3_MODELS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import optuna\n",
    "from optuna.samplers import TPESampler\n",
    "import shap\n",
    "import random\n",
    "from scipy import stats\n",
    "from tqdm import tqdm\n",
    "import statsmodels.api as sm\n",
    "import os\n",
    "\n",
    "import re\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, log_loss, make_scorer\n",
    "\n",
    "from catboost import CatBoostRegressor, CatBoostClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "\n",
    "random.seed(123)\n",
    "\n",
    "#IMPORTAR FUNCIONES DE INTERÉS\n",
    "from trees_models import  xgboost_regression, random_forest_regression, catboost_regression, lightgbm_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Explicativa</th>\n",
       "      <th>CAT_FE</th>\n",
       "      <th>CAT_SHAP</th>\n",
       "      <th>CAT_SHAP_ABS</th>\n",
       "      <th>CAT_MAE_OUT</th>\n",
       "      <th>CAT_MAE_IN</th>\n",
       "      <th>CAT_coef</th>\n",
       "      <th>CAT_PARAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>defence_fdh</td>\n",
       "      <td>22.722745</td>\n",
       "      <td>-0.000700</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.2178***</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>housing_fdh</td>\n",
       "      <td>16.981815</td>\n",
       "      <td>-0.000180</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.1186**</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>general_fdh</td>\n",
       "      <td>11.715212</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0664</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>economy_fdh</td>\n",
       "      <td>9.924588</td>\n",
       "      <td>-0.001986</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0927**</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>energy_fdh</td>\n",
       "      <td>8.529212</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0176</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>education_fdh</td>\n",
       "      <td>7.679006</td>\n",
       "      <td>0.001354</td>\n",
       "      <td>0.001354</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.1181**</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>environment_fdh</td>\n",
       "      <td>6.518073</td>\n",
       "      <td>-0.000568</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.1087***</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>health_fdh</td>\n",
       "      <td>5.206999</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>transportation_fdh</td>\n",
       "      <td>4.775452</td>\n",
       "      <td>-0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0447</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>order_fdh</td>\n",
       "      <td>4.259010</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0775*</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>social_fdh</td>\n",
       "      <td>1.687888</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>0.078119</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>const</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3165***</td>\n",
       "      <td>{'iterations': 60, 'depth': 5, 'learning_rate'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Explicativa     CAT_FE  CAT_SHAP  CAT_SHAP_ABS  CAT_MAE_OUT  \\\n",
       "9          defence_fdh  22.722745 -0.000700      0.000700     0.112055   \n",
       "5          housing_fdh  16.981815 -0.000180      0.000180     0.112055   \n",
       "10         general_fdh  11.715212  0.000872      0.000872     0.112055   \n",
       "7          economy_fdh   9.924588 -0.001986      0.001986     0.112055   \n",
       "0           energy_fdh   8.529212  0.000439      0.000439     0.112055   \n",
       "3        education_fdh   7.679006  0.001354      0.001354     0.112055   \n",
       "6      environment_fdh   6.518073 -0.000568      0.000568     0.112055   \n",
       "4           health_fdh   5.206999  0.000102      0.000102     0.112055   \n",
       "1   transportation_fdh   4.775452 -0.000333      0.000333     0.112055   \n",
       "8            order_fdh   4.259010  0.000919      0.000919     0.112055   \n",
       "2           social_fdh   1.687888  0.000080      0.000080     0.112055   \n",
       "11               const        NaN       NaN           NaN          NaN   \n",
       "\n",
       "    CAT_MAE_IN   CAT_coef                                          CAT_PARAM  \n",
       "9     0.078119  0.2178***  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "5     0.078119   0.1186**  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "10    0.078119     0.0664  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "7     0.078119   0.0927**  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "0     0.078119     0.0176  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "3     0.078119   0.1181**  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "6     0.078119  0.1087***  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "4     0.078119     0.0171  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "1     0.078119     0.0447  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "8     0.078119    0.0775*  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "2     0.078119     0.0604  {'iterations': 60, 'depth': 5, 'learning_rate'...  \n",
       "11         NaN  0.3165***  {'iterations': 60, 'depth': 5, 'learning_rate'...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(Y, X)\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mejor_modelo(modelos, out):\n",
    "    \"\"\"\n",
    "    Retorna el DataFrame con el valor \"out\" más bajo.\n",
    "\n",
    "    Parámetros:\n",
    "    - modelos: lista de DataFrames.\n",
    "    - out: lista de valores \"out\" asociados a cada DataFrame.\n",
    "    \n",
    "    Retorna:\n",
    "    - DataFrame asociado al valor \"out\" más bajo.\n",
    "    \"\"\"\n",
    "    # Encontrar el índice del valor mínimo en 'out'\n",
    "    indice_minimo = out.index(min(out))\n",
    "    \n",
    "    # Retornar el DataFrame correspondiente\n",
    "    return modelos[indice_minimo]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ORGANIZACIÓN DE LOS DATOS + MODELO PRINCIPAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# version = \"23_08_15_V1\"\n",
    "# version = \"23_08_22_V2\"\n",
    "df = pd.read_excel(f\"(20240122)time_levelex(inputorientation) - copia.xlsx\")\n",
    "columnas = list(df.columns)\n",
    "fdhs = [i for i in columnas if i.endswith('_fdh')]\n",
    "ids = [\"Country Code\", \"Time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfr = df[ids+fdhs]\n",
    "\n",
    "nans = dfr.isna().sum()\n",
    "drop = list(nans[nans>70].index)\n",
    "for i in drop:\n",
    "    fdhs.remove(i)\n",
    "\n",
    "dfr = df[ids+fdhs]\n",
    "\n",
    "dfp = dfr.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2024-03-19 19:19:15,112]\u001b[0m A new study created in memory with name: no-name-ab586edf-fb17-4dfd-b7e9-9e1ecfb4cd7f\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:15,640]\u001b[0m Trial 0 finished with value: 0.3306868836692143 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.3306868836692143.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:16,061]\u001b[0m Trial 1 finished with value: 0.3287928825741464 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.3287928825741464.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:16,290]\u001b[0m Trial 2 finished with value: 0.33107779718407815 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3287928825741464.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:16,677]\u001b[0m Trial 3 finished with value: 0.3307665223930305 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3287928825741464.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:16,850]\u001b[0m Trial 4 finished with value: 0.3310722855283842 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3287928825741464.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:17,231]\u001b[0m Trial 5 finished with value: 0.325529362852588 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.325529362852588.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:17,433]\u001b[0m Trial 6 finished with value: 0.13693680445513096 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:17,624]\u001b[0m Trial 7 finished with value: 0.33080192152060467 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:17,838]\u001b[0m Trial 8 finished with value: 0.32791032422693256 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:18,270]\u001b[0m Trial 9 finished with value: 0.18683314391397574 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:18,455]\u001b[0m Trial 10 finished with value: 0.13792114147382517 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:18,625]\u001b[0m Trial 11 finished with value: 0.14071614190038248 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:19,003]\u001b[0m Trial 12 finished with value: 0.17578968225826772 and parameters: {'booster': 'gbtree', 'lambda': 5.3631107495308e-06, 'alpha': 0.10298878000094057, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 0.0012896702521643245, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.13693680445513096.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:19,376]\u001b[0m Trial 13 finished with value: 0.13090773078558907 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.04275837367095418, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.026393646386352426, 'gamma': 7.1514352509497836e-06, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.13090773078558907.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:19,680]\u001b[0m Trial 14 finished with value: 0.15350857116531105 and parameters: {'booster': 'gbtree', 'lambda': 1.999524162439115e-07, 'alpha': 0.01630030018822387, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.016265990183224426, 'gamma': 3.5620480894375304e-06, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.13090773078558907.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:19,976]\u001b[0m Trial 15 finished with value: 0.1339223029602885 and parameters: {'booster': 'gbtree', 'lambda': 1.9050189965122934e-07, 'alpha': 0.007068366051888947, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.023925548242448445, 'gamma': 2.8392654201975797e-05, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.13090773078558907.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:20,263]\u001b[0m Trial 16 finished with value: 0.1858868470435202 and parameters: {'booster': 'gbtree', 'lambda': 1.0067553535768692e-08, 'alpha': 0.0309476207594851, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.01012531203559635, 'gamma': 9.762310242569957e-06, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.13090773078558907.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:20,851]\u001b[0m Trial 17 finished with value: 0.2933929501255609 and parameters: {'booster': 'gbtree', 'lambda': 2.5825276421140184e-07, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.5, 'max_depth': 8, 'eta': 0.0015659063859213768, 'gamma': 1.4856638446443875e-07, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.13090773078558907.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:21,202]\u001b[0m Trial 18 finished with value: 0.11872850110327456 and parameters: {'booster': 'gbtree', 'lambda': 5.255601495536208e-07, 'alpha': 0.011100726752099799, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.08432966657872422, 'gamma': 1.6482877710207397e-05, 'grow_policy': 'lossguide'}. Best is trial 18 with value: 0.11872850110327456.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:21,469]\u001b[0m Trial 19 finished with value: 0.1168198621424718 and parameters: {'booster': 'gbtree', 'lambda': 9.111298723288855e-07, 'alpha': 0.031112123044937545, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.13686018649380136, 'gamma': 6.325674943229882e-07, 'grow_policy': 'lossguide'}. Best is trial 19 with value: 0.1168198621424718.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2024-03-19 19:19:21,718]\u001b[0m A new study created in memory with name: no-name-1fd8b842-a195-4c33-802a-f948273bf7d2\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:22,032]\u001b[0m Trial 0 finished with value: 0.13181881243767435 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.13181881243767435.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:22,282]\u001b[0m Trial 1 finished with value: 0.12676786060770848 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.12676786060770848.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:22,713]\u001b[0m Trial 2 finished with value: 0.1376526129233828 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.12676786060770848.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:23,020]\u001b[0m Trial 3 finished with value: 0.1263610694671789 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:23,222]\u001b[0m Trial 4 finished with value: 0.1412770490466879 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:23,631]\u001b[0m Trial 5 finished with value: 0.13584216455443263 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:23,986]\u001b[0m Trial 6 finished with value: 0.12745991469600754 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:24,113]\u001b[0m Trial 7 finished with value: 0.1300200139693143 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:24,405]\u001b[0m Trial 8 finished with value: 0.12726709051710644 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:24,851]\u001b[0m Trial 9 finished with value: 0.13026404879115108 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.1263610694671789.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:25,057]\u001b[0m Trial 10 finished with value: 0.12279965705524531 and parameters: {'n_estimators': 36, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.12279965705524531.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:25,201]\u001b[0m Trial 11 finished with value: 0.12008258053678204 and parameters: {'n_estimators': 24, 'max_depth': 15, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:25,327]\u001b[0m Trial 12 finished with value: 0.12094239848231975 and parameters: {'n_estimators': 20, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:25,435]\u001b[0m Trial 13 finished with value: 0.12032837269355849 and parameters: {'n_estimators': 21, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:25,606]\u001b[0m Trial 14 finished with value: 0.1299174711602174 and parameters: {'n_estimators': 33, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:25,891]\u001b[0m Trial 15 finished with value: 0.1252155541921963 and parameters: {'n_estimators': 39, 'max_depth': 16, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:26,067]\u001b[0m Trial 16 finished with value: 0.1262959606299786 and parameters: {'n_estimators': 29, 'max_depth': 11, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:26,342]\u001b[0m Trial 17 finished with value: 0.12939641371579294 and parameters: {'n_estimators': 56, 'max_depth': 16, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:26,553]\u001b[0m Trial 18 finished with value: 0.1239416618679801 and parameters: {'n_estimators': 44, 'max_depth': 12, 'min_samples_split': 7, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:26,702]\u001b[0m Trial 19 finished with value: 0.13013464833227806 and parameters: {'n_estimators': 26, 'max_depth': 20, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 11 with value: 0.12008258053678204.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:26,794]\u001b[0m A new study created in memory with name: no-name-d3d12b90-1f75-4003-a8ab-d655138167e1\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:27,217]\u001b[0m Trial 0 finished with value: 0.11854662674121526 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:28,702]\u001b[0m Trial 1 finished with value: 0.16550868253448728 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:29,237]\u001b[0m Trial 2 finished with value: 0.12300148878885381 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:30,184]\u001b[0m Trial 3 finished with value: 0.13987090875474498 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:31,092]\u001b[0m Trial 4 finished with value: 0.12241838727663419 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:33,080]\u001b[0m Trial 5 finished with value: 0.1685690812822802 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:36,007]\u001b[0m Trial 6 finished with value: 0.1426797155763849 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:36,483]\u001b[0m Trial 7 finished with value: 0.12019022272043366 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:38,912]\u001b[0m Trial 8 finished with value: 0.134810215899516 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:40,169]\u001b[0m Trial 9 finished with value: 0.13425535100253808 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:40,619]\u001b[0m Trial 10 finished with value: 0.11926687790534012 and parameters: {'iterations': 65, 'depth': 3, 'learning_rate': 0.29201626538499564, 'l2_leaf_reg': 5, 'border_count': 93}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:40,987]\u001b[0m Trial 11 finished with value: 0.1192684481753223 and parameters: {'iterations': 63, 'depth': 3, 'learning_rate': 0.2927355101746894, 'l2_leaf_reg': 5, 'border_count': 92}. Best is trial 0 with value: 0.11854662674121526.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:41,856]\u001b[0m Trial 12 finished with value: 0.11369476207400449 and parameters: {'iterations': 63, 'depth': 5, 'learning_rate': 0.29907541881368893, 'l2_leaf_reg': 6, 'border_count': 96}. Best is trial 12 with value: 0.11369476207400449.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:42,480]\u001b[0m Trial 13 finished with value: 0.11205458957032 and parameters: {'iterations': 60, 'depth': 5, 'learning_rate': 0.2638741215161054, 'l2_leaf_reg': 7, 'border_count': 100}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:42,966]\u001b[0m Trial 14 finished with value: 0.11206638983540383 and parameters: {'iterations': 59, 'depth': 6, 'learning_rate': 0.29494156717642106, 'l2_leaf_reg': 14, 'border_count': 198}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:43,463]\u001b[0m Trial 15 finished with value: 0.1424611224195839 and parameters: {'iterations': 55, 'depth': 7, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 14, 'border_count': 197}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:44,000]\u001b[0m Trial 16 finished with value: 0.11602531727265575 and parameters: {'iterations': 57, 'depth': 6, 'learning_rate': 0.26298259532080737, 'l2_leaf_reg': 20, 'border_count': 194}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:44,403]\u001b[0m Trial 17 finished with value: 0.11637527184565533 and parameters: {'iterations': 58, 'depth': 5, 'learning_rate': 0.17101072333143386, 'l2_leaf_reg': 14, 'border_count': 183}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:45,042]\u001b[0m Trial 18 finished with value: 0.1406189435877204 and parameters: {'iterations': 72, 'depth': 7, 'learning_rate': 0.0864407987326337, 'l2_leaf_reg': 13, 'border_count': 227}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n",
      "\u001b[32m[I 2024-03-19 19:19:45,499]\u001b[0m Trial 19 finished with value: 0.11356264089928332 and parameters: {'iterations': 66, 'depth': 5, 'learning_rate': 0.2648506207170317, 'l2_leaf_reg': 10, 'border_count': 117}. Best is trial 13 with value: 0.11205458957032.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "features = dfp[fdhs[:-1]]\n",
    "target = dfp[fdhs[-1]]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(features, target, test_size=0.1, random_state=2)\n",
    "\n",
    "\n",
    "trials_n = 20\n",
    "fperdida = \"MAE\"\n",
    "\n",
    "\n",
    "g = xgboost_regression(X_train, Y_train, X_test, Y_test, fperdida, trials_n)\n",
    "rf = random_forest_regression(X_train, Y_train, X_test, Y_test, fperdida, trials_n)\n",
    "cb = catboost_regression(X_train, Y_train, X_test, Y_test, fperdida, trials_n)\n",
    "\n",
    "modelos = [cb, g, rf]\n",
    "out = cb[\"CAT_MAE_OUT\"].iloc[0], g[\"XGB_MAE_OUT\"].iloc[0], rf[\"RF_MAE_OUT\"].iloc[0]\n",
    "\n",
    "BM = mejor_modelo(modelos, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n"
     ]
    }
   ],
   "source": [
    "with pd.ExcelWriter('RESULTADOS_ML.xlsx') as writer:  \n",
    "    g.to_excel(writer, sheet_name='G')\n",
    "    cb.to_excel(writer, sheet_name='CB')\n",
    "    rf.to_excel(writer, sheet_name='RF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "BMT = BM.iloc[0:-1, [0,4,6,1]]\n",
    "\n",
    "tabla_total = dfr.groupby(\"Country Code\").sum()\n",
    "tabla_total = tabla_total.sort_values(\"Total_fdh\", ascending = False).iloc[:, 1:].T\n",
    "tabla_total = tabla_total.reset_index().rename(columns = {\"index\": \"Explicativa\"})\n",
    "tabla_total = pd.merge(BMT, tabla_total, \"outer\", \"Explicativa\")\n",
    "# tabla_total.to_excel(f\"TABLA_GENERAL_ML.xlsx\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n"
     ]
    }
   ],
   "source": [
    "tabla_total.to_excel(\"prueba.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODELO SECTORIAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para limpiar el nombre de la columna\n",
    "def clean_column_name(name):\n",
    "    # Quitar contenido entre paréntesis y corchetes\n",
    "    name = re.sub(r'\\[.*?\\]|\\(.*?\\)', '', name)\n",
    "    \n",
    "    # Quitar caracteres que no sean alfanuméricos y espacios\n",
    "    name = re.sub(r'[^a-zA-Z0-9 ]', '', name)\n",
    "    \n",
    "    # Quitar '_fdh' si está presente\n",
    "    # name = name.replace('_fdh', '')\n",
    "    \n",
    "    # Convertir a título y reemplazar espacios con guiones bajos\n",
    "    name = name.strip().title().replace(' ', '_')\n",
    "    \n",
    "    return name\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelos por catgoría"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'version' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-c22d2aabb9ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mxls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{version}//general.xlsx\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msheet_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mtrials_n\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'version' is not defined"
     ]
    }
   ],
   "source": [
    "xls = pd.ExcelFile(f\"{version}//general.xlsx\")\n",
    "\n",
    "sheet_names = xls.sheet_names\n",
    "\n",
    "trials_n = 20\n",
    "fperdida = \"MAE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-08-24 08:40:11,846]\u001b[0m A new study created in memory with name: no-name-be83779e-9eb6-4844-8506-506cafd83860\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:12,839]\u001b[0m Trial 0 finished with value: 0.3445349396488259 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.3445349396488259.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:14,027]\u001b[0m Trial 1 finished with value: 0.3425097724788267 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.3425097724788267.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:14,525]\u001b[0m Trial 2 finished with value: 0.3449734962039949 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3425097724788267.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:15,461]\u001b[0m Trial 3 finished with value: 0.34462150490179955 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3425097724788267.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:15,937]\u001b[0m Trial 4 finished with value: 0.3449666478711027 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3425097724788267.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:16,734]\u001b[0m Trial 5 finished with value: 0.3387768891107215 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.3387768891107215.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:17,189]\u001b[0m Trial 6 finished with value: 0.07465613435399353 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:17,710]\u001b[0m Trial 7 finished with value: 0.34470096929029204 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:18,244]\u001b[0m Trial 8 finished with value: 0.3413996801158037 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:18,872]\u001b[0m Trial 9 finished with value: 0.1731684524318345 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:19,188]\u001b[0m Trial 10 finished with value: 0.08311977854894906 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:19,531]\u001b[0m Trial 11 finished with value: 0.0845505119140912 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:20,162]\u001b[0m Trial 12 finished with value: 0.09085752640286242 and parameters: {'booster': 'gbtree', 'lambda': 5.3631107495308e-06, 'alpha': 0.10298878000094057, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 0.0012896702521643245, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:20,906]\u001b[0m Trial 13 finished with value: 0.09888991878505998 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.04275837367095418, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.026393646386352426, 'gamma': 7.1514352509497836e-06, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:21,247]\u001b[0m Trial 14 finished with value: 0.1241272066002882 and parameters: {'booster': 'gbtree', 'lambda': 4.203765214923554e-05, 'alpha': 0.0073163273009043675, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.021011438666673567, 'gamma': 0.003276536485721788, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.07465613435399353.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:21,662]\u001b[0m Trial 15 finished with value: 0.07410049698556209 and parameters: {'booster': 'gbtree', 'lambda': 3.8146747332708435e-07, 'alpha': 0.8683759394897397, 'colsample_bytree': 0.3, 'max_depth': 3, 'eta': 0.11777469387937922, 'gamma': 2.8392654201975797e-05, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.07410049698556209.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:22,252]\u001b[0m Trial 16 finished with value: 0.2775974327048121 and parameters: {'booster': 'gbtree', 'lambda': 3.1759337851853345e-07, 'alpha': 0.010612678555075361, 'colsample_bytree': 0.4, 'max_depth': 4, 'eta': 0.003012315490040272, 'gamma': 1.2935759658412034e-05, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.07410049698556209.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:22,782]\u001b[0m Trial 17 finished with value: 0.08270326182357195 and parameters: {'booster': 'gbtree', 'lambda': 1.6802636524317446e-08, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.5, 'max_depth': 3, 'eta': 0.09184795530190366, 'gamma': 1.3919953108242773e-05, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.07410049698556209.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:23,398]\u001b[0m Trial 18 finished with value: 0.2997236808193549 and parameters: {'booster': 'gbtree', 'lambda': 5.255601495536208e-07, 'alpha': 0.9230740248221362, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.0020425067297214342, 'gamma': 2.486186832830885e-07, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.07410049698556209.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:24,434]\u001b[0m Trial 19 finished with value: 0.0883985319580921 and parameters: {'booster': 'gbtree', 'lambda': 3.857323659391491e-05, 'alpha': 0.03588354100956784, 'colsample_bytree': 0.2, 'max_depth': 9, 'eta': 0.08682975972176804, 'gamma': 0.007588578386228201, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.07410049698556209.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:40:24,762]\u001b[0m A new study created in memory with name: no-name-46fc766c-c23a-4bc3-af3d-c1ee19c8a5fe\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:25,581]\u001b[0m Trial 0 finished with value: 0.0730902475684228 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.0730902475684228.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:26,203]\u001b[0m Trial 1 finished with value: 0.05524188253081524 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:27,210]\u001b[0m Trial 2 finished with value: 0.08727746008520691 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:28,038]\u001b[0m Trial 3 finished with value: 0.06559609027173548 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:28,554]\u001b[0m Trial 4 finished with value: 0.08941618680012793 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:29,324]\u001b[0m Trial 5 finished with value: 0.08490280169135926 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:32,263]\u001b[0m Trial 6 finished with value: 0.059813639743505766 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:33,863]\u001b[0m Trial 7 finished with value: 0.06198472704485084 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:37,631]\u001b[0m Trial 8 finished with value: 0.05750681704424405 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:39,944]\u001b[0m Trial 9 finished with value: 0.07249520773810415 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.05524188253081524.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:40,179]\u001b[0m Trial 10 finished with value: 0.04899043516214116 and parameters: {'n_estimators': 45, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.04899043516214116.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:40,413]\u001b[0m Trial 11 finished with value: 0.04906982496966692 and parameters: {'n_estimators': 44, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.04899043516214116.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:40,663]\u001b[0m Trial 12 finished with value: 0.04791523623632151 and parameters: {'n_estimators': 37, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:40,820]\u001b[0m Trial 13 finished with value: 0.048006604139898636 and parameters: {'n_estimators': 28, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:40,960]\u001b[0m Trial 14 finished with value: 0.05171022164118153 and parameters: {'n_estimators': 23, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:41,148]\u001b[0m Trial 15 finished with value: 0.0504931766800896 and parameters: {'n_estimators': 33, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:41,320]\u001b[0m Trial 16 finished with value: 0.06067899133853787 and parameters: {'n_estimators': 31, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:41,507]\u001b[0m Trial 17 finished with value: 0.051668238525762 and parameters: {'n_estimators': 33, 'max_depth': 10, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:41,789]\u001b[0m Trial 18 finished with value: 0.052086868550626185 and parameters: {'n_estimators': 59, 'max_depth': 17, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:42,023]\u001b[0m Trial 19 finished with value: 0.07103803741765613 and parameters: {'n_estimators': 39, 'max_depth': 14, 'min_samples_split': 7, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 12 with value: 0.04791523623632151.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:42,304]\u001b[0m A new study created in memory with name: no-name-6fc6099b-739f-4fb4-b0d5-14b50c714bd1\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:42,633]\u001b[0m Trial 0 finished with value: 0.04720316286996328 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:43,211]\u001b[0m Trial 1 finished with value: 0.07311774278642225 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:43,555]\u001b[0m Trial 2 finished with value: 0.05911377639319205 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:44,196]\u001b[0m Trial 3 finished with value: 0.05973098051590991 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:44,743]\u001b[0m Trial 4 finished with value: 0.054978289143524595 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:46,149]\u001b[0m Trial 5 finished with value: 0.08525328301503936 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:48,244]\u001b[0m Trial 6 finished with value: 0.06039981874200767 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:48,770]\u001b[0m Trial 7 finished with value: 0.0517250395606506 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:50,396]\u001b[0m Trial 8 finished with value: 0.059090928683159044 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:51,588]\u001b[0m Trial 9 finished with value: 0.05183595032259225 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:51,823]\u001b[0m Trial 10 finished with value: 0.050945602890510644 and parameters: {'iterations': 65, 'depth': 3, 'learning_rate': 0.29201626538499564, 'l2_leaf_reg': 5, 'border_count': 93}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:52,073]\u001b[0m Trial 11 finished with value: 0.05039231169816872 and parameters: {'iterations': 63, 'depth': 3, 'learning_rate': 0.2927355101746894, 'l2_leaf_reg': 5, 'border_count': 92}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:52,385]\u001b[0m Trial 12 finished with value: 0.04932959760483363 and parameters: {'iterations': 61, 'depth': 5, 'learning_rate': 0.29908300209136973, 'l2_leaf_reg': 6, 'border_count': 96}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:52,761]\u001b[0m Trial 13 finished with value: 0.05184320335096515 and parameters: {'iterations': 59, 'depth': 5, 'learning_rate': 0.26387640604492335, 'l2_leaf_reg': 7, 'border_count': 100}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:53,214]\u001b[0m Trial 14 finished with value: 0.05427230401027156 and parameters: {'iterations': 79, 'depth': 6, 'learning_rate': 0.29738443245688373, 'l2_leaf_reg': 14, 'border_count': 198}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:53,542]\u001b[0m Trial 15 finished with value: 0.047621321143725436 and parameters: {'iterations': 72, 'depth': 5, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 2, 'border_count': 29}. Best is trial 0 with value: 0.04720316286996328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:53,924]\u001b[0m Trial 16 finished with value: 0.046795875086373694 and parameters: {'iterations': 73, 'depth': 4, 'learning_rate': 0.09734977035441812, 'l2_leaf_reg': 2, 'border_count': 21}. Best is trial 16 with value: 0.046795875086373694.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:54,315]\u001b[0m Trial 17 finished with value: 0.04723993731155796 and parameters: {'iterations': 89, 'depth': 4, 'learning_rate': 0.08647621045706062, 'l2_leaf_reg': 3, 'border_count': 20}. Best is trial 16 with value: 0.046795875086373694.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:54,877]\u001b[0m Trial 18 finished with value: 0.1955256992638049 and parameters: {'iterations': 100, 'depth': 7, 'learning_rate': 0.010213173143133997, 'l2_leaf_reg': 9, 'border_count': 64}. Best is trial 16 with value: 0.046795875086373694.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:40:55,268]\u001b[0m Trial 19 finished with value: 0.05444667342773062 and parameters: {'iterations': 79, 'depth': 4, 'learning_rate': 0.17512189880099663, 'l2_leaf_reg': 20, 'border_count': 119}. Best is trial 16 with value: 0.046795875086373694.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:40:55,608]\u001b[0m A new study created in memory with name: no-name-329021c2-194e-4926-a0e3-cac1522535fa\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:56,046]\u001b[0m Trial 0 finished with value: 0.39798208255328055 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.39798208255328055.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:56,468]\u001b[0m Trial 1 finished with value: 0.39472381655232364 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.39472381655232364.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:56,702]\u001b[0m Trial 2 finished with value: 0.39866529422798164 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.39472381655232364.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:57,187]\u001b[0m Trial 3 finished with value: 0.398103039659829 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.39472381655232364.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:57,359]\u001b[0m Trial 4 finished with value: 0.39865469217653565 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.39472381655232364.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:57,765]\u001b[0m Trial 5 finished with value: 0.38898914579437865 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.38898914579437865.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:58,076]\u001b[0m Trial 6 finished with value: 0.012647272055645814 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:58,284]\u001b[0m Trial 7 finished with value: 0.3982284077633576 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:58,550]\u001b[0m Trial 8 finished with value: 0.3930839737863991 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:58,816]\u001b[0m Trial 9 finished with value: 0.13836384940555857 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:58,972]\u001b[0m Trial 10 finished with value: 0.02788809062222823 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:59,144]\u001b[0m Trial 11 finished with value: 0.0352472042890613 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:59,582]\u001b[0m Trial 12 finished with value: 0.015334776859990741 and parameters: {'booster': 'gbtree', 'lambda': 5.3631107495308e-06, 'alpha': 0.10298878000094057, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 0.0012896702521643245, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:40:59,863]\u001b[0m Trial 13 finished with value: 0.030816391117892732 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.042680256920104825, 'colsample_bytree': 0.5, 'max_depth': 9, 'eta': 0.026040110235398966, 'gamma': 0.0019255968152891395, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:00,219]\u001b[0m Trial 14 finished with value: 0.037578991460013526 and parameters: {'booster': 'gbtree', 'lambda': 1.9122574085396057e-05, 'alpha': 0.010673562371581187, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.02355462366691312, 'gamma': 8.368134896692912e-06, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012647272055645814.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:00,521]\u001b[0m Trial 15 finished with value: 0.007419228357843668 and parameters: {'booster': 'gbtree', 'lambda': 3.8146747332708435e-07, 'alpha': 0.023633967683934706, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11777469387937922, 'gamma': 0.0036224097727239643, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.007419228357843668.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:00,880]\u001b[0m Trial 16 finished with value: 0.2952047585739081 and parameters: {'booster': 'gbtree', 'lambda': 3.1759337851853345e-07, 'alpha': 0.007229508684507139, 'colsample_bytree': 0.4, 'max_depth': 4, 'eta': 0.003012315490040272, 'gamma': 0.005441964254521712, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.007419228357843668.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:01,131]\u001b[0m Trial 17 finished with value: 0.0029482063238758217 and parameters: {'booster': 'gbtree', 'lambda': 1.6802636524317446e-08, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.1061285420424653, 'gamma': 1.9248439937955043e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0029482063238758217.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:01,418]\u001b[0m Trial 18 finished with value: 0.3236942392790658 and parameters: {'booster': 'gbtree', 'lambda': 1.0514990199219779e-08, 'alpha': 1.3760840580544568e-08, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.002078672903266466, 'gamma': 1.1927699967197258e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0029482063238758217.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:01,840]\u001b[0m Trial 19 finished with value: 0.002966882276323885 and parameters: {'booster': 'gbtree', 'lambda': 6.854904100027099e-08, 'alpha': 1.13854524317208e-08, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.08033094834826023, 'gamma': 2.1151120784998337e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0029482063238758217.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:02,106]\u001b[0m A new study created in memory with name: no-name-8f0f8af8-612b-4d51-9bb6-13553b0e3380\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:02,497]\u001b[0m Trial 0 finished with value: 0.014677856796091864 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.014677856796091864.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:02,794]\u001b[0m Trial 1 finished with value: 0.022778711992531713 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 0 with value: 0.014677856796091864.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:03,603]\u001b[0m Trial 2 finished with value: 0.033043316030493305 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.014677856796091864.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:04,087]\u001b[0m Trial 3 finished with value: 0.008858850210770302 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:04,337]\u001b[0m Trial 4 finished with value: 0.04246832633941291 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:04,853]\u001b[0m Trial 5 finished with value: 0.03397830987822867 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:05,244]\u001b[0m Trial 6 finished with value: 0.03405649646086327 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:05,416]\u001b[0m Trial 7 finished with value: 0.037346129861866 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:05,807]\u001b[0m Trial 8 finished with value: 0.028801246434215253 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:06,463]\u001b[0m Trial 9 finished with value: 0.01592068451334033 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.008858850210770302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:06,916]\u001b[0m Trial 10 finished with value: 0.006368619300824126 and parameters: {'n_estimators': 36, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:07,424]\u001b[0m Trial 11 finished with value: 0.006513020719836111 and parameters: {'n_estimators': 24, 'max_depth': 15, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:07,914]\u001b[0m Trial 12 finished with value: 0.006855062283900333 and parameters: {'n_estimators': 20, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:08,511]\u001b[0m Trial 13 finished with value: 0.006383090593546153 and parameters: {'n_estimators': 33, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:09,193]\u001b[0m Trial 14 finished with value: 0.010048012682598724 and parameters: {'n_estimators': 39, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:09,525]\u001b[0m Trial 15 finished with value: 0.007605585736542853 and parameters: {'n_estimators': 35, 'max_depth': 12, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:10,075]\u001b[0m Trial 16 finished with value: 0.007881406835751488 and parameters: {'n_estimators': 57, 'max_depth': 17, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:10,354]\u001b[0m Trial 17 finished with value: 0.012419559941255195 and parameters: {'n_estimators': 35, 'max_depth': 13, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:10,831]\u001b[0m Trial 18 finished with value: 0.008226222619299896 and parameters: {'n_estimators': 45, 'max_depth': 10, 'min_samples_split': 7, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:11,154]\u001b[0m Trial 19 finished with value: 0.014417995973527322 and parameters: {'n_estimators': 28, 'max_depth': 19, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 10 with value: 0.006368619300824126.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:11,429]\u001b[0m A new study created in memory with name: no-name-90661560-05e1-4862-b412-f5b772f85c7b\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:11,840]\u001b[0m Trial 0 finished with value: 0.004370657491614642 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:12,466]\u001b[0m Trial 1 finished with value: 0.013015271934209413 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:12,925]\u001b[0m Trial 2 finished with value: 0.01166028323410521 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:13,697]\u001b[0m Trial 3 finished with value: 0.006730286025115858 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:14,785]\u001b[0m Trial 4 finished with value: 0.005147474831383892 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:16,121]\u001b[0m Trial 5 finished with value: 0.02828415450508367 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:18,570]\u001b[0m Trial 6 finished with value: 0.0060811225159326 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:18,976]\u001b[0m Trial 7 finished with value: 0.005652944592669395 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:20,274]\u001b[0m Trial 8 finished with value: 0.005984953642133794 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:20,914]\u001b[0m Trial 9 finished with value: 0.0064345269350645365 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:21,211]\u001b[0m Trial 10 finished with value: 0.005764222210424775 and parameters: {'iterations': 65, 'depth': 3, 'learning_rate': 0.29201626538499564, 'l2_leaf_reg': 5, 'border_count': 93}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:21,602]\u001b[0m Trial 11 finished with value: 0.0050956574612486035 and parameters: {'iterations': 82, 'depth': 6, 'learning_rate': 0.2814556732624116, 'l2_leaf_reg': 12, 'border_count': 81}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:22,044]\u001b[0m Trial 12 finished with value: 0.004796637001089148 and parameters: {'iterations': 81, 'depth': 5, 'learning_rate': 0.298952010483044, 'l2_leaf_reg': 15, 'border_count': 96}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:22,513]\u001b[0m Trial 13 finished with value: 0.005672776122864598 and parameters: {'iterations': 61, 'depth': 5, 'learning_rate': 0.2996396615086033, 'l2_leaf_reg': 15, 'border_count': 102}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:22,826]\u001b[0m Trial 14 finished with value: 0.009615867939264859 and parameters: {'iterations': 76, 'depth': 3, 'learning_rate': 0.26179710614798013, 'l2_leaf_reg': 20, 'border_count': 28}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:23,481]\u001b[0m Trial 15 finished with value: 0.006481299989471123 and parameters: {'iterations': 93, 'depth': 5, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 15, 'border_count': 114}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:24,216]\u001b[0m Trial 16 finished with value: 0.004754171742054671 and parameters: {'iterations': 80, 'depth': 4, 'learning_rate': 0.251434348157376, 'l2_leaf_reg': 7, 'border_count': 194}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:24,653]\u001b[0m Trial 17 finished with value: 0.005434503440056432 and parameters: {'iterations': 74, 'depth': 4, 'learning_rate': 0.17574116781510846, 'l2_leaf_reg': 6, 'border_count': 206}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:25,325]\u001b[0m Trial 18 finished with value: 0.005457140234698355 and parameters: {'iterations': 100, 'depth': 7, 'learning_rate': 0.0864407987326337, 'l2_leaf_reg': 7, 'border_count': 189}. Best is trial 0 with value: 0.004370657491614642.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:25,669]\u001b[0m Trial 19 finished with value: 0.003646178671674164 and parameters: {'iterations': 86, 'depth': 4, 'learning_rate': 0.250920143669739, 'l2_leaf_reg': 3, 'border_count': 193}. Best is trial 19 with value: 0.003646178671674164.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:41:26,076]\u001b[0m A new study created in memory with name: no-name-9ba04283-0485-4e4d-9e2f-6c2d531ea0c6\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:26,779]\u001b[0m Trial 0 finished with value: 0.31577230786245075 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.31577230786245075.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:27,264]\u001b[0m Trial 1 finished with value: 0.31340397826211186 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.31340397826211186.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:27,545]\u001b[0m Trial 2 finished with value: 0.31630727270406406 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.31340397826211186.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:28,108]\u001b[0m Trial 3 finished with value: 0.3158900338797564 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.31340397826211186.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:28,311]\u001b[0m Trial 4 finished with value: 0.316299798497831 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.31340397826211186.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:28,764]\u001b[0m Trial 5 finished with value: 0.30858507350842257 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.30858507350842257.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:29,014]\u001b[0m Trial 6 finished with value: 0.013087824599772174 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:29,405]\u001b[0m Trial 7 finished with value: 0.31598087830433114 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:29,718]\u001b[0m Trial 8 finished with value: 0.31194310286862453 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:30,062]\u001b[0m Trial 9 finished with value: 0.11041046896296879 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:30,249]\u001b[0m Trial 10 finished with value: 0.030838322322300927 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:30,421]\u001b[0m Trial 11 finished with value: 0.04109729189033314 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:30,890]\u001b[0m Trial 12 finished with value: 0.015399627136048961 and parameters: {'booster': 'gbtree', 'lambda': 5.3631107495308e-06, 'alpha': 0.10298878000094057, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 0.0012896702521643245, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:31,187]\u001b[0m Trial 13 finished with value: 0.025109119976018127 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.042680256920104825, 'colsample_bytree': 0.5, 'max_depth': 9, 'eta': 0.026040110235398966, 'gamma': 0.0019255968152891395, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:31,562]\u001b[0m Trial 14 finished with value: 0.0305774573255058 and parameters: {'booster': 'gbtree', 'lambda': 1.9122574085396057e-05, 'alpha': 0.010673562371581187, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.02355462366691312, 'gamma': 8.368134896692912e-06, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.013087824599772174.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:31,828]\u001b[0m Trial 15 finished with value: 0.007150530508749922 and parameters: {'booster': 'gbtree', 'lambda': 3.8146747332708435e-07, 'alpha': 0.023633967683934706, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11777469387937922, 'gamma': 0.0036224097727239643, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.007150530508749922.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:32,180]\u001b[0m Trial 16 finished with value: 0.23432907005394513 and parameters: {'booster': 'gbtree', 'lambda': 3.1759337851853345e-07, 'alpha': 0.007229508684507139, 'colsample_bytree': 0.4, 'max_depth': 4, 'eta': 0.003012315490040272, 'gamma': 0.005441964254521712, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.007150530508749922.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:32,586]\u001b[0m Trial 17 finished with value: 0.0032833812791246433 and parameters: {'booster': 'gbtree', 'lambda': 1.6802636524317446e-08, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.1061285420424653, 'gamma': 1.9248439937955043e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0032833812791246433.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:32,945]\u001b[0m Trial 18 finished with value: 0.2571271965196353 and parameters: {'booster': 'gbtree', 'lambda': 1.0514990199219779e-08, 'alpha': 1.3760840580544568e-08, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.002078672903266466, 'gamma': 1.1927699967197258e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0032833812791246433.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:33,321]\u001b[0m Trial 19 finished with value: 0.0032954120813510437 and parameters: {'booster': 'gbtree', 'lambda': 6.854904100027099e-08, 'alpha': 1.13854524317208e-08, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.08033094834826023, 'gamma': 2.1151120784998337e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0032833812791246433.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "\u001b[32m[I 2023-08-24 08:41:33,555]\u001b[0m A new study created in memory with name: no-name-7e20e8f5-1930-4231-9da9-4957882af4ba\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:34,024]\u001b[0m Trial 0 finished with value: 0.012299532262117876 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.012299532262117876.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:34,400]\u001b[0m Trial 1 finished with value: 0.016064495869002982 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 0 with value: 0.012299532262117876.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:34,994]\u001b[0m Trial 2 finished with value: 0.026274073179025286 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.012299532262117876.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:35,604]\u001b[0m Trial 3 finished with value: 0.006806943102931712 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:35,901]\u001b[0m Trial 4 finished with value: 0.024780681491536175 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:36,588]\u001b[0m Trial 5 finished with value: 0.02105704250414023 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:37,073]\u001b[0m Trial 6 finished with value: 0.020688292045926784 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:37,265]\u001b[0m Trial 7 finished with value: 0.023789300325411693 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:37,667]\u001b[0m Trial 8 finished with value: 0.018872190720949252 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:38,261]\u001b[0m Trial 9 finished with value: 0.01248029246519918 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.006806943102931712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:38,511]\u001b[0m Trial 10 finished with value: 0.00494438887357093 and parameters: {'n_estimators': 36, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.00494438887357093.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:38,746]\u001b[0m Trial 11 finished with value: 0.00527580216762692 and parameters: {'n_estimators': 24, 'max_depth': 15, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.00494438887357093.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:39,027]\u001b[0m Trial 12 finished with value: 0.005228860105357732 and parameters: {'n_estimators': 20, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.00494438887357093.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:39,340]\u001b[0m Trial 13 finished with value: 0.004854733466008212 and parameters: {'n_estimators': 38, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:39,590]\u001b[0m Trial 14 finished with value: 0.008616618329362962 and parameters: {'n_estimators': 38, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:39,855]\u001b[0m Trial 15 finished with value: 0.005644392943656801 and parameters: {'n_estimators': 37, 'max_depth': 12, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:40,074]\u001b[0m Trial 16 finished with value: 0.006554261904621728 and parameters: {'n_estimators': 33, 'max_depth': 17, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:40,512]\u001b[0m Trial 17 finished with value: 0.010103053251709041 and parameters: {'n_estimators': 56, 'max_depth': 13, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:40,793]\u001b[0m Trial 18 finished with value: 0.006004012357167464 and parameters: {'n_estimators': 47, 'max_depth': 10, 'min_samples_split': 7, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:41,215]\u001b[0m Trial 19 finished with value: 0.012462346998043423 and parameters: {'n_estimators': 66, 'max_depth': 19, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 13 with value: 0.004854733466008212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:41,371]\u001b[0m A new study created in memory with name: no-name-116b7bd0-f7c0-4abb-9d0e-9cf4eafc40d9\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:41,731]\u001b[0m Trial 0 finished with value: 0.007099685839189122 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.007099685839189122.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:42,356]\u001b[0m Trial 1 finished with value: 0.012220240925012288 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.007099685839189122.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:42,778]\u001b[0m Trial 2 finished with value: 0.00954000027631474 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.007099685839189122.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:43,481]\u001b[0m Trial 3 finished with value: 0.008315546149427944 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.007099685839189122.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:44,028]\u001b[0m Trial 4 finished with value: 0.006581519020781523 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 4 with value: 0.006581519020781523.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:44,794]\u001b[0m Trial 5 finished with value: 0.024015300759459676 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 4 with value: 0.006581519020781523.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:46,608]\u001b[0m Trial 6 finished with value: 0.006081736120010603 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 6 with value: 0.006081736120010603.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:46,967]\u001b[0m Trial 7 finished with value: 0.007392912251218682 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 6 with value: 0.006081736120010603.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:48,304]\u001b[0m Trial 8 finished with value: 0.005531435442293286 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:49,043]\u001b[0m Trial 9 finished with value: 0.007392263990924536 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:49,418]\u001b[0m Trial 10 finished with value: 0.00681502382303925 and parameters: {'iterations': 65, 'depth': 6, 'learning_rate': 0.29197113937431496, 'l2_leaf_reg': 14, 'border_count': 193}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:49,956]\u001b[0m Trial 11 finished with value: 0.009379627829858224 and parameters: {'iterations': 67, 'depth': 8, 'learning_rate': 0.12162770839646941, 'l2_leaf_reg': 14, 'border_count': 113}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:50,503]\u001b[0m Trial 12 finished with value: 0.0067850491990027436 and parameters: {'iterations': 66, 'depth': 8, 'learning_rate': 0.1552000681109931, 'l2_leaf_reg': 6, 'border_count': 120}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:52,254]\u001b[0m Trial 13 finished with value: 0.007330568594509951 and parameters: {'iterations': 99, 'depth': 10, 'learning_rate': 0.08895584457096815, 'l2_leaf_reg': 20, 'border_count': 188}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:52,691]\u001b[0m Trial 14 finished with value: 0.006842661926111265 and parameters: {'iterations': 74, 'depth': 7, 'learning_rate': 0.17180562705718247, 'l2_leaf_reg': 13, 'border_count': 93}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:53,895]\u001b[0m Trial 15 finished with value: 0.1388400968886376 and parameters: {'iterations': 91, 'depth': 9, 'learning_rate': 0.012355453682242917, 'l2_leaf_reg': 9, 'border_count': 181}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:54,114]\u001b[0m Trial 16 finished with value: 0.011677642952285442 and parameters: {'iterations': 59, 'depth': 3, 'learning_rate': 0.1306266533668836, 'l2_leaf_reg': 12, 'border_count': 212}. Best is trial 8 with value: 0.005531435442293286.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:54,567]\u001b[0m Trial 17 finished with value: 0.005347109553699732 and parameters: {'iterations': 78, 'depth': 7, 'learning_rate': 0.22442204468061522, 'l2_leaf_reg': 5, 'border_count': 141}. Best is trial 17 with value: 0.005347109553699732.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:55,068]\u001b[0m Trial 18 finished with value: 0.005250900335515711 and parameters: {'iterations': 75, 'depth': 5, 'learning_rate': 0.29319344407758613, 'l2_leaf_reg': 5, 'border_count': 90}. Best is trial 18 with value: 0.005250900335515711.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:41:55,428]\u001b[0m Trial 19 finished with value: 0.004489147174887887 and parameters: {'iterations': 79, 'depth': 5, 'learning_rate': 0.2953325097470043, 'l2_leaf_reg': 4, 'border_count': 92}. Best is trial 19 with value: 0.004489147174887887.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:41:56,100]\u001b[0m A new study created in memory with name: no-name-23164047-419b-4da0-9a6c-ad3340182041\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:56,694]\u001b[0m Trial 0 finished with value: 0.3800440056544718 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.3800440056544718.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:57,218]\u001b[0m Trial 1 finished with value: 0.3791016079505632 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.3791016079505632.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:57,609]\u001b[0m Trial 2 finished with value: 0.38024227194149646 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3791016079505632.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:58,109]\u001b[0m Trial 3 finished with value: 0.3800749740399741 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3791016079505632.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:58,359]\u001b[0m Trial 4 finished with value: 0.38024004232028696 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3791016079505632.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:58,844]\u001b[0m Trial 5 finished with value: 0.37742631171972146 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.37742631171972146.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:59,235]\u001b[0m Trial 6 finished with value: 0.23417078277904924 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:59,500]\u001b[0m Trial 7 finished with value: 0.3800753604022864 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:41:59,852]\u001b[0m Trial 8 finished with value: 0.37868405222142326 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:00,336]\u001b[0m Trial 9 finished with value: 0.27454933171684753 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:00,524]\u001b[0m Trial 10 finished with value: 0.24272959972434419 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:00,790]\u001b[0m Trial 11 finished with value: 0.2411056292440026 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:01,040]\u001b[0m Trial 12 finished with value: 0.2554497980065524 and parameters: {'booster': 'gbtree', 'lambda': 6.570086239949949e-06, 'alpha': 0.5964381891647306, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 6.490830553928742e-06, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.23417078277904924.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:01,446]\u001b[0m Trial 13 finished with value: 0.21808694427586764 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.04343530749447208, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.026393646386352426, 'gamma': 0.0013697661836378806, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.21808694427586764.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:01,853]\u001b[0m Trial 14 finished with value: 0.23822571628537795 and parameters: {'booster': 'gbtree', 'lambda': 1.999524162439115e-07, 'alpha': 0.016441719230670167, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.016265990183224426, 'gamma': 0.003276536485721788, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.21808694427586764.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:02,266]\u001b[0m Trial 15 finished with value: 0.22201588368186456 and parameters: {'booster': 'gbtree', 'lambda': 1.9050189965122934e-07, 'alpha': 0.023633967683934706, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.023925548242448445, 'gamma': 0.0037909823596958517, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.21808694427586764.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:02,895]\u001b[0m Trial 16 finished with value: 0.26796115742099724 and parameters: {'booster': 'gbtree', 'lambda': 1.0067553535768692e-08, 'alpha': 0.03631411070082745, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.01012531203559635, 'gamma': 1.1780314733109965e-05, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.21808694427586764.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:03,556]\u001b[0m Trial 17 finished with value: 0.35544340780215467 and parameters: {'booster': 'gbtree', 'lambda': 2.5825276421140184e-07, 'alpha': 0.020143582252113244, 'colsample_bytree': 0.5, 'max_depth': 8, 'eta': 0.0015659063859213768, 'gamma': 0.0021893837257224965, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.21808694427586764.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:03,979]\u001b[0m Trial 18 finished with value: 0.2080770226004106 and parameters: {'booster': 'gbtree', 'lambda': 5.255601495536208e-07, 'alpha': 0.12694141954629312, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.08432966657872422, 'gamma': 3.1688857459930654e-05, 'grow_policy': 'lossguide'}. Best is trial 18 with value: 0.2080770226004106.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:04,353]\u001b[0m Trial 19 finished with value: 0.20989917597427707 and parameters: {'booster': 'gbtree', 'lambda': 9.111298723288855e-07, 'alpha': 0.0952253845364154, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.13686018649380136, 'gamma': 1.9721181628707107e-05, 'grow_policy': 'lossguide'}. Best is trial 18 with value: 0.2080770226004106.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:04,961]\u001b[0m A new study created in memory with name: no-name-92ec41e5-f0bb-4dc0-89bf-af0b46a01879\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:05,410]\u001b[0m Trial 0 finished with value: 0.2186782001927452 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.2186782001927452.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:05,782]\u001b[0m Trial 1 finished with value: 0.21326056200695712 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:06,412]\u001b[0m Trial 2 finished with value: 0.22656807560822143 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:06,844]\u001b[0m Trial 3 finished with value: 0.21519217696145002 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:07,152]\u001b[0m Trial 4 finished with value: 0.2284783003806746 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:07,761]\u001b[0m Trial 5 finished with value: 0.22668831748020565 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:08,248]\u001b[0m Trial 6 finished with value: 0.2171052034296211 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:08,421]\u001b[0m Trial 7 finished with value: 0.215223399757432 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:08,800]\u001b[0m Trial 8 finished with value: 0.21459675312991597 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:09,332]\u001b[0m Trial 9 finished with value: 0.220182810301592 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.21326056200695712.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:09,602]\u001b[0m Trial 10 finished with value: 0.20551475327448473 and parameters: {'n_estimators': 45, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.20551475327448473.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:09,948]\u001b[0m Trial 11 finished with value: 0.20549189229324635 and parameters: {'n_estimators': 44, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 11 with value: 0.20549189229324635.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:10,243]\u001b[0m Trial 12 finished with value: 0.20260864766478584 and parameters: {'n_estimators': 37, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:10,438]\u001b[0m Trial 13 finished with value: 0.20294355525797145 and parameters: {'n_estimators': 27, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:10,588]\u001b[0m Trial 14 finished with value: 0.20662736276061913 and parameters: {'n_estimators': 21, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:10,823]\u001b[0m Trial 15 finished with value: 0.20283407985974236 and parameters: {'n_estimators': 32, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:11,009]\u001b[0m Trial 16 finished with value: 0.21183920396491052 and parameters: {'n_estimators': 31, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:11,379]\u001b[0m Trial 17 finished with value: 0.20403171288979902 and parameters: {'n_estimators': 35, 'max_depth': 10, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:11,783]\u001b[0m Trial 18 finished with value: 0.20860644728032043 and parameters: {'n_estimators': 61, 'max_depth': 17, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:11,989]\u001b[0m Trial 19 finished with value: 0.21567194550114224 and parameters: {'n_estimators': 35, 'max_depth': 14, 'min_samples_split': 7, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 12 with value: 0.20260864766478584.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:12,173]\u001b[0m A new study created in memory with name: no-name-302bbb9b-d035-4b84-a5e7-53aef1801ec1\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:12,683]\u001b[0m Trial 0 finished with value: 0.20900430131360556 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.20900430131360556.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:13,947]\u001b[0m Trial 1 finished with value: 0.23240111852048417 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.20900430131360556.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:14,487]\u001b[0m Trial 2 finished with value: 0.20854176828853577 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 2 with value: 0.20854176828853577.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:15,521]\u001b[0m Trial 3 finished with value: 0.21916131921437307 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 2 with value: 0.20854176828853577.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:16,293]\u001b[0m Trial 4 finished with value: 0.21230837859961718 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 2 with value: 0.20854176828853577.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:17,932]\u001b[0m Trial 5 finished with value: 0.23898535702618062 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 2 with value: 0.20854176828853577.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:21,090]\u001b[0m Trial 6 finished with value: 0.21960282877943516 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 2 with value: 0.20854176828853577.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:21,516]\u001b[0m Trial 7 finished with value: 0.2078363982480804 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 7 with value: 0.2078363982480804.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:23,771]\u001b[0m Trial 8 finished with value: 0.2174955625825931 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 7 with value: 0.2078363982480804.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:25,051]\u001b[0m Trial 9 finished with value: 0.21166717821657346 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 7 with value: 0.2078363982480804.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:25,489]\u001b[0m Trial 10 finished with value: 0.21058687115977048 and parameters: {'iterations': 100, 'depth': 3, 'learning_rate': 0.29197113937431496, 'l2_leaf_reg': 20, 'border_count': 198}. Best is trial 7 with value: 0.2078363982480804.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:25,853]\u001b[0m Trial 11 finished with value: 0.28527197051622233 and parameters: {'iterations': 66, 'depth': 5, 'learning_rate': 0.02040621675215476, 'l2_leaf_reg': 15, 'border_count': 244}. Best is trial 7 with value: 0.2078363982480804.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:26,209]\u001b[0m Trial 12 finished with value: 0.20382150293860965 and parameters: {'iterations': 63, 'depth': 6, 'learning_rate': 0.07674622217016316, 'l2_leaf_reg': 2, 'border_count': 195}. Best is trial 12 with value: 0.20382150293860965.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:26,601]\u001b[0m Trial 13 finished with value: 0.21383145593329134 and parameters: {'iterations': 61, 'depth': 6, 'learning_rate': 0.09471288184072323, 'l2_leaf_reg': 15, 'border_count': 192}. Best is trial 12 with value: 0.20382150293860965.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:27,074]\u001b[0m Trial 14 finished with value: 0.21346793268032593 and parameters: {'iterations': 58, 'depth': 4, 'learning_rate': 0.08129447141352121, 'l2_leaf_reg': 14, 'border_count': 194}. Best is trial 12 with value: 0.20382150293860965.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:27,773]\u001b[0m Trial 15 finished with value: 0.20932794149048695 and parameters: {'iterations': 100, 'depth': 7, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 18, 'border_count': 104}. Best is trial 12 with value: 0.20382150293860965.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:28,197]\u001b[0m Trial 16 finished with value: 0.2038339308887753 and parameters: {'iterations': 74, 'depth': 5, 'learning_rate': 0.1560626812828991, 'l2_leaf_reg': 5, 'border_count': 110}. Best is trial 12 with value: 0.20382150293860965.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:28,689]\u001b[0m Trial 17 finished with value: 0.20836540001912637 and parameters: {'iterations': 74, 'depth': 7, 'learning_rate': 0.17101072333143386, 'l2_leaf_reg': 4, 'border_count': 107}. Best is trial 12 with value: 0.20382150293860965.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:29,049]\u001b[0m Trial 18 finished with value: 0.20346594436648183 and parameters: {'iterations': 60, 'depth': 5, 'learning_rate': 0.15604414322208643, 'l2_leaf_reg': 3, 'border_count': 104}. Best is trial 18 with value: 0.20346594436648183.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:29,420]\u001b[0m Trial 19 finished with value: 0.2028954291374195 and parameters: {'iterations': 60, 'depth': 5, 'learning_rate': 0.07674584131954669, 'l2_leaf_reg': 3, 'border_count': 90}. Best is trial 19 with value: 0.2028954291374195.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:42:29,770]\u001b[0m A new study created in memory with name: no-name-bc828a54-9c80-4758-99a5-8d4057fc2d14\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:30,217]\u001b[0m Trial 0 finished with value: 0.42808980672841807 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.42808980672841807.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:30,717]\u001b[0m Trial 1 finished with value: 0.4263205806102897 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.4263205806102897.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:31,020]\u001b[0m Trial 2 finished with value: 0.4284686829025827 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.4263205806102897.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:31,436]\u001b[0m Trial 3 finished with value: 0.4281639586771702 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.4263205806102897.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:31,619]\u001b[0m Trial 4 finished with value: 0.42846353268402 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.4263205806102897.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:32,029]\u001b[0m Trial 5 finished with value: 0.4229980860476746 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.4229980860476746.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:32,340]\u001b[0m Trial 6 finished with value: 0.1370662371301067 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.1370662371301067.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:32,549]\u001b[0m Trial 7 finished with value: 0.42824976945396065 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.1370662371301067.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:32,757]\u001b[0m Trial 8 finished with value: 0.4255254598296155 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.1370662371301067.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:33,046]\u001b[0m Trial 9 finished with value: 0.2755503240161987 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.1370662371301067.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:33,187]\u001b[0m Trial 10 finished with value: 0.11226294346755727 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11226294346755727.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:33,354]\u001b[0m Trial 11 finished with value: 0.1111596537954767 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 11 with value: 0.1111596537954767.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:33,628]\u001b[0m Trial 12 finished with value: 0.1110590049057989 and parameters: {'booster': 'gbtree', 'lambda': 1.0473667575279822e-06, 'alpha': 0.627911977428592, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.9598391039242934, 'gamma': 5.72358962790807e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:33,948]\u001b[0m Trial 13 finished with value: 0.19950607360940076 and parameters: {'booster': 'gbtree', 'lambda': 5.881246930677318e-07, 'alpha': 0.8366851036962906, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.024051209580507136, 'gamma': 8.469877671193761e-06, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:34,093]\u001b[0m Trial 14 finished with value: 0.20859389619300314 and parameters: {'booster': 'gbtree', 'lambda': 6.856447376105159e-08, 'alpha': 0.055403032751810494, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.023556088834085213, 'gamma': 1.2953604208031761e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:34,355]\u001b[0m Trial 15 finished with value: 0.1546116360002186 and parameters: {'booster': 'gbtree', 'lambda': 8.714787395098991e-07, 'alpha': 0.018078665943274605, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11459388905949243, 'gamma': 0.0013771048319540476, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:34,502]\u001b[0m Trial 16 finished with value: 0.3918182633762854 and parameters: {'booster': 'gbtree', 'lambda': 4.357322664232636e-05, 'alpha': 0.06642780790393904, 'colsample_bytree': 0.5, 'max_depth': 1, 'eta': 0.0024058585824945074, 'gamma': 2.3382067099705154e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:34,781]\u001b[0m Trial 17 finished with value: 0.38601200907863936 and parameters: {'booster': 'gbtree', 'lambda': 1.7962033878898724e-06, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.3, 'max_depth': 2, 'eta': 0.0022870721298695173, 'gamma': 6.392844482486238e-07, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:35,038]\u001b[0m Trial 18 finished with value: 0.11219602556385418 and parameters: {'booster': 'gbtree', 'lambda': 1.1351974686168584e-08, 'alpha': 0.933560085535797, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.7526891303709383, 'gamma': 0.00242396433969875, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:35,563]\u001b[0m Trial 19 finished with value: 0.4284715279489318 and parameters: {'booster': 'gbtree', 'lambda': 9.895887397004856e-08, 'alpha': 0.008449386600699363, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 2.1186614061329933e-08, 'gamma': 6.810458776822587e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.1110590049057989.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:42:35,674]\u001b[0m A new study created in memory with name: no-name-90fc8dec-22f9-42e9-a10b-7a04ff942d63\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:36,217]\u001b[0m Trial 0 finished with value: 0.12890491655733605 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.12890491655733605.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:36,522]\u001b[0m Trial 1 finished with value: 0.08757978733283706 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:37,057]\u001b[0m Trial 2 finished with value: 0.16205444582249398 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:37,575]\u001b[0m Trial 3 finished with value: 0.11457929841765857 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:37,859]\u001b[0m Trial 4 finished with value: 0.15917303243666137 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:38,444]\u001b[0m Trial 5 finished with value: 0.14913892771456472 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:38,865]\u001b[0m Trial 6 finished with value: 0.09169505154715508 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:39,074]\u001b[0m Trial 7 finished with value: 0.09674079124199793 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:39,514]\u001b[0m Trial 8 finished with value: 0.08954372346113183 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:40,147]\u001b[0m Trial 9 finished with value: 0.12611837641694096 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08757978733283706.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:40,592]\u001b[0m Trial 10 finished with value: 0.08503436657810905 and parameters: {'n_estimators': 45, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.08503436657810905.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:40,893]\u001b[0m Trial 11 finished with value: 0.08523708888613445 and parameters: {'n_estimators': 44, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.08503436657810905.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:41,174]\u001b[0m Trial 12 finished with value: 0.08406858188379436 and parameters: {'n_estimators': 37, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.08406858188379436.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:41,342]\u001b[0m Trial 13 finished with value: 0.08514195149029456 and parameters: {'n_estimators': 28, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.08406858188379436.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:41,532]\u001b[0m Trial 14 finished with value: 0.08671987050800023 and parameters: {'n_estimators': 37, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 12 with value: 0.08406858188379436.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:41,903]\u001b[0m Trial 15 finished with value: 0.08442444229816054 and parameters: {'n_estimators': 60, 'max_depth': 16, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 12 with value: 0.08406858188379436.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:42,263]\u001b[0m Trial 16 finished with value: 0.11721944506777775 and parameters: {'n_estimators': 61, 'max_depth': 18, 'min_samples_split': 7, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 12 with value: 0.08406858188379436.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:42,539]\u001b[0m Trial 17 finished with value: 0.08389361235568127 and parameters: {'n_estimators': 33, 'max_depth': 12, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 17 with value: 0.08389361235568127.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:42,741]\u001b[0m Trial 18 finished with value: 0.08397412500196233 and parameters: {'n_estimators': 32, 'max_depth': 11, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 17 with value: 0.08389361235568127.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:42,894]\u001b[0m Trial 19 finished with value: 0.126287739934022 and parameters: {'n_estimators': 21, 'max_depth': 11, 'min_samples_split': 11, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 17 with value: 0.08389361235568127.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:43,006]\u001b[0m A new study created in memory with name: no-name-3808bdf3-6540-493a-b199-52c8abbc5976\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:43,400]\u001b[0m Trial 0 finished with value: 0.09634050472271172 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:44,083]\u001b[0m Trial 1 finished with value: 0.1191355850855702 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:44,552]\u001b[0m Trial 2 finished with value: 0.09850672710796432 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:45,479]\u001b[0m Trial 3 finished with value: 0.09783459631428394 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:46,173]\u001b[0m Trial 4 finished with value: 0.09638609138658932 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:47,454]\u001b[0m Trial 5 finished with value: 0.14009421139160724 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:49,697]\u001b[0m Trial 6 finished with value: 0.09813004020699834 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.09634050472271172.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:50,076]\u001b[0m Trial 7 finished with value: 0.09126096567599347 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 7 with value: 0.09126096567599347.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:51,900]\u001b[0m Trial 8 finished with value: 0.09319995027422379 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 7 with value: 0.09126096567599347.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:52,779]\u001b[0m Trial 9 finished with value: 0.08430637848441946 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:53,231]\u001b[0m Trial 10 finished with value: 0.09425943189771875 and parameters: {'iterations': 65, 'depth': 6, 'learning_rate': 0.29197113937431496, 'l2_leaf_reg': 2, 'border_count': 97}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:53,657]\u001b[0m Trial 11 finished with value: 0.09085079395152 and parameters: {'iterations': 100, 'depth': 3, 'learning_rate': 0.1809245764669805, 'l2_leaf_reg': 17, 'border_count': 192}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:54,042]\u001b[0m Trial 12 finished with value: 0.09164570732538867 and parameters: {'iterations': 100, 'depth': 3, 'learning_rate': 0.16541289596319753, 'l2_leaf_reg': 15, 'border_count': 205}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:54,780]\u001b[0m Trial 13 finished with value: 0.09867190760753967 and parameters: {'iterations': 89, 'depth': 7, 'learning_rate': 0.09471288184072323, 'l2_leaf_reg': 20, 'border_count': 190}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:55,101]\u001b[0m Trial 14 finished with value: 0.09449357865256656 and parameters: {'iterations': 80, 'depth': 5, 'learning_rate': 0.18011771462539874, 'l2_leaf_reg': 14, 'border_count': 29}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:55,353]\u001b[0m Trial 15 finished with value: 0.087085564862543 and parameters: {'iterations': 62, 'depth': 3, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 4, 'border_count': 102}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:55,829]\u001b[0m Trial 16 finished with value: 0.09102253459673156 and parameters: {'iterations': 62, 'depth': 7, 'learning_rate': 0.09763270451712786, 'l2_leaf_reg': 4, 'border_count': 101}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:56,184]\u001b[0m Trial 17 finished with value: 0.09051966837084051 and parameters: {'iterations': 58, 'depth': 5, 'learning_rate': 0.11996162911762855, 'l2_leaf_reg': 2, 'border_count': 105}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:56,611]\u001b[0m Trial 18 finished with value: 0.2720579015185956 and parameters: {'iterations': 73, 'depth': 8, 'learning_rate': 0.010287122376721428, 'l2_leaf_reg': 6, 'border_count': 22}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:42:56,906]\u001b[0m Trial 19 finished with value: 0.0998100736469258 and parameters: {'iterations': 56, 'depth': 5, 'learning_rate': 0.07674584131954669, 'l2_leaf_reg': 5, 'border_count': 82}. Best is trial 9 with value: 0.08430637848441946.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:42:57,969]\u001b[0m A new study created in memory with name: no-name-8cce752e-82c6-4c75-b11d-0426b559c9a3\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:42:58,473]\u001b[0m Trial 0 finished with value: 0.37627484891934454 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.37627484891934454.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:42:59,086]\u001b[0m Trial 1 finished with value: 0.3732855789278555 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.3732855789278555.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:42:59,296]\u001b[0m Trial 2 finished with value: 0.3769210829656324 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3732855789278555.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:42:59,733]\u001b[0m Trial 3 finished with value: 0.3764009980864022 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3732855789278555.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:42:59,926]\u001b[0m Trial 4 finished with value: 0.37691188219791405 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3732855789278555.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:00,491]\u001b[0m Trial 5 finished with value: 0.3677248334687168 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.3677248334687168.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:00,708]\u001b[0m Trial 6 finished with value: 0.012856280548362293 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:00,880]\u001b[0m Trial 7 finished with value: 0.37652129293046005 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:01,128]\u001b[0m Trial 8 finished with value: 0.3716839435330534 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:01,645]\u001b[0m Trial 9 finished with value: 0.13071804903059622 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:01,894]\u001b[0m Trial 10 finished with value: 0.028723596502660747 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:02,099]\u001b[0m Trial 11 finished with value: 0.03600233247941514 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:02,538]\u001b[0m Trial 12 finished with value: 0.014472500132510781 and parameters: {'booster': 'gbtree', 'lambda': 5.3631107495308e-06, 'alpha': 0.10298878000094057, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 0.0012896702521643245, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:02,937]\u001b[0m Trial 13 finished with value: 0.029148967256031912 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.042680256920104825, 'colsample_bytree': 0.5, 'max_depth': 9, 'eta': 0.026040110235398966, 'gamma': 0.0019255968152891395, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:03,371]\u001b[0m Trial 14 finished with value: 0.035567094943909146 and parameters: {'booster': 'gbtree', 'lambda': 1.9122574085396057e-05, 'alpha': 0.010673562371581187, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.02355462366691312, 'gamma': 8.368134896692912e-06, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.012856280548362293.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:03,668]\u001b[0m Trial 15 finished with value: 0.007498727567618799 and parameters: {'booster': 'gbtree', 'lambda': 3.8146747332708435e-07, 'alpha': 0.023633967683934706, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11777469387937922, 'gamma': 0.0036224097727239643, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.007498727567618799.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:03,964]\u001b[0m Trial 16 finished with value: 0.2788555502878534 and parameters: {'booster': 'gbtree', 'lambda': 3.1759337851853345e-07, 'alpha': 0.007229508684507139, 'colsample_bytree': 0.4, 'max_depth': 4, 'eta': 0.003012315490040272, 'gamma': 0.005441964254521712, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.007498727567618799.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:04,374]\u001b[0m Trial 17 finished with value: 0.0033778563459697514 and parameters: {'booster': 'gbtree', 'lambda': 1.6802636524317446e-08, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.1061285420424653, 'gamma': 1.9248439937955043e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0033778563459697514.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:04,705]\u001b[0m Trial 18 finished with value: 0.3060456167403993 and parameters: {'booster': 'gbtree', 'lambda': 1.0514990199219779e-08, 'alpha': 1.3760840580544568e-08, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.002078672903266466, 'gamma': 1.1927699967197258e-05, 'grow_policy': 'lossguide'}. Best is trial 17 with value: 0.0033778563459697514.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:05,028]\u001b[0m Trial 19 finished with value: 0.0033725333870844166 and parameters: {'booster': 'gbtree', 'lambda': 6.854904100027099e-08, 'alpha': 1.13854524317208e-08, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.08033094834826023, 'gamma': 2.1151120784998337e-05, 'grow_policy': 'lossguide'}. Best is trial 19 with value: 0.0033725333870844166.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "\u001b[32m[I 2023-08-24 08:43:05,295]\u001b[0m A new study created in memory with name: no-name-c9ef711f-317a-4d18-98b5-823134ac6e52\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:05,749]\u001b[0m Trial 0 finished with value: 0.015303318879141825 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.015303318879141825.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:06,054]\u001b[0m Trial 1 finished with value: 0.017341333538227945 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 0 with value: 0.015303318879141825.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:06,553]\u001b[0m Trial 2 finished with value: 0.023293930945522595 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.015303318879141825.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:06,952]\u001b[0m Trial 3 finished with value: 0.010062561772825959 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:07,228]\u001b[0m Trial 4 finished with value: 0.03705246794858496 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:07,844]\u001b[0m Trial 5 finished with value: 0.02348476996033469 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:08,274]\u001b[0m Trial 6 finished with value: 0.023241807532320597 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:08,414]\u001b[0m Trial 7 finished with value: 0.03130382663506675 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:08,818]\u001b[0m Trial 8 finished with value: 0.020318104886987612 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:09,247]\u001b[0m Trial 9 finished with value: 0.014493901264942752 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.010062561772825959.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:09,462]\u001b[0m Trial 10 finished with value: 0.00635849146294212 and parameters: {'n_estimators': 36, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.00635849146294212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:09,597]\u001b[0m Trial 11 finished with value: 0.006779338648305967 and parameters: {'n_estimators': 24, 'max_depth': 15, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.00635849146294212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:09,787]\u001b[0m Trial 12 finished with value: 0.00655480887913041 and parameters: {'n_estimators': 20, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.00635849146294212.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:10,056]\u001b[0m Trial 13 finished with value: 0.00632291025712195 and parameters: {'n_estimators': 38, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:10,268]\u001b[0m Trial 14 finished with value: 0.01128981472410224 and parameters: {'n_estimators': 38, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:10,500]\u001b[0m Trial 15 finished with value: 0.007539303604586847 and parameters: {'n_estimators': 37, 'max_depth': 12, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:10,679]\u001b[0m Trial 16 finished with value: 0.00926967456634626 and parameters: {'n_estimators': 33, 'max_depth': 17, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:10,959]\u001b[0m Trial 17 finished with value: 0.01343146356045801 and parameters: {'n_estimators': 56, 'max_depth': 13, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:11,219]\u001b[0m Trial 18 finished with value: 0.009002425938255275 and parameters: {'n_estimators': 47, 'max_depth': 10, 'min_samples_split': 7, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:11,581]\u001b[0m Trial 19 finished with value: 0.014550707463038048 and parameters: {'n_estimators': 66, 'max_depth': 19, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 13 with value: 0.00632291025712195.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:11,720]\u001b[0m A new study created in memory with name: no-name-1902ad85-0fc8-40b8-a6d7-3e1ab2de8030\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:11,996]\u001b[0m Trial 0 finished with value: 0.005738289361321748 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:12,506]\u001b[0m Trial 1 finished with value: 0.013298840852902427 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:12,878]\u001b[0m Trial 2 finished with value: 0.012356535780535041 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:13,437]\u001b[0m Trial 3 finished with value: 0.007499959624495892 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:14,121]\u001b[0m Trial 4 finished with value: 0.006234108744923997 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:15,042]\u001b[0m Trial 5 finished with value: 0.028295492208237183 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:16,669]\u001b[0m Trial 6 finished with value: 0.006129759707004749 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:17,039]\u001b[0m Trial 7 finished with value: 0.006491013622554352 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:18,233]\u001b[0m Trial 8 finished with value: 0.005860254917316517 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:18,876]\u001b[0m Trial 9 finished with value: 0.00672273076868565 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:19,081]\u001b[0m Trial 10 finished with value: 0.007532803625444885 and parameters: {'iterations': 65, 'depth': 3, 'learning_rate': 0.29201626538499564, 'l2_leaf_reg': 5, 'border_count': 93}. Best is trial 0 with value: 0.005738289361321748.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:19,445]\u001b[0m Trial 11 finished with value: 0.005010142992725011 and parameters: {'iterations': 68, 'depth': 6, 'learning_rate': 0.28204236882299116, 'l2_leaf_reg': 7, 'border_count': 184}. Best is trial 11 with value: 0.005010142992725011.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:19,735]\u001b[0m Trial 12 finished with value: 0.004825151783741031 and parameters: {'iterations': 61, 'depth': 5, 'learning_rate': 0.29895950864469806, 'l2_leaf_reg': 6, 'border_count': 200}. Best is trial 12 with value: 0.004825151783741031.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:20,054]\u001b[0m Trial 13 finished with value: 0.006084512718900263 and parameters: {'iterations': 61, 'depth': 6, 'learning_rate': 0.29963969133631474, 'l2_leaf_reg': 14, 'border_count': 202}. Best is trial 12 with value: 0.004825151783741031.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:20,323]\u001b[0m Trial 14 finished with value: 0.005186776220096621 and parameters: {'iterations': 58, 'depth': 5, 'learning_rate': 0.2658802186628621, 'l2_leaf_reg': 5, 'border_count': 198}. Best is trial 12 with value: 0.004825151783741031.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:20,748]\u001b[0m Trial 15 finished with value: 0.004533357795861528 and parameters: {'iterations': 70, 'depth': 7, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 2, 'border_count': 194}. Best is trial 15 with value: 0.004533357795861528.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:21,172]\u001b[0m Trial 16 finished with value: 0.007041284707960446 and parameters: {'iterations': 57, 'depth': 7, 'learning_rate': 0.09734977035441812, 'l2_leaf_reg': 2, 'border_count': 109}. Best is trial 15 with value: 0.004533357795861528.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:21,624]\u001b[0m Trial 17 finished with value: 0.005750148373099101 and parameters: {'iterations': 75, 'depth': 7, 'learning_rate': 0.08833305558729443, 'l2_leaf_reg': 3, 'border_count': 225}. Best is trial 15 with value: 0.004533357795861528.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:21,934]\u001b[0m Trial 18 finished with value: 0.20950727669737032 and parameters: {'iterations': 72, 'depth': 5, 'learning_rate': 0.010287122376721428, 'l2_leaf_reg': 6, 'border_count': 177}. Best is trial 15 with value: 0.004533357795861528.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:22,402]\u001b[0m Trial 19 finished with value: 0.008890035094989533 and parameters: {'iterations': 62, 'depth': 8, 'learning_rate': 0.17337812936106473, 'l2_leaf_reg': 20, 'border_count': 217}. Best is trial 15 with value: 0.004533357795861528.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:43:23,025]\u001b[0m A new study created in memory with name: no-name-ef25e205-6850-4108-a9c3-187bedb30008\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:23,514]\u001b[0m Trial 0 finished with value: 0.3374150879028122 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.3374150879028122.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:24,008]\u001b[0m Trial 1 finished with value: 0.33594995774206104 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.33594995774206104.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:24,459]\u001b[0m Trial 2 finished with value: 0.3377365806899358 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.33594995774206104.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:24,888]\u001b[0m Trial 3 finished with value: 0.337476991616827 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.33594995774206104.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:25,084]\u001b[0m Trial 4 finished with value: 0.33773232647645673 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.33594995774206104.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:25,645]\u001b[0m Trial 5 finished with value: 0.33315107662680804 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.33315107662680804.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:25,883]\u001b[0m Trial 6 finished with value: 0.12646582584665403 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:26,105]\u001b[0m Trial 7 finished with value: 0.33748894362199877 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:26,362]\u001b[0m Trial 8 finished with value: 0.3352418424752539 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:26,949]\u001b[0m Trial 9 finished with value: 0.19104220057527285 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:27,154]\u001b[0m Trial 10 finished with value: 0.12960035759441024 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:27,328]\u001b[0m Trial 11 finished with value: 0.1315620765486619 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:27,667]\u001b[0m Trial 12 finished with value: 0.16930727343885807 and parameters: {'booster': 'gbtree', 'lambda': 5.3631107495308e-06, 'alpha': 0.10298878000094057, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.9596976602699849, 'gamma': 0.0012896702521643245, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.12646582584665403.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:28,019]\u001b[0m Trial 13 finished with value: 0.11011798812117383 and parameters: {'booster': 'gbtree', 'lambda': 2.464915781025747e-07, 'alpha': 0.04275837367095418, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.026393646386352426, 'gamma': 7.1514352509497836e-06, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.11011798812117383.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:28,605]\u001b[0m Trial 14 finished with value: 0.14146471433402388 and parameters: {'booster': 'gbtree', 'lambda': 1.999524162439115e-07, 'alpha': 0.01630030018822387, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.016265990183224426, 'gamma': 3.5620480894375304e-06, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.11011798812117383.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:28,945]\u001b[0m Trial 15 finished with value: 0.11295432983678139 and parameters: {'booster': 'gbtree', 'lambda': 1.9050189965122934e-07, 'alpha': 0.007068366051888947, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.023925548242448445, 'gamma': 2.8392654201975797e-05, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.11011798812117383.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:29,291]\u001b[0m Trial 16 finished with value: 0.18340388187989412 and parameters: {'booster': 'gbtree', 'lambda': 1.0067553535768692e-08, 'alpha': 0.0309476207594851, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.01012531203559635, 'gamma': 9.762310242569957e-06, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.11011798812117383.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:30,163]\u001b[0m Trial 17 finished with value: 0.30224479616321 and parameters: {'booster': 'gbtree', 'lambda': 2.5825276421140184e-07, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.5, 'max_depth': 8, 'eta': 0.0015659063859213768, 'gamma': 1.4856638446443875e-07, 'grow_policy': 'lossguide'}. Best is trial 13 with value: 0.11011798812117383.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:30,574]\u001b[0m Trial 18 finished with value: 0.09439028161039016 and parameters: {'booster': 'gbtree', 'lambda': 5.255601495536208e-07, 'alpha': 0.011100726752099799, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.08432966657872422, 'gamma': 1.6482877710207397e-05, 'grow_policy': 'lossguide'}. Best is trial 18 with value: 0.09439028161039016.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:30,932]\u001b[0m Trial 19 finished with value: 0.09742870702137858 and parameters: {'booster': 'gbtree', 'lambda': 9.111298723288855e-07, 'alpha': 0.031112123044937545, 'colsample_bytree': 0.5, 'max_depth': 5, 'eta': 0.13686018649380136, 'gamma': 6.325674943229882e-07, 'grow_policy': 'lossguide'}. Best is trial 18 with value: 0.09439028161039016.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:31,524]\u001b[0m A new study created in memory with name: no-name-6cd9774e-84fd-4b19-b720-bbc7b7a9e967\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:31,914]\u001b[0m Trial 0 finished with value: 0.10849020624996498 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.10849020624996498.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:32,202]\u001b[0m Trial 1 finished with value: 0.10452508026715575 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.10452508026715575.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:32,991]\u001b[0m Trial 2 finished with value: 0.11984909914654414 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.10452508026715575.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:33,455]\u001b[0m Trial 3 finished with value: 0.10032635714605487 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:33,866]\u001b[0m Trial 4 finished with value: 0.12422074921439402 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:34,422]\u001b[0m Trial 5 finished with value: 0.11883400853678672 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:34,892]\u001b[0m Trial 6 finished with value: 0.11044053568994046 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:35,035]\u001b[0m Trial 7 finished with value: 0.114030132865186 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:35,417]\u001b[0m Trial 8 finished with value: 0.1071016768691948 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:35,976]\u001b[0m Trial 9 finished with value: 0.10705318968466981 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.10032635714605487.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:36,208]\u001b[0m Trial 10 finished with value: 0.09744880486811532 and parameters: {'n_estimators': 36, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.09744880486811532.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:36,344]\u001b[0m Trial 11 finished with value: 0.09757451167511522 and parameters: {'n_estimators': 24, 'max_depth': 15, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.09744880486811532.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:36,484]\u001b[0m Trial 12 finished with value: 0.09734566926494799 and parameters: {'n_estimators': 20, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 12 with value: 0.09734566926494799.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:36,734]\u001b[0m Trial 13 finished with value: 0.09646276058378933 and parameters: {'n_estimators': 38, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:36,980]\u001b[0m Trial 14 finished with value: 0.10217438958942639 and parameters: {'n_estimators': 36, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:37,229]\u001b[0m Trial 15 finished with value: 0.09795020298134398 and parameters: {'n_estimators': 37, 'max_depth': 12, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:37,391]\u001b[0m Trial 16 finished with value: 0.09825266935272244 and parameters: {'n_estimators': 21, 'max_depth': 17, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:37,756]\u001b[0m Trial 17 finished with value: 0.10537853970601513 and parameters: {'n_estimators': 48, 'max_depth': 13, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:38,069]\u001b[0m Trial 18 finished with value: 0.10011410617747059 and parameters: {'n_estimators': 30, 'max_depth': 10, 'min_samples_split': 7, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:38,446]\u001b[0m Trial 19 finished with value: 0.10761339275594634 and parameters: {'n_estimators': 63, 'max_depth': 19, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 13 with value: 0.09646276058378933.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:38,624]\u001b[0m A new study created in memory with name: no-name-a75fd789-3dee-4800-b7fc-2b6698a628d2\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:38,979]\u001b[0m Trial 0 finished with value: 0.09327655138777703 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:39,897]\u001b[0m Trial 1 finished with value: 0.11435106134701388 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:40,455]\u001b[0m Trial 2 finished with value: 0.10129420351583508 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:41,807]\u001b[0m Trial 3 finished with value: 0.10711351011377394 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:42,968]\u001b[0m Trial 4 finished with value: 0.09878740796137105 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:44,769]\u001b[0m Trial 5 finished with value: 0.12789048813069606 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:48,088]\u001b[0m Trial 6 finished with value: 0.10510072009052919 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:48,725]\u001b[0m Trial 7 finished with value: 0.0958958379395351 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:51,110]\u001b[0m Trial 8 finished with value: 0.1028393279329528 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:52,435]\u001b[0m Trial 9 finished with value: 0.09939357816704615 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:52,721]\u001b[0m Trial 10 finished with value: 0.09373797078103131 and parameters: {'iterations': 65, 'depth': 3, 'learning_rate': 0.29201626538499564, 'l2_leaf_reg': 5, 'border_count': 93}. Best is trial 0 with value: 0.09327655138777703.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:53,029]\u001b[0m Trial 11 finished with value: 0.09319780739790025 and parameters: {'iterations': 63, 'depth': 3, 'learning_rate': 0.2927355101746894, 'l2_leaf_reg': 5, 'border_count': 92}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:53,409]\u001b[0m Trial 12 finished with value: 0.09431007524562746 and parameters: {'iterations': 61, 'depth': 5, 'learning_rate': 0.29908300209136973, 'l2_leaf_reg': 6, 'border_count': 96}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:53,666]\u001b[0m Trial 13 finished with value: 0.09683606285355846 and parameters: {'iterations': 60, 'depth': 3, 'learning_rate': 0.263317715812268, 'l2_leaf_reg': 2, 'border_count': 100}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:54,121]\u001b[0m Trial 14 finished with value: 0.09597075386063651 and parameters: {'iterations': 79, 'depth': 5, 'learning_rate': 0.2657205029945847, 'l2_leaf_reg': 14, 'border_count': 198}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:54,529]\u001b[0m Trial 15 finished with value: 0.09867065504685384 and parameters: {'iterations': 71, 'depth': 4, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 7, 'border_count': 29}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:54,986]\u001b[0m Trial 16 finished with value: 0.10254530230021719 and parameters: {'iterations': 57, 'depth': 7, 'learning_rate': 0.08839536016087551, 'l2_leaf_reg': 4, 'border_count': 73}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:55,322]\u001b[0m Trial 17 finished with value: 0.09374807678867515 and parameters: {'iterations': 69, 'depth': 4, 'learning_rate': 0.17101072333143386, 'l2_leaf_reg': 9, 'border_count': 117}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:55,739]\u001b[0m Trial 18 finished with value: 0.09751870631106212 and parameters: {'iterations': 92, 'depth': 3, 'learning_rate': 0.26680260122671756, 'l2_leaf_reg': 13, 'border_count': 49}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:43:56,150]\u001b[0m Trial 19 finished with value: 0.09765051337423547 and parameters: {'iterations': 77, 'depth': 5, 'learning_rate': 0.23400678650970547, 'l2_leaf_reg': 20, 'border_count': 182}. Best is trial 11 with value: 0.09319780739790025.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:43:56,602]\u001b[0m A new study created in memory with name: no-name-050f5221-b15c-4f28-aa4e-4ca731d6f7cd\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:57,087]\u001b[0m Trial 0 finished with value: 0.3436775924032725 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.3436775924032725.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:57,616]\u001b[0m Trial 1 finished with value: 0.34239365624043755 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.34239365624043755.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:57,849]\u001b[0m Trial 2 finished with value: 0.3439735120723341 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.34239365624043755.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:58,240]\u001b[0m Trial 3 finished with value: 0.3437387555821194 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.34239365624043755.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:58,416]\u001b[0m Trial 4 finished with value: 0.34396927779133174 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.34239365624043755.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:58,904]\u001b[0m Trial 5 finished with value: 0.33971860417728633 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.33971860417728633.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:59,156]\u001b[0m Trial 6 finished with value: 0.16306467363381885 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.16306467363381885.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:59,345]\u001b[0m Trial 7 finished with value: 0.34375975146188537 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.16306467363381885.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:59,578]\u001b[0m Trial 8 finished with value: 0.3416123495506292 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.16306467363381885.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:43:59,886]\u001b[0m Trial 9 finished with value: 0.22529557659775698 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.16306467363381885.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:00,132]\u001b[0m Trial 10 finished with value: 0.1542078843888254 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.1542078843888254.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:00,298]\u001b[0m Trial 11 finished with value: 0.1552181264663596 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.1542078843888254.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:00,453]\u001b[0m Trial 12 finished with value: 0.15458695655222707 and parameters: {'booster': 'gbtree', 'lambda': 1.0473667575279822e-06, 'alpha': 0.627911977428592, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.9598391039242934, 'gamma': 5.72358962790807e-05, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.1542078843888254.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:00,721]\u001b[0m Trial 13 finished with value: 0.17733658003210642 and parameters: {'booster': 'gbtree', 'lambda': 5.881246930677318e-07, 'alpha': 0.8366851036962906, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.024302329488734866, 'gamma': 6.1532405566926866e-06, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.1542078843888254.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:00,878]\u001b[0m Trial 14 finished with value: 0.1825528270185756 and parameters: {'booster': 'gbtree', 'lambda': 2.4460732276629095e-07, 'alpha': 0.055403032751810494, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.023556088834085213, 'gamma': 1.2953604208031761e-05, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.1542078843888254.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:01,212]\u001b[0m Trial 15 finished with value: 0.151256397975708 and parameters: {'booster': 'gbtree', 'lambda': 1.2146049819487215e-08, 'alpha': 0.018917752082735745, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11507637647773479, 'gamma': 0.0013771048319540476, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.151256397975708.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:01,633]\u001b[0m Trial 16 finished with value: 0.29326026524721077 and parameters: {'booster': 'gbtree', 'lambda': 1.0045903232665417e-08, 'alpha': 0.03938671767111285, 'colsample_bytree': 0.5, 'max_depth': 4, 'eta': 0.0026125009228956456, 'gamma': 0.001950890495315153, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.151256397975708.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:02,065]\u001b[0m Trial 17 finished with value: 0.1564381686417855 and parameters: {'booster': 'gbtree', 'lambda': 1.3874028180821784e-07, 'alpha': 0.0289891567095563, 'colsample_bytree': 0.3, 'max_depth': 8, 'eta': 0.1061285420424653, 'gamma': 0.0040790188618974295, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.151256397975708.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:02,345]\u001b[0m Trial 18 finished with value: 0.3117316476228957 and parameters: {'booster': 'gbtree', 'lambda': 3.898737975660885e-05, 'alpha': 0.01155098710837322, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.002050582775232043, 'gamma': 2.486186832830885e-07, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.151256397975708.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:02,689]\u001b[0m Trial 19 finished with value: 0.15480416610141845 and parameters: {'booster': 'gbtree', 'lambda': 2.4528717098834345e-08, 'alpha': 0.1728427537385378, 'colsample_bytree': 0.3, 'max_depth': 5, 'eta': 0.09300333381026714, 'gamma': 8.456646262665646e-05, 'grow_policy': 'lossguide'}. Best is trial 15 with value: 0.151256397975708.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:02,949]\u001b[0m A new study created in memory with name: no-name-ca6d7fb4-7eea-4ba9-aaef-202b57e037fc\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:03,378]\u001b[0m Trial 0 finished with value: 0.13276272865498256 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.13276272865498256.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:03,719]\u001b[0m Trial 1 finished with value: 0.12920949914166505 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.12920949914166505.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:04,179]\u001b[0m Trial 2 finished with value: 0.14127089137069437 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.12920949914166505.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:04,612]\u001b[0m Trial 3 finished with value: 0.12730671570734853 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:04,893]\u001b[0m Trial 4 finished with value: 0.14108652339244612 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:05,447]\u001b[0m Trial 5 finished with value: 0.13788532201629156 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:06,136]\u001b[0m Trial 6 finished with value: 0.13326665266140134 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:06,304]\u001b[0m Trial 7 finished with value: 0.13530316776925333 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:06,661]\u001b[0m Trial 8 finished with value: 0.13066084133309808 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:07,267]\u001b[0m Trial 9 finished with value: 0.13051374123185763 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 3 with value: 0.12730671570734853.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:07,509]\u001b[0m Trial 10 finished with value: 0.12478682990822315 and parameters: {'n_estimators': 36, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 10 with value: 0.12478682990822315.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:07,664]\u001b[0m Trial 11 finished with value: 0.12395251237213518 and parameters: {'n_estimators': 24, 'max_depth': 15, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:07,798]\u001b[0m Trial 12 finished with value: 0.12443759856676244 and parameters: {'n_estimators': 20, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:07,927]\u001b[0m Trial 13 finished with value: 0.12435157794098668 and parameters: {'n_estimators': 21, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:08,247]\u001b[0m Trial 14 finished with value: 0.12779369928860182 and parameters: {'n_estimators': 33, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:08,501]\u001b[0m Trial 15 finished with value: 0.12575893022329712 and parameters: {'n_estimators': 39, 'max_depth': 16, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:08,675]\u001b[0m Trial 16 finished with value: 0.12730874325482824 and parameters: {'n_estimators': 29, 'max_depth': 11, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:08,955]\u001b[0m Trial 17 finished with value: 0.1302415957103399 and parameters: {'n_estimators': 56, 'max_depth': 16, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:09,239]\u001b[0m Trial 18 finished with value: 0.12634483918384434 and parameters: {'n_estimators': 44, 'max_depth': 12, 'min_samples_split': 7, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:09,413]\u001b[0m Trial 19 finished with value: 0.1312525984634263 and parameters: {'n_estimators': 26, 'max_depth': 20, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 11 with value: 0.12395251237213518.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:09,533]\u001b[0m A new study created in memory with name: no-name-8de3e6e4-4d52-472a-b0cb-9467776dd92b\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:09,902]\u001b[0m Trial 0 finished with value: 0.11877040095124503 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.11877040095124503.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:10,763]\u001b[0m Trial 1 finished with value: 0.14258000645133426 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.11877040095124503.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:11,179]\u001b[0m Trial 2 finished with value: 0.12155132834564665 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.11877040095124503.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:12,170]\u001b[0m Trial 3 finished with value: 0.12322750304006956 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.11877040095124503.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:12,837]\u001b[0m Trial 4 finished with value: 0.1160363919926574 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:14,321]\u001b[0m Trial 5 finished with value: 0.150609068395657 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:17,085]\u001b[0m Trial 6 finished with value: 0.12259982189310759 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:17,464]\u001b[0m Trial 7 finished with value: 0.12752972519843223 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:19,332]\u001b[0m Trial 8 finished with value: 0.1174457486335253 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:20,445]\u001b[0m Trial 9 finished with value: 0.11815495330733945 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:21,034]\u001b[0m Trial 10 finished with value: 0.11797891481478513 and parameters: {'iterations': 65, 'depth': 7, 'learning_rate': 0.29197113937431496, 'l2_leaf_reg': 13, 'border_count': 89}. Best is trial 4 with value: 0.1160363919926574.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:21,586]\u001b[0m Trial 11 finished with value: 0.11561108030708302 and parameters: {'iterations': 68, 'depth': 7, 'learning_rate': 0.20674370488411517, 'l2_leaf_reg': 8, 'border_count': 100}. Best is trial 11 with value: 0.11561108030708302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:22,016]\u001b[0m Trial 12 finished with value: 0.12503166067821686 and parameters: {'iterations': 61, 'depth': 7, 'learning_rate': 0.09660367687695079, 'l2_leaf_reg': 6, 'border_count': 96}. Best is trial 11 with value: 0.11561108030708302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:22,458]\u001b[0m Trial 13 finished with value: 0.12074001239515342 and parameters: {'iterations': 78, 'depth': 6, 'learning_rate': 0.18670723739461753, 'l2_leaf_reg': 10, 'border_count': 102}. Best is trial 11 with value: 0.11561108030708302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:23,140]\u001b[0m Trial 14 finished with value: 0.11931446943554473 and parameters: {'iterations': 58, 'depth': 8, 'learning_rate': 0.29023325973686065, 'l2_leaf_reg': 14, 'border_count': 198}. Best is trial 11 with value: 0.11561108030708302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:23,507]\u001b[0m Trial 15 finished with value: 0.11942382577280271 and parameters: {'iterations': 74, 'depth': 5, 'learning_rate': 0.22515027447969355, 'l2_leaf_reg': 10, 'border_count': 73}. Best is trial 11 with value: 0.11561108030708302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:24,380]\u001b[0m Trial 16 finished with value: 0.12581867179598497 and parameters: {'iterations': 68, 'depth': 8, 'learning_rate': 0.16983321847869223, 'l2_leaf_reg': 20, 'border_count': 116}. Best is trial 11 with value: 0.11561108030708302.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:24,991]\u001b[0m Trial 17 finished with value: 0.11359482721835949 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.26231476482795474, 'l2_leaf_reg': 5, 'border_count': 20}. Best is trial 17 with value: 0.11359482721835949.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:25,349]\u001b[0m Trial 18 finished with value: 0.10698758737080243 and parameters: {'iterations': 92, 'depth': 5, 'learning_rate': 0.26708983346547266, 'l2_leaf_reg': 4, 'border_count': 25}. Best is trial 18 with value: 0.10698758737080243.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:25,664]\u001b[0m Trial 19 finished with value: 0.12176761146124106 and parameters: {'iterations': 100, 'depth': 3, 'learning_rate': 0.26409539615539435, 'l2_leaf_reg': 3, 'border_count': 26}. Best is trial 18 with value: 0.10698758737080243.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:44:26,171]\u001b[0m A new study created in memory with name: no-name-fde2f27c-4978-4bf9-b0dc-3a3fe0510ca3\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:26,610]\u001b[0m Trial 0 finished with value: 0.4293975937872543 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.4293975937872543.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:27,022]\u001b[0m Trial 1 finished with value: 0.4272684445585872 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.4272684445585872.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:27,293]\u001b[0m Trial 2 finished with value: 0.4298532051636816 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.4272684445585872.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:27,774]\u001b[0m Trial 3 finished with value: 0.4294810741502015 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.4272684445585872.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:27,996]\u001b[0m Trial 4 finished with value: 0.4298464401347822 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.4272684445585872.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:28,508]\u001b[0m Trial 5 finished with value: 0.423450645292305 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.423450645292305.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:28,847]\u001b[0m Trial 6 finished with value: 0.15023047755852348 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.15023047755852348.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:29,118]\u001b[0m Trial 7 finished with value: 0.4295069907339808 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.15023047755852348.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:29,631]\u001b[0m Trial 8 finished with value: 0.4261745701249702 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.15023047755852348.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:30,380]\u001b[0m Trial 9 finished with value: 0.24484888826472292 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.15023047755852348.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:30,644]\u001b[0m Trial 10 finished with value: 0.11384082983983025 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:30,886]\u001b[0m Trial 11 finished with value: 0.11536841987434959 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:31,096]\u001b[0m Trial 12 finished with value: 0.11736307074375084 and parameters: {'booster': 'gbtree', 'lambda': 1.0473667575279822e-06, 'alpha': 0.627911977428592, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.9598391039242934, 'gamma': 5.72358962790807e-05, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:31,495]\u001b[0m Trial 13 finished with value: 0.15764385652967675 and parameters: {'booster': 'gbtree', 'lambda': 1.3332187753258847e-06, 'alpha': 0.8311269838034957, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.024644366898092296, 'gamma': 0.00016690921260296285, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:31,702]\u001b[0m Trial 14 finished with value: 0.1673032072873322 and parameters: {'booster': 'gbtree', 'lambda': 1.547817717422271e-07, 'alpha': 0.05131508087940074, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.022197262159735248, 'gamma': 8.114105709883805e-06, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:32,075]\u001b[0m Trial 15 finished with value: 0.14883477627630354 and parameters: {'booster': 'gbtree', 'lambda': 3.214664036266455e-05, 'alpha': 0.06606309242833071, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11507637647773479, 'gamma': 0.0026544794242065396, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:32,283]\u001b[0m Trial 16 finished with value: 0.3684217446163309 and parameters: {'booster': 'gbtree', 'lambda': 2.0523861367373643e-06, 'alpha': 0.03938671767111285, 'colsample_bytree': 0.5, 'max_depth': 1, 'eta': 0.0024058585824945074, 'gamma': 2.7904460691909303e-06, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:32,530]\u001b[0m Trial 17 finished with value: 0.37885974702460423 and parameters: {'booster': 'gbtree', 'lambda': 2.5355948611549184e-08, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.3, 'max_depth': 2, 'eta': 0.002115815129582509, 'gamma': 2.4442153639401705e-05, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:32,806]\u001b[0m Trial 18 finished with value: 0.21463927913039527 and parameters: {'booster': 'gbtree', 'lambda': 2.8841769226954487e-07, 'alpha': 0.009279255328989551, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.7526891303709383, 'gamma': 0.0020862643916283153, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:33,455]\u001b[0m Trial 19 finished with value: 0.42985689442772007 and parameters: {'booster': 'gbtree', 'lambda': 4.873869248534369e-06, 'alpha': 0.14791001261737077, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 2.158684781591343e-08, 'gamma': 3.889324322696354e-07, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.11384082983983025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:33,572]\u001b[0m A new study created in memory with name: no-name-65ebbf35-78a7-4332-b201-d6fdd462626f\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:33,973]\u001b[0m Trial 0 finished with value: 0.09116957249780135 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.09116957249780135.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:34,356]\u001b[0m Trial 1 finished with value: 0.08388266764248908 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:35,139]\u001b[0m Trial 2 finished with value: 0.10090312483297785 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:35,635]\u001b[0m Trial 3 finished with value: 0.08632416184148599 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:36,080]\u001b[0m Trial 4 finished with value: 0.10720341751207586 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:36,669]\u001b[0m Trial 5 finished with value: 0.10011609275640194 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:37,204]\u001b[0m Trial 6 finished with value: 0.0871588859490523 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:37,372]\u001b[0m Trial 7 finished with value: 0.08743536850398503 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:37,761]\u001b[0m Trial 8 finished with value: 0.08550752783164735 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:38,424]\u001b[0m Trial 9 finished with value: 0.09138292435267485 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.08388266764248908.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:38,733]\u001b[0m Trial 10 finished with value: 0.07807905031712521 and parameters: {'n_estimators': 45, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.07807905031712521.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:39,027]\u001b[0m Trial 11 finished with value: 0.07805971275028577 and parameters: {'n_estimators': 44, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 11 with value: 0.07805971275028577.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:39,273]\u001b[0m Trial 12 finished with value: 0.07691260252878836 and parameters: {'n_estimators': 37, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.07691260252878836.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:39,449]\u001b[0m Trial 13 finished with value: 0.07600574056162263 and parameters: {'n_estimators': 27, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 13 with value: 0.07600574056162263.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:39,618]\u001b[0m Trial 14 finished with value: 0.07926056361678224 and parameters: {'n_estimators': 21, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 13 with value: 0.07600574056162263.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:39,813]\u001b[0m Trial 15 finished with value: 0.0764475496381197 and parameters: {'n_estimators': 32, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 13 with value: 0.07600574056162263.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:40,091]\u001b[0m Trial 16 finished with value: 0.09068494805748635 and parameters: {'n_estimators': 32, 'max_depth': 11, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 13 with value: 0.07600574056162263.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:40,315]\u001b[0m Trial 17 finished with value: 0.0764475496381197 and parameters: {'n_estimators': 32, 'max_depth': 13, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 13 with value: 0.07600574056162263.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:40,783]\u001b[0m Trial 18 finished with value: 0.07553585785251245 and parameters: {'n_estimators': 59, 'max_depth': 17, 'min_samples_split': 4, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 18 with value: 0.07553585785251245.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:41,229]\u001b[0m Trial 19 finished with value: 0.0916101050806773 and parameters: {'n_estimators': 62, 'max_depth': 18, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 18 with value: 0.07553585785251245.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:41,459]\u001b[0m A new study created in memory with name: no-name-522aad5f-3af6-4912-984c-3c56e913cbc3\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:41,927]\u001b[0m Trial 0 finished with value: 0.08268636918999185 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.08268636918999185.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:42,994]\u001b[0m Trial 1 finished with value: 0.10854167190056734 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.08268636918999185.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:43,741]\u001b[0m Trial 2 finished with value: 0.08757777099748354 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.08268636918999185.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:44,772]\u001b[0m Trial 3 finished with value: 0.09397594116327908 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.08268636918999185.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:45,525]\u001b[0m Trial 4 finished with value: 0.08210290174814099 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:47,078]\u001b[0m Trial 5 finished with value: 0.12264590111910378 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:49,957]\u001b[0m Trial 6 finished with value: 0.09243372274824187 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:50,358]\u001b[0m Trial 7 finished with value: 0.08512936607448784 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:52,542]\u001b[0m Trial 8 finished with value: 0.08841732220359622 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:53,486]\u001b[0m Trial 9 finished with value: 0.08478629409983096 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:54,169]\u001b[0m Trial 10 finished with value: 0.08450225273770484 and parameters: {'iterations': 65, 'depth': 7, 'learning_rate': 0.29197113937431496, 'l2_leaf_reg': 13, 'border_count': 89}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:54,473]\u001b[0m Trial 11 finished with value: 0.08659505268451485 and parameters: {'iterations': 82, 'depth': 3, 'learning_rate': 0.2814556732624116, 'l2_leaf_reg': 7, 'border_count': 84}. Best is trial 4 with value: 0.08210290174814099.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:54,963]\u001b[0m Trial 12 finished with value: 0.0756890237601774 and parameters: {'iterations': 81, 'depth': 6, 'learning_rate': 0.24582538735273046, 'l2_leaf_reg': 6, 'border_count': 89}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:55,656]\u001b[0m Trial 13 finished with value: 0.0793971455482272 and parameters: {'iterations': 61, 'depth': 7, 'learning_rate': 0.18388800119214155, 'l2_leaf_reg': 4, 'border_count': 102}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:56,049]\u001b[0m Trial 14 finished with value: 0.0802920372411868 and parameters: {'iterations': 61, 'depth': 6, 'learning_rate': 0.10016796915552625, 'l2_leaf_reg': 4, 'border_count': 198}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:56,382]\u001b[0m Trial 15 finished with value: 0.0764160969935278 and parameters: {'iterations': 60, 'depth': 5, 'learning_rate': 0.1673715594499257, 'l2_leaf_reg': 2, 'border_count': 102}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:56,924]\u001b[0m Trial 16 finished with value: 0.07692030918345454 and parameters: {'iterations': 75, 'depth': 5, 'learning_rate': 0.08839536016087551, 'l2_leaf_reg': 2, 'border_count': 113}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:57,189]\u001b[0m Trial 17 finished with value: 0.07909028072794853 and parameters: {'iterations': 58, 'depth': 5, 'learning_rate': 0.16316578482338412, 'l2_leaf_reg': 5, 'border_count': 20}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:57,558]\u001b[0m Trial 18 finished with value: 0.2509372824914267 and parameters: {'iterations': 72, 'depth': 5, 'learning_rate': 0.010872618238215004, 'l2_leaf_reg': 6, 'border_count': 121}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:44:57,885]\u001b[0m Trial 19 finished with value: 0.0820621065364681 and parameters: {'iterations': 67, 'depth': 3, 'learning_rate': 0.2549097198712308, 'l2_leaf_reg': 14, 'border_count': 191}. Best is trial 12 with value: 0.0756890237601774.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:44:58,733]\u001b[0m A new study created in memory with name: no-name-eae228b9-de47-4b1d-b26c-f0aeb2e37e82\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:59,186]\u001b[0m Trial 0 finished with value: 0.3899207555165904 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.3899207555165904.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:59,719]\u001b[0m Trial 1 finished with value: 0.3888979909513515 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.3888979909513515.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:44:59,993]\u001b[0m Trial 2 finished with value: 0.39013965259548167 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3888979909513515.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:00,416]\u001b[0m Trial 3 finished with value: 0.389963073742722 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3888979909513515.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:00,608]\u001b[0m Trial 4 finished with value: 0.39013658426230086 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.3888979909513515.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:01,119]\u001b[0m Trial 5 finished with value: 0.38719464700607253 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.38719464700607253.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:01,369]\u001b[0m Trial 6 finished with value: 0.24055661413182025 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.24055661413182025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:01,568]\u001b[0m Trial 7 finished with value: 0.3899488519244086 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.24055661413182025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:01,820]\u001b[0m Trial 8 finished with value: 0.3882921472887407 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.24055661413182025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:02,153]\u001b[0m Trial 9 finished with value: 0.29681428693577283 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.24055661413182025.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:02,331]\u001b[0m Trial 10 finished with value: 0.21354612479845125 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.21354612479845125.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:02,513]\u001b[0m Trial 11 finished with value: 0.211621921433791 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 11 with value: 0.211621921433791.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:02,677]\u001b[0m Trial 12 finished with value: 0.21074269292415956 and parameters: {'booster': 'gbtree', 'lambda': 1.0473667575279822e-06, 'alpha': 0.627911977428592, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.9598391039242934, 'gamma': 5.72358962790807e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:03,035]\u001b[0m Trial 13 finished with value: 0.2483635851954534 and parameters: {'booster': 'gbtree', 'lambda': 5.881246930677318e-07, 'alpha': 0.8366851036962906, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.024051209580507136, 'gamma': 8.469877671193761e-06, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:03,193]\u001b[0m Trial 14 finished with value: 0.2510967797459022 and parameters: {'booster': 'gbtree', 'lambda': 6.856447376105159e-08, 'alpha': 0.055403032751810494, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.023556088834085213, 'gamma': 1.2953604208031761e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:03,483]\u001b[0m Trial 15 finished with value: 0.23688979479207006 and parameters: {'booster': 'gbtree', 'lambda': 8.714787395098991e-07, 'alpha': 0.018078665943274605, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11459388905949243, 'gamma': 0.0013771048319540476, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:03,636]\u001b[0m Trial 16 finished with value: 0.35987889860283956 and parameters: {'booster': 'gbtree', 'lambda': 4.357322664232636e-05, 'alpha': 0.06642780790393904, 'colsample_bytree': 0.5, 'max_depth': 1, 'eta': 0.0024058585824945074, 'gamma': 2.3382067099705154e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:03,901]\u001b[0m Trial 17 finished with value: 0.362649391911604 and parameters: {'booster': 'gbtree', 'lambda': 1.7962033878898724e-06, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.3, 'max_depth': 2, 'eta': 0.0022870721298695173, 'gamma': 6.392844482486238e-07, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:04,130]\u001b[0m Trial 18 finished with value: 0.22675343953445737 and parameters: {'booster': 'gbtree', 'lambda': 1.1351974686168584e-08, 'alpha': 0.933560085535797, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.7526891303709383, 'gamma': 0.00242396433969875, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:04,644]\u001b[0m Trial 19 finished with value: 0.39014157143184375 and parameters: {'booster': 'gbtree', 'lambda': 9.895887397004856e-08, 'alpha': 0.008449386600699363, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 2.1186614061329933e-08, 'gamma': 6.810458776822587e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.21074269292415956.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:04,778]\u001b[0m A new study created in memory with name: no-name-6e1eb316-4a28-4c64-8271-2fbd4f8cea85\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:05,154]\u001b[0m Trial 0 finished with value: 0.19777013858537804 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.19777013858537804.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:05,556]\u001b[0m Trial 1 finished with value: 0.18893463125640328 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:06,035]\u001b[0m Trial 2 finished with value: 0.20560651064928495 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:06,431]\u001b[0m Trial 3 finished with value: 0.19262927968124752 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:06,696]\u001b[0m Trial 4 finished with value: 0.20670629148779765 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:07,372]\u001b[0m Trial 5 finished with value: 0.2014739586676696 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:07,852]\u001b[0m Trial 6 finished with value: 0.189796210329074 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:08,022]\u001b[0m Trial 7 finished with value: 0.19133345184986125 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 1 with value: 0.18893463125640328.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:08,400]\u001b[0m Trial 8 finished with value: 0.18863962827211234 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 8 with value: 0.18863962827211234.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:09,027]\u001b[0m Trial 9 finished with value: 0.19752396964847954 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 8 with value: 0.18863962827211234.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:09,251]\u001b[0m Trial 10 finished with value: 0.18791352562252384 and parameters: {'n_estimators': 35, 'max_depth': 14, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.18791352562252384.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:09,455]\u001b[0m Trial 11 finished with value: 0.18861160820923453 and parameters: {'n_estimators': 37, 'max_depth': 14, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.18791352562252384.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:09,648]\u001b[0m Trial 12 finished with value: 0.18892756847247907 and parameters: {'n_estimators': 27, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.18791352562252384.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:09,874]\u001b[0m Trial 13 finished with value: 0.1876925248564203 and parameters: {'n_estimators': 38, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 13 with value: 0.1876925248564203.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:10,170]\u001b[0m Trial 14 finished with value: 0.18836963453331668 and parameters: {'n_estimators': 38, 'max_depth': 13, 'min_samples_split': 7, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 13 with value: 0.1876925248564203.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:10,518]\u001b[0m Trial 15 finished with value: 0.18730501226941051 and parameters: {'n_estimators': 58, 'max_depth': 11, 'min_samples_split': 10, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 15 with value: 0.18730501226941051.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:10,865]\u001b[0m Trial 16 finished with value: 0.19696002044361582 and parameters: {'n_estimators': 60, 'max_depth': 11, 'min_samples_split': 11, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 15 with value: 0.18730501226941051.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:11,162]\u001b[0m Trial 17 finished with value: 0.1889359783359802 and parameters: {'n_estimators': 52, 'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 15 with value: 0.18730501226941051.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:11,516]\u001b[0m Trial 18 finished with value: 0.1880345529130828 and parameters: {'n_estimators': 65, 'max_depth': 12, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 15 with value: 0.18730501226941051.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:11,870]\u001b[0m Trial 19 finished with value: 0.20143448943452502 and parameters: {'n_estimators': 45, 'max_depth': 16, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 15 with value: 0.18730501226941051.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:12,098]\u001b[0m A new study created in memory with name: no-name-513d677c-ffa6-4c16-a23f-df5c21334e75\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:12,417]\u001b[0m Trial 0 finished with value: 0.19611143911135553 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.19611143911135553.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:13,348]\u001b[0m Trial 1 finished with value: 0.21288942142266357 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.19611143911135553.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:13,809]\u001b[0m Trial 2 finished with value: 0.19685142170339634 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.19611143911135553.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:14,825]\u001b[0m Trial 3 finished with value: 0.19624099835700734 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.19611143911135553.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:15,647]\u001b[0m Trial 4 finished with value: 0.19433586668137612 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 4 with value: 0.19433586668137612.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:17,216]\u001b[0m Trial 5 finished with value: 0.222214719845796 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 4 with value: 0.19433586668137612.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:20,046]\u001b[0m Trial 6 finished with value: 0.19688336349569605 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 4 with value: 0.19433586668137612.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:20,630]\u001b[0m Trial 7 finished with value: 0.19198037100743823 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 7 with value: 0.19198037100743823.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:22,670]\u001b[0m Trial 8 finished with value: 0.19300022824547147 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 7 with value: 0.19198037100743823.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:23,790]\u001b[0m Trial 9 finished with value: 0.18854923180731395 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:24,208]\u001b[0m Trial 10 finished with value: 0.19520226839595808 and parameters: {'iterations': 65, 'depth': 6, 'learning_rate': 0.29197113937431496, 'l2_leaf_reg': 2, 'border_count': 97}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:24,676]\u001b[0m Trial 11 finished with value: 0.1951531935736266 and parameters: {'iterations': 100, 'depth': 3, 'learning_rate': 0.1809245764669805, 'l2_leaf_reg': 17, 'border_count': 192}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:25,178]\u001b[0m Trial 12 finished with value: 0.19099989560716205 and parameters: {'iterations': 99, 'depth': 5, 'learning_rate': 0.09660367687695079, 'l2_leaf_reg': 15, 'border_count': 20}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:25,603]\u001b[0m Trial 13 finished with value: 0.19566392764909937 and parameters: {'iterations': 89, 'depth': 7, 'learning_rate': 0.09471288184072323, 'l2_leaf_reg': 14, 'border_count': 21}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:26,001]\u001b[0m Trial 14 finished with value: 0.3202129276158218 and parameters: {'iterations': 80, 'depth': 5, 'learning_rate': 0.010499577329491583, 'l2_leaf_reg': 20, 'border_count': 29}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:27,045]\u001b[0m Trial 15 finished with value: 0.19325686303487377 and parameters: {'iterations': 95, 'depth': 7, 'learning_rate': 0.11195723892114703, 'l2_leaf_reg': 14, 'border_count': 98}. Best is trial 9 with value: 0.18854923180731395.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:27,448]\u001b[0m Trial 16 finished with value: 0.18730825199494117 and parameters: {'iterations': 62, 'depth': 5, 'learning_rate': 0.16546295551012585, 'l2_leaf_reg': 5, 'border_count': 100}. Best is trial 16 with value: 0.18730825199494117.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:28,453]\u001b[0m Trial 17 finished with value: 0.1951784998276915 and parameters: {'iterations': 61, 'depth': 8, 'learning_rate': 0.17101072333143386, 'l2_leaf_reg': 4, 'border_count': 103}. Best is trial 16 with value: 0.18730825199494117.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:28,853]\u001b[0m Trial 18 finished with value: 0.19419959328484251 and parameters: {'iterations': 56, 'depth': 5, 'learning_rate': 0.15441597419235686, 'l2_leaf_reg': 3, 'border_count': 77}. Best is trial 16 with value: 0.18730825199494117.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:29,158]\u001b[0m Trial 19 finished with value: 0.19855639563414998 and parameters: {'iterations': 72, 'depth': 3, 'learning_rate': 0.20306407244367108, 'l2_leaf_reg': 6, 'border_count': 119}. Best is trial 16 with value: 0.18730825199494117.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n",
      "\u001b[32m[I 2023-08-24 08:45:29,540]\u001b[0m A new study created in memory with name: no-name-7710386c-92a7-466d-8cad-e0393dfd4c98\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:30,025]\u001b[0m Trial 0 finished with value: 0.46393846343178463 and parameters: {'booster': 'gbtree', 'lambda': 0.0022378515338249295, 'alpha': 6.426696054575679e-07, 'colsample_bytree': 0.2, 'max_depth': 7, 'eta': 1.7293744113845677e-05, 'gamma': 0.07041793212210282, 'grow_policy': 'depthwise'}. Best is trial 0 with value: 0.46393846343178463.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:30,421]\u001b[0m Trial 1 finished with value: 0.46175946003011986 and parameters: {'booster': 'gbtree', 'lambda': 1.0140224196140568e-05, 'alpha': 2.3807211516435423e-07, 'colsample_bytree': 0.3, 'max_depth': 6, 'eta': 9.885460054886953e-05, 'gamma': 0.570299456766166, 'grow_policy': 'lossguide'}. Best is trial 1 with value: 0.46175946003011986.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:30,655]\u001b[0m Trial 2 finished with value: 0.46439298655009914 and parameters: {'booster': 'gbtree', 'lambda': 0.006179217180405472, 'alpha': 2.7859710429708224e-05, 'colsample_bytree': 0.4, 'max_depth': 3, 'eta': 1.310125888757471e-07, 'gamma': 0.04205144256137155, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.46175946003011986.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:31,018]\u001b[0m Trial 3 finished with value: 0.4640204333444518 and parameters: {'booster': 'gbtree', 'lambda': 0.00035534052545457454, 'alpha': 0.00014484588048983434, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 1.4184402234372041e-05, 'gamma': 0.4396217678359854, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.46175946003011986.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:31,336]\u001b[0m Trial 4 finished with value: 0.46438669842564373 and parameters: {'booster': 'gbtree', 'lambda': 0.00025105564101180947, 'alpha': 0.0022646417982425406, 'colsample_bytree': 0.2, 'max_depth': 2, 'eta': 3.983827205864752e-07, 'gamma': 0.0529164932364242, 'grow_policy': 'depthwise'}. Best is trial 1 with value: 0.46175946003011986.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:31,685]\u001b[0m Trial 5 finished with value: 0.457914071210402 and parameters: {'booster': 'gbtree', 'lambda': 1.3229463312970766e-07, 'alpha': 0.00027205869330600656, 'colsample_bytree': 0.2, 'max_depth': 6, 'eta': 0.00024730593374414626, 'gamma': 0.04315132058222182, 'grow_policy': 'depthwise'}. Best is trial 5 with value: 0.457914071210402.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:31,904]\u001b[0m Trial 6 finished with value: 0.1651365070099178 and parameters: {'booster': 'gbtree', 'lambda': 1.6953276497533082e-05, 'alpha': 0.0028690300020082107, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.28650629357258234, 'gamma': 0.01782241907316918, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.1651365070099178.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:32,094]\u001b[0m Trial 7 finished with value: 0.46403313902860904 and parameters: {'booster': 'gbtree', 'lambda': 0.012947821835818179, 'alpha': 0.000674173998310349, 'colsample_bytree': 0.5, 'max_depth': 2, 'eta': 1.1096205505673902e-05, 'gamma': 0.0006774602829554277, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.1651365070099178.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:32,441]\u001b[0m Trial 8 finished with value: 0.46063464292242623 and parameters: {'booster': 'gbtree', 'lambda': 1.0116890473626486e-08, 'alpha': 5.462556654656038e-05, 'colsample_bytree': 0.2, 'max_depth': 3, 'eta': 0.00014160378738991983, 'gamma': 2.2035198635149216e-08, 'grow_policy': 'lossguide'}. Best is trial 6 with value: 0.1651365070099178.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:32,882]\u001b[0m Trial 9 finished with value: 0.2393854328751477 and parameters: {'booster': 'gbtree', 'lambda': 0.3940296489573078, 'alpha': 6.562002185420414e-06, 'colsample_bytree': 0.4, 'max_depth': 5, 'eta': 0.010710320142215693, 'gamma': 7.789431011058871e-07, 'grow_policy': 'depthwise'}. Best is trial 6 with value: 0.1651365070099178.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:33,046]\u001b[0m Trial 10 finished with value: 0.13773944228882176 and parameters: {'booster': 'gbtree', 'lambda': 3.0397251965664963e-06, 'alpha': 0.29578903036658566, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.7357689563900227, 'gamma': 0.0002489918320173566, 'grow_policy': 'lossguide'}. Best is trial 10 with value: 0.13773944228882176.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:33,201]\u001b[0m Trial 11 finished with value: 0.13343601859724774 and parameters: {'booster': 'gbtree', 'lambda': 3.967941847843044e-06, 'alpha': 0.5113201712374126, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.8349418965551187, 'gamma': 0.00019375472509024016, 'grow_policy': 'lossguide'}. Best is trial 11 with value: 0.13343601859724774.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:33,350]\u001b[0m Trial 12 finished with value: 0.13046578338455797 and parameters: {'booster': 'gbtree', 'lambda': 1.0473667575279822e-06, 'alpha': 0.627911977428592, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.9598391039242934, 'gamma': 5.72358962790807e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:33,600]\u001b[0m Trial 13 finished with value: 0.18785895863358185 and parameters: {'booster': 'gbtree', 'lambda': 5.881246930677318e-07, 'alpha': 0.8366851036962906, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 0.024051209580507136, 'gamma': 8.469877671193761e-06, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:33,775]\u001b[0m Trial 14 finished with value: 0.18547299221333668 and parameters: {'booster': 'gbtree', 'lambda': 6.856447376105159e-08, 'alpha': 0.055403032751810494, 'colsample_bytree': 0.3, 'max_depth': 1, 'eta': 0.023556088834085213, 'gamma': 1.2953604208031761e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:34,148]\u001b[0m Trial 15 finished with value: 0.16952545321398102 and parameters: {'booster': 'gbtree', 'lambda': 8.714787395098991e-07, 'alpha': 0.018078665943274605, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.11459388905949243, 'gamma': 0.0013771048319540476, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:34,296]\u001b[0m Trial 16 finished with value: 0.3982555112864532 and parameters: {'booster': 'gbtree', 'lambda': 4.357322664232636e-05, 'alpha': 0.06642780790393904, 'colsample_bytree': 0.5, 'max_depth': 1, 'eta': 0.0024058585824945074, 'gamma': 2.3382067099705154e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:34,487]\u001b[0m Trial 17 finished with value: 0.4096444496848459 and parameters: {'booster': 'gbtree', 'lambda': 1.7962033878898724e-06, 'alpha': 2.805189234102766e-08, 'colsample_bytree': 0.3, 'max_depth': 2, 'eta': 0.0022870721298695173, 'gamma': 6.392844482486238e-07, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:34,676]\u001b[0m Trial 18 finished with value: 0.15504004123966705 and parameters: {'booster': 'gbtree', 'lambda': 1.1351974686168584e-08, 'alpha': 0.933560085535797, 'colsample_bytree': 0.3, 'max_depth': 4, 'eta': 0.7526891303709383, 'gamma': 0.00242396433969875, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:35,090]\u001b[0m Trial 19 finished with value: 0.46439715192977693 and parameters: {'booster': 'gbtree', 'lambda': 9.895887397004856e-08, 'alpha': 0.008449386600699363, 'colsample_bytree': 0.3, 'max_depth': 9, 'eta': 2.1186614061329933e-08, 'gamma': 6.810458776822587e-05, 'grow_policy': 'lossguide'}. Best is trial 12 with value: 0.13046578338455797.\u001b[0m\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n",
      "Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "\u001b[32m[I 2023-08-24 08:45:35,181]\u001b[0m A new study created in memory with name: no-name-b8acd1e2-fc85-4aa2-9290-98b770dc4af6\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:35,527]\u001b[0m Trial 0 finished with value: 0.10872296199537233 and parameters: {'n_estimators': 74, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 0 with value: 0.10872296199537233.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:35,886]\u001b[0m Trial 1 finished with value: 0.0929362800937404 and parameters: {'n_estimators': 52, 'max_depth': 18, 'min_samples_split': 9, 'min_samples_leaf': 6, 'max_features': 'auto'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:36,511]\u001b[0m Trial 2 finished with value: 0.1157221975687414 and parameters: {'n_estimators': 94, 'max_depth': 3, 'min_samples_split': 13, 'min_samples_leaf': 7, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:37,058]\u001b[0m Trial 3 finished with value: 0.10147476084734912 and parameters: {'n_estimators': 75, 'max_depth': 16, 'min_samples_split': 8, 'min_samples_leaf': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:37,357]\u001b[0m Trial 4 finished with value: 0.123980454693096 and parameters: {'n_estimators': 46, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:37,902]\u001b[0m Trial 5 finished with value: 0.11877879854860467 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 13, 'min_samples_leaf': 8, 'max_features': 'log2'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:38,389]\u001b[0m Trial 6 finished with value: 0.09997385236202533 and parameters: {'n_estimators': 79, 'max_depth': 9, 'min_samples_split': 9, 'min_samples_leaf': 8, 'max_features': 'auto'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:38,617]\u001b[0m Trial 7 finished with value: 0.1004604914493757 and parameters: {'n_estimators': 24, 'max_depth': 6, 'min_samples_split': 4, 'min_samples_leaf': 9, 'max_features': 'auto'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:39,120]\u001b[0m Trial 8 finished with value: 0.09825940829110237 and parameters: {'n_estimators': 64, 'max_depth': 20, 'min_samples_split': 3, 'min_samples_leaf': 7, 'max_features': 'auto'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:39,653]\u001b[0m Trial 9 finished with value: 0.10748777950442762 and parameters: {'n_estimators': 87, 'max_depth': 10, 'min_samples_split': 2, 'min_samples_leaf': 5, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.0929362800937404.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:39,949]\u001b[0m Trial 10 finished with value: 0.08950407993326694 and parameters: {'n_estimators': 45, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.08950407993326694.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:40,207]\u001b[0m Trial 11 finished with value: 0.0895184305675321 and parameters: {'n_estimators': 44, 'max_depth': 15, 'min_samples_split': 9, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 10 with value: 0.08950407993326694.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:40,529]\u001b[0m Trial 12 finished with value: 0.08882227669104677 and parameters: {'n_estimators': 37, 'max_depth': 14, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 12 with value: 0.08882227669104677.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:40,719]\u001b[0m Trial 13 finished with value: 0.08729185370658722 and parameters: {'n_estimators': 28, 'max_depth': 13, 'min_samples_split': 6, 'min_samples_leaf': 2, 'max_features': 'auto'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:40,939]\u001b[0m Trial 14 finished with value: 0.09170583125416903 and parameters: {'n_estimators': 23, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'auto'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:41,310]\u001b[0m Trial 15 finished with value: 0.08823644102697642 and parameters: {'n_estimators': 33, 'max_depth': 12, 'min_samples_split': 6, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:41,561]\u001b[0m Trial 16 finished with value: 0.10030682750184244 and parameters: {'n_estimators': 32, 'max_depth': 11, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'log2'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:41,825]\u001b[0m Trial 17 finished with value: 0.08834916860594662 and parameters: {'n_estimators': 32, 'max_depth': 13, 'min_samples_split': 5, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:42,184]\u001b[0m Trial 18 finished with value: 0.09387972563249113 and parameters: {'n_estimators': 59, 'max_depth': 9, 'min_samples_split': 11, 'min_samples_leaf': 3, 'max_features': 'auto'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:42,347]\u001b[0m Trial 19 finished with value: 0.10093200015663738 and parameters: {'n_estimators': 21, 'max_depth': 17, 'min_samples_split': 7, 'min_samples_leaf': 5, 'max_features': 'log2'}. Best is trial 13 with value: 0.08729185370658722.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:42,464]\u001b[0m A new study created in memory with name: no-name-bdc3e21a-4f0b-4bb8-a64d-d6b4f478760d\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:42,777]\u001b[0m Trial 0 finished with value: 0.10504291320021152 and parameters: {'iterations': 84, 'depth': 4, 'learning_rate': 0.259196634989793, 'l2_leaf_reg': 7, 'border_count': 54}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:43,634]\u001b[0m Trial 1 finished with value: 0.1469017826730544 and parameters: {'iterations': 50, 'depth': 9, 'learning_rate': 0.12737347728918552, 'l2_leaf_reg': 18, 'border_count': 147}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:44,078]\u001b[0m Trial 2 finished with value: 0.11065956812320958 and parameters: {'iterations': 75, 'depth': 6, 'learning_rate': 0.0599056842297737, 'l2_leaf_reg': 4, 'border_count': 238}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:45,000]\u001b[0m Trial 3 finished with value: 0.12312811321463722 and parameters: {'iterations': 51, 'depth': 9, 'learning_rate': 0.19499927427804942, 'l2_leaf_reg': 11, 'border_count': 248}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:45,643]\u001b[0m Trial 4 finished with value: 0.11142456941483925 and parameters: {'iterations': 83, 'depth': 8, 'learning_rate': 0.2199213379102454, 'l2_leaf_reg': 10, 'border_count': 65}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:47,034]\u001b[0m Trial 5 finished with value: 0.1591498195811921 and parameters: {'iterations': 90, 'depth': 10, 'learning_rate': 0.03968865940024177, 'l2_leaf_reg': 8, 'border_count': 52}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:49,882]\u001b[0m Trial 6 finished with value: 0.1239557960458394 and parameters: {'iterations': 92, 'depth': 10, 'learning_rate': 0.14060924421771326, 'l2_leaf_reg': 12, 'border_count': 142}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:50,366]\u001b[0m Trial 7 finished with value: 0.10827294659919534 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.24324268707755636, 'l2_leaf_reg': 16, 'border_count': 163}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:52,599]\u001b[0m Trial 8 finished with value: 0.12189803764428714 and parameters: {'iterations': 70, 'depth': 10, 'learning_rate': 0.22208264783973847, 'l2_leaf_reg': 8, 'border_count': 149}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:53,795]\u001b[0m Trial 9 finished with value: 0.11816890542896963 and parameters: {'iterations': 84, 'depth': 10, 'learning_rate': 0.18939505317980468, 'l2_leaf_reg': 2, 'border_count': 32}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:54,105]\u001b[0m Trial 10 finished with value: 0.11290913617092359 and parameters: {'iterations': 65, 'depth': 3, 'learning_rate': 0.29201626538499564, 'l2_leaf_reg': 5, 'border_count': 93}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:54,674]\u001b[0m Trial 11 finished with value: 0.10913714170311745 and parameters: {'iterations': 100, 'depth': 4, 'learning_rate': 0.2859950695985241, 'l2_leaf_reg': 17, 'border_count': 203}. Best is trial 0 with value: 0.10504291320021152.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:55,197]\u001b[0m Trial 12 finished with value: 0.09822651268582745 and parameters: {'iterations': 100, 'depth': 5, 'learning_rate': 0.25069849253398785, 'l2_leaf_reg': 15, 'border_count': 107}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:56,039]\u001b[0m Trial 13 finished with value: 0.10889122818374376 and parameters: {'iterations': 89, 'depth': 6, 'learning_rate': 0.2644800146520119, 'l2_leaf_reg': 14, 'border_count': 100}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:56,532]\u001b[0m Trial 14 finished with value: 0.10438335487214385 and parameters: {'iterations': 80, 'depth': 5, 'learning_rate': 0.17058306962590541, 'l2_leaf_reg': 20, 'border_count': 107}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:56,848]\u001b[0m Trial 15 finished with value: 0.11393524823687516 and parameters: {'iterations': 62, 'depth': 5, 'learning_rate': 0.10009190723969709, 'l2_leaf_reg': 19, 'border_count': 105}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:57,394]\u001b[0m Trial 16 finished with value: 0.11565880703186639 and parameters: {'iterations': 76, 'depth': 7, 'learning_rate': 0.19084962607197659, 'l2_leaf_reg': 20, 'border_count': 115}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:58,110]\u001b[0m Trial 17 finished with value: 0.10415663393242292 and parameters: {'iterations': 95, 'depth': 5, 'learning_rate': 0.1646456507106392, 'l2_leaf_reg': 15, 'border_count': 181}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:58,771]\u001b[0m Trial 18 finished with value: 0.11339543265005762 and parameters: {'iterations': 94, 'depth': 7, 'learning_rate': 0.0864407987326337, 'l2_leaf_reg': 14, 'border_count': 186}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "\u001b[32m[I 2023-08-24 08:45:59,419]\u001b[0m Trial 19 finished with value: 0.10624376882819002 and parameters: {'iterations': 97, 'depth': 5, 'learning_rate': 0.12289355874779709, 'l2_leaf_reg': 15, 'border_count': 210}. Best is trial 12 with value: 0.09822651268582745.\u001b[0m\n",
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n"
     ]
    }
   ],
   "source": [
    "SD = pd.DataFrame()\n",
    "\n",
    "\n",
    "time_series = f\"{version}//4_ML\"\n",
    "if not os.path.exists(time_series):\n",
    "    os.mkdir(time_series)\n",
    "\n",
    "for name in sheet_names[1:]:\n",
    "    dfr = pd.read_excel(f\"{version}//general.xlsx\", sheet_name=name)\n",
    "    fdhs = [i for i in list(dfr.columns) if i.endswith('_fdh')]\n",
    "\n",
    "    dfr2 = dfr[fdhs]\n",
    "\n",
    "    try:\n",
    "\n",
    "        nans = dfr2.isna().sum()\n",
    "        drop = list(nans[nans>60].index)\n",
    "        for i in drop:\n",
    "            fdhs.remove(i)\n",
    "\n",
    "        dfr2 = dfr[fdhs]\n",
    "\n",
    "        dfp = dfr.dropna()\n",
    "\n",
    "        fdhs.remove(f\"{name}_fdh\")\n",
    "        features = dfp[fdhs.copy()]\n",
    "        target = dfp[f\"{name}_fdh\"]\n",
    "\n",
    "        # Aplicar la función de limpieza a los nombres de las columnas\n",
    "        features.columns = [clean_column_name(col) for col in features.columns]\n",
    "        features = features.loc[:, ~features.columns.duplicated()]\n",
    "\n",
    "        X_train, X_test, Y_train, Y_test = train_test_split(features, target, test_size=0.1, random_state=2)\n",
    "\n",
    "        g = xgboost_regression(X_train, Y_train, X_test, Y_test, fperdida, trials_n)\n",
    "        rf = random_forest_regression(X_train, Y_train, X_test, Y_test, fperdida, trials_n)\n",
    "        cb = catboost_regression(X_train, Y_train, X_test, Y_test, fperdida, trials_n)\n",
    "\n",
    "        modelos = [cb, g, rf]\n",
    "        out = cb[\"CAT_MAE_OUT\"].iloc[0], g[\"XGB_MAE_OUT\"].iloc[0], rf[\"RF_MAE_OUT\"].iloc[0]\n",
    "\n",
    "        BM = mejor_modelo(modelos, out)\n",
    "        BM.to_excel(f\"{version}//4_ML//ML_{name}.xlsx\")\n",
    "\n",
    "        BM = BM.iloc[:-1, [0,1]]\n",
    "        BM.columns = [\"target\", \"value\"]\n",
    "        BM[\"source\"] = name\n",
    "\n",
    "        # BM\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(\"Ocurrió un error:\", str(e))\n",
    "        BM = pd.DataFrame()\n",
    "\n",
    "\n",
    "    SD = pd.concat([SD, BM])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n"
     ]
    }
   ],
   "source": [
    "SD.to_excel(f\"{version}//TABLE_GENERAL_ML_CAT.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graficas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tabla_total = pd.read_excel(f\"{version}/TABLA_GENERAL_ML.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = tabla_total.iloc[:-1, [0,3]]\n",
    "T.columns = [\"target\", \"value\"]\n",
    "T[\"target\"]  = T[\"target\"].apply(lambda x: x.replace(\"_fdh\", \"\"))\n",
    "T[\"source\"] = \"total\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "SD = pd.read_excel(f\"{version}//TABLE_GENERAL_ML_CAT.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "SD = pd.concat([T,SD])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "SD.source = [\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"Total\",\n",
    "    \"General\",\n",
    "    \"General\",\n",
    "    \"General\",\n",
    "    \"Defence\",\n",
    "    \"Order\",\n",
    "    \"Economy\",\n",
    "    \"Economy\",\n",
    "    \"Economy\",\n",
    "    \"Economy\",\n",
    "    \"Economy\",\n",
    "    \"Economy\",\n",
    "    \"Environment\",\n",
    "    \"Environment\",\n",
    "    \"Environment\",\n",
    "    \"Housing\",\n",
    "    \"Health\",\n",
    "    \"Health\",\n",
    "    \"Health\",\n",
    "    \"Health\",\n",
    "    \"Health\",\n",
    "    \"Health\",\n",
    "    \"Education\",\n",
    "    \"Education\",\n",
    "    \"Education\",\n",
    "    \"Education\",\n",
    "    \"Social\",\n",
    "    \"Social\",\n",
    "    \"Social\",\n",
    "    \"Social\",\n",
    "    \"Transportation\",\n",
    "    \"Transportation\",\n",
    "    \"Transportation\",\n",
    "    \"Transportation\",\n",
    "    \"Energy\",\n",
    "    \"Energy\",\n",
    "    \"Energy\",\n",
    "    \"Energy\",\n",
    "    \"Energy\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "SD.target = [\n",
    "    \"Defence\",\n",
    "    \"Education\",\n",
    "    \"Health\",\n",
    "    \"Social\",\n",
    "    \"General\",\n",
    "    \"Housing\",\n",
    "    \"Environment\",\n",
    "    \"Energy\",\n",
    "    \"Order\",\n",
    "    \"Transportation\",\n",
    "    \"Economy\",\n",
    "    \"Safe Drinking Water Usage\",\n",
    "    \"Population Living in Slums\",\n",
    "    \"Access to Electricity\",\n",
    "    \"Political Stability\",\n",
    "    \"Voice and Accountability\",\n",
    "    \"GDP Growth\",\n",
    "    \"GDP per Capita\",\n",
    "    \"Inflation Variability\",\n",
    "    \"Statistical Capacity\",\n",
    "    \"Growth Variability\",\n",
    "    \"Unemployment Rate\",\n",
    "    \"Forest Area\",\n",
    "    \"CO2 Emissions\",\n",
    "    \"Air Pollution Exposure\",\n",
    "    \"Improved Housing Rate\",\n",
    "    \"CVD, Cancer, Diabetes Survival\",\n",
    "    \"Maternal Mortality Risk\",\n",
    "    \"Life Expectancy\",\n",
    "    \"Infant Mortality Rate\",\n",
    "    \"Maternal Mortality Ratio\",\n",
    "    \"Stunting Prevalence\",\n",
    "    \"Secondary School Enrollment\",\n",
    "    \"Educational System Quality\",\n",
    "    \"Education Performance\",\n",
    "    \"Primary Completion Rate\",\n",
    "    \"Multidimensional Poverty\",\n",
    "    \"Informal Economy\",\n",
    "    \"National Poverty Rate\",\n",
    "    \"Gini Index\",\n",
    "    \"Logistics Performance\",\n",
    "    \"Road Traffic Mortality\",\n",
    "    \"Trade Efficiency\",\n",
    "    \"Infrastructure Quality\",\n",
    "    \"Energy Usage\",\n",
    "    \"Access to Electricity (Urban)\",\n",
    "    \"Fossil Fuel Consumption\",\n",
    "    \"Net Energy Imports\",\n",
    "    \"Renewable Energy Consumption\"\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.7' currently installed).\n"
     ]
    }
   ],
   "source": [
    "SD.to_excel(f\"{version}//TABLA_GRAFICA_PLOTLY.xlsx\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>target</th>\n",
       "      <th>value</th>\n",
       "      <th>source</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Defence</td>\n",
       "      <td>16.975993</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Education</td>\n",
       "      <td>11.341545</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Health</td>\n",
       "      <td>11.282190</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Social</td>\n",
       "      <td>11.186658</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>General</td>\n",
       "      <td>10.169585</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Housing</td>\n",
       "      <td>8.248822</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Environment</td>\n",
       "      <td>7.815834</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Energy</td>\n",
       "      <td>6.677748</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Order</td>\n",
       "      <td>6.317300</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>5.735271</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Economy</td>\n",
       "      <td>4.249055</td>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Safe Drinking Water Usage</td>\n",
       "      <td>45.228058</td>\n",
       "      <td>General</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population Living in Slums</td>\n",
       "      <td>41.745299</td>\n",
       "      <td>General</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Access to Electricity</td>\n",
       "      <td>13.026643</td>\n",
       "      <td>General</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Political Stability</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>Defence</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Voice and Accountability</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>Order</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GDP Growth</td>\n",
       "      <td>42.133311</td>\n",
       "      <td>Economy</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GDP per Capita</td>\n",
       "      <td>19.315668</td>\n",
       "      <td>Economy</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Inflation Variability</td>\n",
       "      <td>13.508541</td>\n",
       "      <td>Economy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Statistical Capacity</td>\n",
       "      <td>13.227654</td>\n",
       "      <td>Economy</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Growth Variability</td>\n",
       "      <td>7.761395</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Unemployment Rate</td>\n",
       "      <td>4.053431</td>\n",
       "      <td>Economy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Forest Area</td>\n",
       "      <td>93.392150</td>\n",
       "      <td>Environment</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>4.129420</td>\n",
       "      <td>Environment</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Air Pollution Exposure</td>\n",
       "      <td>2.478430</td>\n",
       "      <td>Environment</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Improved Housing Rate</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>Housing</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CVD, Cancer, Diabetes Survival</td>\n",
       "      <td>37.002514</td>\n",
       "      <td>Health</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Maternal Mortality Risk</td>\n",
       "      <td>29.294683</td>\n",
       "      <td>Health</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Life Expectancy</td>\n",
       "      <td>13.872990</td>\n",
       "      <td>Health</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Infant Mortality Rate</td>\n",
       "      <td>8.642700</td>\n",
       "      <td>Health</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maternal Mortality Ratio</td>\n",
       "      <td>7.155081</td>\n",
       "      <td>Health</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Stunting Prevalence</td>\n",
       "      <td>4.032032</td>\n",
       "      <td>Health</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Secondary School Enrollment</td>\n",
       "      <td>28.806657</td>\n",
       "      <td>Education</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Educational System Quality</td>\n",
       "      <td>24.868080</td>\n",
       "      <td>Education</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Education Performance</td>\n",
       "      <td>24.060683</td>\n",
       "      <td>Education</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Primary Completion Rate</td>\n",
       "      <td>22.264579</td>\n",
       "      <td>Education</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Multidimensional Poverty</td>\n",
       "      <td>85.603296</td>\n",
       "      <td>Social</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Informal Economy</td>\n",
       "      <td>8.221102</td>\n",
       "      <td>Social</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>National Poverty Rate</td>\n",
       "      <td>3.702765</td>\n",
       "      <td>Social</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Gini Index</td>\n",
       "      <td>2.472837</td>\n",
       "      <td>Social</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Logistics Performance</td>\n",
       "      <td>67.349554</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Road Traffic Mortality</td>\n",
       "      <td>17.799345</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Trade Efficiency</td>\n",
       "      <td>8.600393</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Infrastructure Quality</td>\n",
       "      <td>6.250707</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Energy Usage</td>\n",
       "      <td>71.299095</td>\n",
       "      <td>Energy</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Access to Electricity (Urban)</td>\n",
       "      <td>12.037866</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Fossil Fuel Consumption</td>\n",
       "      <td>7.267970</td>\n",
       "      <td>Energy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Net Energy Imports</td>\n",
       "      <td>6.283282</td>\n",
       "      <td>Energy</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Renewable Energy Consumption</td>\n",
       "      <td>3.111787</td>\n",
       "      <td>Energy</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            target       value          source  Unnamed: 0\n",
       "0                          Defence   16.975993           Total         NaN\n",
       "1                        Education   11.341545           Total         NaN\n",
       "2                           Health   11.282190           Total         NaN\n",
       "3                           Social   11.186658           Total         NaN\n",
       "4                          General   10.169585           Total         NaN\n",
       "5                          Housing    8.248822           Total         NaN\n",
       "6                      Environment    7.815834           Total         NaN\n",
       "7                           Energy    6.677748           Total         NaN\n",
       "8                            Order    6.317300           Total         NaN\n",
       "9                   Transportation    5.735271           Total         NaN\n",
       "10                         Economy    4.249055           Total         NaN\n",
       "0        Safe Drinking Water Usage   45.228058         General         0.0\n",
       "1       Population Living in Slums   41.745299         General         2.0\n",
       "2            Access to Electricity   13.026643         General         1.0\n",
       "3              Political Stability  100.000000         Defence         0.0\n",
       "4         Voice and Accountability  100.000000           Order         0.0\n",
       "5                       GDP Growth   42.133311         Economy         2.0\n",
       "6                   GDP per Capita   19.315668         Economy         3.0\n",
       "7            Inflation Variability   13.508541         Economy         1.0\n",
       "8             Statistical Capacity   13.227654         Economy         0.0\n",
       "9               Growth Variability    7.761395         Economy         5.0\n",
       "10               Unemployment Rate    4.053431         Economy         4.0\n",
       "11                     Forest Area   93.392150     Environment         0.0\n",
       "12                   CO2 Emissions    4.129420     Environment         1.0\n",
       "13          Air Pollution Exposure    2.478430     Environment         2.0\n",
       "14           Improved Housing Rate  100.000000         Housing         0.0\n",
       "15  CVD, Cancer, Diabetes Survival   37.002514          Health         3.0\n",
       "16         Maternal Mortality Risk   29.294683          Health         2.0\n",
       "17                 Life Expectancy   13.872990          Health         0.0\n",
       "18           Infant Mortality Rate    8.642700          Health         1.0\n",
       "19        Maternal Mortality Ratio    7.155081          Health         4.0\n",
       "20             Stunting Prevalence    4.032032          Health         5.0\n",
       "21     Secondary School Enrollment   28.806657       Education         0.0\n",
       "22      Educational System Quality   24.868080       Education         2.0\n",
       "23           Education Performance   24.060683       Education         3.0\n",
       "24         Primary Completion Rate   22.264579       Education         1.0\n",
       "25        Multidimensional Poverty   85.603296          Social         1.0\n",
       "26                Informal Economy    8.221102          Social         2.0\n",
       "27           National Poverty Rate    3.702765          Social         3.0\n",
       "28                      Gini Index    2.472837          Social         0.0\n",
       "29           Logistics Performance   67.349554  Transportation         1.0\n",
       "30          Road Traffic Mortality   17.799345  Transportation         2.0\n",
       "31                Trade Efficiency    8.600393  Transportation         3.0\n",
       "32          Infrastructure Quality    6.250707  Transportation         0.0\n",
       "33                    Energy Usage   71.299095          Energy         3.0\n",
       "34   Access to Electricity (Urban)   12.037866          Energy         1.0\n",
       "35         Fossil Fuel Consumption    7.267970          Energy         4.0\n",
       "36              Net Energy Imports    6.283282          Energy         2.0\n",
       "37    Renewable Energy Consumption    3.111787          Energy         0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
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        },
        "width": 1500
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     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Asumiendo que tu DataFrame 'SD' ya está definido anteriormente...\n",
    "\n",
    "# Crear etiquetas para cada nodo único colocando primero las etiquetas 'target'\n",
    "labels = list(SD['target'].unique()) + list(SD['source'].unique())\n",
    "\n",
    "# Crear listas de fuentes y objetivos\n",
    "sources = SD['source'].apply(lambda x: labels.index(x))\n",
    "targets = SD['target'].apply(lambda x: labels.index(x))\n",
    "\n",
    "# Crear lista de valores\n",
    "values = SD['value']\n",
    "\n",
    "# Definir gamas de colores para las categorías.\n",
    "# Obtener categorías únicas desde la columna 'source'\n",
    "unique_sources = SD['source'].unique()\n",
    "\n",
    "\n",
    "# Definir los datos para el diagrama de Sankey\n",
    "sankey_data = dict(\n",
    "    label = labels,\n",
    "    source = sources, \n",
    "    target = targets,\n",
    "    value = values\n",
    ")\n",
    "\n",
    "# Crear el objeto de figura y añadir el diagrama de Sankey\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node = dict(\n",
    "      pad = 30,\n",
    "      thickness = 70,\n",
    "      line = dict(color = \"black\", width = 1),\n",
    "      label = sankey_data['label']\n",
    "      # color = colors\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = sankey_data['source'],\n",
    "      target = sankey_data['target'],\n",
    "      value = sankey_data['value']\n",
    "    )\n",
    ")])\n",
    "\n",
    "# Ajustar las dimensiones del layout\n",
    "fig.update_layout(\n",
    "    width=1500,\n",
    "    height=1200   \n",
    ")\n",
    "\n",
    "# Mostrar el diagrama\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~jorgealbertoguerra18/3/'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import chart_studio\n",
    "chart_studio.tools.set_credentials_file(username='jorgealbertoguerra18', api_key='uosUmZZXDGL6o1Ahv3NE')\n",
    "\n",
    "import chart_studio.plotly as py\n",
    "import plotly.express as px\n",
    "\n",
    "# Extract the main categories from SD DataFrame\n",
    "main_categories = SD[SD[\"source\"] == \"Total\"]\n",
    "\n",
    "# Extract the sub-categories from SD DataFrame\n",
    "sub_categories = SD[SD[\"source\"] != \"Total\"]\n",
    "\n",
    "# Create a total sum for all categories\n",
    "all_categories_value = SD[\"value\"].sum()\n",
    "\n",
    "# Create lists for 'character', 'parent', and 'value' for Plotly sunburst\n",
    "character = [\"All Categories\"] + main_categories[\"target\"].tolist() + sub_categories[\"target\"].tolist()\n",
    "parent = [\"\"] + [\"All Categories\"]*len(main_categories) + sub_categories[\"source\"].tolist()\n",
    "value = [all_categories_value] + main_categories[\"value\"].tolist() + sub_categories[\"value\"].tolist()\n",
    "\n",
    "# Create the DataFrame for Plotly\n",
    "data = {\n",
    "    'character': character,\n",
    "    'parent': parent,\n",
    "    'value': value\n",
    "}\n",
    "\n",
    "# Create the sunburst chart\n",
    "fig = px.sunburst(data, names='character', parents='parent', values='value', width=1100, height=1200)\n",
    "\n",
    "# Upload and display the chart on Plotly platform\n",
    "py.plot(fig, filename='sunburst_chart', auto_open=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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