5855433\ttotal: 9.44s\tremaining: 10m 20s\n",
      "15:\tlearn: 0.5781164\ttotal: 10.1s\tremaining: 10m 19s\n",
      "16:\tlearn: 0.5715703\ttotal: 10.7s\tremaining: 10m 17s\n",
      "17:\tlearn: 0.5653782\ttotal: 11.3s\tremaining: 10m 18s\n",
      "18:\tlearn: 0.5592852\ttotal: 11.9s\tremaining: 10m 16s\n",
      "19:\tlearn: 0.5538863\ttotal: 12.6s\tremaining: 10m 15s\n",
      "20:\tlearn: 0.5481603\ttotal: 13.2s\tremaining: 10m 17s\n",
      "21:\tlearn: 0.5421576\ttotal: 13.9s\tremaining: 10m 16s\n",
      "22:\tlearn: 0.5364446\ttotal: 14.5s\tremaining: 10m 16s\n",
      "23:\tlearn: 0.5316159\ttotal: 15.1s\tremaining: 10m 15s\n",
      "24:\tlearn: 0.5260597\ttotal: 15.7s\tremaining: 10m 12s\n",
      "25:\tlearn: 0.5216863\ttotal: 16.3s\tremaining: 10m 10s\n",
      "26:\tlearn: 0.5160305\ttotal: 16.9s\tremaining: 10m 7s\n",
      "27:\tlearn: 0.5102545\ttotal: 17.4s\tremaining: 10m 5s\n",
      "28:\tlearn: 0.5050897\ttotal: 18.1s\tremaining: 10m 5s\n",
      "29:\tlearn: 0.5008924\ttotal: 18.6s\tremaining: 10m 2s\n",
      "30:\tlearn: 0.4954054\ttotal: 19.3s\tremaining: 10m 3s\n",
      "31:\tlearn: 0.4904189\ttotal: 19.9s\tremaining: 10m 1s\n",
      "32:\tlearn: 0.4854585\ttotal: 20.5s\tremaining: 10m\n",
      "33:\tlearn: 0.4807022\ttotal: 21s\tremaining: 9m 57s\n",
      "34:\tlearn: 0.4763349\ttotal: 21.7s\tremaining: 9m 57s\n",
      "35:\tlearn: 0.4719977\ttotal: 22.2s\tremaining: 9m 55s\n",
      "36:\tlearn: 0.4667034\ttotal: 22.8s\tremaining: 9m 52s\n",
      "37:\tlearn: 0.4626714\ttotal: 23.4s\tremaining: 9m 52s\n",
      "38:\tlearn: 0.4577887\ttotal: 24s\tremaining: 9m 50s\n",
      "39:\tlearn: 0.4534833\ttotal: 24.6s\tremaining: 9m 50s\n",
      "40:\tlearn: 0.4487700\ttotal: 25.2s\tremaining: 9m 48s\n",
      "41:\tlearn: 0.4443160\ttotal: 25.8s\tremaining: 9m 48s\n",
      "42:\tlearn: 0.4400529\ttotal: 26.5s\tremaining: 9m 49s\n",
      "43:\tlearn: 0.4354023\ttotal: 27.1s\tremaining: 9m 48s\n",
      "44:\tlearn: 0.4317100\ttotal: 27.6s\tremaining: 9m 46s\n",
      "45:\tlearn: 0.4277061\ttotal: 28.2s\tremaining: 9m 45s\n",
      "46:\tlearn: 0.4244923\ttotal: 28.8s\tremaining: 9m 44s\n",
      "47:\tlearn: 0.4207433\ttotal: 29.4s\tremaining: 9m 43s\n",
      "48:\tlearn: 0.4169818\ttotal: 30s\tremaining: 9m 42s\n",
      "49:\tlearn: 0.4137716\ttotal: 30.6s\tremaining: 9m 41s\n",
      "50:\tlearn: 0.4095944\ttotal: 31.2s\tremaining: 9m 41s\n",
      "51:\tlearn: 0.4056278\ttotal: 31.8s\tremaining: 9m 39s\n",
      "52:\tlearn: 0.4020598\ttotal: 32.4s\tremaining: 9m 39s\n",
      "53:\tlearn: 0.3987163\ttotal: 33s\tremaining: 9m 38s\n",
      "54:\tlearn: 0.3961542\ttotal: 33.6s\tremaining: 9m 37s\n",
      "55:\tlearn: 0.3929423\ttotal: 34.2s\tremaining: 9m 36s\n",
      "56:\tlearn: 0.3899360\ttotal: 34.7s\tremaining: 9m 34s\n",
      "57:\tlearn: 0.3868429\ttotal: 35.3s\tremaining: 9m 33s\n",
      "58:\tlearn: 0.3835713\ttotal: 35.9s\tremaining: 9m 33s\n",
      "59:\tlearn: 0.3805913\ttotal: 36.6s\tremaining: 9m 32s\n",
      "60:\tlearn: 0.3774203\ttotal: 37.2s\tremaining: 9m 32s\n",
      "61:\tlearn: 0.3746500\ttotal: 37.8s\tremaining: 9m 31s\n",
      "62:\tlearn: 0.3716006\ttotal: 38.3s\tremaining: 9m 29s\n",
      "63:\tlearn: 0.3687423\ttotal: 38.9s\tremaining: 9m 28s\n",
      "64:\tlearn: 0.3660209\ttotal: 39.5s\tremaining: 9m 27s\n",
      "65:\tlearn: 0.3632290\ttotal: 40.1s\tremaining: 9m 27s\n",
      "66:\tlearn: 0.3601452\ttotal: 40.7s\tremaining: 9m 26s\n",
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      "68:\tlearn: 0.3546233\ttotal: 41.9s\tremaining: 9m 24s\n",
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      "70:\tlearn: 0.3501198\ttotal: 43s\tremaining: 9m 22s\n",
      "71:\tlearn: 0.3477840\ttotal: 43.7s\tremaining: 9m 22s\n",
      "72:\tlearn: 0.3451620\ttotal: 44.2s\tremaining: 9m 21s\n",
      "73:\tlearn: 0.3425315\ttotal: 44.8s\tremaining: 9m 21s\n",
      "74:\tlearn: 0.3398616\ttotal: 45.4s\tremaining: 9m 20s\n",
      "75:\tlearn: 0.3377142\ttotal: 46s\tremaining: 9m 19s\n",
      "76:\tlearn: 0.3350042\ttotal: 46.6s\tremaining: 9m 19s\n",
      "77:\tlearn: 0.3324198\ttotal: 47.3s\tremaining: 9m 18s\n",
      "78:\tlearn: 0.3295246\ttotal: 47.9s\tremaining: 9m 18s\n",
      "79:\tlearn: 0.3273135\ttotal: 48.5s\tremaining: 9m 17s\n",
      "80:\tlearn: 0.3245887\ttotal: 49s\tremaining: 9m 16s\n"
     ]
    }
   ],
   "source": [
    "clf_base_model = CatBoostClassifier(max_depth=9, random_state=42)\n",
    "clf_base_model.fit(X_train, y_train, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.95      0.91       244\n",
      "         1.0       0.68      0.43      0.53        60\n",
      "\n",
      "    accuracy                           0.85       304\n",
      "   macro avg       0.78      0.69      0.72       304\n",
      "weighted avg       0.84      0.85      0.83       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# VGG19\n",
    "print(classification_report(y_val, clf_base_model.predict(X_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.88      0.98      0.93       244\n",
      "         1.0       0.87      0.45      0.59        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.88      0.72      0.76       304\n",
      "weighted avg       0.88      0.88      0.86       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# with max_depth=9\n",
    "print(classification_report(y_val, clf_base_model.predict(X_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export model\n",
    "clf_base_model.save_model('catboost_base_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.88      0.97      0.92       244\n",
      "         1.0       0.79      0.45      0.57        60\n",
      "\n",
      "    accuracy                           0.87       304\n",
      "   macro avg       0.84      0.71      0.75       304\n",
      "weighted avg       0.86      0.87      0.85       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# VGG19 after feature engineering\n",
    "print(classification_report(y_val, clf_base_model.predict(X_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export model after feature engineering\n",
    "clf_base_model.save_model('catboost_after_fe_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, clf_base_model.predict(X_val))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.89      0.97      0.93       244\n",
      "         1.0       0.81      0.50      0.62        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.85      0.74      0.77       304\n",
      "weighted avg       0.87      0.88      0.87       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DENSENET after feature engineering\n",
    "print(classification_report(y_val, clf_base_model.predict(X_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, clf_base_model.predict(X_val))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DENSENET export model after feature engineering\n",
    "clf_base_model.save_model('densenet_catboost_after_fe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1ef4852d450>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model\n",
    "clf_base_model = CatBoostClassifier()\n",
    "clf_base_model.load_model('densenet_catboost_after_fe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.91      0.93       244\n",
      "         1.0       0.68      0.75      0.71        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.81      0.83      0.82       304\n",
      "weighted avg       0.89      0.88      0.88       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DENSENET after feature engineering with SMOTE\n",
    "print(classification_report(y_val, clf_base_model.predict(X_val)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda3\\envs\\tf\\lib\\site-packages\\lightgbm\\engine.py:181: 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",
      "d:\\miniconda3\\envs\\tf\\lib\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 500 rounds\n",
      "[100]\ttraining's rmse: 0.36109\tvalid_1's rmse: 0.391534\n",
      "[200]\ttraining's rmse: 0.298995\tvalid_1's rmse: 0.353301\n",
      "[300]\ttraining's rmse: 0.262192\tvalid_1's rmse: 0.338773\n",
      "[400]\ttraining's rmse: 0.235998\tvalid_1's rmse: 0.330125\n",
      "[500]\ttraining's rmse: 0.216404\tvalid_1's rmse: 0.32533\n",
      "[600]\ttraining's rmse: 0.200333\tvalid_1's rmse: 0.322218\n",
      "[700]\ttraining's rmse: 0.186182\tvalid_1's rmse: 0.320103\n",
      "[800]\ttraining's rmse: 0.173636\tvalid_1's rmse: 0.318225\n",
      "[900]\ttraining's rmse: 0.162464\tvalid_1's rmse: 0.317584\n",
      "[1000]\ttraining's rmse: 0.15247\tvalid_1's rmse: 0.316088\n",
      "[1100]\ttraining's rmse: 0.14335\tvalid_1's rmse: 0.315225\n",
      "[1200]\ttraining's rmse: 0.134852\tvalid_1's rmse: 0.314919\n",
      "[1300]\ttraining's rmse: 0.127195\tvalid_1's rmse: 0.314501\n",
      "[1400]\ttraining's rmse: 0.120228\tvalid_1's rmse: 0.31406\n",
      "[1500]\ttraining's rmse: 0.113879\tvalid_1's rmse: 0.313753\n",
      "[1600]\ttraining's rmse: 0.107934\tvalid_1's rmse: 0.313135\n",
      "[1700]\ttraining's rmse: 0.102496\tvalid_1's rmse: 0.312912\n",
      "[1800]\ttraining's rmse: 0.0974576\tvalid_1's rmse: 0.312384\n",
      "[1900]\ttraining's rmse: 0.0927098\tvalid_1's rmse: 0.311978\n",
      "[2000]\ttraining's rmse: 0.088352\tvalid_1's rmse: 0.311661\n",
      "[2100]\ttraining's rmse: 0.0844671\tvalid_1's rmse: 0.311422\n",
      "[2200]\ttraining's rmse: 0.0809668\tvalid_1's rmse: 0.311235\n",
      "[2300]\ttraining's rmse: 0.0778865\tvalid_1's rmse: 0.311156\n",
      "[2400]\ttraining's rmse: 0.0752431\tvalid_1's rmse: 0.311174\n",
      "[2500]\ttraining's rmse: 0.0736712\tvalid_1's rmse: 0.311259\n",
      "[2600]\ttraining's rmse: 0.0736712\tvalid_1's rmse: 0.311259\n",
      "[2700]\ttraining's rmse: 0.0736712\tvalid_1's rmse: 0.311259\n",
      "Early stopping, best iteration is:\n",
      "[2281]\ttraining's rmse: 0.0784708\tvalid_1's rmse: 0.311109\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    \"application\": \"regression\",\n",
    "    \"boosting\": \"gbdt\",\n",
    "    \"metric\": \"rmse\",\n",
    "    \"num_leaves\": 70,\n",
    "    \"max_depth\": 9,\n",
    "    \"learning_rate\": 0.01,\n",
    "    \"bagging_fraction\": 0.85,\n",
    "    \"feature_fraction\": 0.8,\n",
    "    \"min_split_gain\": 0.02,\n",
    "    \"min_child_samples\": 150,\n",
    "    \"min_child_weight\": 0.02,\n",
    "    \"lambda_l2\": 0.0475,\n",
    "    \"verbosity\": -1,\n",
    "    \"data_random_seed\": 17,\n",
    "}\n",
    "\n",
    "# Additional parameters:\n",
    "early_stop = 500\n",
    "verbose_eval = 100\n",
    "num_rounds = 10000\n",
    "\n",
    "d_train = lgb.Dataset(X_train, label=y_train)\n",
    "d_valid = lgb.Dataset(X_val, label=y_val)\n",
    "valid_sets = [d_train, d_valid]\n",
    "\n",
    "lgbm_model = lgb.train(\n",
    "    params,\n",
    "    train_set=d_train,\n",
    "    num_boost_round=num_rounds,\n",
    "    valid_sets=valid_sets,\n",
    "    verbose_eval=verbose_eval,\n",
    "    early_stopping_rounds=early_stop,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.96      0.94       244\n",
      "           1       0.80      0.65      0.72        60\n",
      "\n",
      "    accuracy                           0.90       304\n",
      "   macro avg       0.86      0.80      0.83       304\n",
      "weighted avg       0.89      0.90      0.89       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, lgbm_model.predict(X_val).round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.91      0.91       244\n",
      "           1       0.63      0.65      0.64        60\n",
      "\n",
      "    accuracy                           0.86       304\n",
      "   macro avg       0.77      0.78      0.77       304\n",
      "weighted avg       0.86      0.86      0.86       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SMOTE\n",
    "print(classification_report(y_val, lgbm_model.predict(X_val).round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD4CAYAAADSIzzWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWTElEQVR4nO3de7jVVZ3H8fcHECtsAkUREa8RkzSFl8rRvA2jkZqXLib5KBrTEQdKe/LupKNPNTaljjlGnQYFTDAUUSQyCC1Fw0QlBPEC5AXmACreIeWc/Z0/9g9nezzn7H0O+5zF/vF5+azn7L1+e6/fkuc8Hxbrt36/pYjAzMy6XrfUHTAz21o5gM3MEnEAm5kl4gA2M0vEAWxmlkiPzj7BxpdWeJmFvc8HdzkkdRdsC9T4ziptbhvtyZxt+u612efbHJ0ewGZmXarQlLoHFXMAm1m+RCF1DyrmADazfCk4gM3MkgiPgM3MEmlqTN2DinkZmpnlS6Gp8tIGSQMl3SvpCUlLJJ2d1f9Y0pOSFkmaLql3Vr+HpA2SFmbl5+W66hGwmeVL9aYgGoHvRsSjkj4MPCJpDjAHuCgiGiX9CLgIuCD7zvKIGFrpCRzAZpYvVboIFxENQEP2+g1JS4EBETG75GPzga909ByegjCzXIkoVFwk1UlaUFLqWmpT0h7AvsBDzQ59A/htyfs9JT0m6Y+Syt5t5BGwmeVLO0bAEVEP1Lf1GUnbAdOAcyLi9ZL6SyhOU9ycVTUAu0XEy5L2B+6QNKT0O805gM0sX5o2Vq0pSdtQDN+bI+L2kvrTgWOBYZHtahERbwNvZ68fkbQc+BiwoLX2HcBmli9VuggnScB4YGlEXF1SPxw4HzgsItaX1O8IrIuIJkl7AYOAFW2dwwFsZvlSvTvhDgZOBR6XtDCruxj4KbAtMKeY0cyPiNHAocAVkjYCBWB0RKxr6wQOYDPLlyqNgCNiHtDS09JmtfL5aRSnKyrmADazfPGzIMzM0ohC9S7CdTYHsJnli0fAZmaJ+GloZmaJeEcMM7NEPAI2M0vEc8BmZonU0APZHcBmli8eAZuZpRHhi3BmZml4BGxmlohXQZiZJeIRsJlZIl4FYWaWSA1NQXhTTjPLl0Kh8tIGSQMl3SvpCUlLJJ2d1W8vaY6kZ7KffbJ6SfqppGWSFknar1xXHcBmli9VCmCKG25+NyL2AQ4ExkjaB7gQmBsRg4C52XuAL1DchmgQUAeMK3cCB7CZ5UsUKi9tNRPREBGPZq/fAJYCA4DjgYnZxyYCJ2SvjwcmRdF8oLek/m2dw3PAZpYv7bgIJ6mO4mh1k/psq/rmn9sD2Bd4COgXEQ3ZodVAv+z1AOCFkq+tzOoaaIUD2MzypR3L0LKwfV/glpK0HcW93s6JiNezjTg3fT8kRQd76gA2s5yp4ioISdtQDN+bI+L2rHqNpP4R0ZBNMazN6lcBA0u+vmtW1yrPAZtZvlRvFYSA8cDSiLi65NAMYGT2eiRwZ0n9adlqiAOB10qmKlrkEbCZ5Uv17oQ7GDgVeFzSwqzuYuBKYKqkUcBzwEnZsVnA0cAyYD1wRrkTOIDNLF+iw1OyzZqJeYBaOTyshc8HMKY953AAm1m+NPpWZDOzNGroVmQHsJnli5+GZmaWSJXmgLuCA9jM8sUjYDOzRBzAZmZpRJM35TQzS8MjYDOzRLwMzcwskYJXQZiZpeEpCDOzRGroIpwfR1klDWte5IyxF3DcKXUcf8qZ3DT1DgCuq5/EiaedxZdHjuGb51zM2hdffs/3Hl/6FJ869Bhm33t/gl5bV/tl/VX878q/sPCxue/W9enTm7tnTWHpknncPWsKvXt/JGEPc6B6e8J1OgdwlfTo3p3zvvVNZtxcz+T6a7jl9pks/+tznHHKl5k+aRzTJl7PYQd/lnE3Tn73O01NTVzzsxs56NNlN0+1nJg0aSrHHHvKe+ouOH8M99w7j48P+Rz33DuPC85v1wO1rLlCVF4ScwBXyY59t2efwR8FoFevD7HX7gNZ8+LLbNer17uf2bDhb5TsZsLk22Zw5OEHs32f3l3cW0vl/nkPse6VV99T98Uvfp5JN90KwKSbbuW444Yn6FmOVGlTzq5Qdg5Y0t9T3O1zQFa1CpgREUs7s2O1bFXDGpY+s5xPDhkMwLW/mMCMu+fy4V69uOG6KwFY8+JLzL3vQW647kcsXvp0yu5aYv126svq1cVdbVavXku/nfom7lGNq+LIVtINwLHA2oj4RFb3a2Bw9pHewKsRMTTbuHMp8FR2bH5EjG6r/TZHwJIuAG6h+FDiP2dFwBRJF7bxvTpJCyQt+J9JU9r+P8yZ9es38J1Lvs8F3z7z3dHv2WeeztzpN3HMUUcwedpdAPzo2l/wnbO+Qbdu/keIvVfU0MNktkRRKFRcKjABeM8/SSLiaxExNCKGUtwv7vaSw8s3HSsXvlB+BDwKGBIRG0srJV0NLKG4Ncf7lO40uvGlFVvNb9PGxkbOueT7HHPUERx5+MHvO37sUUdw1rmXMvZfTmXJk89w3mXFP75XXnud+//0MN27d2fYoQd1dbctsTVrX2LnnXdi9eq17LzzTu+7UGvtVMVVEBFxXzayfZ9sz7iTgH/qaPvlhl8FYJcW6vtnxywTEVz6H//FXrsPZOTJX3q3/rkX/n9T1Hvu/xN77r4rAL+7bQKzp01k9rSJHHX45/i3c8c4fLdSM++azWmnfhWA0079Knfd9bvEPapxXXcR7hBgTUQ8U1K3p6THJP1R0iHlGig3Aj4HmCvpGeCFrG434KPA2A50OLceW7SEu+6ey6C99+DLI4tXsc8+cyS3z5zNs8+vRN3ELjvvxKXnfStxTy2lX910PYcd+o/07bs9z65YwOVX/IQf/fh6bpn8c844fQTPP7+Sk79e9l+u1pZ2LC+TVAfUlVTVZ/+Cr8QIoHSOtQHYLSJelrQ/cIekIRHxeqvnLzffJKkb8BneexHu4YioaJy/NU1BWOU+uEvZwYFthRrfWdXaJpgVe+vSkyvOnF5X3FL2fNkUxMxNF+Gyuh4Us3D/iFjZyvf+AJwbEQtaa7vsKoiIKADzy33OzGyL0DXLy/4ZeLI0fCXtCKyLiCZJewGDgBVtNeJL8GaWL1WcA5Y0BfgTMFjSSkmjskMn897pB4BDgUWSFgK3AaMjYl1b7ftZEGaWK9FY1VUQI1qpP72FumkUl6VVzAFsZvmyBdxiXCkHsJnlyxZwi3GlHMBmli8eAZuZpREOYDOzRKp4Ea6zOYDNLF88AjYzS8QBbGaWRi09ztMBbGb54hGwmVkiDmAzszSi0TdimJmlUTv56wA2s3zxjRhmZqk4gM3MEvEUhJlZGrU0BeEdMcwsV6IxKi7lSLpB0lpJi0vq/l3SKkkLs3J0ybGLJC2T9JSkz5dr3wFsZvlSaEcpbwIwvIX6ayJiaFZmAUjah+JWRUOy7/xMUve2GncAm1muRKHyUratiPuANvd1K3E8cEtEvB0RfwWWUdxRvlUOYDPLl3aMgCXVSVpQUuoqPMtYSYuyKYo+Wd0A4IWSz6zM6lrlADazXGnPCDgi6iPigJJSX8EpxgF7A0OBBuCqjvbVqyDMLFeisZPbj1iz6bWkXwIzs7ergIElH901q2uVR8BmlivVnANuiaT+JW9PBDatkJgBnCxpW0l7AoOAP7fVlkfAZpYr1dwUWdIU4HCgr6SVwGXA4ZKGAgE8C5wJEBFLJE0FngAagTER0eb+SA5gM8uXUPWaihjRQvX4Nj7/A+AHlbbvADazXKnmCLizOYDNLFeiUL0RcGdzAJtZrhSaHMBmZkl4CsLMLBFPQZiZJVJDu9I7gM0sXzwCNjNLxBfhzMwS8QjYzCyRqOKdcJ3NAWxmueJlaGZmiRQ8AjYzS8NTEGZmiXgVhJlZIrW0CsI7YphZrhRCFZdysk0310paXFL3Y0lPZptyTpfUO6vfQ9IGSQuz8vNy7TuAzSxXIlRxqcAEYHizujnAJyLik8DTwEUlx5ZHxNCsjC7XuAPYzHIlovJSvq24D1jXrG52xLtbf86nuPlmhziAzSxX2jMFIalO0oKSUtfO030D+G3J+z0lPSbpj5IOKfdlX4Qzs1wptOMiXETUA/UdOY+kSyhuvnlzVtUA7BYRL0vaH7hD0pCIeL21NhzAZpYrXXEjhqTTgWOBYRHFyYyIeBt4O3v9iKTlwMeABa210+kB3H+v5vPXZvDJHfZM3QXLqc6+EUPScOB84LCIWF9SvyOwLiKaJO0FDAJWtNWWR8BmlivVHAFLmgIcDvSVtBK4jOKqh22BOZIA5mcrHg4FrpC0ESgAoyNiXYsNZxzAZpYr1dwQIyJGtFA9vpXPTgOmtad9B7CZ5UpToXYWdzmAzSxXauhplA5gM8uXoHaeBeEANrNcKXhXZDOzNAoeAZuZpeEpCDOzRJocwGZmaXgVhJlZIg5gM7NEPAdsZpZIDW0J5wA2s3zxMjQzs0SaUnegHRzAZpYrBXkEbGaWRA3diexNOc0sXwrtKOVIukHSWkmLS+q2lzRH0jPZzz5ZvST9VNIySYsk7VeufQewmeVKQZWXCkwAmu+rdiEwNyIGAXOz9wBfoLgN0SCgDhhXrnEHsJnlShOquJQTEfcBzbcVOh6YmL2eCJxQUj8piuYDvSX1b6t9B7CZ5Up7RsCS6iQtKCl1FZyiX0Q0ZK9XA/2y1wOAF0o+tzKra5UvwplZrrTnVuSIqAfqO3quiAhJHb7u5xGwmeVKtKN00JpNUwvZz7VZ/SpgYMnnds3qWuUANrNcqfJFuJbMAEZmr0cCd5bUn5athjgQeK1kqqJFnoIws1yp5tPQJE0BDgf6SloJXAZcCUyVNAp4Djgp+/gs4GhgGbAeOKNc+w5gM8uVpireCBcRI1o5NKyFzwYwpj3tO4DNLFf8PGAzs0QcwGZmidTSsyAcwGaWK34gu5lZIp6CMDNLxA9kNzNLxFMQZmaJeArCzCwRr4IwM0ukUEMR7AA2s1zxRTgzs0Q8B2xmlohXQZiZJeI5YDOzRGonfh3AZpYzngM2M0ukqUpjYEmDgV+XVO0FXAr0Br4JvJjVXxwRszpyDgewmeVKtUbAEfEUMBRAUneKG2xOp7jV0DUR8ZPNPYcD2MxypZMuwg0DlkfEc1L1lll4V2Qzy5X2bEsvqU7SgpJS10qzJwNTSt6PlbRI0g2S+nS0rw5gM8uVQjtKRNRHxAElpb55e5J6AscBt2ZV44C9KU5PNABXdbSvnoIws1yp1kW4El8AHo2INQCbfgJI+iUws6MNO4DNLFc6YQ54BCXTD5L6R0RD9vZEYHFHG/YURCfZZcDO3DFzEg/8eRbzHvoNdWedBsBxJwxn3kO/Ye2rTzJ0308k7qV1tZ7b9mTSrHpu+f0Ebv3DTYw+9xsAfPrg/bh59nim3juJy6+9hO7duyfuae1qzxxwOZJ6AUcCt5dU/6ekxyUtAo4AvtPRvnoE3EmaGpu49JIrWfSXJ9huu17Mve92/nDPAyx94hlOP2UsV117ReouWgLvvP0OZ37lbDas30CPHt0Zf+c4HvzDn7n82ksYfdI5PL/iBUafN4pjTxrOnVN+k7q7NamaI+CIeAvYoVndqdVq3yPgTrJmzYss+ssTALz55ls8/dRy+u/Sj2eeXs6yZX9N3DtLacP6DQD02KYHPbbpTqGpwMaNjTy/4gUAHrrvYYYdc3jCHta29lyES80B3AUG7jaAf/jkPjyy4C+pu2JbgG7dujFlzo38/vG7eOiPC1j82BP06NGdj39qMADDjj2CfrvslLiXtSva8V9qHQ5gSWe0cezdtXV/e+e1jp4iF3r1+hATbrqOSy78IW++8Vbq7tgWoFAoMOLIMxi+35cYsu/H2Xvwnlw0+jLOvfzbTJpVz/o311No2hLGZ7Wpiai4pLY5I+DLWztQurbuAz0/shmnqG09evTgxl9dx21T7+I3d81O3R3bwrz5+psseOBRDjriQBY9soRRJ4zhtKPreHT+Qp7LpiOs/WppCqLNi3DZVb4WDwH9qt+dfLn2+h/y9FPLGXf9jam7YluI3jv0pnFjI2++/ibbfqAnBx72aSb898302aE3r7z8Ktv03IbTx5zC+Gsnpe5qzSpE+pFtpcqtgugHfB54pVm9gAc7pUc58dkD9+drI05gyeInuXfenQD84Iqr6dmzJ1f++Hvs0Hd7Jt9az+LHl3LSiaMS99a6yo477ZAtM+uGunVjzox7uP/3D3LO9/6VQ448CKkbt02azsMPPJq6qzWrduIXFG38bSFpPHBjRMxr4djkiPh6uRP0/buP1dKfh3WR3Xr5IpO936MN8zb7STdf3/3EijNn8nPTk25g1OYIOCJaHZpVEr5mZl1tS1jdUCnfiGFmudLoADYzS8MjYDOzRLaE5WWVcgCbWa60tbBgS+MANrNc6aQtiTqFA9jMcmVLuMW4Ug5gM8sVj4DNzBKp5hywpGeBN4AmoDEiDpC0PfBrYA/gWeCkiGh+t3BF/DhKM8uVTngYzxERMTQiDsjeXwjMjYhBwNzsfYc4gM0sV7rgecDHAxOz1xOBEzrakAPYzHKlQFRcSp9dnpW6Zs0FMFvSIyXH+pVsyrmazXgypOeAzSxXmqLyyYWIqAfq2/jI5yJilaSdgDmSnmz2/ZDU4aG0R8BmlivVnIKIiFXZz7XAdOAzwBpJ/aG4RT2wtqN9dQCbWa4UIioubZHUS9KHN70GjgIWAzOAkdnHRgJ3drSvnoIws1yp4irgfsB0SVDMyskRcbekh4GpkkYBzwEndfQEDmAzy5Vq3YgRESuAT7VQ/zIwrBrncACbWa74Tjgzs0TaswoiNQewmeWKH8huZpaInwdsZpaI54DNzBLxCNjMLJGmGtoVzgFsZrlS7g63LYkD2MxyxasgzMwS8QjYzCwRj4DNzBLxCNjMLBHfimxmloinIMzMEokaGgF7Rwwzy5X2bMrZFkkDJd0r6QlJSySdndX/u6RVkhZm5eiO9tUjYDPLlSreitwIfDciHs22JnpE0pzs2DUR8ZPNPYED2MxypYo7YjQADdnrNyQtBQZUpfGMpyDMLFeaCoWKi6Q6SQtKSl1LbUraA9gXeCirGitpkaQbJPXpaF8dwGaWK+3Zlj4i6iPigJJS37w9SdsB04BzIuJ1YBywNzCU4gj5qo721VMQZpYr1XwcpaRtKIbvzRFxe9b+mpLjvwRmdrR9B7CZ5Uq15oBV3I9+PLA0Iq4uqe+fzQ8DnAgs7ug5HMBmlitVHAEfDJwKPC5pYVZ3MTBC0lAggGeBMzt6AgewmeVKU6E6N2JExDxALRyaVZUT4AA2s5zxnnBmZol4Tzgzs0T8OEozs0T8NDQzs0Q8AjYzS6RQQ4+jdACbWa74IpyZWSIOYDOzRGonfkG19LdFrZNU19LTlmzr5t+LrZcfR9m1WnzWqG31/HuxlXIAm5kl4gA2M0vEAdy1PM9nLfHvxVbKF+HMzBLxCNjMLBEHsJlZIg7gLiJpuKSnJC2TdGHq/lh62ZbmayV1eE8xq20O4C4gqTtwPfAFYB+Ke0rtk7ZXtgWYAAxP3QlLxwHcNT4DLIuIFRHxDnALcHziPlliEXEfsC51PywdB3DXGAC8UPJ+ZVZnZlsxB7CZWSIO4K6xChhY8n7XrM7MtmIO4K7xMDBI0p6SegInAzMS98nMEnMAd4GIaATGAr8DlgJTI2JJ2l5ZapKmAH8CBktaKWlU6j5Z1/KtyGZmiXgEbGaWiAPYzCwRB7CZWSIOYDOzRBzAZmaJOIDNzBJxAJuZJfJ/tmNy4oo2+7IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, lgbm_model.predict(X_val).round())\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,18))\n",
    "lgb.plot_importance(lgbm_model, max_num_features=50, height=0.8, ax=ax)\n",
    "ax.grid(False)\n",
    "plt.title(\"LightGBM - Feature Importance\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x1f3f1f08460>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save model\n",
    "lgbm_model.save_model('lgbm_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_model = lgb.Booster(model_file='lgbm_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.90      0.95      0.92       244\n",
      "         1.0       0.75      0.55      0.63        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.82      0.75      0.78       304\n",
      "weighted avg       0.87      0.88      0.87       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# svd\n",
    "print(classification_report(y_val, lgbm_model.predict(X_val).round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0., -0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0., -0.,\n",
       "        1.,  0.,  0.,  1., -0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,  0.,\n",
       "        0., -0., -0.,  1.,  0.,  0., -0.,  0.,  0.,  1., -0., -0.,  0.,\n",
       "        1.,  0.,  0., -0.,  0.,  0., -0.,  0., -0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  1.,  0.,  1.,  0.,  1., -0.,  1.,  0.,  0.,  0.,  0.,  0.,\n",
       "       -0.,  1.,  0.,  0., -0.,  0.,  0.,  1.,  1.,  0.,  0.,  0., -0.,\n",
       "        1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0., -0.,  0., -0., -0.,\n",
       "        0., -0., -0.,  0.,  0.,  0.,  1., -0.,  1.,  0., -0.,  0.,  1.,\n",
       "        0.,  1., -0.,  0.,  0.,  1., -0.,  0.,  0.,  0.,  1.,  0., -0.,\n",
       "       -0.,  0.,  1.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0.,  1., -0.,\n",
       "        0.,  0.,  0., -0.,  0.,  0.,  0.,  1., -0.,  0.,  0., -0.,  0.,\n",
       "        0., -0., -0.,  0., -0.,  0.,  0.,  0.,  0., -0.,  1., -0.,  0.,\n",
       "        0.,  0.,  1.,  0.,  1.,  0., -0.,  0.,  1.,  0., -0.,  0., -0.,\n",
       "       -0.,  1.,  0., -0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,\n",
       "        0.,  0.,  0.,  1., -0.,  1.,  0.,  0.,  0.,  0., -0.,  0., -0.,\n",
       "       -0., -0.,  1.,  0.,  0.,  0., -0., -0., -0.,  0.,  0.,  0., -0.,\n",
       "       -0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., -0.,  1., -0.,\n",
       "        0.,  1.,  1.,  0.,  0.,  1.,  1., -0., -0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  1.,  0., -0.,  1.,  1.,  0.,  0.,  1.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0., -0.,  0.,  0.,\n",
       "        0.,  0., -0.,  0.,  0.,  0.,  0.,  1.,  1., -0.,  0.,  0.,  0.,\n",
       "       -0.,  0.,  0.,  0.,  0., -0.,  0.,  0., -0.,  0.,  0., -0.,  0.,\n",
       "        0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  0., -0.,  0.,  1.,  0.,\n",
       "        0.,  0.,  0.,  1.,  0.])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_model.predict(X_val).round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### unused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_model = lgb.LGBMClassifier()\n",
    "lgbm_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.90      0.95      0.92       244\n",
      "         1.0       0.75      0.55      0.63        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.82      0.75      0.78       304\n",
      "weighted avg       0.87      0.88      0.87       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, lgbm_model.predict(X_val).round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost (worse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:0.49547\tvalid-rmse:0.49687\n",
      "[1000]\ttrain-rmse:0.01161\tvalid-rmse:0.30536\n",
      "[1782]\ttrain-rmse:0.00233\tvalid-rmse:0.30526\n"
     ]
    }
   ],
   "source": [
    "xgb_params = {\n",
    "    \"eval_metric\": \"rmse\",\n",
    "    \"seed\": 1337,\n",
    "    \"eta\": 0.0123,\n",
    "    \"subsample\": 0.8,\n",
    "    \"colsample_bytree\": 0.85,\n",
    "    \"tree_method\": \"gpu_hist\",\n",
    "}\n",
    "\n",
    "n_splits = 10\n",
    "verbose_eval = 1000\n",
    "num_rounds = 60000\n",
    "early_stop = 500\n",
    "\n",
    "d_train = xgb.DMatrix(data=X_train, label=y_train, feature_names=X_train.columns)\n",
    "d_valid = xgb.DMatrix(data=X_val, label=y_val, feature_names=X_val.columns)\n",
    "\n",
    "evals = [(d_train, \"train\"), (d_valid, \"valid\")]\n",
    "xgb_model = xgb.train(\n",
    "    dtrain=d_train,\n",
    "    num_boost_round=num_rounds,\n",
    "    evals=evals,\n",
    "    early_stopping_rounds=early_stop,\n",
    "    verbose_eval=verbose_eval,\n",
    "    params=xgb_params,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.89      0.96      0.92       244\n",
      "         1.0       0.76      0.52      0.61        60\n",
      "\n",
      "    accuracy                           0.87       304\n",
      "   macro avg       0.82      0.74      0.77       304\n",
      "weighted avg       0.86      0.87      0.86       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, xgb_model.predict(d_valid).round()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.91      0.91      0.91       244\n",
      "         1.0       0.63      0.63      0.63        60\n",
      "\n",
      "    accuracy                           0.86       304\n",
      "   macro avg       0.77      0.77      0.77       304\n",
      "weighted avg       0.86      0.86      0.86       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# SMOTE\n",
    "print(classification_report(y_val, xgb_model.predict(d_valid).round()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning (unused)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CatBoost (Worse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda3\\envs\\tf\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n",
      "186 fits failed out of a total of 840.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "186 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\miniconda3\\envs\\tf\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"d:\\miniconda3\\envs\\tf\\lib\\site-packages\\catboost\\core.py\", line 5007, in fit\n",
      "    self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,\n",
      "  File \"d:\\miniconda3\\envs\\tf\\lib\\site-packages\\catboost\\core.py\", line 2278, in _fit\n",
      "    self._train(\n",
      "  File \"d:\\miniconda3\\envs\\tf\\lib\\site-packages\\catboost\\core.py\", line 1705, in _train\n",
      "    self._object._train(train_pool, test_pool, params, allow_clear_pool, init_model._object if init_model else None)\n",
      "  File \"_catboost.pyx\", line 4585, in _catboost._CatBoost._train\n",
      "  File \"_catboost.pyx\", line 4634, in _catboost._CatBoost._train\n",
      "_catboost.CatBoostError: bad allocation\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "d:\\miniconda3\\envs\\tf\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.78747708 0.78747504 0.78912113 0.79324451 0.78418286 0.79077334\n",
      " 0.79736177 0.80642953 0.786652   0.79901194 0.8089007  0.82207962\n",
      " 0.78912725 0.80807359 0.81549322 0.82537383 0.79159638 0.81137392\n",
      " 0.82043556 0.82866805 0.79406959 0.81219696 0.82043352 0.82948702\n",
      " 0.7965428  0.81302001 0.82290266 0.83854663 0.79736585 0.81219492\n",
      " 0.8253718  0.8426639  0.79901194 0.81632034 0.82866601 0.84184289\n",
      " 0.8031292  0.82373385 0.83360225 0.84101984 0.78912725 0.79736381\n",
      " 0.79406959 0.80477733 0.79571365 0.80971968 0.80643157 0.81467017\n",
      " 0.79324655 0.80889052 0.8146661  0.8245569  0.79489671 0.80971968\n",
      " 0.81548914 0.82702604 0.79654077 0.81054476 0.81960844 0.8294911\n",
      " 0.79818889 0.81466202 0.82866805 0.83196227 0.7990099  0.82042945\n",
      " 0.8294911  0.83443141 0.80066007 0.82372571 0.83031618 0.83607953\n",
      " 0.8089007  0.82455079 0.83196227 0.83772562 0.81220103 0.82784297\n",
      " 0.83526056 0.84019476 0.7932547  0.79819297 0.79242554 0.80066414\n",
      " 0.79407163 0.798195   0.79983702 0.80643157 0.79901805 0.81054476\n",
      " 0.80642953 0.81631422 0.8006621  0.81219492 0.81549322 0.82043352\n",
      " 0.80148515 0.8179542  0.81714134 0.82702196 0.80313124 0.82043149\n",
      " 0.81960844 0.83360632 0.80560241 0.82290062 0.83113515 0.83936764\n",
      " 0.80560037 0.82784093 0.83277717 0.83936764 0.80972171 0.82701585\n",
      " 0.83442122 0.84100762 0.81302001 0.8278389  0.83689647 0.83854256\n",
      " 0.79818686 0.80066007 0.80066007 0.80065803 0.80066618 0.80560445\n",
      " 0.80807155 0.81795624 0.80478344 0.81137595 0.81630811 0.8253718\n",
      " 0.79983702 0.80972171 0.8212525  0.8253718  0.80231431 0.81714338\n",
      " 0.82289859 0.82948295 0.80725869 0.81961252 0.83031618 0.83195616\n",
      " 0.80890478 0.8187854  0.83278532 0.83525649 0.81220103 0.82455486\n",
      " 0.83360632 0.83690462 0.81549729 0.82537587 0.83442733 0.83525853\n",
      " 0.81467221 0.82866805 0.83937375 0.83526056 0.80890682 0.80725461\n",
      " 0.80643157 0.80890274 0.80725054 0.80477733 0.80477529 0.81054476\n",
      " 0.80725258 0.80889867 0.81384713        nan 0.80560649        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan 0.82948499        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.81137799 0.80478752 0.80808173 0.80477937 0.80560445 0.8039604\n",
      " 0.8089007  0.80477326 0.80642546 0.80395225 0.80807562 0.81548507\n",
      " 0.80560241 0.80725258 0.81219492 0.81383898 0.80972171 0.8105468\n",
      " 0.81219492 0.81878336 0.81137188 0.81054883 0.81796235 0.81960844\n",
      " 0.80972579 0.81219492 0.82208165 0.82289859 0.81302408 0.81384305\n",
      " 0.82372978 0.81960844 0.81384713 0.81713523 0.82455486 0.82372978\n",
      " 0.81631626 0.82042741 0.82784501 0.82867416]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=&lt;catboost.core.CatBoostClassifier object at 0x000001EF4852D450&gt;,\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;depth&#x27;: [4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;iterations&#x27;: [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
       "                                        100],\n",
       "                         &#x27;learning_rate&#x27;: [0.01, 0.02, 0.03, 0.04]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=&lt;catboost.core.CatBoostClassifier object at 0x000001EF4852D450&gt;,\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;depth&#x27;: [4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;iterations&#x27;: [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
       "                                        100],\n",
       "                         &#x27;learning_rate&#x27;: [0.01, 0.02, 0.03, 0.04]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000001EF4852D450&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x000001EF4852D450&gt;</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=<catboost.core.CatBoostClassifier object at 0x000001EF4852D450>,\n",
       "             n_jobs=-1,\n",
       "             param_grid={'depth': [4, 5, 6, 7, 8, 9, 10],\n",
       "                         'iterations': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
       "                                        100],\n",
       "                         'learning_rate': [0.01, 0.02, 0.03, 0.04]})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = {\n",
    "    \"depth\": [4, 5, 6, 7, 8, 9, 10],\n",
    "    \"learning_rate\": [0.01, 0.02, 0.03, 0.04],\n",
    "    \"iterations\": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],\n",
    "}\n",
    "\n",
    "clf_grid_search = GridSearchCV(clf_base_model, parameters, cv=3, n_jobs=-1)\n",
    "clf_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'depth': 4, 'iterations': 80, 'learning_rate': 0.04}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8426638960192315"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1ef50dc5a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "clf_grid_search.best_estimator_.save_model('catboost_grid_search_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.98      0.90       244\n",
      "         1.0       0.71      0.25      0.37        60\n",
      "\n",
      "    accuracy                           0.83       304\n",
      "   macro avg       0.78      0.61      0.64       304\n",
      "weighted avg       0.82      0.83      0.80       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, clf_grid_search.predict(X_val)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 540 candidates, totalling 1620 fits\n",
      "[CV 1/3; 1/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 1/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.744) total time=   3.5s\n",
      "[CV 2/3; 1/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 1/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.1s\n",
      "[CV 3/3; 1/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 1/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.738) total time=   3.1s\n",
      "[CV 1/3; 2/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 2/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.714) total time=   4.0s\n",
      "[CV 2/3; 2/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 2/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   4.3s\n",
      "[CV 3/3; 2/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 2/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.3s\n",
      "[CV 1/3; 3/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 3/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.738) total time=   4.2s\n",
      "[CV 2/3; 3/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 3/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   4.1s\n",
      "[CV 3/3; 3/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 3/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.8s\n",
      "[CV 1/3; 4/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 4/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.686) total time=   5.9s\n",
      "[CV 2/3; 4/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 4/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.820) F1: (test=0.679) total time=   5.7s\n",
      "[CV 3/3; 4/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 4/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.708) total time=   5.4s\n",
      "[CV 1/3; 5/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 5/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   6.5s\n",
      "[CV 2/3; 5/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 5/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.713) total time=   6.6s\n",
      "[CV 3/3; 5/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 5/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   6.9s\n",
      "[CV 1/3; 6/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 6/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.704) total time=   5.6s\n",
      "[CV 2/3; 6/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 6/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.817) F1: (test=0.677) total time=   5.7s\n",
      "[CV 3/3; 6/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 6/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   6.4s\n",
      "[CV 1/3; 7/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 7/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.686) total time=   7.9s\n",
      "[CV 2/3; 7/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 7/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.820) F1: (test=0.687) total time=   7.6s\n",
      "[CV 3/3; 7/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 7/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.712) total time=   8.2s\n",
      "[CV 1/3; 8/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 8/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.694) total time=   6.9s\n",
      "[CV 2/3; 8/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 8/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.709) total time=   6.9s\n",
      "[CV 3/3; 8/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 8/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.691) total time=   7.0s\n",
      "[CV 1/3; 9/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 9/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.682) total time=   6.8s\n",
      "[CV 2/3; 9/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 9/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.695) total time=   5.8s\n",
      "[CV 3/3; 9/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 9/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.706) total time=   5.9s\n",
      "[CV 1/3; 10/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 10/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.677) total time=   9.9s\n",
      "[CV 2/3; 10/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 10/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.820) F1: (test=0.687) total time=  10.1s\n",
      "[CV 3/3; 10/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 10/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.711) total time=   9.9s\n",
      "[CV 1/3; 11/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 11/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.694) total time=   6.5s\n",
      "[CV 2/3; 11/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 11/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.709) total time=   6.5s\n",
      "[CV 3/3; 11/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 11/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.691) total time=   6.6s\n",
      "[CV 1/3; 12/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 12/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.682) total time=   5.6s\n",
      "[CV 2/3; 12/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 12/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.695) total time=   5.6s\n",
      "[CV 3/3; 12/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 12/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.706) total time=   5.9s\n",
      "[CV 1/3; 13/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 13/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.820) F1: (test=0.659) total time=  11.8s\n",
      "[CV 2/3; 13/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 13/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=  11.7s\n",
      "[CV 3/3; 13/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 13/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=  11.7s\n",
      "[CV 1/3; 14/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 14/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.694) total time=   6.5s\n",
      "[CV 2/3; 14/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 14/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.709) total time=   6.5s\n",
      "[CV 3/3; 14/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 14/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.691) total time=   6.7s\n",
      "[CV 1/3; 15/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 15/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.682) total time=   5.7s\n",
      "[CV 2/3; 15/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 15/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.695) total time=   5.7s\n",
      "[CV 3/3; 15/540] START learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 15/540] END learning_rate=0.05, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.706) total time=   6.0s\n",
      "[CV 1/3; 16/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 16/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.701) total time=   2.8s\n",
      "[CV 2/3; 16/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 16/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.7s\n",
      "[CV 3/3; 16/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 16/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.7s\n",
      "[CV 1/3; 17/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 17/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.726) total time=   3.5s\n",
      "[CV 2/3; 17/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 17/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.6s\n",
      "[CV 3/3; 17/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 17/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.6s\n",
      "[CV 1/3; 18/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 18/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.726) total time=   3.5s\n",
      "[CV 2/3; 18/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 18/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.5s\n",
      "[CV 3/3; 18/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 18/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.6s\n",
      "[CV 1/3; 19/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 19/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   4.7s\n",
      "[CV 2/3; 19/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 19/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   4.7s\n",
      "[CV 3/3; 19/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 19/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   4.7s\n",
      "[CV 1/3; 20/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 20/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.6s\n",
      "[CV 2/3; 20/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 20/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.8s\n",
      "[CV 3/3; 20/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 20/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 21/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 21/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 21/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 21/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.5s\n",
      "[CV 3/3; 21/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 21/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.5s\n",
      "[CV 1/3; 22/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 22/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   6.6s\n",
      "[CV 2/3; 22/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 22/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   6.6s\n",
      "[CV 3/3; 22/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 22/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.718) total time=   6.5s\n",
      "[CV 1/3; 23/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 23/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.7s\n",
      "[CV 2/3; 23/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 23/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.8s\n",
      "[CV 3/3; 23/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 23/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 24/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 24/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 24/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 24/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.5s\n",
      "[CV 3/3; 24/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 24/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.5s\n",
      "[CV 1/3; 25/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 25/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.705) total time=   7.3s\n",
      "[CV 2/3; 25/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 25/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   7.4s\n",
      "[CV 3/3; 25/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 25/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   7.2s\n",
      "[CV 1/3; 26/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 26/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.7s\n",
      "[CV 2/3; 26/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 26/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.8s\n",
      "[CV 3/3; 26/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 26/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 27/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 27/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 27/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 27/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.4s\n",
      "[CV 3/3; 27/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 27/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.6s\n",
      "[CV 1/3; 28/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 28/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.705) total time=   7.5s\n",
      "[CV 2/3; 28/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 28/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   7.2s\n",
      "[CV 3/3; 28/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 28/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   7.2s\n",
      "[CV 1/3; 29/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 29/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.8s\n",
      "[CV 2/3; 29/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 29/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.8s\n",
      "[CV 3/3; 29/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 29/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 30/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 30/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 30/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 30/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.4s\n",
      "[CV 3/3; 30/540] START learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 30/540] END learning_rate=0.05, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.6s\n",
      "[CV 1/3; 31/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 31/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.5s\n",
      "[CV 2/3; 31/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 31/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.5s\n",
      "[CV 3/3; 31/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 31/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   2.5s\n",
      "[CV 1/3; 32/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 32/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.1s\n",
      "[CV 2/3; 32/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 32/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.2s\n",
      "[CV 3/3; 32/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 32/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.3s\n",
      "[CV 1/3; 33/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 33/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.721) total time=   3.1s\n",
      "[CV 2/3; 33/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 33/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.710) total time=   3.2s\n",
      "[CV 3/3; 33/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 33/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.2s\n",
      "[CV 1/3; 34/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 34/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.726) total time=   4.3s\n",
      "[CV 2/3; 34/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 34/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   4.4s\n",
      "[CV 3/3; 34/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 34/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.824) F1: (test=0.688) total time=   4.4s\n",
      "[CV 1/3; 35/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 35/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.8s\n",
      "[CV 2/3; 35/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 35/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.8s\n",
      "[CV 3/3; 35/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 35/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   3.9s\n",
      "[CV 1/3; 36/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 36/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 36/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 36/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.6s\n",
      "[CV 3/3; 36/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 36/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.7s\n",
      "[CV 1/3; 37/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 37/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.9s\n",
      "[CV 2/3; 37/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 37/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.9s\n",
      "[CV 3/3; 37/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 37/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=   4.8s\n",
      "[CV 1/3; 38/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 38/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.7s\n",
      "[CV 2/3; 38/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 38/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.8s\n",
      "[CV 3/3; 38/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 38/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   4.0s\n",
      "[CV 1/3; 39/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 39/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 39/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 39/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.7s\n",
      "[CV 3/3; 39/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 39/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.8s\n",
      "[CV 1/3; 40/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 40/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   5.0s\n",
      "[CV 2/3; 40/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 40/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.9s\n",
      "[CV 3/3; 40/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 40/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=   4.9s\n",
      "[CV 1/3; 41/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 41/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.8s\n",
      "[CV 2/3; 41/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 41/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.9s\n",
      "[CV 3/3; 41/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 41/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   3.9s\n",
      "[CV 1/3; 42/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 42/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.6s\n",
      "[CV 2/3; 42/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 42/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.6s\n",
      "[CV 3/3; 42/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 42/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.8s\n",
      "[CV 1/3; 43/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 43/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   5.0s\n",
      "[CV 2/3; 43/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 43/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   5.0s\n",
      "[CV 3/3; 43/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 43/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=   5.0s\n",
      "[CV 1/3; 44/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 44/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.8s\n",
      "[CV 2/3; 44/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 44/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.9s\n",
      "[CV 3/3; 44/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 44/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   4.0s\n",
      "[CV 1/3; 45/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 45/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.6s\n",
      "[CV 2/3; 45/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 45/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.7s\n",
      "[CV 3/3; 45/540] START learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 45/540] END learning_rate=0.05, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.8s\n",
      "[CV 1/3; 46/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 46/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.726) total time=   2.1s\n",
      "[CV 2/3; 46/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 46/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.2s\n",
      "[CV 3/3; 46/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 46/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   2.2s\n",
      "[CV 1/3; 47/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 47/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.8s\n",
      "[CV 2/3; 47/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 47/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.822) F1: (test=0.689) total time=   2.9s\n",
      "[CV 3/3; 47/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 47/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.9s\n",
      "[CV 1/3; 48/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 48/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.726) total time=   2.8s\n",
      "[CV 2/3; 48/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 48/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.9s\n",
      "[CV 3/3; 48/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 48/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.718) total time=   2.9s\n",
      "[CV 1/3; 49/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 49/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 2/3; 49/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 49/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.817) F1: (test=0.677) total time=   2.3s\n",
      "[CV 3/3; 49/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 49/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.4s\n",
      "[CV 1/3; 50/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 50/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.708) total time=   3.0s\n",
      "[CV 2/3; 50/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 50/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.830) F1: (test=0.704) total time=   3.1s\n",
      "[CV 3/3; 50/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 50/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.727) total time=   3.1s\n",
      "[CV 1/3; 51/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 51/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.691) total time=   2.9s\n",
      "[CV 2/3; 51/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 51/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.709) total time=   3.0s\n",
      "[CV 3/3; 51/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 51/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   3.0s\n",
      "[CV 1/3; 52/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 52/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.3s\n",
      "[CV 2/3; 52/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 52/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.817) F1: (test=0.677) total time=   2.4s\n",
      "[CV 3/3; 52/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 52/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.5s\n",
      "[CV 1/3; 53/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 53/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.708) total time=   3.0s\n",
      "[CV 2/3; 53/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 53/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.830) F1: (test=0.704) total time=   3.1s\n",
      "[CV 3/3; 53/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 53/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.727) total time=   3.2s\n",
      "[CV 1/3; 54/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 54/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.691) total time=   2.9s\n",
      "[CV 2/3; 54/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 54/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.709) total time=   3.0s\n",
      "[CV 3/3; 54/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 54/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   3.0s\n",
      "[CV 1/3; 55/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 55/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 2/3; 55/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 55/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.817) F1: (test=0.677) total time=   2.4s\n",
      "[CV 3/3; 55/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 55/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.4s\n",
      "[CV 1/3; 56/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 56/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.708) total time=   3.0s\n",
      "[CV 2/3; 56/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 56/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.830) F1: (test=0.704) total time=   3.1s\n",
      "[CV 3/3; 56/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 56/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.727) total time=   3.1s\n",
      "[CV 1/3; 57/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 57/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.691) total time=   2.9s\n",
      "[CV 2/3; 57/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 57/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.709) total time=   3.1s\n",
      "[CV 3/3; 57/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 57/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   3.1s\n",
      "[CV 1/3; 58/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 58/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.3s\n",
      "[CV 2/3; 58/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 58/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.817) F1: (test=0.677) total time=   2.4s\n",
      "[CV 3/3; 58/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 58/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.4s\n",
      "[CV 1/3; 59/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 59/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.708) total time=   3.0s\n",
      "[CV 2/3; 59/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 59/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.830) F1: (test=0.704) total time=   3.1s\n",
      "[CV 3/3; 59/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 59/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.727) total time=   3.2s\n",
      "[CV 1/3; 60/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 60/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.691) total time=   2.9s\n",
      "[CV 2/3; 60/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 60/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.709) total time=   3.1s\n",
      "[CV 3/3; 60/540] START learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 60/540] END learning_rate=0.05, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   3.1s\n",
      "[CV 1/3; 61/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 61/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.720) total time=   2.0s\n",
      "[CV 2/3; 61/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 61/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.0s\n",
      "[CV 3/3; 61/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 61/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.724) total time=   2.0s\n",
      "[CV 1/3; 62/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 62/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.685) total time=   2.6s\n",
      "[CV 2/3; 62/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 62/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.6s\n",
      "[CV 3/3; 62/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 62/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.7s\n",
      "[CV 1/3; 63/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 63/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.6s\n",
      "[CV 2/3; 63/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 63/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.7s\n",
      "[CV 3/3; 63/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 63/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.8s\n",
      "[CV 1/3; 64/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 64/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.1s\n",
      "[CV 2/3; 64/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 64/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.1s\n",
      "[CV 3/3; 64/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 64/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.2s\n",
      "[CV 1/3; 65/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 65/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.698) total time=   2.7s\n",
      "[CV 2/3; 65/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 65/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.7s\n",
      "[CV 3/3; 65/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 65/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.7s\n",
      "[CV 1/3; 66/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 66/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.735) total time=   2.7s\n",
      "[CV 2/3; 66/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 66/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   2.7s\n",
      "[CV 3/3; 66/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 66/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.7s\n",
      "[CV 1/3; 67/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 67/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.1s\n",
      "[CV 2/3; 67/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 67/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.2s\n",
      "[CV 3/3; 67/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 67/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.2s\n",
      "[CV 1/3; 68/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 68/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.698) total time=   2.7s\n",
      "[CV 2/3; 68/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 68/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.7s\n",
      "[CV 3/3; 68/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 68/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.8s\n",
      "[CV 1/3; 69/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 69/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.735) total time=   2.6s\n",
      "[CV 2/3; 69/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 69/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   2.7s\n",
      "[CV 3/3; 69/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 69/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.7s\n",
      "[CV 1/3; 70/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 70/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.1s\n",
      "[CV 2/3; 70/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 70/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.1s\n",
      "[CV 3/3; 70/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 70/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.1s\n",
      "[CV 1/3; 71/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 71/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.698) total time=   2.7s\n",
      "[CV 2/3; 71/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 71/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.7s\n",
      "[CV 3/3; 71/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 71/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.7s\n",
      "[CV 1/3; 72/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 72/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.735) total time=   2.7s\n",
      "[CV 2/3; 72/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 72/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   2.7s\n",
      "[CV 3/3; 72/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 72/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.7s\n",
      "[CV 1/3; 73/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 73/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.1s\n",
      "[CV 2/3; 73/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 73/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.1s\n",
      "[CV 3/3; 73/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 73/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.2s\n",
      "[CV 1/3; 74/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 74/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.698) total time=   2.7s\n",
      "[CV 2/3; 74/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 74/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.7s\n",
      "[CV 3/3; 74/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 74/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.7s\n",
      "[CV 1/3; 75/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 75/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.735) total time=   2.7s\n",
      "[CV 2/3; 75/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 75/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   2.7s\n",
      "[CV 3/3; 75/540] START learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 75/540] END learning_rate=0.05, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.7s\n",
      "[CV 1/3; 76/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 76/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   1.9s\n",
      "[CV 2/3; 76/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 76/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   1.8s\n",
      "[CV 3/3; 76/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 76/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.0s\n",
      "[CV 1/3; 77/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 77/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.4s\n",
      "[CV 2/3; 77/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 77/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.4s\n",
      "[CV 3/3; 77/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 77/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.851) F1: (test=0.741) total time=   2.5s\n",
      "[CV 1/3; 78/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 78/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.709) total time=   2.4s\n",
      "[CV 2/3; 78/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 78/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.4s\n",
      "[CV 3/3; 78/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 78/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   2.5s\n",
      "[CV 1/3; 79/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 79/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   2.0s\n",
      "[CV 2/3; 79/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 79/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   1.9s\n",
      "[CV 3/3; 79/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 79/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 80/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 80/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 2/3; 80/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 80/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 3/3; 80/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 80/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.5s\n",
      "[CV 1/3; 81/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 81/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.723) total time=   2.3s\n",
      "[CV 2/3; 81/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 81/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.4s\n",
      "[CV 3/3; 81/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 81/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   2.5s\n",
      "[CV 1/3; 82/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 82/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   2.0s\n",
      "[CV 2/3; 82/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 82/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   1.9s\n",
      "[CV 3/3; 82/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 82/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 83/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 83/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 2/3; 83/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 83/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 3/3; 83/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 83/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.4s\n",
      "[CV 1/3; 84/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 84/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.723) total time=   2.4s\n",
      "[CV 2/3; 84/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 84/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.4s\n",
      "[CV 3/3; 84/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 84/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   2.5s\n",
      "[CV 1/3; 85/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 85/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   1.9s\n",
      "[CV 2/3; 85/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 85/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   1.9s\n",
      "[CV 3/3; 85/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 85/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 86/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 86/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 2/3; 86/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 86/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.5s\n",
      "[CV 3/3; 86/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 86/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.5s\n",
      "[CV 1/3; 87/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 87/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.723) total time=   2.3s\n",
      "[CV 2/3; 87/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 87/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.4s\n",
      "[CV 3/3; 87/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 87/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   2.5s\n",
      "[CV 1/3; 88/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 88/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   1.9s\n",
      "[CV 2/3; 88/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 88/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   1.9s\n",
      "[CV 3/3; 88/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 88/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 89/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 89/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.5s\n",
      "[CV 2/3; 89/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 89/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.4s\n",
      "[CV 3/3; 89/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 89/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.4s\n",
      "[CV 1/3; 90/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 90/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.723) total time=   2.4s\n",
      "[CV 2/3; 90/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 90/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.4s\n",
      "[CV 3/3; 90/540] START learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 90/540] END learning_rate=0.05, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.732) total time=   2.5s\n",
      "[CV 1/3; 91/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 91/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.735) total time=   3.0s\n",
      "[CV 2/3; 91/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 91/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.0s\n",
      "[CV 3/3; 91/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 91/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   3.0s\n",
      "[CV 1/3; 92/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 92/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.714) total time=   4.0s\n",
      "[CV 2/3; 92/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 92/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   4.3s\n",
      "[CV 3/3; 92/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 92/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.1s\n",
      "[CV 1/3; 93/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 93/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.738) total time=   4.1s\n",
      "[CV 2/3; 93/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 93/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   4.0s\n",
      "[CV 3/3; 93/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 93/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.1s\n",
      "[CV 1/3; 94/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 94/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.700) total time=   5.4s\n",
      "[CV 2/3; 94/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 94/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.708) total time=   5.3s\n",
      "[CV 3/3; 94/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 94/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.697) total time=   5.5s\n",
      "[CV 1/3; 95/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 95/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.717) total time=   6.1s\n",
      "[CV 2/3; 95/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 95/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.817) F1: (test=0.691) total time=   6.1s\n",
      "[CV 3/3; 95/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 95/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   6.4s\n",
      "[CV 1/3; 96/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 96/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.711) total time=   5.5s\n",
      "[CV 2/3; 96/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 96/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.694) total time=   5.5s\n",
      "[CV 3/3; 96/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 96/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.720) total time=   5.8s\n",
      "[CV 1/3; 97/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 97/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.706) total time=   7.4s\n",
      "[CV 2/3; 97/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 97/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   7.1s\n",
      "[CV 3/3; 97/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 97/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   7.3s\n",
      "[CV 1/3; 98/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 98/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.692) total time=   6.2s\n",
      "[CV 2/3; 98/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 98/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.817) F1: (test=0.688) total time=   6.2s\n",
      "[CV 3/3; 98/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 98/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.687) total time=   6.5s\n",
      "[CV 1/3; 99/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 99/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   5.6s\n",
      "[CV 2/3; 99/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 99/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.820) F1: (test=0.690) total time=   5.7s\n",
      "[CV 3/3; 99/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 99/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.9s\n",
      "[CV 1/3; 100/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 100/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.699) total time=   8.8s\n",
      "[CV 2/3; 100/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 100/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.704) total time=   8.3s\n",
      "[CV 3/3; 100/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 100/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.695) total time=   9.1s\n",
      "[CV 1/3; 101/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 101/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.692) total time=   6.3s\n",
      "[CV 2/3; 101/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 101/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.817) F1: (test=0.688) total time=   6.2s\n",
      "[CV 3/3; 101/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 101/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.687) total time=   6.6s\n",
      "[CV 1/3; 102/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 102/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   5.6s\n",
      "[CV 2/3; 102/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 102/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.820) F1: (test=0.690) total time=   5.7s\n",
      "[CV 3/3; 102/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 102/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.9s\n",
      "[CV 1/3; 103/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 103/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.678) total time=   9.2s\n",
      "[CV 2/3; 103/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 103/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   8.7s\n",
      "[CV 3/3; 103/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 103/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.693) total time=   9.7s\n",
      "[CV 1/3; 104/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 104/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.692) total time=   6.3s\n",
      "[CV 2/3; 104/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 104/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.817) F1: (test=0.688) total time=   6.2s\n",
      "[CV 3/3; 104/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 104/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.687) total time=   6.5s\n",
      "[CV 1/3; 105/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 105/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   5.6s\n",
      "[CV 2/3; 105/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 105/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.820) F1: (test=0.690) total time=   5.7s\n",
      "[CV 3/3; 105/540] START learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 105/540] END learning_rate=0.05, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.9s\n",
      "[CV 1/3; 106/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 106/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.701) total time=   2.7s\n",
      "[CV 2/3; 106/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 106/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.7s\n",
      "[CV 3/3; 106/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 106/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.8s\n",
      "[CV 1/3; 107/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 107/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.726) total time=   3.6s\n",
      "[CV 2/3; 107/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 107/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.6s\n",
      "[CV 3/3; 107/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 107/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.834) F1: (test=0.709) total time=   3.6s\n",
      "[CV 1/3; 108/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 108/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.726) total time=   3.5s\n",
      "[CV 2/3; 108/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 108/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.5s\n",
      "[CV 3/3; 108/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 108/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.6s\n",
      "[CV 1/3; 109/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 109/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.6s\n",
      "[CV 2/3; 109/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 109/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.704) total time=   4.6s\n",
      "[CV 3/3; 109/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 109/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.718) total time=   4.7s\n",
      "[CV 1/3; 110/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 110/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.6s\n",
      "[CV 2/3; 110/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 110/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.706) total time=   4.7s\n",
      "[CV 3/3; 110/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 110/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.0s\n",
      "[CV 1/3; 111/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 111/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.750) total time=   4.3s\n",
      "[CV 2/3; 111/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 111/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.4s\n",
      "[CV 3/3; 111/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 111/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.741) total time=   4.6s\n",
      "[CV 1/3; 112/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 112/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.729) total time=   6.0s\n",
      "[CV 2/3; 112/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 112/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.712) total time=   5.7s\n",
      "[CV 3/3; 112/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 112/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.729) total time=   6.1s\n",
      "[CV 1/3; 113/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 113/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.6s\n",
      "[CV 2/3; 113/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 113/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.706) total time=   4.7s\n",
      "[CV 3/3; 113/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 113/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   4.9s\n",
      "[CV 1/3; 114/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 114/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.750) total time=   4.3s\n",
      "[CV 2/3; 114/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 114/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.4s\n",
      "[CV 3/3; 114/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 114/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.741) total time=   4.6s\n",
      "[CV 1/3; 115/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 115/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.729) total time=   6.0s\n",
      "[CV 2/3; 115/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 115/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.729) total time=   5.9s\n",
      "[CV 3/3; 115/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 115/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.729) total time=   6.2s\n",
      "[CV 1/3; 116/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 116/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.7s\n",
      "[CV 2/3; 116/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 116/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.706) total time=   4.7s\n",
      "[CV 3/3; 116/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 116/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.0s\n",
      "[CV 1/3; 117/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 117/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.750) total time=   4.3s\n",
      "[CV 2/3; 117/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 117/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.4s\n",
      "[CV 3/3; 117/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 117/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.741) total time=   4.6s\n",
      "[CV 1/3; 118/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 118/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.729) total time=   6.0s\n",
      "[CV 2/3; 118/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 118/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.729) total time=   5.9s\n",
      "[CV 3/3; 118/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 118/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.729) total time=   6.3s\n",
      "[CV 1/3; 119/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 119/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.7s\n",
      "[CV 2/3; 119/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 119/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.706) total time=   4.8s\n",
      "[CV 3/3; 119/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 119/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.0s\n",
      "[CV 1/3; 120/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 120/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.750) total time=   4.3s\n",
      "[CV 2/3; 120/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 120/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.4s\n",
      "[CV 3/3; 120/540] START learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 120/540] END learning_rate=0.05, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.741) total time=   4.6s\n",
      "[CV 1/3; 121/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 121/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.5s\n",
      "[CV 2/3; 121/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 121/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.5s\n",
      "[CV 3/3; 121/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 121/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.6s\n",
      "[CV 1/3; 122/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 122/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.730) total time=   3.3s\n",
      "[CV 2/3; 122/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 122/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.2s\n",
      "[CV 3/3; 122/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 122/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   3.2s\n",
      "[CV 1/3; 123/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 123/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.721) total time=   3.1s\n",
      "[CV 2/3; 123/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 123/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   3.2s\n",
      "[CV 3/3; 123/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 123/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.749) total time=   3.2s\n",
      "[CV 1/3; 124/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 124/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   4.2s\n",
      "[CV 2/3; 124/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 124/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.2s\n",
      "[CV 3/3; 124/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 124/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=   4.3s\n",
      "[CV 1/3; 125/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 125/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.735) total time=   3.7s\n",
      "[CV 2/3; 125/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 125/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.8s\n",
      "[CV 3/3; 125/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 125/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.729) total time=   3.9s\n",
      "[CV 1/3; 126/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 126/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 126/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 126/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.6s\n",
      "[CV 3/3; 126/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 126/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.829) F1: (test=0.693) total time=   3.7s\n",
      "[CV 1/3; 127/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 127/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.5s\n",
      "[CV 2/3; 127/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 127/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.747) total time=   4.4s\n",
      "[CV 3/3; 127/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 127/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=   4.6s\n",
      "[CV 1/3; 128/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 128/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.735) total time=   3.8s\n",
      "[CV 2/3; 128/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 128/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.9s\n",
      "[CV 3/3; 128/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 128/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.0s\n",
      "[CV 1/3; 129/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 129/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 129/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 129/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.6s\n",
      "[CV 3/3; 129/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 129/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.829) F1: (test=0.693) total time=   3.7s\n",
      "[CV 1/3; 130/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 130/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.5s\n",
      "[CV 2/3; 130/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 130/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.747) total time=   4.4s\n",
      "[CV 3/3; 130/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 130/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=   4.6s\n",
      "[CV 1/3; 131/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 131/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.735) total time=   3.8s\n",
      "[CV 2/3; 131/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 131/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.8s\n",
      "[CV 3/3; 131/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 131/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.729) total time=   3.9s\n",
      "[CV 1/3; 132/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 132/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.6s\n",
      "[CV 2/3; 132/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 132/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.8s\n",
      "[CV 3/3; 132/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 132/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.829) F1: (test=0.693) total time=   3.7s\n",
      "[CV 1/3; 133/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 133/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.7s\n",
      "[CV 2/3; 133/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 133/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.747) total time=   4.4s\n",
      "[CV 3/3; 133/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 133/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=   4.7s\n",
      "[CV 1/3; 134/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 134/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.735) total time=   3.8s\n",
      "[CV 2/3; 134/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 134/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.9s\n",
      "[CV 3/3; 134/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 134/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.0s\n",
      "[CV 1/3; 135/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 135/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.6s\n",
      "[CV 2/3; 135/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 135/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.7s\n",
      "[CV 3/3; 135/540] START learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 135/540] END learning_rate=0.05, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.829) F1: (test=0.693) total time=   3.7s\n",
      "[CV 1/3; 136/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 136/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.744) total time=   3.0s\n",
      "[CV 2/3; 136/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 136/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.0s\n",
      "[CV 3/3; 136/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 136/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.738) total time=   3.0s\n",
      "[CV 1/3; 137/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 137/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.714) total time=   4.1s\n",
      "[CV 2/3; 137/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 137/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   4.1s\n",
      "[CV 3/3; 137/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 137/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.1s\n",
      "[CV 1/3; 138/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 138/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.738) total time=   4.0s\n",
      "[CV 2/3; 138/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 138/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   4.0s\n",
      "[CV 3/3; 138/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 138/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.1s\n",
      "[CV 1/3; 139/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 139/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.686) total time=   5.5s\n",
      "[CV 2/3; 139/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 139/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.820) F1: (test=0.679) total time=   5.5s\n",
      "[CV 3/3; 139/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 139/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.708) total time=   5.4s\n",
      "[CV 1/3; 140/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 140/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   6.3s\n",
      "[CV 2/3; 140/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 140/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.713) total time=   6.3s\n",
      "[CV 3/3; 140/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 140/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   6.5s\n",
      "[CV 1/3; 141/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 141/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.704) total time=   5.6s\n",
      "[CV 2/3; 141/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 141/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.817) F1: (test=0.677) total time=   5.5s\n",
      "[CV 3/3; 141/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 141/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   6.1s\n",
      "[CV 1/3; 142/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 142/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.708) total time=   8.1s\n",
      "[CV 2/3; 142/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 142/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.689) total time=   7.6s\n",
      "[CV 3/3; 142/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 142/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.712) total time=   7.8s\n",
      "[CV 1/3; 143/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 143/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.694) total time=   6.5s\n",
      "[CV 2/3; 143/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 143/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.709) total time=   6.5s\n",
      "[CV 3/3; 143/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 143/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.691) total time=   6.8s\n",
      "[CV 1/3; 144/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 144/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.682) total time=   5.7s\n",
      "[CV 2/3; 144/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 144/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.695) total time=   5.6s\n",
      "[CV 3/3; 144/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 144/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.706) total time=   5.9s\n",
      "[CV 1/3; 145/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 145/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.675) total time=   9.8s\n",
      "[CV 2/3; 145/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 145/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.817) F1: (test=0.684) total time=   9.5s\n",
      "[CV 3/3; 145/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 145/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.718) total time=   9.7s\n",
      "[CV 1/3; 146/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 146/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.694) total time=   6.5s\n",
      "[CV 2/3; 146/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 146/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.709) total time=   6.5s\n",
      "[CV 3/3; 146/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 146/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.691) total time=   6.7s\n",
      "[CV 1/3; 147/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 147/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.682) total time=   5.6s\n",
      "[CV 2/3; 147/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 147/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.695) total time=   5.6s\n",
      "[CV 3/3; 147/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 147/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.706) total time=   5.9s\n",
      "[CV 1/3; 148/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 148/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.673) total time=  11.7s\n",
      "[CV 2/3; 148/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 148/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.698) total time=  11.6s\n",
      "[CV 3/3; 148/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 148/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.729) total time=  11.4s\n",
      "[CV 1/3; 149/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 149/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.694) total time=   6.6s\n",
      "[CV 2/3; 149/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 149/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.709) total time=   6.7s\n",
      "[CV 3/3; 149/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 149/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.691) total time=   6.7s\n",
      "[CV 1/3; 150/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 150/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.682) total time=   5.7s\n",
      "[CV 2/3; 150/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 150/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.695) total time=   5.7s\n",
      "[CV 3/3; 150/540] START learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 150/540] END learning_rate=0.05, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.706) total time=   5.9s\n",
      "[CV 1/3; 151/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 151/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.701) total time=   2.8s\n",
      "[CV 2/3; 151/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 151/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.716) total time=   2.7s\n",
      "[CV 3/3; 151/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 151/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.8s\n",
      "[CV 1/3; 152/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 152/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.726) total time=   3.6s\n",
      "[CV 2/3; 152/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 152/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.5s\n",
      "[CV 3/3; 152/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 152/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.7s\n",
      "[CV 1/3; 153/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 153/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.726) total time=   3.5s\n",
      "[CV 2/3; 153/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 153/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.5s\n",
      "[CV 3/3; 153/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 153/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.6s\n",
      "[CV 1/3; 154/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 154/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   4.6s\n",
      "[CV 2/3; 154/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 154/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   4.7s\n",
      "[CV 3/3; 154/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 154/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   4.7s\n",
      "[CV 1/3; 155/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 155/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.7s\n",
      "[CV 2/3; 155/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 155/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.7s\n",
      "[CV 3/3; 155/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 155/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 156/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 156/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 156/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 156/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.4s\n",
      "[CV 3/3; 156/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 156/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.6s\n",
      "[CV 1/3; 157/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 157/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   6.5s\n",
      "[CV 2/3; 157/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 157/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   6.5s\n",
      "[CV 3/3; 157/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 157/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.718) total time=   6.5s\n",
      "[CV 1/3; 158/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 158/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.7s\n",
      "[CV 2/3; 158/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 158/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.8s\n",
      "[CV 3/3; 158/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 158/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 159/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 159/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 159/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 159/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.4s\n",
      "[CV 3/3; 159/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 159/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.6s\n",
      "[CV 1/3; 160/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 160/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.705) total time=   7.3s\n",
      "[CV 2/3; 160/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 160/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   7.2s\n",
      "[CV 3/3; 160/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 160/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   7.2s\n",
      "[CV 1/3; 161/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 161/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.7s\n",
      "[CV 2/3; 161/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 161/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.8s\n",
      "[CV 3/3; 161/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 161/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   4.9s\n",
      "[CV 1/3; 162/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 162/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.3s\n",
      "[CV 2/3; 162/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 162/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.4s\n",
      "[CV 3/3; 162/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 162/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.6s\n",
      "[CV 1/3; 163/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 163/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.705) total time=   7.3s\n",
      "[CV 2/3; 163/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 163/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   7.1s\n",
      "[CV 3/3; 163/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 163/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   7.2s\n",
      "[CV 1/3; 164/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 164/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   4.7s\n",
      "[CV 2/3; 164/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 164/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.9s\n",
      "[CV 3/3; 164/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 164/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   5.0s\n",
      "[CV 1/3; 165/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 165/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.717) total time=   4.6s\n",
      "[CV 2/3; 165/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 165/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.8s\n",
      "[CV 3/3; 165/540] START learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 165/540] END learning_rate=0.05, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.729) total time=   4.6s\n",
      "[CV 1/3; 166/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 166/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.6s\n",
      "[CV 2/3; 166/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 166/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.6s\n",
      "[CV 3/3; 166/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 166/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   2.7s\n",
      "[CV 1/3; 167/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 167/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.2s\n",
      "[CV 2/3; 167/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 167/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.2s\n",
      "[CV 3/3; 167/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 167/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.3s\n",
      "[CV 1/3; 168/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 168/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.721) total time=   3.1s\n",
      "[CV 2/3; 168/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 168/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.710) total time=   3.2s\n",
      "[CV 3/3; 168/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 168/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.2s\n",
      "[CV 1/3; 169/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 169/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.726) total time=   4.4s\n",
      "[CV 2/3; 169/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 169/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   4.4s\n",
      "[CV 3/3; 169/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 169/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.824) F1: (test=0.688) total time=   4.4s\n",
      "[CV 1/3; 170/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 170/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.8s\n",
      "[CV 2/3; 170/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 170/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   4.0s\n",
      "[CV 3/3; 170/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 170/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   4.0s\n",
      "[CV 1/3; 171/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 171/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.6s\n",
      "[CV 2/3; 171/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 171/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.6s\n",
      "[CV 3/3; 171/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 171/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.7s\n",
      "[CV 1/3; 172/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 172/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.9s\n",
      "[CV 2/3; 172/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 172/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.9s\n",
      "[CV 3/3; 172/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 172/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=   4.8s\n",
      "[CV 1/3; 173/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 173/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.7s\n",
      "[CV 2/3; 173/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 173/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.9s\n",
      "[CV 3/3; 173/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 173/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   4.0s\n",
      "[CV 1/3; 174/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 174/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 174/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 174/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.6s\n",
      "[CV 3/3; 174/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 174/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.7s\n",
      "[CV 1/3; 175/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 175/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.9s\n",
      "[CV 2/3; 175/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 175/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   5.0s\n",
      "[CV 3/3; 175/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 175/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=   5.0s\n",
      "[CV 1/3; 176/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 176/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.8s\n",
      "[CV 2/3; 176/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 176/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.9s\n",
      "[CV 3/3; 176/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 176/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   4.0s\n",
      "[CV 1/3; 177/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 177/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 177/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 177/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.6s\n",
      "[CV 3/3; 177/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 177/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.8s\n",
      "[CV 1/3; 178/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 178/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.9s\n",
      "[CV 2/3; 178/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 178/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   5.0s\n",
      "[CV 3/3; 178/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 178/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.694) total time=   4.9s\n",
      "[CV 1/3; 179/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 179/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.723) total time=   3.8s\n",
      "[CV 2/3; 179/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 179/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.9s\n",
      "[CV 3/3; 179/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 179/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.714) total time=   3.9s\n",
      "[CV 1/3; 180/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 180/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   3.5s\n",
      "[CV 2/3; 180/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 180/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.6s\n",
      "[CV 3/3; 180/540] START learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 180/540] END learning_rate=0.05, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.703) total time=   3.8s\n",
      "[CV 1/3; 181/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 181/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.735) total time=   3.1s\n",
      "[CV 2/3; 181/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 181/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.752) total time=   3.0s\n",
      "[CV 3/3; 181/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 181/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.752) total time=   3.1s\n",
      "[CV 1/3; 182/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 182/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.9s\n",
      "[CV 2/3; 182/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 182/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   3.9s\n",
      "[CV 3/3; 182/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 182/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.733) total time=   4.1s\n",
      "[CV 1/3; 183/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 183/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.717) total time=   4.2s\n",
      "[CV 2/3; 183/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 183/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.744) total time=   4.0s\n",
      "[CV 3/3; 183/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 183/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.730) total time=   4.0s\n",
      "[CV 1/3; 184/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 184/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   5.5s\n",
      "[CV 2/3; 184/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 184/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   5.9s\n",
      "[CV 3/3; 184/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 184/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.692) total time=   5.8s\n",
      "[CV 1/3; 185/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 185/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   5.0s\n",
      "[CV 2/3; 185/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 185/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.723) total time=   4.8s\n",
      "[CV 3/3; 185/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 185/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   5.0s\n",
      "[CV 1/3; 186/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 186/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.716) total time=   4.0s\n",
      "[CV 2/3; 186/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 186/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.705) total time=   4.0s\n",
      "[CV 3/3; 186/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 186/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.706) total time=   4.2s\n",
      "[CV 1/3; 187/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 187/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.732) total time=   7.7s\n",
      "[CV 2/3; 187/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 187/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.695) total time=   7.6s\n",
      "[CV 3/3; 187/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 187/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.709) total time=   7.7s\n",
      "[CV 1/3; 188/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 188/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   4.8s\n",
      "[CV 2/3; 188/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 188/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.9s\n",
      "[CV 3/3; 188/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 188/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   5.1s\n",
      "[CV 1/3; 189/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 189/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.0s\n",
      "[CV 2/3; 189/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 189/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   4.1s\n",
      "[CV 3/3; 189/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 189/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.3s\n",
      "[CV 1/3; 190/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 190/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.697) total time=   9.6s\n",
      "[CV 2/3; 190/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 190/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   9.4s\n",
      "[CV 3/3; 190/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 190/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.709) total time=   9.5s\n",
      "[CV 1/3; 191/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 191/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   4.8s\n",
      "[CV 2/3; 191/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 191/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.9s\n",
      "[CV 3/3; 191/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 191/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   5.2s\n",
      "[CV 1/3; 192/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 192/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.8s\n",
      "[CV 2/3; 192/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 192/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   4.8s\n",
      "[CV 3/3; 192/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 192/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.7s\n",
      "[CV 1/3; 193/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 193/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.702) total time=  11.9s\n",
      "[CV 2/3; 193/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 193/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.714) total time=  11.9s\n",
      "[CV 3/3; 193/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 193/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.712) total time=  11.4s\n",
      "[CV 1/3; 194/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 194/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   5.8s\n",
      "[CV 2/3; 194/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 194/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   5.3s\n",
      "[CV 3/3; 194/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 194/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   5.2s\n",
      "[CV 1/3; 195/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 195/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   5.1s\n",
      "[CV 2/3; 195/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 195/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   5.7s\n",
      "[CV 3/3; 195/540] START learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 195/540] END learning_rate=0.1, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   5.3s\n",
      "[CV 1/3; 196/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 196/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.9s\n",
      "[CV 2/3; 196/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 196/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.8s\n",
      "[CV 3/3; 196/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 196/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.760) total time=   3.1s\n",
      "[CV 1/3; 197/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 197/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.4s\n",
      "[CV 2/3; 197/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 197/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.752) total time=   3.5s\n",
      "[CV 3/3; 197/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 197/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.744) total time=   3.5s\n",
      "[CV 1/3; 198/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 198/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.3s\n",
      "[CV 2/3; 198/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 198/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   3.4s\n",
      "[CV 3/3; 198/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 198/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.718) total time=   3.4s\n",
      "[CV 1/3; 199/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 199/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   4.8s\n",
      "[CV 2/3; 199/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 199/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.747) total time=   5.3s\n",
      "[CV 3/3; 199/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 199/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   4.9s\n",
      "[CV 1/3; 200/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 200/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.0s\n",
      "[CV 2/3; 200/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 200/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.3s\n",
      "[CV 3/3; 200/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 200/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.3s\n",
      "[CV 1/3; 201/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 201/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.6s\n",
      "[CV 2/3; 201/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 201/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.6s\n",
      "[CV 3/3; 201/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 201/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.9s\n",
      "[CV 1/3; 202/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 202/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.693) total time=   7.1s\n",
      "[CV 2/3; 202/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 202/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.717) total time=   7.0s\n",
      "[CV 3/3; 202/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 202/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   7.0s\n",
      "[CV 1/3; 203/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 203/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.1s\n",
      "[CV 2/3; 203/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 203/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.3s\n",
      "[CV 3/3; 203/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 203/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.4s\n",
      "[CV 1/3; 204/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 204/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.6s\n",
      "[CV 2/3; 204/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 204/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.7s\n",
      "[CV 3/3; 204/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 204/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.8s\n",
      "[CV 1/3; 205/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 205/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   7.9s\n",
      "[CV 2/3; 205/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 205/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.730) total time=   8.0s\n",
      "[CV 3/3; 205/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 205/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   7.7s\n",
      "[CV 1/3; 206/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 206/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   3.9s\n",
      "[CV 2/3; 206/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 206/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.4s\n",
      "[CV 3/3; 206/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 206/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.3s\n",
      "[CV 1/3; 207/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 207/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.4s\n",
      "[CV 2/3; 207/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 207/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.6s\n",
      "[CV 3/3; 207/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 207/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.8s\n",
      "[CV 1/3; 208/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 208/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   8.0s\n",
      "[CV 2/3; 208/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 208/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.730) total time=   7.9s\n",
      "[CV 3/3; 208/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 208/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   7.5s\n",
      "[CV 1/3; 209/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 209/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   3.9s\n",
      "[CV 2/3; 209/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 209/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.1s\n",
      "[CV 3/3; 209/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 209/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.8s\n",
      "[CV 1/3; 210/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 210/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.7s\n",
      "[CV 2/3; 210/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 210/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.9s\n",
      "[CV 3/3; 210/540] START learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 210/540] END learning_rate=0.1, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.9s\n",
      "[CV 1/3; 211/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 211/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   2.6s\n",
      "[CV 2/3; 211/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 211/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.7s\n",
      "[CV 3/3; 211/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 211/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.8s\n",
      "[CV 1/3; 212/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 212/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.1s\n",
      "[CV 2/3; 212/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 212/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   3.1s\n",
      "[CV 3/3; 212/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 212/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.3s\n",
      "[CV 1/3; 213/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 213/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.0s\n",
      "[CV 2/3; 213/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 213/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.0s\n",
      "[CV 3/3; 213/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 213/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.0s\n",
      "[CV 1/3; 214/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 214/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   4.5s\n",
      "[CV 2/3; 214/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 214/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   4.5s\n",
      "[CV 3/3; 214/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 214/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.5s\n",
      "[CV 1/3; 215/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 215/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.3s\n",
      "[CV 2/3; 215/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 215/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.6s\n",
      "[CV 3/3; 215/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 215/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.6s\n",
      "[CV 1/3; 216/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 216/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.0s\n",
      "[CV 2/3; 216/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 216/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.4s\n",
      "[CV 3/3; 216/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 216/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.3s\n",
      "[CV 1/3; 217/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 217/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.5s\n",
      "[CV 2/3; 217/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 217/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.733) total time=   5.5s\n",
      "[CV 3/3; 217/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 217/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   5.3s\n",
      "[CV 1/3; 218/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 218/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 218/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 218/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.6s\n",
      "[CV 3/3; 218/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 218/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.7s\n",
      "[CV 1/3; 219/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 219/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.1s\n",
      "[CV 2/3; 219/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 219/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.2s\n",
      "[CV 3/3; 219/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 219/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.3s\n",
      "[CV 1/3; 220/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 220/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.8s\n",
      "[CV 2/3; 220/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 220/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.733) total time=   6.4s\n",
      "[CV 3/3; 220/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 220/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   6.1s\n",
      "[CV 1/3; 221/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 221/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.8s\n",
      "[CV 2/3; 221/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 221/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.7s\n",
      "[CV 3/3; 221/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 221/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.8s\n",
      "[CV 1/3; 222/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 222/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.2s\n",
      "[CV 2/3; 222/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 222/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.6s\n",
      "[CV 3/3; 222/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 222/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.8s\n",
      "[CV 1/3; 223/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 223/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   6.8s\n",
      "[CV 2/3; 223/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 223/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.733) total time=   6.2s\n",
      "[CV 3/3; 223/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 223/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   5.3s\n",
      "[CV 1/3; 224/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 224/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.5s\n",
      "[CV 2/3; 224/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 224/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   4.5s\n",
      "[CV 3/3; 224/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 224/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.9s\n",
      "[CV 1/3; 225/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 225/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.1s\n",
      "[CV 2/3; 225/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 225/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.3s\n",
      "[CV 3/3; 225/540] START learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 225/540] END learning_rate=0.1, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.3s\n",
      "[CV 1/3; 226/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 226/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.683) total time=   2.2s\n",
      "[CV 2/3; 226/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 226/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.707) total time=   2.2s\n",
      "[CV 3/3; 226/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 226/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.744) total time=   2.3s\n",
      "[CV 1/3; 227/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 227/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.9s\n",
      "[CV 2/3; 227/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 227/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   2.9s\n",
      "[CV 3/3; 227/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 227/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.733) total time=   3.1s\n",
      "[CV 1/3; 228/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 228/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.709) total time=   2.8s\n",
      "[CV 2/3; 228/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 228/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.9s\n",
      "[CV 3/3; 228/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 228/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.749) total time=   3.0s\n",
      "[CV 1/3; 229/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 229/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   2.4s\n",
      "[CV 2/3; 229/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 229/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.701) total time=   2.4s\n",
      "[CV 3/3; 229/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 229/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.5s\n",
      "[CV 1/3; 230/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 230/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   3.1s\n",
      "[CV 2/3; 230/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 230/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.1s\n",
      "[CV 3/3; 230/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 230/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.749) total time=   3.1s\n",
      "[CV 1/3; 231/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 231/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   3.0s\n",
      "[CV 2/3; 231/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 231/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.1s\n",
      "[CV 3/3; 231/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 231/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   3.0s\n",
      "[CV 1/3; 232/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 232/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   2.5s\n",
      "[CV 2/3; 232/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 232/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.701) total time=   2.5s\n",
      "[CV 3/3; 232/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 232/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.5s\n",
      "[CV 1/3; 233/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 233/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   3.2s\n",
      "[CV 2/3; 233/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 233/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.2s\n",
      "[CV 3/3; 233/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 233/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.749) total time=   3.0s\n",
      "[CV 1/3; 234/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 234/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   2.9s\n",
      "[CV 2/3; 234/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 234/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.0s\n",
      "[CV 3/3; 234/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 234/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   3.0s\n",
      "[CV 1/3; 235/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 235/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   2.6s\n",
      "[CV 2/3; 235/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 235/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.701) total time=   2.4s\n",
      "[CV 3/3; 235/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 235/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.5s\n",
      "[CV 1/3; 236/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 236/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   3.2s\n",
      "[CV 2/3; 236/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 236/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.1s\n",
      "[CV 3/3; 236/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 236/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.749) total time=   3.0s\n",
      "[CV 1/3; 237/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 237/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   2.9s\n",
      "[CV 2/3; 237/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 237/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   2.8s\n",
      "[CV 3/3; 237/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 237/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.9s\n",
      "[CV 1/3; 238/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 238/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=   2.4s\n",
      "[CV 2/3; 238/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 238/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.701) total time=   2.4s\n",
      "[CV 3/3; 238/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 238/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.5s\n",
      "[CV 1/3; 239/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 239/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   3.1s\n",
      "[CV 2/3; 239/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 239/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.1s\n",
      "[CV 3/3; 239/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 239/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.749) total time=   3.1s\n",
      "[CV 1/3; 240/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 240/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   2.9s\n",
      "[CV 2/3; 240/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 240/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   2.9s\n",
      "[CV 3/3; 240/540] START learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 240/540] END learning_rate=0.1, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   3.0s\n",
      "[CV 1/3; 241/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 241/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   2.2s\n",
      "[CV 2/3; 241/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 241/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.704) total time=   2.0s\n",
      "[CV 3/3; 241/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 241/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.3s\n",
      "[CV 1/3; 242/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 242/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.747) total time=   2.8s\n",
      "[CV 2/3; 242/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 242/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.712) total time=   2.7s\n",
      "[CV 3/3; 242/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 242/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.749) total time=   2.7s\n",
      "[CV 1/3; 243/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 243/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   2.5s\n",
      "[CV 2/3; 243/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 243/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.5s\n",
      "[CV 3/3; 243/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 243/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.5s\n",
      "[CV 1/3; 244/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 244/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.0s\n",
      "[CV 2/3; 244/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 244/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   2.0s\n",
      "[CV 3/3; 244/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 244/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.744) total time=   2.1s\n",
      "[CV 1/3; 245/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 245/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   2.5s\n",
      "[CV 2/3; 245/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 245/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.6s\n",
      "[CV 3/3; 245/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 245/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.5s\n",
      "[CV 1/3; 246/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 246/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.4s\n",
      "[CV 2/3; 246/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 246/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.4s\n",
      "[CV 3/3; 246/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 246/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.4s\n",
      "[CV 1/3; 247/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 247/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.0s\n",
      "[CV 2/3; 247/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 247/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   2.0s\n",
      "[CV 3/3; 247/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 247/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.744) total time=   2.0s\n",
      "[CV 1/3; 248/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 248/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   2.7s\n",
      "[CV 2/3; 248/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 248/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.6s\n",
      "[CV 3/3; 248/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 248/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.8s\n",
      "[CV 1/3; 249/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 249/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.6s\n",
      "[CV 2/3; 249/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 249/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.6s\n",
      "[CV 3/3; 249/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 249/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.8s\n",
      "[CV 1/3; 250/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 250/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.2s\n",
      "[CV 2/3; 250/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 250/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   2.4s\n",
      "[CV 3/3; 250/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 250/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.744) total time=   2.3s\n",
      "[CV 1/3; 251/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 251/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.0s\n",
      "[CV 2/3; 251/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 251/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.8s\n",
      "[CV 3/3; 251/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 251/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.7s\n",
      "[CV 1/3; 252/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 252/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.6s\n",
      "[CV 2/3; 252/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 252/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.6s\n",
      "[CV 3/3; 252/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 252/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.7s\n",
      "[CV 1/3; 253/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 253/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.2s\n",
      "[CV 2/3; 253/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 253/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   2.1s\n",
      "[CV 3/3; 253/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 253/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.744) total time=   2.2s\n",
      "[CV 1/3; 254/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 254/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   2.8s\n",
      "[CV 2/3; 254/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 254/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.719) total time=   2.8s\n",
      "[CV 3/3; 254/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 254/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.746) total time=   2.7s\n",
      "[CV 1/3; 255/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 255/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.7s\n",
      "[CV 2/3; 255/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 255/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.7s\n",
      "[CV 3/3; 255/540] START learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 255/540] END learning_rate=0.1, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.6s\n",
      "[CV 1/3; 256/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 256/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.724) total time=   1.9s\n",
      "[CV 2/3; 256/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 256/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.747) total time=   1.9s\n",
      "[CV 3/3; 256/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 256/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 1/3; 257/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 257/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.4s\n",
      "[CV 2/3; 257/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 257/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.4s\n",
      "[CV 3/3; 257/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 257/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.6s\n",
      "[CV 1/3; 258/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 258/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.4s\n",
      "[CV 2/3; 258/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 258/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.4s\n",
      "[CV 3/3; 258/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 258/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.5s\n",
      "[CV 1/3; 259/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 259/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 259/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 259/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.755) total time=   1.9s\n",
      "[CV 3/3; 259/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 259/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.3s\n",
      "[CV 1/3; 260/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 260/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   2.4s\n",
      "[CV 2/3; 260/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 260/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.4s\n",
      "[CV 3/3; 260/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 260/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.4s\n",
      "[CV 1/3; 261/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 261/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.710) total time=   2.3s\n",
      "[CV 2/3; 261/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 261/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.752) total time=   2.5s\n",
      "[CV 3/3; 261/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 261/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.4s\n",
      "[CV 1/3; 262/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 262/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 262/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 262/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.755) total time=   1.9s\n",
      "[CV 3/3; 262/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 262/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.0s\n",
      "[CV 1/3; 263/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 263/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   2.4s\n",
      "[CV 2/3; 263/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 263/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.5s\n",
      "[CV 3/3; 263/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 263/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.5s\n",
      "[CV 1/3; 264/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 264/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.710) total time=   2.3s\n",
      "[CV 2/3; 264/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 264/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.752) total time=   2.4s\n",
      "[CV 3/3; 264/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 264/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.4s\n",
      "[CV 1/3; 265/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 265/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 265/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 265/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.755) total time=   1.9s\n",
      "[CV 3/3; 265/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 265/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.1s\n",
      "[CV 1/3; 266/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 266/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   2.5s\n",
      "[CV 2/3; 266/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 266/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.5s\n",
      "[CV 3/3; 266/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 266/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.4s\n",
      "[CV 1/3; 267/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 267/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.710) total time=   2.4s\n",
      "[CV 2/3; 267/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 267/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.752) total time=   2.3s\n",
      "[CV 3/3; 267/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 267/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.4s\n",
      "[CV 1/3; 268/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 268/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 268/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 268/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.755) total time=   2.0s\n",
      "[CV 3/3; 268/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 268/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.715) total time=   2.1s\n",
      "[CV 1/3; 269/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 269/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   2.4s\n",
      "[CV 2/3; 269/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 269/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.4s\n",
      "[CV 3/3; 269/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 269/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.5s\n",
      "[CV 1/3; 270/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 270/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.830) F1: (test=0.710) total time=   2.3s\n",
      "[CV 2/3; 270/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 270/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.752) total time=   2.5s\n",
      "[CV 3/3; 270/540] START learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 270/540] END learning_rate=0.1, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.4s\n",
      "[CV 1/3; 271/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 271/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.735) total time=   3.1s\n",
      "[CV 2/3; 271/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 271/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.752) total time=   3.1s\n",
      "[CV 3/3; 271/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 271/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.752) total time=   3.1s\n",
      "[CV 1/3; 272/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 272/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.0s\n",
      "[CV 2/3; 272/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 272/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   4.1s\n",
      "[CV 3/3; 272/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 272/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.733) total time=   4.0s\n",
      "[CV 1/3; 273/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 273/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.6s\n",
      "[CV 2/3; 273/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 273/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.8s\n",
      "[CV 3/3; 273/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 273/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.730) total time=   3.8s\n",
      "[CV 1/3; 274/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 274/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.696) total time=   5.9s\n",
      "[CV 2/3; 274/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 274/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.738) total time=   5.6s\n",
      "[CV 3/3; 274/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 274/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   5.6s\n",
      "[CV 1/3; 275/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 275/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.835) F1: (test=0.706) total time=   4.8s\n",
      "[CV 2/3; 275/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 275/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.9s\n",
      "[CV 3/3; 275/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 275/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.724) total time=   5.0s\n",
      "[CV 1/3; 276/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 276/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.723) total time=   4.1s\n",
      "[CV 2/3; 276/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 276/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.735) total time=   4.2s\n",
      "[CV 3/3; 276/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 276/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.2s\n",
      "[CV 1/3; 277/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 277/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.700) total time=   7.7s\n",
      "[CV 2/3; 277/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 277/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.709) total time=   7.5s\n",
      "[CV 3/3; 277/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 277/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.704) total time=   7.5s\n",
      "[CV 1/3; 278/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 278/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.7s\n",
      "[CV 2/3; 278/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 278/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   5.1s\n",
      "[CV 3/3; 278/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 278/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.2s\n",
      "[CV 1/3; 279/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 279/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.1s\n",
      "[CV 2/3; 279/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 279/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   4.2s\n",
      "[CV 3/3; 279/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 279/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.824) F1: (test=0.695) total time=   4.5s\n",
      "[CV 1/3; 280/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 280/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.701) total time=   9.5s\n",
      "[CV 2/3; 280/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 280/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   8.8s\n",
      "[CV 3/3; 280/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 280/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.819) F1: (test=0.693) total time=   9.2s\n",
      "[CV 1/3; 281/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 281/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.9s\n",
      "[CV 2/3; 281/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 281/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   5.2s\n",
      "[CV 3/3; 281/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 281/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.2s\n",
      "[CV 1/3; 282/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 282/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.1s\n",
      "[CV 2/3; 282/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 282/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   4.4s\n",
      "[CV 3/3; 282/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 282/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.824) F1: (test=0.695) total time=   4.4s\n",
      "[CV 1/3; 283/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 283/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.720) total time=   9.3s\n",
      "[CV 2/3; 283/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 283/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   9.1s\n",
      "[CV 3/3; 283/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 283/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.712) total time=  10.2s\n",
      "[CV 1/3; 284/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 284/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   5.0s\n",
      "[CV 2/3; 284/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 284/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   5.1s\n",
      "[CV 3/3; 284/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 284/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.2s\n",
      "[CV 1/3; 285/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 285/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.2s\n",
      "[CV 2/3; 285/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 285/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.699) total time=   4.2s\n",
      "[CV 3/3; 285/540] START learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 285/540] END learning_rate=0.1, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.824) F1: (test=0.695) total time=   4.6s\n",
      "[CV 1/3; 286/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 286/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.717) total time=   2.9s\n",
      "[CV 2/3; 286/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 286/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.8s\n",
      "[CV 3/3; 286/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 286/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   2.9s\n",
      "[CV 1/3; 287/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 287/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.4s\n",
      "[CV 2/3; 287/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 287/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.755) total time=   3.8s\n",
      "[CV 3/3; 287/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 287/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.741) total time=   3.8s\n",
      "[CV 1/3; 288/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 288/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.3s\n",
      "[CV 2/3; 288/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 288/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   3.4s\n",
      "[CV 3/3; 288/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 288/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.718) total time=   3.4s\n",
      "[CV 1/3; 289/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 289/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   4.9s\n",
      "[CV 2/3; 289/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 289/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.9s\n",
      "[CV 3/3; 289/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 289/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.721) total time=   4.7s\n",
      "[CV 1/3; 290/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 290/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.717) total time=   4.0s\n",
      "[CV 2/3; 290/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 290/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.733) total time=   4.2s\n",
      "[CV 3/3; 290/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 290/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.3s\n",
      "[CV 1/3; 291/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 291/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.5s\n",
      "[CV 2/3; 291/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 291/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.7s\n",
      "[CV 3/3; 291/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 291/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.735) total time=   4.0s\n",
      "[CV 1/3; 292/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 292/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.738) total time=   6.4s\n",
      "[CV 2/3; 292/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 292/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   6.1s\n",
      "[CV 3/3; 292/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 292/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.718) total time=   6.5s\n",
      "[CV 1/3; 293/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 293/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.717) total time=   4.0s\n",
      "[CV 2/3; 293/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 293/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.733) total time=   4.2s\n",
      "[CV 3/3; 293/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 293/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.3s\n",
      "[CV 1/3; 294/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 294/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.5s\n",
      "[CV 2/3; 294/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 294/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.7s\n",
      "[CV 3/3; 294/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 294/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.8s\n",
      "[CV 1/3; 295/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 295/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   6.6s\n",
      "[CV 2/3; 295/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 295/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   6.5s\n",
      "[CV 3/3; 295/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 295/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.718) total time=   6.8s\n",
      "[CV 1/3; 296/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 296/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.717) total time=   4.0s\n",
      "[CV 2/3; 296/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 296/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.733) total time=   4.4s\n",
      "[CV 3/3; 296/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 296/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.3s\n",
      "[CV 1/3; 297/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 297/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.5s\n",
      "[CV 2/3; 297/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 297/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.7s\n",
      "[CV 3/3; 297/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 297/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.8s\n",
      "[CV 1/3; 298/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 298/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   6.4s\n",
      "[CV 2/3; 298/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 298/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   6.4s\n",
      "[CV 3/3; 298/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 298/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.718) total time=   6.8s\n",
      "[CV 1/3; 299/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 299/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.717) total time=   4.2s\n",
      "[CV 2/3; 299/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 299/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.733) total time=   4.1s\n",
      "[CV 3/3; 299/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 299/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.3s\n",
      "[CV 1/3; 300/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 300/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.4s\n",
      "[CV 2/3; 300/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 300/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.7s\n",
      "[CV 3/3; 300/540] START learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 300/540] END learning_rate=0.1, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.9s\n",
      "[CV 1/3; 301/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 301/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.719) total time=   2.6s\n",
      "[CV 2/3; 301/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 301/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.5s\n",
      "[CV 3/3; 301/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 301/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.724) total time=   2.7s\n",
      "[CV 1/3; 302/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 302/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.0s\n",
      "[CV 2/3; 302/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 302/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   3.1s\n",
      "[CV 3/3; 302/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 302/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.2s\n",
      "[CV 1/3; 303/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 303/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.1s\n",
      "[CV 2/3; 303/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 303/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.0s\n",
      "[CV 3/3; 303/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 303/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.2s\n",
      "[CV 1/3; 304/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 304/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.698) total time=   4.4s\n",
      "[CV 2/3; 304/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 304/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.747) total time=   4.4s\n",
      "[CV 3/3; 304/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 304/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.744) total time=   4.6s\n",
      "[CV 1/3; 305/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 305/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.5s\n",
      "[CV 2/3; 305/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 305/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.5s\n",
      "[CV 3/3; 305/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 305/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.7s\n",
      "[CV 1/3; 306/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 306/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.0s\n",
      "[CV 2/3; 306/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 306/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   3.2s\n",
      "[CV 3/3; 306/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 306/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.3s\n",
      "[CV 1/3; 307/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 307/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.7s\n",
      "[CV 2/3; 307/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 307/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.7s\n",
      "[CV 3/3; 307/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 307/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.692) total time=   5.2s\n",
      "[CV 1/3; 308/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 308/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 308/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 308/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.6s\n",
      "[CV 3/3; 308/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 308/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.7s\n",
      "[CV 1/3; 309/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 309/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.0s\n",
      "[CV 2/3; 309/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 309/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   3.1s\n",
      "[CV 3/3; 309/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 309/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.3s\n",
      "[CV 1/3; 310/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 310/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.7s\n",
      "[CV 2/3; 310/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 310/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.9s\n",
      "[CV 3/3; 310/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 310/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.692) total time=   5.0s\n",
      "[CV 1/3; 311/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 311/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.5s\n",
      "[CV 2/3; 311/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 311/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.7s\n",
      "[CV 3/3; 311/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 311/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.8s\n",
      "[CV 1/3; 312/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 312/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.1s\n",
      "[CV 2/3; 312/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 312/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   3.3s\n",
      "[CV 3/3; 312/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 312/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.3s\n",
      "[CV 1/3; 313/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 313/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.8s\n",
      "[CV 2/3; 313/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 313/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   4.8s\n",
      "[CV 3/3; 313/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 313/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.692) total time=   4.8s\n",
      "[CV 1/3; 314/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 314/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 314/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 314/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.6s\n",
      "[CV 3/3; 314/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 314/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   3.8s\n",
      "[CV 1/3; 315/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 315/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.0s\n",
      "[CV 2/3; 315/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 315/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   3.2s\n",
      "[CV 3/3; 315/540] START learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 315/540] END learning_rate=0.1, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.3s\n",
      "[CV 1/3; 316/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 316/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.735) total time=   3.1s\n",
      "[CV 2/3; 316/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 316/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.752) total time=   3.2s\n",
      "[CV 3/3; 316/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 316/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.752) total time=   3.1s\n",
      "[CV 1/3; 317/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 317/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   4.1s\n",
      "[CV 2/3; 317/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 317/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   4.0s\n",
      "[CV 3/3; 317/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 317/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.733) total time=   4.2s\n",
      "[CV 1/3; 318/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 318/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.717) total time=   3.6s\n",
      "[CV 2/3; 318/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 318/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.8s\n",
      "[CV 3/3; 318/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 318/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.730) total time=   4.3s\n",
      "[CV 1/3; 319/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 319/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.723) total time=   5.6s\n",
      "[CV 2/3; 319/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 319/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.721) total time=   5.6s\n",
      "[CV 3/3; 319/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 319/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.692) total time=   5.7s\n",
      "[CV 1/3; 320/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 320/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.8s\n",
      "[CV 2/3; 320/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 320/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.723) total time=   4.9s\n",
      "[CV 3/3; 320/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 320/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.712) total time=   5.1s\n",
      "[CV 1/3; 321/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 321/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.716) total time=   4.0s\n",
      "[CV 2/3; 321/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 321/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.825) F1: (test=0.705) total time=   4.1s\n",
      "[CV 3/3; 321/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 321/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.706) total time=   4.3s\n",
      "[CV 1/3; 322/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 322/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.723) total time=   8.0s\n",
      "[CV 2/3; 322/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 322/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   7.9s\n",
      "[CV 3/3; 322/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 322/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.709) total time=   7.9s\n",
      "[CV 1/3; 323/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 323/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   5.1s\n",
      "[CV 2/3; 323/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 323/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.724) total time=   5.0s\n",
      "[CV 3/3; 323/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 323/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   5.3s\n",
      "[CV 1/3; 324/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 324/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.2s\n",
      "[CV 2/3; 324/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 324/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   4.2s\n",
      "[CV 3/3; 324/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 324/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.7s\n",
      "[CV 1/3; 325/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 325/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.707) total time=  10.7s\n",
      "[CV 2/3; 325/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 325/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.699) total time=  11.3s\n",
      "[CV 3/3; 325/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 325/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=  11.1s\n",
      "[CV 1/3; 326/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 326/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   5.1s\n",
      "[CV 2/3; 326/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 326/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.724) total time=   5.5s\n",
      "[CV 3/3; 326/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 326/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   5.7s\n",
      "[CV 1/3; 327/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 327/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.5s\n",
      "[CV 2/3; 327/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 327/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   4.4s\n",
      "[CV 3/3; 327/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 327/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.8s\n",
      "[CV 1/3; 328/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 328/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.698) total time=  12.0s\n",
      "[CV 2/3; 328/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 328/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=  10.9s\n",
      "[CV 3/3; 328/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 328/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.834) F1: (test=0.712) total time=  10.8s\n",
      "[CV 1/3; 329/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 329/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.715) total time=   4.8s\n",
      "[CV 2/3; 329/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 329/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.724) total time=   4.9s\n",
      "[CV 3/3; 329/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 329/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   5.1s\n",
      "[CV 1/3; 330/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 330/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.727) total time=   4.0s\n",
      "[CV 2/3; 330/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 330/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   4.1s\n",
      "[CV 3/3; 330/540] START learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 330/540] END learning_rate=0.1, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.5s\n",
      "[CV 1/3; 331/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 331/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.9s\n",
      "[CV 2/3; 331/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 331/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.3s\n",
      "[CV 3/3; 331/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 331/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.760) total time=   3.4s\n",
      "[CV 1/3; 332/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 332/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.8s\n",
      "[CV 2/3; 332/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 332/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.752) total time=   3.7s\n",
      "[CV 3/3; 332/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 332/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.744) total time=   3.8s\n",
      "[CV 1/3; 333/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 333/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.715) total time=   3.3s\n",
      "[CV 2/3; 333/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 333/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   3.6s\n",
      "[CV 3/3; 333/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 333/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.834) F1: (test=0.718) total time=   3.6s\n",
      "[CV 1/3; 334/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 334/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   5.8s\n",
      "[CV 2/3; 334/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 334/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.747) total time=   5.4s\n",
      "[CV 3/3; 334/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 334/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   5.1s\n",
      "[CV 1/3; 335/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 335/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.1s\n",
      "[CV 2/3; 335/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 335/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.3s\n",
      "[CV 3/3; 335/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 335/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.5s\n",
      "[CV 1/3; 336/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 336/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.5s\n",
      "[CV 2/3; 336/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 336/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.8s\n",
      "[CV 3/3; 336/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 336/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.0s\n",
      "[CV 1/3; 337/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 337/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.693) total time=   7.2s\n",
      "[CV 2/3; 337/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 337/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.717) total time=   7.2s\n",
      "[CV 3/3; 337/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 337/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   7.9s\n",
      "[CV 1/3; 338/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 338/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.1s\n",
      "[CV 2/3; 338/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 338/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.3s\n",
      "[CV 3/3; 338/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 338/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.3s\n",
      "[CV 1/3; 339/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 339/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.4s\n",
      "[CV 2/3; 339/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 339/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.6s\n",
      "[CV 3/3; 339/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 339/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.7s\n",
      "[CV 1/3; 340/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 340/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   7.7s\n",
      "[CV 2/3; 340/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 340/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.724) total time=   7.5s\n",
      "[CV 3/3; 340/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 340/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   7.6s\n",
      "[CV 1/3; 341/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 341/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   3.9s\n",
      "[CV 2/3; 341/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 341/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.1s\n",
      "[CV 3/3; 341/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 341/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.3s\n",
      "[CV 1/3; 342/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 342/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.8s\n",
      "[CV 2/3; 342/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 342/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.3s\n",
      "[CV 3/3; 342/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 342/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.9s\n",
      "[CV 1/3; 343/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 343/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   8.4s\n",
      "[CV 2/3; 343/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 343/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.724) total time=   7.9s\n",
      "[CV 3/3; 343/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 343/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   7.6s\n",
      "[CV 1/3; 344/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 344/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.729) total time=   4.0s\n",
      "[CV 2/3; 344/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 344/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   4.3s\n",
      "[CV 3/3; 344/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 344/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   4.3s\n",
      "[CV 1/3; 345/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 345/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.755) total time=   3.5s\n",
      "[CV 2/3; 345/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 345/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.6s\n",
      "[CV 3/3; 345/540] START learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 345/540] END learning_rate=0.1, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.8s\n",
      "[CV 1/3; 346/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 346/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   2.6s\n",
      "[CV 2/3; 346/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 346/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.6s\n",
      "[CV 3/3; 346/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 346/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.6s\n",
      "[CV 1/3; 347/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 347/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.724) total time=   3.1s\n",
      "[CV 2/3; 347/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 347/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   3.1s\n",
      "[CV 3/3; 347/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 347/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   3.2s\n",
      "[CV 1/3; 348/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 348/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.8s\n",
      "[CV 2/3; 348/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 348/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.9s\n",
      "[CV 3/3; 348/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 348/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.0s\n",
      "[CV 1/3; 349/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 349/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.715) total time=   4.4s\n",
      "[CV 2/3; 349/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 349/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   4.5s\n",
      "[CV 3/3; 349/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 349/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.4s\n",
      "[CV 1/3; 350/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 350/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 350/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 350/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.5s\n",
      "[CV 3/3; 350/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 350/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.5s\n",
      "[CV 1/3; 351/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 351/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.9s\n",
      "[CV 2/3; 351/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 351/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.1s\n",
      "[CV 3/3; 351/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 351/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.2s\n",
      "[CV 1/3; 352/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 352/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.3s\n",
      "[CV 2/3; 352/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 352/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.733) total time=   5.3s\n",
      "[CV 3/3; 352/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 352/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   5.2s\n",
      "[CV 1/3; 353/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 353/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 353/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 353/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.5s\n",
      "[CV 3/3; 353/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 353/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.5s\n",
      "[CV 1/3; 354/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 354/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.0s\n",
      "[CV 2/3; 354/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 354/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.1s\n",
      "[CV 3/3; 354/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 354/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.2s\n",
      "[CV 1/3; 355/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 355/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.3s\n",
      "[CV 2/3; 355/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 355/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.733) total time=   5.2s\n",
      "[CV 3/3; 355/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 355/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   5.2s\n",
      "[CV 1/3; 356/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 356/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 356/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 356/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.5s\n",
      "[CV 3/3; 356/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 356/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.6s\n",
      "[CV 1/3; 357/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 357/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.0s\n",
      "[CV 2/3; 357/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 357/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.1s\n",
      "[CV 3/3; 357/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 357/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.2s\n",
      "[CV 1/3; 358/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 358/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.703) total time=   5.3s\n",
      "[CV 2/3; 358/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 358/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.733) total time=   5.4s\n",
      "[CV 3/3; 358/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 358/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   5.2s\n",
      "[CV 1/3; 359/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 359/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.741) total time=   3.4s\n",
      "[CV 2/3; 359/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 359/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.749) total time=   3.5s\n",
      "[CV 3/3; 359/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 359/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   3.6s\n",
      "[CV 1/3; 360/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 360/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.718) total time=   3.0s\n",
      "[CV 2/3; 360/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 360/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.718) total time=   3.2s\n",
      "[CV 3/3; 360/540] START learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 360/540] END learning_rate=0.1, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.747) total time=   3.2s\n",
      "[CV 1/3; 361/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 361/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.747) total time=   2.6s\n",
      "[CV 2/3; 361/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 361/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   2.6s\n",
      "[CV 3/3; 361/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 361/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.772) total time=   2.7s\n",
      "[CV 1/3; 362/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 362/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.5s\n",
      "[CV 2/3; 362/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 362/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.5s\n",
      "[CV 3/3; 362/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 362/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.770) total time=   2.7s\n",
      "[CV 1/3; 363/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 363/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 363/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 363/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.766) total time=   2.1s\n",
      "[CV 3/3; 363/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 363/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.755) total time=   2.3s\n",
      "[CV 1/3; 364/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 364/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.747) total time=   3.4s\n",
      "[CV 2/3; 364/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 364/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.4s\n",
      "[CV 3/3; 364/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 364/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.730) total time=   3.4s\n",
      "[CV 1/3; 365/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 365/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.714) total time=   2.6s\n",
      "[CV 2/3; 365/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 365/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.749) total time=   2.6s\n",
      "[CV 3/3; 365/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 365/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.829) F1: (test=0.707) total time=   2.9s\n",
      "[CV 1/3; 366/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 366/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   2.4s\n",
      "[CV 2/3; 366/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 366/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.3s\n",
      "[CV 3/3; 366/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 366/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   2.3s\n",
      "[CV 1/3; 367/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 367/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.750) total time=   4.6s\n",
      "[CV 2/3; 367/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 367/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.7s\n",
      "[CV 3/3; 367/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 367/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.707) total time=   4.6s\n",
      "[CV 1/3; 368/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 368/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   2.7s\n",
      "[CV 2/3; 368/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 368/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.7s\n",
      "[CV 3/3; 368/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 368/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.9s\n",
      "[CV 1/3; 369/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 369/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.755) total time=   2.3s\n",
      "[CV 2/3; 369/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 369/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.3s\n",
      "[CV 3/3; 369/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 369/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.3s\n",
      "[CV 1/3; 370/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 370/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.741) total time=   5.5s\n",
      "[CV 2/3; 370/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 370/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.822) F1: (test=0.689) total time=   5.6s\n",
      "[CV 3/3; 370/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 370/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.704) total time=   5.5s\n",
      "[CV 1/3; 371/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 371/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   2.8s\n",
      "[CV 2/3; 371/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 371/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.7s\n",
      "[CV 3/3; 371/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 371/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.9s\n",
      "[CV 1/3; 372/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 372/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.755) total time=   2.4s\n",
      "[CV 2/3; 372/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 372/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.3s\n",
      "[CV 3/3; 372/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 372/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.3s\n",
      "[CV 1/3; 373/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 373/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.741) total time=   6.3s\n",
      "[CV 2/3; 373/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 373/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   6.2s\n",
      "[CV 3/3; 373/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 373/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   6.3s\n",
      "[CV 1/3; 374/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 374/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   2.7s\n",
      "[CV 2/3; 374/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 374/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.8s\n",
      "[CV 3/3; 374/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 374/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.9s\n",
      "[CV 1/3; 375/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 375/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.755) total time=   2.2s\n",
      "[CV 2/3; 375/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 375/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.3s\n",
      "[CV 3/3; 375/540] START learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 375/540] END learning_rate=0.2, max_depth=-1, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.3s\n",
      "[CV 1/3; 376/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 376/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   2.5s\n",
      "[CV 2/3; 376/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 376/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.5s\n",
      "[CV 3/3; 376/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 376/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.6s\n",
      "[CV 1/3; 377/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 377/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 2/3; 377/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 377/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.4s\n",
      "[CV 3/3; 377/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 377/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.4s\n",
      "[CV 1/3; 378/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 378/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   1.9s\n",
      "[CV 2/3; 378/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 378/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.0s\n",
      "[CV 3/3; 378/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 378/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.768) total time=   2.0s\n",
      "[CV 1/3; 379/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 379/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.738) total time=   3.6s\n",
      "[CV 2/3; 379/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 379/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.724) total time=   3.6s\n",
      "[CV 3/3; 379/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 379/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.755) total time=   3.6s\n",
      "[CV 1/3; 380/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 380/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.2s\n",
      "[CV 2/3; 380/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 380/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 380/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 380/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 381/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 381/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 381/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 381/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 381/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 381/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.1s\n",
      "[CV 1/3; 382/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 382/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.7s\n",
      "[CV 2/3; 382/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 382/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.7s\n",
      "[CV 3/3; 382/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 382/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=   4.8s\n",
      "[CV 1/3; 383/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 383/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.3s\n",
      "[CV 2/3; 383/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 383/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 383/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 383/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 384/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 384/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 384/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 384/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 384/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 384/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.1s\n",
      "[CV 1/3; 385/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 385/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.738) total time=   5.4s\n",
      "[CV 2/3; 385/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 385/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.702) total time=   5.3s\n",
      "[CV 3/3; 385/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 385/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.4s\n",
      "[CV 1/3; 386/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 386/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.3s\n",
      "[CV 2/3; 386/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 386/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 386/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 386/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 387/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 387/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   2.0s\n",
      "[CV 2/3; 387/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 387/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 387/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 387/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.1s\n",
      "[CV 1/3; 388/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 388/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.738) total time=   5.4s\n",
      "[CV 2/3; 388/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 388/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.702) total time=   5.3s\n",
      "[CV 3/3; 388/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 388/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.3s\n",
      "[CV 1/3; 389/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 389/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.3s\n",
      "[CV 2/3; 389/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 389/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 389/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 389/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 390/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 390/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 390/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 390/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 390/540] START learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 390/540] END learning_rate=0.2, max_depth=-1, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.1s\n",
      "[CV 1/3; 391/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 391/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.4s\n",
      "[CV 2/3; 391/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 391/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.752) total time=   2.5s\n",
      "[CV 3/3; 391/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 391/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.5s\n",
      "[CV 1/3; 392/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 392/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.752) total time=   2.0s\n",
      "[CV 2/3; 392/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 392/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   2.2s\n",
      "[CV 3/3; 392/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 392/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.2s\n",
      "[CV 1/3; 393/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 393/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   1.8s\n",
      "[CV 2/3; 393/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 393/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.760) total time=   1.8s\n",
      "[CV 3/3; 393/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 393/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   1.9s\n",
      "[CV 1/3; 394/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 394/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.7s\n",
      "[CV 2/3; 394/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 394/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   3.8s\n",
      "[CV 3/3; 394/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 394/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.765) total time=   3.7s\n",
      "[CV 1/3; 395/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 395/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.2s\n",
      "[CV 2/3; 395/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 395/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.3s\n",
      "[CV 3/3; 395/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 395/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.3s\n",
      "[CV 1/3; 396/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 396/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.8s\n",
      "[CV 2/3; 396/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 396/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.9s\n",
      "[CV 3/3; 396/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 396/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.0s\n",
      "[CV 1/3; 397/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 397/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   4.2s\n",
      "[CV 2/3; 397/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 397/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.3s\n",
      "[CV 3/3; 397/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 397/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   4.3s\n",
      "[CV 1/3; 398/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 398/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.3s\n",
      "[CV 2/3; 398/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 398/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.3s\n",
      "[CV 3/3; 398/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 398/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.3s\n",
      "[CV 1/3; 399/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 399/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.8s\n",
      "[CV 2/3; 399/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 399/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.8s\n",
      "[CV 3/3; 399/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 399/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 400/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 400/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   4.2s\n",
      "[CV 2/3; 400/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 400/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.3s\n",
      "[CV 3/3; 400/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 400/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   4.2s\n",
      "[CV 1/3; 401/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 401/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.3s\n",
      "[CV 2/3; 401/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 401/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.3s\n",
      "[CV 3/3; 401/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 401/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.4s\n",
      "[CV 1/3; 402/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 402/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.8s\n",
      "[CV 2/3; 402/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 402/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.8s\n",
      "[CV 3/3; 402/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 402/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.0s\n",
      "[CV 1/3; 403/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 403/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   4.3s\n",
      "[CV 2/3; 403/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 403/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.3s\n",
      "[CV 3/3; 403/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 403/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   4.4s\n",
      "[CV 1/3; 404/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 404/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.3s\n",
      "[CV 2/3; 404/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 404/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.4s\n",
      "[CV 3/3; 404/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 404/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.4s\n",
      "[CV 1/3; 405/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 405/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.8s\n",
      "[CV 2/3; 405/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 405/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.9s\n",
      "[CV 3/3; 405/540] START learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 405/540] END learning_rate=0.2, max_depth=-1, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.0s\n",
      "[CV 1/3; 406/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 406/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.1s\n",
      "[CV 2/3; 406/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 406/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.726) total time=   2.1s\n",
      "[CV 3/3; 406/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 406/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.746) total time=   2.1s\n",
      "[CV 1/3; 407/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 407/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.721) total time=   2.1s\n",
      "[CV 2/3; 407/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 407/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.825) F1: (test=0.708) total time=   2.1s\n",
      "[CV 3/3; 407/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 407/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   2.3s\n",
      "[CV 1/3; 408/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 408/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.708) total time=   1.8s\n",
      "[CV 2/3; 408/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 408/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.717) total time=   1.9s\n",
      "[CV 3/3; 408/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 408/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.738) total time=   2.0s\n",
      "[CV 1/3; 409/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 409/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.1s\n",
      "[CV 2/3; 409/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 409/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 409/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 409/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.765) total time=   2.3s\n",
      "[CV 1/3; 410/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 410/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.1s\n",
      "[CV 2/3; 410/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 410/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.2s\n",
      "[CV 3/3; 410/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 410/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.749) total time=   2.2s\n",
      "[CV 1/3; 411/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 411/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.727) total time=   1.8s\n",
      "[CV 2/3; 411/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 411/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.741) total time=   1.8s\n",
      "[CV 3/3; 411/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 411/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.0s\n",
      "[CV 1/3; 412/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 412/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.1s\n",
      "[CV 2/3; 412/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 412/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 412/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 412/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.765) total time=   2.3s\n",
      "[CV 1/3; 413/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 413/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.0s\n",
      "[CV 2/3; 413/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 413/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.1s\n",
      "[CV 3/3; 413/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 413/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.749) total time=   2.2s\n",
      "[CV 1/3; 414/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 414/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.727) total time=   1.8s\n",
      "[CV 2/3; 414/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 414/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.741) total time=   1.8s\n",
      "[CV 3/3; 414/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 414/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.0s\n",
      "[CV 1/3; 415/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 415/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.1s\n",
      "[CV 2/3; 415/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 415/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 415/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 415/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.765) total time=   2.3s\n",
      "[CV 1/3; 416/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 416/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.0s\n",
      "[CV 2/3; 416/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 416/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.1s\n",
      "[CV 3/3; 416/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 416/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.749) total time=   2.2s\n",
      "[CV 1/3; 417/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 417/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.727) total time=   1.8s\n",
      "[CV 2/3; 417/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 417/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.741) total time=   1.8s\n",
      "[CV 3/3; 417/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 417/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.0s\n",
      "[CV 1/3; 418/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 418/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.718) total time=   2.1s\n",
      "[CV 2/3; 418/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 418/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 418/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 418/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.765) total time=   2.3s\n",
      "[CV 1/3; 419/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 419/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.0s\n",
      "[CV 2/3; 419/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 419/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.718) total time=   2.1s\n",
      "[CV 3/3; 419/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 419/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.749) total time=   2.2s\n",
      "[CV 1/3; 420/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 420/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.727) total time=   1.8s\n",
      "[CV 2/3; 420/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 420/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.741) total time=   1.9s\n",
      "[CV 3/3; 420/540] START learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 420/540] END learning_rate=0.2, max_depth=5, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.0s\n",
      "[CV 1/3; 421/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 421/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.825) F1: (test=0.695) total time=   2.0s\n",
      "[CV 2/3; 421/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 421/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.710) total time=   2.0s\n",
      "[CV 3/3; 421/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 421/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.0s\n",
      "[CV 1/3; 422/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 422/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.721) total time=   2.0s\n",
      "[CV 2/3; 422/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 422/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.761) total time=   2.0s\n",
      "[CV 3/3; 422/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 422/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.746) total time=   2.1s\n",
      "[CV 1/3; 423/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 423/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.827) F1: (test=0.698) total time=   1.7s\n",
      "[CV 2/3; 423/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 423/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.744) total time=   1.8s\n",
      "[CV 3/3; 423/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 423/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.768) total time=   1.8s\n",
      "[CV 1/3; 424/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 424/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 424/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 424/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.0s\n",
      "[CV 3/3; 424/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 424/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.749) total time=   2.1s\n",
      "[CV 1/3; 425/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 425/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 425/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 425/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   2.0s\n",
      "[CV 3/3; 425/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 425/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.1s\n",
      "[CV 1/3; 426/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 426/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.7s\n",
      "[CV 2/3; 426/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 426/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.757) total time=   1.8s\n",
      "[CV 3/3; 426/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 426/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.757) total time=   1.8s\n",
      "[CV 1/3; 427/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 427/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 427/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 427/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.0s\n",
      "[CV 3/3; 427/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 427/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.749) total time=   2.2s\n",
      "[CV 1/3; 428/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 428/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 428/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 428/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   2.0s\n",
      "[CV 3/3; 428/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 428/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.1s\n",
      "[CV 1/3; 429/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 429/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.7s\n",
      "[CV 2/3; 429/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 429/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.757) total time=   1.7s\n",
      "[CV 3/3; 429/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 429/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.757) total time=   1.8s\n",
      "[CV 1/3; 430/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 430/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 430/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 430/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.0s\n",
      "[CV 3/3; 430/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 430/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.749) total time=   2.2s\n",
      "[CV 1/3; 431/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 431/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 431/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 431/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   2.0s\n",
      "[CV 3/3; 431/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 431/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.1s\n",
      "[CV 1/3; 432/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 432/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.7s\n",
      "[CV 2/3; 432/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 432/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.757) total time=   1.7s\n",
      "[CV 3/3; 432/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 432/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.757) total time=   1.8s\n",
      "[CV 1/3; 433/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 433/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 433/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 433/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.0s\n",
      "[CV 3/3; 433/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 433/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.749) total time=   2.1s\n",
      "[CV 1/3; 434/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 434/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.0s\n",
      "[CV 2/3; 434/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 434/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.724) total time=   2.0s\n",
      "[CV 3/3; 434/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 434/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.1s\n",
      "[CV 1/3; 435/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 435/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.7s\n",
      "[CV 2/3; 435/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 435/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.757) total time=   1.7s\n",
      "[CV 3/3; 435/540] START learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 435/540] END learning_rate=0.2, max_depth=5, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.757) total time=   1.8s\n",
      "[CV 1/3; 436/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 436/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.727) total time=   1.9s\n",
      "[CV 2/3; 436/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 436/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.744) total time=   1.8s\n",
      "[CV 3/3; 436/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 436/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.749) total time=   1.9s\n",
      "[CV 1/3; 437/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 437/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.752) total time=   1.9s\n",
      "[CV 2/3; 437/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 437/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   1.9s\n",
      "[CV 3/3; 437/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 437/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.851) F1: (test=0.754) total time=   2.0s\n",
      "[CV 1/3; 438/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 438/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   1.6s\n",
      "[CV 2/3; 438/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 438/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   1.6s\n",
      "[CV 3/3; 438/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 438/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.763) total time=   1.7s\n",
      "[CV 1/3; 439/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 439/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.704) total time=   1.9s\n",
      "[CV 2/3; 439/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 439/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   1.9s\n",
      "[CV 3/3; 439/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 439/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.765) total time=   2.0s\n",
      "[CV 1/3; 440/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 440/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.9s\n",
      "[CV 2/3; 440/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 440/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   1.9s\n",
      "[CV 3/3; 440/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 440/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.763) total time=   2.0s\n",
      "[CV 1/3; 441/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 441/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   1.6s\n",
      "[CV 2/3; 441/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 441/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   1.7s\n",
      "[CV 3/3; 441/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 441/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.744) total time=   1.8s\n",
      "[CV 1/3; 442/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 442/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.704) total time=   2.0s\n",
      "[CV 2/3; 442/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 442/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   1.9s\n",
      "[CV 3/3; 442/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 442/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.765) total time=   2.0s\n",
      "[CV 1/3; 443/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 443/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.9s\n",
      "[CV 2/3; 443/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 443/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   1.9s\n",
      "[CV 3/3; 443/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 443/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.763) total time=   2.0s\n",
      "[CV 1/3; 444/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 444/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   1.6s\n",
      "[CV 2/3; 444/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 444/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   1.7s\n",
      "[CV 3/3; 444/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 444/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.744) total time=   1.7s\n",
      "[CV 1/3; 445/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 445/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.704) total time=   2.0s\n",
      "[CV 2/3; 445/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 445/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   1.9s\n",
      "[CV 3/3; 445/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 445/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.765) total time=   2.0s\n",
      "[CV 1/3; 446/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 446/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.9s\n",
      "[CV 2/3; 446/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 446/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   1.9s\n",
      "[CV 3/3; 446/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 446/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.763) total time=   2.0s\n",
      "[CV 1/3; 447/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 447/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   1.6s\n",
      "[CV 2/3; 447/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 447/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   1.7s\n",
      "[CV 3/3; 447/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 447/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.744) total time=   1.8s\n",
      "[CV 1/3; 448/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 448/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.830) F1: (test=0.704) total time=   1.9s\n",
      "[CV 2/3; 448/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 448/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   1.9s\n",
      "[CV 3/3; 448/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 448/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.765) total time=   2.0s\n",
      "[CV 1/3; 449/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 449/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.741) total time=   1.9s\n",
      "[CV 2/3; 449/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 449/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.738) total time=   1.9s\n",
      "[CV 3/3; 449/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 449/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.763) total time=   2.0s\n",
      "[CV 1/3; 450/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 450/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.744) total time=   1.6s\n",
      "[CV 2/3; 450/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 450/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.852) F1: (test=0.747) total time=   1.7s\n",
      "[CV 3/3; 450/540] START learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 450/540] END learning_rate=0.2, max_depth=5, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.744) total time=   1.8s\n",
      "[CV 1/3; 451/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 451/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.747) total time=   2.6s\n",
      "[CV 2/3; 451/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 451/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   2.7s\n",
      "[CV 3/3; 451/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 451/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.869) F1: (test=0.786) total time=   2.7s\n",
      "[CV 1/3; 452/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 452/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.726) total time=   2.5s\n",
      "[CV 2/3; 452/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 452/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.749) total time=   2.6s\n",
      "[CV 3/3; 452/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 452/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.854) F1: (test=0.765) total time=   2.7s\n",
      "[CV 1/3; 453/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 453/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 2/3; 453/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 453/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.766) total time=   2.3s\n",
      "[CV 3/3; 453/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 453/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.758) total time=   2.4s\n",
      "[CV 1/3; 454/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 454/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.750) total time=   3.4s\n",
      "[CV 2/3; 454/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 454/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.752) total time=   3.5s\n",
      "[CV 3/3; 454/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 454/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.724) total time=   3.5s\n",
      "[CV 1/3; 455/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 455/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.732) total time=   2.6s\n",
      "[CV 2/3; 455/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 455/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.6s\n",
      "[CV 3/3; 455/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 455/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.8s\n",
      "[CV 1/3; 456/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 456/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.729) total time=   2.2s\n",
      "[CV 2/3; 456/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 456/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.749) total time=   2.2s\n",
      "[CV 3/3; 456/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 456/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.710) total time=   2.4s\n",
      "[CV 1/3; 457/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 457/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.732) total time=   4.4s\n",
      "[CV 2/3; 457/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 457/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.706) total time=   4.5s\n",
      "[CV 3/3; 457/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 457/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   4.5s\n",
      "[CV 1/3; 458/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 458/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.7s\n",
      "[CV 2/3; 458/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 458/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.747) total time=   2.7s\n",
      "[CV 3/3; 458/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 458/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.829) F1: (test=0.707) total time=   2.8s\n",
      "[CV 1/3; 459/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 459/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.1s\n",
      "[CV 2/3; 459/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 459/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.722) total time=   2.2s\n",
      "[CV 3/3; 459/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 459/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.3s\n",
      "[CV 1/3; 460/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 460/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.711) total time=   5.2s\n",
      "[CV 2/3; 460/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 460/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.747) total time=   5.1s\n",
      "[CV 3/3; 460/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 460/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.744) total time=   5.7s\n",
      "[CV 1/3; 461/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 461/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.7s\n",
      "[CV 2/3; 461/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 461/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.747) total time=   2.8s\n",
      "[CV 3/3; 461/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 461/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.829) F1: (test=0.707) total time=   3.4s\n",
      "[CV 1/3; 462/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 462/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.2s\n",
      "[CV 2/3; 462/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 462/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.722) total time=   2.2s\n",
      "[CV 3/3; 462/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 462/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   3.0s\n",
      "[CV 1/3; 463/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 463/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.723) total time=   5.8s\n",
      "[CV 2/3; 463/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 463/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.724) total time=   5.3s\n",
      "[CV 3/3; 463/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 463/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.721) total time=   5.4s\n",
      "[CV 1/3; 464/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 464/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.708) total time=   2.6s\n",
      "[CV 2/3; 464/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 464/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.747) total time=   2.6s\n",
      "[CV 3/3; 464/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 464/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.829) F1: (test=0.707) total time=   2.9s\n",
      "[CV 1/3; 465/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 465/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.724) total time=   2.1s\n",
      "[CV 2/3; 465/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 465/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.835) F1: (test=0.722) total time=   2.2s\n",
      "[CV 3/3; 465/540] START learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 465/540] END learning_rate=0.2, max_depth=10, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.3s\n",
      "[CV 1/3; 466/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 466/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.6s\n",
      "[CV 2/3; 466/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 466/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.5s\n",
      "[CV 3/3; 466/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 466/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.768) total time=   2.6s\n",
      "[CV 1/3; 467/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 467/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 2/3; 467/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 467/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.735) total time=   2.2s\n",
      "[CV 3/3; 467/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 467/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.4s\n",
      "[CV 1/3; 468/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 468/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.724) total time=   1.8s\n",
      "[CV 2/3; 468/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 468/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   1.9s\n",
      "[CV 3/3; 468/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 468/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.861) F1: (test=0.773) total time=   2.0s\n",
      "[CV 1/3; 469/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 469/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.733) total time=   3.3s\n",
      "[CV 2/3; 469/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 469/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.738) total time=   3.4s\n",
      "[CV 3/3; 469/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 469/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.763) total time=   3.4s\n",
      "[CV 1/3; 470/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 470/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.713) total time=   2.2s\n",
      "[CV 2/3; 470/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 470/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.3s\n",
      "[CV 3/3; 470/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 470/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.4s\n",
      "[CV 1/3; 471/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 471/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   1.8s\n",
      "[CV 2/3; 471/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 471/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   1.9s\n",
      "[CV 3/3; 471/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 471/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.752) total time=   2.0s\n",
      "[CV 1/3; 472/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 472/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   4.3s\n",
      "[CV 2/3; 472/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 472/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.749) total time=   4.2s\n",
      "[CV 3/3; 472/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 472/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.851) F1: (test=0.752) total time=   4.4s\n",
      "[CV 1/3; 473/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 473/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.713) total time=   2.2s\n",
      "[CV 2/3; 473/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 473/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.3s\n",
      "[CV 3/3; 473/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 473/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.5s\n",
      "[CV 1/3; 474/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 474/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   1.9s\n",
      "[CV 2/3; 474/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 474/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.0s\n",
      "[CV 3/3; 474/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 474/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.752) total time=   2.1s\n",
      "[CV 1/3; 475/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 475/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.2s\n",
      "[CV 2/3; 475/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 475/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.744) total time=   4.2s\n",
      "[CV 3/3; 475/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 475/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   4.5s\n",
      "[CV 1/3; 476/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 476/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.713) total time=   2.3s\n",
      "[CV 2/3; 476/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 476/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.4s\n",
      "[CV 3/3; 476/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 476/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.5s\n",
      "[CV 1/3; 477/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 477/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   1.9s\n",
      "[CV 2/3; 477/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 477/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   1.9s\n",
      "[CV 3/3; 477/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 477/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.752) total time=   2.1s\n",
      "[CV 1/3; 478/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 478/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.2s\n",
      "[CV 2/3; 478/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 478/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.744) total time=   4.2s\n",
      "[CV 3/3; 478/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 478/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   4.6s\n",
      "[CV 1/3; 479/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 479/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.713) total time=   2.3s\n",
      "[CV 2/3; 479/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 479/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.747) total time=   2.3s\n",
      "[CV 3/3; 479/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 479/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.5s\n",
      "[CV 1/3; 480/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 480/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.840) F1: (test=0.724) total time=   1.9s\n",
      "[CV 2/3; 480/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 480/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   1.9s\n",
      "[CV 3/3; 480/540] START learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 480/540] END learning_rate=0.2, max_depth=10, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.856) F1: (test=0.752) total time=   2.1s\n",
      "[CV 1/3; 481/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 481/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   2.3s\n",
      "[CV 2/3; 481/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 481/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.4s\n",
      "[CV 3/3; 481/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 481/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.738) total time=   2.4s\n",
      "[CV 1/3; 482/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 482/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.752) total time=   2.1s\n",
      "[CV 2/3; 482/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 482/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   2.1s\n",
      "[CV 3/3; 482/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 482/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.2s\n",
      "[CV 1/3; 483/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 483/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.7s\n",
      "[CV 2/3; 483/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 483/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.760) total time=   1.8s\n",
      "[CV 3/3; 483/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 483/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   1.9s\n",
      "[CV 1/3; 484/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 484/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.6s\n",
      "[CV 2/3; 484/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 484/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.744) total time=   3.5s\n",
      "[CV 3/3; 484/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 484/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.758) total time=   3.6s\n",
      "[CV 1/3; 485/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 485/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.2s\n",
      "[CV 2/3; 485/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 485/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 485/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 485/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.3s\n",
      "[CV 1/3; 486/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 486/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   1.7s\n",
      "[CV 2/3; 486/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 486/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   1.8s\n",
      "[CV 3/3; 486/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 486/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 487/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 487/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.6s\n",
      "[CV 2/3; 487/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 487/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.727) total time=   3.6s\n",
      "[CV 3/3; 487/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 487/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.8s\n",
      "[CV 1/3; 488/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 488/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.1s\n",
      "[CV 2/3; 488/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 488/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 488/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 488/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.3s\n",
      "[CV 1/3; 489/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 489/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   1.7s\n",
      "[CV 2/3; 489/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 489/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   1.8s\n",
      "[CV 3/3; 489/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 489/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 490/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 490/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.6s\n",
      "[CV 2/3; 490/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 490/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.727) total time=   3.7s\n",
      "[CV 3/3; 490/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 490/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.8s\n",
      "[CV 1/3; 491/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 491/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.2s\n",
      "[CV 2/3; 491/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 491/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 491/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 491/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.3s\n",
      "[CV 1/3; 492/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 492/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   1.8s\n",
      "[CV 2/3; 492/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 492/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   1.8s\n",
      "[CV 3/3; 492/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 492/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 493/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 493/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.752) total time=   3.7s\n",
      "[CV 2/3; 493/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 493/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.840) F1: (test=0.727) total time=   3.6s\n",
      "[CV 3/3; 493/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 493/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   3.8s\n",
      "[CV 1/3; 494/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 494/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.741) total time=   2.2s\n",
      "[CV 2/3; 494/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 494/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 3/3; 494/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 494/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.851) F1: (test=0.752) total time=   2.3s\n",
      "[CV 1/3; 495/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 495/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   1.8s\n",
      "[CV 2/3; 495/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 495/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   1.8s\n",
      "[CV 3/3; 495/540] START learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 495/540] END learning_rate=0.2, max_depth=10, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 496/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 496/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.747) total time=   2.6s\n",
      "[CV 2/3; 496/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 496/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   2.6s\n",
      "[CV 3/3; 496/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 496/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.772) total time=   2.7s\n",
      "[CV 1/3; 497/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 497/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.720) total time=   2.4s\n",
      "[CV 2/3; 497/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 497/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.741) total time=   2.5s\n",
      "[CV 3/3; 497/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 497/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.856) F1: (test=0.770) total time=   2.6s\n",
      "[CV 1/3; 498/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 498/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 498/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 498/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.766) total time=   2.2s\n",
      "[CV 3/3; 498/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 498/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.755) total time=   2.2s\n",
      "[CV 1/3; 499/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 499/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.747) total time=   3.5s\n",
      "[CV 2/3; 499/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 499/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.727) total time=   3.5s\n",
      "[CV 3/3; 499/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 499/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0; Accuracy: (test=0.839) F1: (test=0.730) total time=   3.4s\n",
      "[CV 1/3; 500/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 500/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.714) total time=   2.5s\n",
      "[CV 2/3; 500/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 500/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.749) total time=   2.6s\n",
      "[CV 3/3; 500/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 500/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.829) F1: (test=0.707) total time=   2.6s\n",
      "[CV 1/3; 501/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 501/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.714) total time=   2.1s\n",
      "[CV 2/3; 501/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 501/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.1s\n",
      "[CV 3/3; 501/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 501/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.738) total time=   2.2s\n",
      "[CV 1/3; 502/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 502/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   4.4s\n",
      "[CV 2/3; 502/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 502/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   4.4s\n",
      "[CV 3/3; 502/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 502/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.707) total time=   4.5s\n",
      "[CV 1/3; 503/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 503/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   2.6s\n",
      "[CV 2/3; 503/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 503/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.7s\n",
      "[CV 3/3; 503/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 503/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.8s\n",
      "[CV 1/3; 504/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 504/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.755) total time=   2.1s\n",
      "[CV 2/3; 504/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 504/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.2s\n",
      "[CV 3/3; 504/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 504/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.2s\n",
      "[CV 1/3; 505/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 505/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   5.3s\n",
      "[CV 2/3; 505/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 505/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.827) F1: (test=0.704) total time=   5.3s\n",
      "[CV 3/3; 505/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 505/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.704) total time=   5.5s\n",
      "[CV 1/3; 506/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 506/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   2.6s\n",
      "[CV 2/3; 506/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 506/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.7s\n",
      "[CV 3/3; 506/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 506/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.8s\n",
      "[CV 1/3; 507/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 507/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.755) total time=   2.2s\n",
      "[CV 2/3; 507/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 507/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.2s\n",
      "[CV 3/3; 507/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 507/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.3s\n",
      "[CV 1/3; 508/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 508/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.738) total time=   6.1s\n",
      "[CV 2/3; 508/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 508/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.835) F1: (test=0.712) total time=   6.1s\n",
      "[CV 3/3; 508/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 508/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   6.1s\n",
      "[CV 1/3; 509/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 509/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.832) F1: (test=0.703) total time=   2.7s\n",
      "[CV 2/3; 509/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 509/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.827) F1: (test=0.698) total time=   2.7s\n",
      "[CV 3/3; 509/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 509/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.730) total time=   2.8s\n",
      "[CV 1/3; 510/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 510/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.755) total time=   2.2s\n",
      "[CV 2/3; 510/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 510/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   2.3s\n",
      "[CV 3/3; 510/540] START learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 510/540] END learning_rate=0.2, max_depth=20, min_child_samples=5, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.3s\n",
      "[CV 1/3; 511/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 511/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   2.4s\n",
      "[CV 2/3; 511/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 511/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.6s\n",
      "[CV 3/3; 511/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 511/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.757) total time=   2.6s\n",
      "[CV 1/3; 512/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 512/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.844) F1: (test=0.732) total time=   2.2s\n",
      "[CV 2/3; 512/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 512/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.4s\n",
      "[CV 3/3; 512/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 512/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.839) F1: (test=0.730) total time=   2.4s\n",
      "[CV 1/3; 513/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 513/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.832) F1: (test=0.713) total time=   1.9s\n",
      "[CV 2/3; 513/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 513/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.741) total time=   1.9s\n",
      "[CV 3/3; 513/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 513/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.859) F1: (test=0.768) total time=   2.0s\n",
      "[CV 1/3; 514/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 514/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.738) total time=   3.4s\n",
      "[CV 2/3; 514/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 514/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.837) F1: (test=0.724) total time=   3.6s\n",
      "[CV 3/3; 514/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 514/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0; Accuracy: (test=0.854) F1: (test=0.755) total time=   3.5s\n",
      "[CV 1/3; 515/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 515/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.2s\n",
      "[CV 2/3; 515/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 515/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.3s\n",
      "[CV 3/3; 515/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 515/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 516/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 516/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 516/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 516/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   1.9s\n",
      "[CV 3/3; 516/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 516/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 517/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 517/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.724) total time=   4.6s\n",
      "[CV 2/3; 517/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 517/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   4.6s\n",
      "[CV 3/3; 517/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 517/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0; Accuracy: (test=0.829) F1: (test=0.700) total time=   4.6s\n",
      "[CV 1/3; 518/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 518/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.2s\n",
      "[CV 2/3; 518/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 518/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 518/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 518/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.4s\n",
      "[CV 1/3; 519/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 519/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 519/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 519/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 519/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 519/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 520/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 520/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.729) total time=   5.1s\n",
      "[CV 2/3; 520/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 520/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.743) total time=   5.1s\n",
      "[CV 3/3; 520/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 520/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.1s\n",
      "[CV 1/3; 521/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 521/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.2s\n",
      "[CV 2/3; 521/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 521/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 521/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 521/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 522/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 522/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 522/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 522/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 522/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 522/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.0s\n",
      "[CV 1/3; 523/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 523/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.852) F1: (test=0.738) total time=   5.1s\n",
      "[CV 2/3; 523/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 523/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.743) total time=   5.1s\n",
      "[CV 3/3; 523/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 523/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0; Accuracy: (test=0.844) F1: (test=0.735) total time=   5.1s\n",
      "[CV 1/3; 524/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 524/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.847) F1: (test=0.738) total time=   2.2s\n",
      "[CV 2/3; 524/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 524/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.763) total time=   2.4s\n",
      "[CV 3/3; 524/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 524/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.837) F1: (test=0.721) total time=   2.5s\n",
      "[CV 1/3; 525/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 525/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.730) total time=   1.9s\n",
      "[CV 2/3; 525/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 525/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.862) F1: (test=0.764) total time=   2.0s\n",
      "[CV 3/3; 525/540] START learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 525/540] END learning_rate=0.2, max_depth=20, min_child_samples=10, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.849) F1: (test=0.741) total time=   2.1s\n",
      "[CV 1/3; 526/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 1/3; 526/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.735) total time=   2.3s\n",
      "[CV 2/3; 526/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 2/3; 526/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.857) F1: (test=0.752) total time=   2.4s\n",
      "[CV 3/3; 526/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0\n",
      "[CV 3/3; 526/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0; Accuracy: (test=0.856) F1: (test=0.760) total time=   2.4s\n",
      "[CV 1/3; 527/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 1/3; 527/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.859) F1: (test=0.752) total time=   2.0s\n",
      "[CV 2/3; 527/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 2/3; 527/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.852) F1: (test=0.749) total time=   2.1s\n",
      "[CV 3/3; 527/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01\n",
      "[CV 3/3; 527/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.01; Accuracy: (test=0.842) F1: (test=0.735) total time=   2.2s\n",
      "[CV 1/3; 528/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 1/3; 528/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.844) F1: (test=0.732) total time=   1.7s\n",
      "[CV 2/3; 528/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 2/3; 528/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.857) F1: (test=0.760) total time=   1.8s\n",
      "[CV 3/3; 528/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03\n",
      "[CV 3/3; 528/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=20, reg_alpha=0.03; Accuracy: (test=0.837) F1: (test=0.721) total time=   1.8s\n",
      "[CV 1/3; 529/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 1/3; 529/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.849) F1: (test=0.741) total time=   3.5s\n",
      "[CV 2/3; 529/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 2/3; 529/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.732) total time=   3.6s\n",
      "[CV 3/3; 529/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0\n",
      "[CV 3/3; 529/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0; Accuracy: (test=0.859) F1: (test=0.765) total time=   3.5s\n",
      "[CV 1/3; 530/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 1/3; 530/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 530/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 2/3; 530/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.2s\n",
      "[CV 3/3; 530/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01\n",
      "[CV 3/3; 530/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.3s\n",
      "[CV 1/3; 531/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 1/3; 531/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.7s\n",
      "[CV 2/3; 531/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 2/3; 531/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.8s\n",
      "[CV 3/3; 531/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03\n",
      "[CV 3/3; 531/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=40, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 532/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 1/3; 532/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   4.1s\n",
      "[CV 2/3; 532/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 2/3; 532/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.1s\n",
      "[CV 3/3; 532/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0\n",
      "[CV 3/3; 532/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   4.1s\n",
      "[CV 1/3; 533/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 1/3; 533/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.2s\n",
      "[CV 2/3; 533/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 2/3; 533/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.2s\n",
      "[CV 3/3; 533/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01\n",
      "[CV 3/3; 533/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.3s\n",
      "[CV 1/3; 534/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 1/3; 534/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.7s\n",
      "[CV 2/3; 534/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 2/3; 534/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.8s\n",
      "[CV 3/3; 534/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03\n",
      "[CV 3/3; 534/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=60, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 535/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 1/3; 535/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   4.1s\n",
      "[CV 2/3; 535/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 2/3; 535/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.1s\n",
      "[CV 3/3; 535/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0\n",
      "[CV 3/3; 535/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   4.2s\n",
      "[CV 1/3; 536/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 1/3; 536/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.1s\n",
      "[CV 2/3; 536/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 2/3; 536/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.2s\n",
      "[CV 3/3; 536/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01\n",
      "[CV 3/3; 536/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.3s\n",
      "[CV 1/3; 537/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 1/3; 537/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.8s\n",
      "[CV 2/3; 537/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 2/3; 537/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.8s\n",
      "[CV 3/3; 537/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03\n",
      "[CV 3/3; 537/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=80, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n",
      "[CV 1/3; 538/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 1/3; 538/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.832) F1: (test=0.700) total time=   4.1s\n",
      "[CV 2/3; 538/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 2/3; 538/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.847) F1: (test=0.741) total time=   4.1s\n",
      "[CV 3/3; 538/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0\n",
      "[CV 3/3; 538/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0; Accuracy: (test=0.842) F1: (test=0.732) total time=   4.2s\n",
      "[CV 1/3; 539/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 1/3; 539/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.849) F1: (test=0.735) total time=   2.2s\n",
      "[CV 2/3; 539/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 2/3; 539/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.840) F1: (test=0.727) total time=   2.2s\n",
      "[CV 3/3; 539/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01\n",
      "[CV 3/3; 539/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.01; Accuracy: (test=0.824) F1: (test=0.705) total time=   2.3s\n",
      "[CV 1/3; 540/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 1/3; 540/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.842) F1: (test=0.730) total time=   1.8s\n",
      "[CV 2/3; 540/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 2/3; 540/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.847) F1: (test=0.746) total time=   1.8s\n",
      "[CV 3/3; 540/540] START learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03\n",
      "[CV 3/3; 540/540] END learning_rate=0.2, max_depth=20, min_child_samples=15, num_leaves=100, reg_alpha=0.03; Accuracy: (test=0.854) F1: (test=0.757) total time=   1.9s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=LGBMClassifier(),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.05, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [-1, 5, 10, 20],\n",
       "                         &#x27;min_child_samples&#x27;: [5, 10, 15],\n",
       "                         &#x27;num_leaves&#x27;: [20, 40, 60, 80, 100],\n",
       "                         &#x27;reg_alpha&#x27;: [0, 0.01, 0.03]},\n",
       "             refit=&#x27;F1&#x27;, scoring={&#x27;Accuracy&#x27;: &#x27;accuracy&#x27;, &#x27;F1&#x27;: &#x27;f1_macro&#x27;},\n",
       "             verbose=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=LGBMClassifier(),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.05, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [-1, 5, 10, 20],\n",
       "                         &#x27;min_child_samples&#x27;: [5, 10, 15],\n",
       "                         &#x27;num_leaves&#x27;: [20, 40, 60, 80, 100],\n",
       "                         &#x27;reg_alpha&#x27;: [0, 0.01, 0.03]},\n",
       "             refit=&#x27;F1&#x27;, scoring={&#x27;Accuracy&#x27;: &#x27;accuracy&#x27;, &#x27;F1&#x27;: &#x27;f1_macro&#x27;},\n",
       "             verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=LGBMClassifier(),\n",
       "             param_grid={'learning_rate': [0.05, 0.1, 0.2],\n",
       "                         'max_depth': [-1, 5, 10, 20],\n",
       "                         'min_child_samples': [5, 10, 15],\n",
       "                         'num_leaves': [20, 40, 60, 80, 100],\n",
       "                         'reg_alpha': [0, 0.01, 0.03]},\n",
       "             refit='F1', scoring={'Accuracy': 'accuracy', 'F1': 'f1_macro'},\n",
       "             verbose=10)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_model = lgb.LGBMClassifier()\n",
    "\n",
    "# Define the parameters\n",
    "parameters = {\n",
    "    \"num_leaves\": [20, 40, 60, 80, 100],\n",
    "    \"min_child_samples\": [5, 10, 15],\n",
    "    \"max_depth\": [-1, 5, 10, 20],\n",
    "    \"learning_rate\": [0.05, 0.1, 0.2],\n",
    "    \"reg_alpha\": [0, 0.01, 0.03],\n",
    "}\n",
    "\n",
    "# Training config\n",
    "kfold = StratifiedKFold(n_splits=3)\n",
    "scoring = {'Accuracy': 'accuracy', 'F1': 'f1_macro'}\n",
    "refit = 'F1'\n",
    "\n",
    "lgbm_grid_search = GridSearchCV(lgbm_model, parameters, scoring=scoring, cv=kfold, refit=refit, verbose=10)\n",
    "lgbm_grid_search.fit(X=X_train, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.2,\n",
       " 'max_depth': 10,\n",
       " 'min_child_samples': 10,\n",
       " 'num_leaves': 20,\n",
       " 'reg_alpha': 0}"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7565228239670315"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.90      0.95      0.92       244\n",
      "         1.0       0.75      0.55      0.63        60\n",
      "\n",
      "    accuracy                           0.88       304\n",
      "   macro avg       0.82      0.75      0.78       304\n",
      "weighted avg       0.87      0.88      0.87       304\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val, lgbm_grid_search.predict(X_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x1f3f248c670>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save model lgbm\n",
    "lgbm_grid_search.best_estimator_.booster_.save_model('lgbm_grid_search_model.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>image_0</th>\n",
       "      <th>image_1</th>\n",
       "      <th>image_2</th>\n",
       "      <th>image_3</th>\n",
       "      <th>image_4</th>\n",
       "      <th>image_5</th>\n",
       "      <th>image_6</th>\n",
       "      <th>image_7</th>\n",
       "      <th>image_8</th>\n",
       "      <th>image_9</th>\n",
       "      <th>...</th>\n",
       "      <th>text_295</th>\n",
       "      <th>text_296</th>\n",
       "      <th>text_297</th>\n",
       "      <th>text_298</th>\n",
       "      <th>text_299</th>\n",
       "      <th>emotion_anger</th>\n",
       "      <th>emotion_fear</th>\n",
       "      <th>emotion_happy</th>\n",
       "      <th>emotion_love</th>\n",
       "      <th>emotion_sadness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004273</td>\n",
       "      <td>0.114928</td>\n",
       "      <td>0.039943</td>\n",
       "      <td>0.026622</td>\n",
       "      <td>0.012216</td>\n",
       "      <td>0.001952</td>\n",
       "      <td>0.017378</td>\n",
       "      <td>0.004255</td>\n",
       "      <td>0.101986</td>\n",
       "      <td>0.017381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029274</td>\n",
       "      <td>0.002215</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>-0.007852</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.130681</td>\n",
       "      <td>0.054041</td>\n",
       "      <td>0.024013</td>\n",
       "      <td>0.075155</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.008358</td>\n",
       "      <td>0.005364</td>\n",
       "      <td>0.154639</td>\n",
       "      <td>0.009527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012214</td>\n",
       "      <td>0.002714</td>\n",
       "      <td>-0.071238</td>\n",
       "      <td>-0.018362</td>\n",
       "      <td>0.051443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.195683</td>\n",
       "      <td>0.083915</td>\n",
       "      <td>0.018740</td>\n",
       "      <td>0.061179</td>\n",
       "      <td>0.001514</td>\n",
       "      <td>0.009508</td>\n",
       "      <td>0.008178</td>\n",
       "      <td>0.075114</td>\n",
       "      <td>0.024499</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004363</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>-0.051281</td>\n",
       "      <td>-0.004800</td>\n",
       "      <td>0.099719</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.062647</td>\n",
       "      <td>0.027166</td>\n",
       "      <td>0.031916</td>\n",
       "      <td>0.018145</td>\n",
       "      <td>0.002191</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.124661</td>\n",
       "      <td>0.023334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018098</td>\n",
       "      <td>0.002486</td>\n",
       "      <td>0.013426</td>\n",
       "      <td>-0.001015</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003338</td>\n",
       "      <td>0.099204</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.013847</td>\n",
       "      <td>0.046931</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>0.005564</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>0.168099</td>\n",
       "      <td>0.020112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012898</td>\n",
       "      <td>-0.010596</td>\n",
       "      <td>-0.023694</td>\n",
       "      <td>-0.004022</td>\n",
       "      <td>0.034656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 561 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    image_0   image_1   image_2   image_3   image_4   image_5   image_6  \\\n",
       "0  0.004273  0.114928  0.039943  0.026622  0.012216  0.001952  0.017378   \n",
       "1  0.003102  0.130681  0.054041  0.024013  0.075155  0.002030  0.008358   \n",
       "2  0.002691  0.195683  0.083915  0.018740  0.061179  0.001514  0.009508   \n",
       "3  0.003613  0.062647  0.027166  0.031916  0.018145  0.002191  0.008871   \n",
       "4  0.003338  0.099204  0.039586  0.013847  0.046931  0.002498  0.005564   \n",
       "\n",
       "    image_7   image_8   image_9  ...  text_295  text_296  text_297  text_298  \\\n",
       "0  0.004255  0.101986  0.017381  ...  0.029274  0.002215  0.005887 -0.007852   \n",
       "1  0.005364  0.154639  0.009527  ...  0.012214  0.002714 -0.071238 -0.018362   \n",
       "2  0.008178  0.075114  0.024499  ... -0.004363  0.007063 -0.051281 -0.004800   \n",
       "3  0.003298  0.124661  0.023334  ...  0.018098  0.002486  0.013426 -0.001015   \n",
       "4  0.002962  0.168099  0.020112  ...  0.012898 -0.010596 -0.023694 -0.004022   \n",
       "\n",
       "   text_299  emotion_anger  emotion_fear  emotion_happy  emotion_love  \\\n",
       "0  0.003583              0             0              1             0   \n",
       "1  0.051443              0             0              1             0   \n",
       "2  0.099719              0             0              1             0   \n",
       "3  0.006204              0             0              1             0   \n",
       "4  0.034656              0             0              1             0   \n",
       "\n",
       "   emotion_sadness  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "\n",
       "[5 rows x 561 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.concat([test_image_features, test_text_features, test_emotion_df], axis=1)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.to_csv('dataset/densenet_test_features.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.read_csv('dataset/densenet_test_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1011, 561)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on test set\n",
    "preds = clf_base_model.predict(X_test)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lgbm\n",
    "preds = lgbm_model.predict(X_test).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# dl\n",
    "preds = dl_model.predict(X_test).round()\n",
    "preds = preds.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert preds to int\n",
    "preds = preds.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
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       "      <th>1518</th>\n",
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       "      <th>1519</th>\n",
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       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>1216296533735309312</td>\n",
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       "      <th>1521</th>\n",
       "      <td>1234385081482956800</td>\n",
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       "      <th>1522</th>\n",
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      ],
      "text/plain": [
       "                       id  label\n",
       "1518  1213309429254250496      0\n",
       "1519  1216196702647939072      0\n",
       "1520  1216296533735309312      0\n",
       "1521  1234385081482956800      0\n",
       "1522  1214853229567041537      0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame({'id': test_df['id'], 'label': preds})\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submissions/submission9.csv', index=False)"
   ]
  }
 ],
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