test2_scaled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.options.display.max_columns = 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17379, 17)\n"
     ]
    },
    {
     "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/hour.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17379, 11)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.drop(columns=['instant','dteday','casual','registered','yr','atemp'])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
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       "      <td>0.81</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit  temp   hum  \\\n",
       "0       1     1   0        0        6           0           1  0.24  0.81   \n",
       "1       1     1   1        0        6           0           1  0.22  0.80   \n",
       "2       1     1   2        0        6           0           1  0.22  0.80   \n",
       "3       1     1   3        0        6           0           1  0.24  0.75   \n",
       "4       1     1   4        0        6           0           1  0.24  0.75   \n",
       "\n",
       "   windspeed  cnt  \n",
       "0        0.0   16  \n",
       "1        0.0   40  \n",
       "2        0.0   32  \n",
       "3        0.0   13  \n",
       "4        0.0    1  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns=['season','month','hour','holiday','weekday','working','weathersit','temp','humidity','windspeed','count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>working</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
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       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
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       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  month  hour  holiday  weekday  working  weathersit  temp  humidity  \\\n",
       "0       1      1     0        0        6        0           1  0.24      0.81   \n",
       "1       1      1     1        0        6        0           1  0.22      0.80   \n",
       "2       1      1     2        0        6        0           1  0.22      0.80   \n",
       "3       1      1     3        0        6        0           1  0.24      0.75   \n",
       "4       1      1     4        0        6        0           1  0.24      0.75   \n",
       "\n",
       "   windspeed  count  \n",
       "0        0.0     16  \n",
       "1        0.0     40  \n",
       "2        0.0     32  \n",
       "3        0.0     13  \n",
       "4        0.0      1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>12.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>12.000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>23.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>working</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.6600</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.2537</td>\n",
       "      <td>0.8507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0</td>\n",
       "      <td>189.463088</td>\n",
       "      <td>181.387599</td>\n",
       "      <td>1.00</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>142.000</td>\n",
       "      <td>281.0000</td>\n",
       "      <td>977.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count        mean         std   min      25%      50%       75%  \\\n",
       "season      17379.0    2.501640    1.106918  1.00   2.0000    3.000    3.0000   \n",
       "month       17379.0    6.537775    3.438776  1.00   4.0000    7.000   10.0000   \n",
       "hour        17379.0   11.546752    6.914405  0.00   6.0000   12.000   18.0000   \n",
       "holiday     17379.0    0.028770    0.167165  0.00   0.0000    0.000    0.0000   \n",
       "weekday     17379.0    3.003683    2.005771  0.00   1.0000    3.000    5.0000   \n",
       "working     17379.0    0.682721    0.465431  0.00   0.0000    1.000    1.0000   \n",
       "weathersit  17379.0    1.425283    0.639357  1.00   1.0000    1.000    2.0000   \n",
       "temp        17379.0    0.496987    0.192556  0.02   0.3400    0.500    0.6600   \n",
       "humidity    17379.0    0.627229    0.192930  0.00   0.4800    0.630    0.7800   \n",
       "windspeed   17379.0    0.190098    0.122340  0.00   0.1045    0.194    0.2537   \n",
       "count       17379.0  189.463088  181.387599  1.00  40.0000  142.000  281.0000   \n",
       "\n",
       "                 max  \n",
       "season        4.0000  \n",
       "month        12.0000  \n",
       "hour         23.0000  \n",
       "holiday       1.0000  \n",
       "weekday       6.0000  \n",
       "working       1.0000  \n",
       "weathersit    4.0000  \n",
       "temp          1.0000  \n",
       "humidity      1.0000  \n",
       "windspeed     0.8507  \n",
       "count       977.0000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols = ['season','month','holiday','weekday','working','weathersit','hour']\n",
    "numerical_cols = ['temp','humidity','windspeed','count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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279u3r/r27Wu+btasmfbv36/58+ebgeiDDz7QoEGDzEOYzZo1U0JCgmbMmKG4uLjLur8Iasbl3gRvwoQJeuutt3TmzBndeuutSkpKqrDf2bNnlZiYaHcTMiu4nNMTc+fO1enTp5WQkFDD1aE6OMNn52oHqKekpCg1NVW9evWSt7e3UlJSNHbsWP31r3+1C9/ff/+9ioqKdPLkSZ0+fdq8z9UfLwaoLGfYj7UZp8yq0ZWmdUnKzc2Vv7+/+bqwsFCenp52fby8vPTf//5Xhw8frtJ6UXlXchO8cePGaefOnVq3bp1cXV31yCOPVDgOZOXKlTp9+vRVj9+obS7n9MSGDRuUkpIiDw8Pubm5qUWLFpJ+vzKlbH+VHe7/o7LXPJPLeTjLZ2f79u3q1KmTeTVdfHy8OnXqpEmTJl3W+z08PPTPf/5Tf/rTn9S2bVu99NJLGjt2rBYuXGjXr3///urUqZM+//xzffXVV3brvBrOsh9rM44QVaMrvW3+wYMHNXfuXLs7J0dFRWns2LEaOnSoevXqpYMHD5qHyU+cOHHR89eoOZdzO/oyDRs2VMOGDXXDDTeoTZs2CgsL09atWxUREWG3zPfee0933XWXeZrHKi7n9MScOXM0bdo0s/348eOKiorSRx99pG7dukn6/U7Rzz33nIqLi83TAcnJyWrVqtU1cbrsWuEsn52rHaDeuXNnbd269YrfV1WcZT/WZgSianK5VyWUOXbsmPr27av7779fw4cPN+cPHz5cP/30k+666y4VFxfLx8dHTz31lKZMmVLutuZwnMqOMbjQreYPHTqkjRs36rPPPqv6Yp3A1Z6e+OMffen3gaCS1Lx5c/M5XA8//LBeeOEFxcbGasKECdqzZ4/efPNNvfHGG9W5abhCfHaqBvvx6hGIqsmVpPXjx4+rV69e6t69e7nDqzabTTNmzNDLL7+szMxMNWrUSOvXr5f0+3gi1Jyr/RJPTU1VWlqabrvtNjVo0EA//fSTJk6cqObNm5f7ZbZ48WKFhIRccoxabVVV98+5GF9fX61bt05xcXHq0qWLGjZsqEmTJl0zl9zXJrXhsxO/clMlt67qzPrzny7aXhv2Y21GIKoml5vWjx07pl69eqlLly5asmTJBY/6uLq66rrrrpMkffjhh4qIiFCjRo2qbwNQztV+idetW1crVqzQ5MmTVVBQoJCQEPXt21fPP/+83T1KSktLtXTpUg0dOlSurq5Vvh3OoKrun1OmSZMmFS6vffv22rJly5WWhyrGZ6dqsB+rF4HoKlxtWj927Jh69uyp8PBwvfbaa/rll1/MvmWDPn/99Vd9/PHH6tmzp86ePaslS5bo3//+tzZtcvyvGau52i/xdu3aacOGDZd8n4uLi44ePXql5QFOi89O1WA/Vi8C0VW42rSenJysgwcP6uDBg+a4hzJ//Ee/bNkyPfPMMzIMQxEREfrqq690yy23VM1GADVsVsLfHF2C4qe/4+gSADgZAtFVuNq0PnToUA0dOvSi72nYsKFSUlIqUR2q2hePXHhgYk3p//4SR5cAXJHNSVMcXYLuuMvxNVSFh8cnOroELZ8Z7egSqg2XKQEAAMvjCNFVIK0DAHBt4AgRAACwPAIRAACwPIcGos2bN2vgwIEKDQ2VzWbTqlWrLtj38ccfl81m0+zZs+3mnzx5UtHR0fLx8ZGfn59iY2OVn59v1+e7777T7bffLk9PT4WFhWnmzJnVsDUAAKC2cmggKigoUIcOHTRv3ryL9lu5cqW2bt2q0NDQcm3R0dHau3evkpOTlZSUpM2bN9vdiTYvL099+vRReHi40tPT9eqrr2rKlCnl7ggNAACsy6GDqvv163fJ24IfO3ZMo0eP1tq1azVgwAC7tn379mnNmjVKS0tT165dJUlz585V//799dprryk0NFSJiYkqKirS4sWL5e7urrZt2yojI0OzZs3iFv4AAECSk48hKi0t1ZAhQzRu3Di1bdu2XHtKSor8/PzMMCRJkZGRcnFxUWpqqtnnjjvukLu7u9knKipK+/fv16lTpypcb2FhofLy8uwmAABw7XLqQDRjxgy5ubnpySefrLA9MzNTgYGBdvPc3Nzk7++vzMxMs09QUJBdn7LXZX3ON336dPn6+ppTWFjY1W4KAABwYk4biNLT0/Xmm29q6dKlstlsNbruhIQE5ebmmlNtfqbLpQauT5kyRa1bt1a9evXUoEEDRUZGmkfXzldYWKiOHTvKZrMpIyPDnH/27FkNHTpU7dq1k5ubmwYNGlR9GwQAQDVw2kC0ZcsWZWdnq3HjxnJzc5Obm5sOHz6sp59+Wk2aNJH0+wNQs7Oz7d537tw5nTx50nw4anBwsLKysuz6lL0u63M+Dw8P+fj42E211aUGrt9www166623tHv3bn399ddq0qSJ+vTpY/eg2TLjx4+vcGB7SUmJvLy89OSTTyoyMrLKtwEAgOrmtHeqHjJkSLkv16ioKA0ZMkSPPvr7M6UiIiKUk5Oj9PR0denSRZK0YcMGlZaWqlu3bmaf5557TsXFxapTp46k3x+q2qpVKzVo0KAGt8gxLjVw/eGHH7Z7PWvWLC1atEjfffedevfubc5fvXq11q1bp08++USrV6+2e0+9evU0f/58SdI333yjnJycqtsAAABqgEMDUX5+vg4ePGi+PnTokDIyMuTv76/GjRsrICDArn+dOnUUHBysVq1aSZLatGmjvn37avjw4VqwYIGKi4s1atQoDR482DyS8fDDD+uFF15QbGysJkyYoD179ujNN9/UG2+8UXMbWksUFRVp4cKF8vX1VYcOHcz5WVlZGj58uFatWqW6des6sEIAAKqHQ0+Zbd++XZ06dVKnTp0kSfHx8erUqZMmTZp02ctITExU69at1bt3b/Xv31+33Xab3T2GfH19tW7dOh06dEhdunTR008/rUmTJnHJ/R8kJSWpfv368vT01BtvvKHk5GQ1bNhQkmQYhoYOHarHH3/c7mo+AACuJQ49QtSzZ08ZhnHZ/X/++edy8/z9/bV8+fKLvq99+/basmXLlZZnGb169VJGRoZ+/fVXvfvuu3rggQeUmpqqwMBAzZ07V6dPn1ZCQoKjywQAoNo47aBq1Jx69eqpRYsWuvXWW7Vo0SK5ublp0aJFkn4fk5WSkiIPDw+5ubmpRYsWkqSuXbsqJibGkWUDAFBlnHZQNRyntLRUhYWFkqQ5c+Zo2rRpZtvx48cVFRWljz76yBy4DgBAbUcgusZdbOB6QECAXnrpJd19990KCQnRr7/+qnnz5unYsWO6//77JUmNGze2W179+vUlSc2bN9f1119vzv/+++9VVFSkkydP6vTp0+Z9ijp27Fi9GwgAQBUgEF3jtm/frl69epmv4+PjJUkxMTFasGCBfvjhBy1btky//vqrAgICdPPNN2vLli0VPirlYvr376/Dhw+br8sGyl/JGDEAAByFQHSNu9TA9RUrVlzR8po0aVLh8ioa8A4AQG3BoGoAAGB5HCG6xqXPfMzRJajL+PccXQIAABfFESIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5Dg1Emzdv1sCBAxUaGiqbzaZVq1aZbcXFxZowYYLatWunevXqKTQ0VI888oiOHz9ut4yTJ08qOjpaPj4+8vPzU2xsrPLz8+36fPfdd7r99tvl6empsLAwzZw5syY2DwAA1BIODUQFBQXq0KGD5s2bV67tzJkz2rFjhyZOnKgdO3ZoxYoV2r9/v+6++267ftHR0dq7d6+Sk5OVlJSkzZs3a8SIEWZ7Xl6e+vTpo/DwcKWnp+vVV1/VlClTtHDhwmrfPgAAUDu4OXLl/fr1U79+/Sps8/X1VXJyst28t956S7fccouOHDmixo0ba9++fVqzZo3S0tLUtWtXSdLcuXPVv39/vfbaawoNDVViYqKKioq0ePFiubu7q23btsrIyNCsWbPsghMAALCuWjWGKDc3VzabTX5+fpKklJQU+fn5mWFIkiIjI+Xi4qLU1FSzzx133CF3d3ezT1RUlPbv369Tp05VuJ7CwkLl5eXZTQAA4NpVawLR2bNnNWHCBD300EPy8fGRJGVmZiowMNCun5ubm/z9/ZWZmWn2CQoKsutT9rqsz/mmT58uX19fcwoLC6vqzQEAAE6kVgSi4uJiPfDAAzIMQ/Pnz6/29SUkJCg3N9ecjh49Wu3rBAAAjuPQMUSXoywMHT58WBs2bDCPDklScHCwsrOz7fqfO3dOJ0+eVHBwsNknKyvLrk/Z67I+5/Pw8JCHh0dVbgYAAHBiTn2EqCwMHThwQF9++aUCAgLs2iMiIpSTk6P09HRz3oYNG1RaWqpu3bqZfTZv3qzi4mKzT3Jyslq1aqUGDRrUzIYAAACn5tBAlJ+fr4yMDGVkZEiSDh06pIyMDB05ckTFxcX6y1/+ou3btysxMVElJSXKzMxUZmamioqKJElt2rRR3759NXz4cG3btk3ffPONRo0apcGDBys0NFSS9PDDD8vd3V2xsbHau3evPvroI7355puKj4931GYDAAAn49BTZtu3b1evXr3M12UhJSYmRlOmTNFnn30mSerYsaPd+zZu3KiePXtKkhITEzVq1Cj17t1bLi4uuu+++zRnzhyzr6+vr9atW6e4uDh16dJFDRs21KRJk7jkHgAAmBwaiHr27CnDMC7YfrG2Mv7+/lq+fPlF+7Rv315btmy54voAAIA1OPUYIgAAgJpAIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbn0EC0efNmDRw4UKGhobLZbFq1apVdu2EYmjRpkkJCQuTl5aXIyEgdOHDArs/JkycVHR0tHx8f+fn5KTY2Vvn5+XZ9vvvuO91+++3y9PRUWFiYZs6cWd2bBgAAahGHBqKCggJ16NBB8+bNq7B95syZmjNnjhYsWKDU1FTVq1dPUVFROnv2rNknOjpae/fuVXJyspKSkrR582aNGDHCbM/Ly1OfPn0UHh6u9PR0vfrqq5oyZYoWLlxY7dsHAABqBzdHrrxfv37q169fhW2GYWj27Nl6/vnndc8990iS3n//fQUFBWnVqlUaPHiw9u3bpzVr1igtLU1du3aVJM2dO1f9+/fXa6+9ptDQUCUmJqqoqEiLFy+Wu7u72rZtq4yMDM2aNcsuOP1RYWGhCgsLzdd5eXlVvOUAAMCZOO0YokOHDikzM1ORkZHmPF9fX3Xr1k0pKSmSpJSUFPn5+ZlhSJIiIyPl4uKi1NRUs88dd9whd3d3s09UVJT279+vU6dOVbju6dOny9fX15zCwsKqYxMBAICTcNpAlJmZKUkKCgqymx8UFGS2ZWZmKjAw0K7dzc1N/v7+dn0qWsYf13G+hIQE5ebmmtPRo0evfoMAAIDTcugpM2fl4eEhDw8PR5cBAABqiNMeIQoODpYkZWVl2c3Pysoy24KDg5WdnW3Xfu7cOZ08edKuT0XL+OM6AACAtTltIGratKmCg4O1fv16c15eXp5SU1MVEREhSYqIiFBOTo7S09PNPhs2bFBpaam6detm9tm8ebOKi4vNPsnJyWrVqpUaNGhQQ1sDAACcmUMDUX5+vjIyMpSRkSHp94HUGRkZOnLkiGw2m8aMGaNp06bps88+0+7du/XII48oNDRUgwYNkiS1adNGffv21fDhw7Vt2zZ98803GjVqlAYPHqzQ0FBJ0sMPPyx3d3fFxsZq7969+uijj/Tmm28qPj7eQVsNAACcjUPHEG3fvl29evUyX5eFlJiYGC1dulTjx49XQUGBRowYoZycHN12221as2aNPD09zfckJiZq1KhR6t27t1xcXHTfffdpzpw5Zruvr6/WrVunuLg4denSRQ0bNtSkSZMueMk9AACwHocGop49e8owjAu222w2TZ06VVOnTr1gH39/fy1fvvyi62nfvr22bNlS6ToBAMC1zWnHEAEAANQUAhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALC8SgWiO++8Uzk5OeXm5+Xl6c4777zamgAAAGpUpQLRV199paKionLzz549qy1btlx1UQAAADXJ7Uo6f/fdd+Z/f//998rMzDRfl5SUaM2aNbruuuuqrjoAAIAacEWBqGPHjrLZbLLZbBWeGvPy8tLcuXOrrDgAAICacEWB6NChQzIMQ82aNdO2bdvUqFEjs83d3V2BgYFydXWt8iIBAACq0xUFovDwcElSaWlptRQDAADgCFcUiP7owIED2rhxo7Kzs8sFpEmTJl11YQAAADWlUoHo3Xff1ciRI9WwYUMFBwfLZrOZbTabjUAEAABqlUoFomnTpumll17ShAkTqroeAACAGlep+xCdOnVK999/f1XXUk5JSYkmTpyopk2bysvLS82bN9eLL74owzDMPoZhaNKkSQoJCZGXl5ciIyN14MABu+WcPHlS0dHR8vHxkZ+fn2JjY5Wfn1/t9QMAgNqhUoHo/vvv17p166q6lnJmzJih+fPn66233tK+ffs0Y8YMzZw50+7S/pkzZ2rOnDlasGCBUlNTVa9ePUVFRens2bNmn+joaO3du1fJyclKSkrS5s2bNWLEiGqvHwAA1A6VOmXWokULTZw4UVu3blW7du1Up04du/Ynn3yySor79ttvdc8992jAgAGSpCZNmujDDz/Utm3bJP1+dGj27Nl6/vnndc8990iS3n//fQUFBWnVqlUaPHiw9u3bpzVr1igtLU1du3aVJM2dO1f9+/fXa6+9ptDQ0HLrLSwsVGFhofk6Ly+vSrYHAAA4p0oFooULF6p+/fratGmTNm3aZNdms9mqLBB1795dCxcu1I8//qgbbrhBu3bt0tdff61Zs2ZJ+v2+SJmZmYqMjDTf4+vrq27duiklJUWDBw9WSkqK/Pz8zDAkSZGRkXJxcVFqaqr+/Oc/l1vv9OnT9cILL1TJNgAAAOdXqUB06NChqq6jQs8++6zy8vLUunVrubq6qqSkRC+99JKio6MlyXx0SFBQkN37goKCzLbMzEwFBgbatbu5ucnf39/u0SN/lJCQoPj4ePN1Xl6ewsLCqmy7AACAc6n0fYhqwr/+9S8lJiZq+fLlatu2rTIyMjRmzBiFhoYqJiam2tbr4eEhDw+Pals+AABwLpUKRMOGDbto++LFiytVzPnGjRunZ599VoMHD5YktWvXTocPH9b06dMVExOj4OBgSVJWVpZCQkLM92VlZaljx46SpODgYGVnZ9st99y5czp58qT5fgAAYG2Vvuz+j1N2drY2bNigFStWKCcnp8qKO3PmjFxc7Et0dXU174zdtGlTBQcHa/369WZ7Xl6eUlNTFRERIUmKiIhQTk6O0tPTzT4bNmxQaWmpunXrVmW1AgCA2qtSR4hWrlxZbl5paalGjhyp5s2bX3VRZQYOHKiXXnpJjRs3Vtu2bbVz507NmjXLPEJls9k0ZswYTZs2TS1btlTTpk01ceJEhYaGatCgQZKkNm3aqG/fvho+fLgWLFig4uJijRo1SoMHD67wCjMAAGA9VTaGyMXFRfHx8erZs6fGjx9fJcucO3euJk6cqCeeeELZ2dkKDQ3V3/72N7tHg4wfP14FBQUaMWKEcnJydNttt2nNmjXy9PQ0+yQmJmrUqFHq3bu3XFxcdN9992nOnDlVUiMAAKj9qnRQ9U8//aRz585V2fK8vb01e/ZszZ49+4J9bDabpk6dqqlTp16wj7+/v5YvX15ldQEAgGtLpQLRHy9Jl36/QeKJEyf0n//8p1qv/gIAAKgOlQpEO3futHvt4uKiRo0a6fXXX7/kFWgAAADOplKBaOPGjVVdBwAAgMNc1RiiX375Rfv375cktWrVSo0aNaqSogAAAGpSpe5DVFBQoGHDhikkJER33HGH7rjjDoWGhio2NlZnzpyp6hoBAACqVaUCUXx8vDZt2qTPP/9cOTk5ysnJ0aeffqpNmzbp6aefruoaAQAAqlWlTpl98skn+vjjj9WzZ09zXv/+/eXl5aUHHnhA8+fPr6r6AAAAql2ljhCdOXOm3BPmJSkwMJBTZgAAoNapVCCKiIjQ5MmTdfbsWXPeb7/9phdeeMF8hhgAAEBtUalTZrNnz1bfvn11/fXXq0OHDpKkXbt2ycPDQ+vWravSAgEAAKpbpQJRu3btdODAASUmJuqHH36QJD300EOKjo6Wl5dXlRYIAABQ3SoViKZPn66goCANHz7cbv7ixYv1yy+/aMKECVVSHAAAQE2o1Biid955R61bty43v23btlqwYMFVFwUAAFCTKhWIMjMzFRISUm5+o0aNdOLEiasuCgAAoCZVKhCFhYXpm2++KTf/m2++UWho6FUXBQAAUJMqNYZo+PDhGjNmjIqLi3XnnXdKktavX6/x48dzp2oAAFDrVCoQjRs3Tv/7v/+rJ554QkVFRZIkT09PTZgwQQkJCVVaIAAAQHWrVCCy2WyaMWOGJk6cqH379snLy0stW7aUh4dHVdcHAABQ7SoViMrUr19fN998c1XVAgAA4BCVGlQNAABwLSEQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy3P6QHTs2DH99a9/VUBAgLy8vNSuXTtt377dbDcMQ5MmTVJISIi8vLwUGRmpAwcO2C3j5MmTio6Olo+Pj/z8/BQbG6v8/Pya3hQAAOCknDoQnTp1Sj169FCdOnW0evVqff/993r99dfVoEEDs8/MmTM1Z84cLViwQKmpqapXr56ioqJ09uxZs090dLT27t2r5ORkJSUlafPmzRoxYoQjNgkAADghN0cXcDEzZsxQWFiYlixZYs5r2rSp+d+GYWj27Nl6/vnndc8990iS3n//fQUFBWnVqlUaPHiw9u3bpzVr1igtLU1du3aVJM2dO1f9+/fXa6+9ptDQ0JrdKAAA4HSc+gjRZ599pq5du+r+++9XYGCgOnXqpHfffddsP3TokDIzMxUZGWnO8/X1Vbdu3ZSSkiJJSklJkZ+fnxmGJCkyMlIuLi5KTU2tcL2FhYXKy8uzmwAAwLXLqQPR//zP/2j+/Plq2bKl1q5dq5EjR+rJJ5/UsmXLJEmZmZmSpKCgILv3BQUFmW2ZmZkKDAy0a3dzc5O/v7/Z53zTp0+Xr6+vOYWFhVX1pgEAACfi1IGotLRUnTt31ssvv6xOnTppxIgRGj58uBYsWFCt601ISFBubq45HT16tFrXBwAAHMupA1FISIhuvPFGu3lt2rTRkSNHJEnBwcGSpKysLLs+WVlZZltwcLCys7Pt2s+dO6eTJ0+afc7n4eEhHx8fuwkAAFy7nDoQ9ejRQ/v377eb9+OPPyo8PFzS7wOsg4ODtX79erM9Ly9PqampioiIkCRFREQoJydH6enpZp8NGzaotLRU3bp1q4GtAAAAzs6przIbO3asunfvrpdfflkPPPCAtm3bpoULF2rhwoWSJJvNpjFjxmjatGlq2bKlmjZtqokTJyo0NFSDBg2S9PsRpb59+5qn2oqLizVq1CgNHjyYK8wAAIAkJw9EN998s1auXKmEhARNnTpVTZs21ezZsxUdHW32GT9+vAoKCjRixAjl5OTotttu05o1a+Tp6Wn2SUxM1KhRo9S7d2+5uLjovvvu05w5cxyxSQAAwAk5dSCSpLvuukt33XXXBdttNpumTp2qqVOnXrCPv7+/li9fXh3lAQCAa4BTjyECAACoCQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgeQQiAABgebUqEL3yyiuy2WwaM2aMOe/s2bOKi4tTQECA6tevr/vuu09ZWVl27zty5IgGDBigunXrKjAwUOPGjdO5c+dquHoAAOCsak0gSktL0zvvvKP27dvbzR87dqw+//xz/fvf/9amTZt0/Phx3XvvvWZ7SUmJBgwYoKKiIn377bdatmyZli5dqkmTJtX0JgAAACdVKwJRfn6+oqOj9e6776pBgwbm/NzcXC1atEizZs3SnXfeqS5dumjJkiX69ttvtXXrVknSunXr9P333+sf//iHOnbsqH79+unFF1/UvHnzVFRU5KhNAgAATqRWBKK4uDgNGDBAkZGRdvPT09NVXFxsN79169Zq3LixUlJSJEkpKSlq166dgoKCzD5RUVHKy8vT3r17K1xfYWGh8vLy7CYAAHDtcnN0AZfyz3/+Uzt27FBaWlq5tszMTLm7u8vPz89uflBQkDIzM80+fwxDZe1lbRWZPn26XnjhhSqoHgAA1AZOfYTo6NGjeuqpp5SYmChPT88aW29CQoJyc3PN6ejRozW2bgAAUPOcOhClp6crOztbnTt3lpubm9zc3LRp0ybNmTNHbm5uCgoKUlFRkXJycuzel5WVpeDgYElScHBwuavOyl6X9Tmfh4eHfHx87CYAAHDtcupA1Lt3b+3evVsZGRnm1LVrV0VHR5v/XadOHa1fv958z/79+3XkyBFFRERIkiIiIrR7925lZ2ebfZKTk+Xj46Mbb7yxxrcJAAA4H6ceQ+Tt7a2bbrrJbl69evUUEBBgzo+NjVV8fLz8/f3l4+Oj0aNHKyIiQrfeeqskqU+fPrrxxhs1ZMgQzZw5U5mZmXr++ecVFxcnDw+PGt8mAADgfJw6EF2ON954Qy4uLrrvvvtUWFioqKgovf3222a7q6urkpKSNHLkSEVERKhevXqKiYnR1KlTHVg1AABwJrUuEH311Vd2rz09PTVv3jzNmzfvgu8JDw/XF198Uc2VAQCA2sqpxxABAADUBAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPKcORNOnT9fNN98sb29vBQYGatCgQdq/f79dn7NnzyouLk4BAQGqX7++7rvvPmVlZdn1OXLkiAYMGKC6desqMDBQ48aN07lz52pyUwAAgBNz6kC0adMmxcXFaevWrUpOTlZxcbH69OmjgoICs8/YsWP1+eef69///rc2bdqk48eP69577zXbS0pKNGDAABUVFenbb7/VsmXLtHTpUk2aNMkRmwQAAJyQm6MLuJg1a9bYvV66dKkCAwOVnp6uO+64Q7m5uVq0aJGWL1+uO++8U5K0ZMkStWnTRlu3btWtt96qdevW6fvvv9eXX36poKAgdezYUS+++KImTJigKVOmyN3dvdx6CwsLVVhYaL7Oy8ur3g0FAAAO5dRHiM6Xm5srSfL395ckpaenq7i4WJGRkWaf1q1bq3HjxkpJSZEkpaSkqF27dgoKCjL7REVFKS8vT3v37q1wPdOnT5evr685hYWFVdcmAQAAJ1BrAlFpaanGjBmjHj166KabbpIkZWZmyt3dXX5+fnZ9g4KClJmZafb5Yxgqay9rq0hCQoJyc3PN6ejRo1W8NQAAwJk49SmzP4qLi9OePXv09ddfV/u6PDw85OHhUe3rAQAAzqFWHCEaNWqUkpKStHHjRl1//fXm/ODgYBUVFSknJ8euf1ZWloKDg80+5191Vva6rA8AALA2pw5EhmFo1KhRWrlypTZs2KCmTZvatXfp0kV16tTR+vXrzXn79+/XkSNHFBERIUmKiIjQ7t27lZ2dbfZJTk6Wj4+PbrzxxprZEAAA4NSc+pRZXFycli9frk8//VTe3t7mmB9fX195eXnJ19dXsbGxio+Pl7+/v3x8fDR69GhFRETo1ltvlST16dNHN954o4YMGaKZM2cqMzNTzz//vOLi4jgtBgAAJDl5IJo/f74kqWfPnnbzlyxZoqFDh0qS3njjDbm4uOi+++5TYWGhoqKi9Pbbb5t9XV1dlZSUpJEjRyoiIkL16tVTTEyMpk6dWlObAQAAnJxTByLDMC7Zx9PTU/PmzdO8efMu2Cc8PFxffPFFVZYGAACuIU49hggAAKAmEIgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlWSoQzZs3T02aNJGnp6e6deumbdu2ObokAADgBCwTiD766CPFx8dr8uTJ2rFjhzp06KCoqChlZ2c7ujQAAOBglglEs2bN0vDhw/Xoo4/qxhtv1IIFC1S3bl0tXrzY0aUBAAAHc3N0ATWhqKhI6enpSkhIMOe5uLgoMjJSKSkp5foXFhaqsLDQfJ2bmytJysvLs+tXXHimmiq+fOfXdL78s0U1VMmFXarGot8KL9peEy5VoySdKXL+fXm2FvybPFvo/PtRkvJ/K6iBSi7ukvvy7NkaquTCLlVjwZna8fkuPOP8/7/5zrk8f6yx7L8Nw7j0Gw0LOHbsmCHJ+Pbbb+3mjxs3zrjlllvK9Z88ebIhiYmJiYmJiekamI4ePXrJrGCJI0RXKiEhQfHx8ebr0tJSnTx5UgEBAbLZbFWyjry8PIWFheno0aPy8fGpkmVWtdpQo1Q76qTGqlMb6qTGqlMb6qTGqlPVdRqGodOnTys0NPSSfS0RiBo2bChXV1dlZWXZzc/KylJwcHC5/h4eHvLw8LCb5+fnVy21+fj4OPU/Tql21CjVjjqpserUhjqpserUhjqpsepUZZ2+vr6X1c8Sg6rd3d3VpUsXrV+/3pxXWlqq9evXKyIiwoGVAQAAZ2CJI0SSFB8fr5iYGHXt2lW33HKLZs+erYKCAj366KOOLg0AADiYZQLRgw8+qF9++UWTJk1SZmamOnbsqDVr1igoKMgh9Xh4eGjy5MnlTs05k9pQo1Q76qTGqlMb6qTGqlMb6qTGquPIOm2GcTnXogEAAFy7LDGGCAAA4GIIRAAAwPIIRAAAwPIIRAAAwPIIRDVs8+bNGjhwoEJDQ2Wz2bRq1SpHl1TO9OnTdfPNN8vb21uBgYEaNGiQ9u/f7+iy7MyfP1/t27c3b94VERGh1atXO7qsi3rllVdks9k0ZswYR5diZ8qUKbLZbHZT69atHV1WOceOHdNf//pXBQQEyMvLS+3atdP27dsdXZadJk2alNuXNptNcXFxji7NVFJSookTJ6pp06by8vJS8+bN9eKLL17es55q0OnTpzVmzBiFh4fLy8tL3bt3V1pamkNrutTfb8MwNGnSJIWEhMjLy0uRkZE6cOCAU9W4YsUK9enTx3zyQkZGRo3Wd6kai4uLNWHCBLVr10716tVTaGioHnnkER0/frza6yIQ1bCCggJ16NBB8+bNc3QpF7Rp0ybFxcVp69atSk5OVnFxsfr06aOCAsc//LDM9ddfr1deeUXp6enavn277rzzTt1zzz3au3evo0urUFpamt555x21b9/e0aVUqG3btjpx4oQ5ff31144uyc6pU6fUo0cP1alTR6tXr9b333+v119/XQ0aNHB0aXbS0tLs9mNycrIk6f7773dwZf9nxowZmj9/vt566y3t27dPM2bM0MyZMzV37lxHl2bnscceU3Jysj744APt3r1bffr0UWRkpI4dO+awmi7193vmzJmaM2eOFixYoNTUVNWrV09RUVE1+hDeS9VYUFCg2267TTNmzKixmiqq4UI1njlzRjt27NDEiRO1Y8cOrVixQvv379fdd99d/YVVxcNTUTmSjJUrVzq6jEvKzs42JBmbNm1ydCkX1aBBA+O9995zdBnlnD592mjZsqWRnJxs/OlPfzKeeuopR5dkZ/LkyUaHDh0cXcZFTZgwwbjtttscXcYVe+qpp4zmzZsbpaWlji7FNGDAAGPYsGF28+69914jOjraQRWVd+bMGcPV1dVISkqym9+5c2fjueeec1BV9s7/+11aWmoEBwcbr776qjkvJyfH8PDwMD788EMHVHjx75hDhw4ZkoydO3fWaE3nu5zvwW3bthmSjMOHD1drLRwhwiXl5uZKkvz9/R1cScVKSkr0z3/+UwUFBU75KJa4uDgNGDBAkZGRji7lgg4cOKDQ0FA1a9ZM0dHROnLkiKNLsvPZZ5+pa9euuv/++xUYGKhOnTrp3XffdXRZF1VUVKR//OMfGjZsWJU9FLoqdO/eXevXr9ePP/4oSdq1a5e+/vpr9evXz8GV/Z9z586ppKREnp6edvO9vLyc7uhlmUOHDikzM9Puc+7r66tu3bopJSXFgZXVfrm5ubLZbNX2TNEylrlTNSqntLRUY8aMUY8ePXTTTTc5uhw7u3fvVkREhM6ePav69etr5cqVuvHGGx1dlp1//vOf2rFjh8PHPlxMt27dtHTpUrVq1UonTpzQCy+8oNtvv1179uyRt7e3o8uTJP3P//yP5s+fr/j4eP39739XWlqannzySbm7uysmJsbR5VVo1apVysnJ0dChQx1dip1nn31WeXl5at26tVxdXVVSUqKXXnpJ0dHRji7N5O3trYiICL344otq06aNgoKC9OGHHyolJUUtWrRwdHkVyszMlKRyTz8ICgoy23Dlzp49qwkTJuihhx6q9ofSEohwUXFxcdqzZ49T/ipr1aqVMjIylJubq48//lgxMTHatGmT04Sio0eP6qmnnlJycnK5X7rO5I9HBtq3b69u3bopPDxc//rXvxQbG+vAyv5PaWmpunbtqpdfflmS1KlTJ+3Zs0cLFixw2kC0aNEi9evXT6GhoY4uxc6//vUvJSYmavny5Wrbtq0yMjI0ZswYhYaGOtW+/OCDDzRs2DBdd911cnV1VefOnfXQQw8pPT3d0aWhhhQXF+uBBx6QYRiaP39+ta+PU2a4oFGjRikpKUkbN27U9ddf7+hyynF3d1eLFi3UpUsXTZ8+XR06dNCbb77p6LJM6enpys7OVufOneXm5iY3Nzdt2rRJc+bMkZubm0pKShxdYoX8/Px0ww036ODBg44uxRQSElIu6LZp08bpTu2VOXz4sL788ks99thjji6lnHHjxunZZ5/V4MGD1a5dOw0ZMkRjx47V9OnTHV2anebNm2vTpk3Kz8/X0aNHtW3bNhUXF6tZs2aOLq1CwcHBkqSsrCy7+VlZWWYbLl9ZGDp8+LCSk5Or/eiQRCBCBQzD0KhRo7Ry5Upt2LBBTZs2dXRJl6W0tFSFhYWOLsPUu3dv7d69WxkZGebUtWtXRUdHKyMjQ66uro4usUL5+fn66aefFBIS4uhSTD169Ch364cff/xR4eHhDqro4pYsWaLAwEANGDDA0aWUc+bMGbm42P/pd3V1VWlpqYMqurh69eopJCREp06d0tq1a3XPPfc4uqQKNW3aVMHBwVq/fr05Ly8vT6mpqU45ttGZlYWhAwcO6Msvv1RAQECNrJdTZjUsPz/f7pf3oUOHlJGRIX9/fzVu3NiBlf2fuLg4LV++XJ9++qm8vb3N89++vr7y8vJycHW/S0hIUL9+/dS4cWOdPn1ay5cv11dffaW1a9c6ujSTt7d3uXFX9erVU0BAgFONx3rmmWc0cOBAhYeH6/jx45o8ebJcXV310EMPObo009ixY9W9e3e9/PLLeuCBB7Rt2zYtXLhQCxcudHRp5ZSWlmrJkiWKiYmRm5vz/YkdOHCgXnrpJTVu3Fht27bVzp07NWvWLA0bNszRpdlZu3atDMNQq1atdPDgQY0bN06tW7fWo48+6rCaLvX3e8yYMZo2bZpatmyppk2bauLEiQoNDdWgQYOcpsaTJ0/qyJEj5n19yn5oBAcH19iRrIvVGBISor/85S/asWOHkpKSVFJSYn4H+fv7y93dvfoKq9Zr2FDOxo0bDUnlppiYGEeXZqqoPknGkiVLHF2aadiwYUZ4eLjh7u5uNGrUyOjdu7exbt06R5d1Sc542f2DDz5ohISEGO7u7sZ1111nPPjgg8bBgwcdXVY5n3/+uXHTTTcZHh4eRuvWrY2FCxc6uqQKrV271pBk7N+/39GlVCgvL8946qmnjMaNGxuenp5Gs2bNjOeee84oLCx0dGl2PvroI6NZs2aGu7u7ERwcbMTFxRk5OTkOrelSf79LS0uNiRMnGkFBQYaHh4fRu3fvGv93cKkalyxZUmH75MmTnaLGstsBVDRt3LixWuuyGYaT3Z4UAACghjGGCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAqacqUKerYsaOjywBQBQhEAHAZbDabVq1a5egyAFQTAhEAALA8AhGAWqVnz54aPXq0xowZowYNGigoKEjvvvuuCgoK9Oijj8rb21stWrTQ6tWrzfds2rRJt9xyizw8PBQSEqJnn31W586ds1vmk08+qfHjx8vf31/BwcGaMmWK2d6kSRNJ0p///GfZbDbzdZkPPvhATZo0ka+vrwYPHqzTp09X5y4AUA0IRABqnWXLlqlhw4batm2bRo8erZEjR+r+++9X9+7dtWPHDvXp00dDhgzRmTNndOzYMfXv318333yzdu3apfnz52vRokWaNm1auWXWq1dPqampmjlzpqZOnark5GRJUlpamiRpyZIlOnHihPlakn766SetWrVKSUlJSkpK0qZNm/TKK6/U3M4AUCV42j2AWqVnz54qKSnRli1bJEklJSXy9fXVvffeq/fff1+SlJmZqZCQEKWkpOjzzz/XJ598on379slms0mS3n77bU2YMEG5ublycXEpt0xJuuWWW3TnnXea4cZms2nlypUaNGiQ2WfKlCl69dVXlZmZKW9vb0nS+PHjtXnzZm3durUmdgeAKsIRIgC1Tvv27c3/dnV1VUBAgNq1a2fOCwoKkiRlZ2dr3759ioiIMMOQJPXo0UP5+fn673//W+EyJSkkJETZ2dmXrKVJkyZmGLqS9wFwLgQiALVOnTp17F7bbDa7eWXhp7S09KqWeTnvr+z7ADgXAhGAa1qbNm2UkpKiP44O+Oabb+Tt7a3rr7/+spdTp04dlZSUVEeJAJwAgQjANe2JJ57Q0aNHNXr0aP3www/69NNPNXnyZMXHx8vF5fL/BDZp0kTr169XZmamTp06VY0VA3AEAhGAa9p1112nL774Qtu2bVOHDh30+OOPKzY2Vs8///wVLef1119XcnKywsLC1KlTp2qqFoCjcJUZAACwPI4QAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy/v//6h2TB9v5pIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in categorical_cols[:-1]:\n",
    "    ax = sns.countplot(data=data, x = i,palette='deep')\n",
    "    ax.bar_label(ax.containers[0])\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season          4\n",
       "month          12\n",
       "hour           24\n",
       "holiday         2\n",
       "weekday         7\n",
       "working         2\n",
       "weathersit      4\n",
       "temp           50\n",
       "humidity       89\n",
       "windspeed      30\n",
       "count         869\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: x.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season  working\n",
       "1       0          137683\n",
       "        1          333665\n",
       "2       0          287976\n",
       "        1          630613\n",
       "3       0          312056\n",
       "        1          749073\n",
       "4       0          262554\n",
       "        1          579059\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=['season','working'])['count'].agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season  weathersit\n",
       "1       1             325278\n",
       "        2             124205\n",
       "        3              21642\n",
       "        4                223\n",
       "2       1             664463\n",
       "        2             207363\n",
       "        3              46763\n",
       "3       1             801941\n",
       "        2             212932\n",
       "        3              46256\n",
       "4       1             546491\n",
       "        2             251452\n",
       "        3              43670\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=['season','weathersit'])['count'].agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "working  weathersit\n",
       "0        1              747219\n",
       "         2              211982\n",
       "         3               41045\n",
       "         4                  23\n",
       "1        1             1590954\n",
       "         2              583970\n",
       "         3              117286\n",
       "         4                 200\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=['working','weathersit'])['count'].agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20c6f955180>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['windspeed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20c6f78ef80>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['temp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(data=data,x='season',y='count',hue='working')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=data,x='season',hue='working')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in numerical_cols:\n",
    "    ax = sns.boxplot(data=data, x = i,palette='deep')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='count'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data,x='count')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skewness and kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season       -0.005416\n",
       "month        -0.009253\n",
       "hour         -0.010680\n",
       "holiday       5.638538\n",
       "weekday      -0.002998\n",
       "working      -0.785258\n",
       "weathersit    1.228047\n",
       "temp         -0.006021\n",
       "humidity     -0.111287\n",
       "windspeed     0.574905\n",
       "count         1.277412\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season        -1.334251\n",
       "month         -1.201878\n",
       "hour          -1.198021\n",
       "holiday       29.796540\n",
       "weekday       -1.255997\n",
       "working       -1.383529\n",
       "weathersit     0.350151\n",
       "temp          -0.941844\n",
       "humidity      -0.826117\n",
       "windspeed      0.590820\n",
       "count          1.417203\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>season</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>working</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.3284</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17110</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17111</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17125</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17134</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.3284</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17370</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4326 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  month  hour  holiday  weekday  working  weathersit  temp  \\\n",
       "1070        1      2     8        0        4        1           1  0.32   \n",
       "1103        1      2    17        0        5        1           1  0.66   \n",
       "1305        1      2    14        0        0        0           1  0.44   \n",
       "1634        1      3    15        0        0        0           1  0.48   \n",
       "1673        1      3     8        0        2        1           1  0.26   \n",
       "...       ...    ...   ...      ...      ...      ...         ...   ...   \n",
       "17110       4     12    17        0        4        1           2  0.34   \n",
       "17111       4     12    18        0        4        1           2  0.34   \n",
       "17125       1     12     8        0        5        1           2  0.32   \n",
       "17134       1     12    17        0        5        1           2  0.26   \n",
       "17370       1     12    15        0        1        1           2  0.28   \n",
       "\n",
       "       humidity  windspeed  count  \n",
       "1070       0.57     0.0896    285  \n",
       "1103       0.34     0.3582    327  \n",
       "1305       0.47     0.0896    282  \n",
       "1634       0.39     0.3284    304  \n",
       "1673       0.70     0.0896    282  \n",
       "...         ...        ...    ...  \n",
       "17110      0.66     0.1940    475  \n",
       "17111      0.71     0.1940    381  \n",
       "17125      0.57     0.2836    472  \n",
       "17134      0.56     0.3284    321  \n",
       "17370      0.45     0.1343    315  \n",
       "\n",
       "[4326 rows x 11 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['count']>281]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "mask = np.zeros_like(data.corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(data.corr(), cmap='viridis', annot=True,mask=mask)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Season and Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season  month\n",
       "1       1        134933\n",
       "        2        151352\n",
       "        3        142065\n",
       "        12        42998\n",
       "2       3         86855\n",
       "        4        269094\n",
       "        5        331686\n",
       "        6        230954\n",
       "3       6        115388\n",
       "        7        344948\n",
       "        8        351194\n",
       "        9        249599\n",
       "4       9         96392\n",
       "        10       322352\n",
       "        11       254831\n",
       "        12       168038\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(by=['season','month'])['count'].agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>month</th>\n",
       "      <th>season</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830386</td>\n",
       "      <td>0.120638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>0.830386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.178056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.178056</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           month    season     count\n",
       "month   1.000000  0.830386  0.120638\n",
       "season  0.830386  1.000000  0.178056\n",
       "count   0.120638  0.178056  1.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['month','season','count']].corr()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### need to drop season column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>working</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  hour  holiday  weekday  working  weathersit  temp  humidity  \\\n",
       "0          1     0        0        6        0           1  0.24      0.81   \n",
       "1          1     1        0        6        0           1  0.22      0.80   \n",
       "2          1     2        0        6        0           1  0.22      0.80   \n",
       "3          1     3        0        6        0           1  0.24      0.75   \n",
       "4          1     4        0        6        0           1  0.24      0.75   \n",
       "...      ...   ...      ...      ...      ...         ...   ...       ...   \n",
       "17374     12    19        0        1        1           2  0.26      0.60   \n",
       "17375     12    20        0        1        1           2  0.26      0.60   \n",
       "17376     12    21        0        1        1           1  0.26      0.60   \n",
       "17377     12    22        0        1        1           1  0.26      0.56   \n",
       "17378     12    23        0        1        1           1  0.26      0.65   \n",
       "\n",
       "       windspeed  count  \n",
       "0         0.0000     16  \n",
       "1         0.0000     40  \n",
       "2         0.0000     32  \n",
       "3         0.0000     13  \n",
       "4         0.0000      1  \n",
       "...          ...    ...  \n",
       "17374     0.1642    119  \n",
       "17375     0.1642     89  \n",
       "17376     0.1642     90  \n",
       "17377     0.1343     61  \n",
       "17378     0.1343     49  \n",
       "\n",
       "[17379 rows x 10 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns='season')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['season', 'month', 'holiday', 'weekday', 'working', 'weathersit', 'hour']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'season'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['month', 'holiday', 'weekday', 'working', 'weathersit', 'hour']\n",
      "['temp', 'humidity', 'windspeed', 'count']\n"
     ]
    }
   ],
   "source": [
    "print(categorical_cols)\n",
    "print(numerical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12165, 9), (5214, 9), (12165,), (5214,))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,shuffle=True,random_state=25)\n",
    "X_train.shape,X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor\n",
    "from catboost import CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val_models(X, y, cv, scoring='neg_mean_squared_error',n_jobs=-1):\n",
    "    xgb = XGBRegressor()\n",
    "    xgb_scores = cross_val_score(xgb, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for XGBRegressor: \", xgb_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for XGBRegressor: \", np.sqrt(-np.mean(xgb_scores)))\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    #catb = CatBoostRegressor()\n",
    "    #catb_scores = cross_val_score(catb, X, y, cv=cv, scoring=scoring)\n",
    "    #print(\"Cross validation score for CATBoostRegressor: \", catb_scores)\n",
    "    #print()\n",
    "    #print(\"RMSE Cross validation score for CATBoostRegressor: \", np.sqrt(-np.mean(catb_scores)))\n",
    "    #print(\"\\n\\n\")\n",
    "\n",
    "    rf = RandomForestRegressor()\n",
    "    rf_scores = cross_val_score(rf, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for RandomForestRegressor: \", rf_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for RandomForestRegressor: \", np.sqrt(-np.mean(rf_scores)))\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    lr = LinearRegression()\n",
    "    lr_scores = cross_val_score(lr, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for LinearRegression: \", lr_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for LinearRegression: \", np.sqrt(-np.mean(lr_scores)))\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    svm = SVR()\n",
    "    svm_scores = cross_val_score(svm, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for SVR: \", svm_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for SVR: \", np.sqrt(-np.mean(svm_scores)))\n",
    "    print(\"\\n\\n\")\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation score for XGBRegressor:  [-3619.13546492 -4149.92502921 -3777.58565105 -4170.5805415\n",
      " -4116.26074183 -4576.30320934 -4091.93984216 -4211.65565248\n",
      " -4503.55876901 -4189.6515091 ]\n",
      "\n",
      "RMSE Cross validation score for XGBRegressor:  64.34795755157289\n",
      "\n",
      "\n",
      "\n",
      "Cross validation score for RandomForestRegressor:  [-4199.39538222 -4571.90746207 -4198.79654789 -4559.30807976\n",
      " -4268.87242981 -4929.21357527 -4591.53243286 -4762.83263229\n",
      " -5359.1988646  -4844.78989506]\n",
      "\n",
      "RMSE Cross validation score for RandomForestRegressor:  68.0337028992527\n",
      "\n",
      "\n",
      "\n",
      "Cross validation score for LinearRegression:  [-20412.20456008 -21300.36501427 -20754.94070182 -22082.01660096\n",
      " -22531.90793029 -21601.13185329 -21171.72393364 -23958.3404189\n",
      " -24308.27356081 -21056.59013908]\n",
      "\n",
      "RMSE Cross validation score for LinearRegression:  148.04644362940894\n",
      "\n",
      "\n",
      "\n",
      "Cross validation score for SVR:  [-19193.31724409 -20706.90012759 -19664.92883274 -20896.56608402\n",
      " -20829.9101967  -19982.59745075 -21469.71149414 -23697.60010857\n",
      " -23798.56087102 -20673.05564496]\n",
      "\n",
      "RMSE Cross validation score for SVR:  145.22849171377993\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cross_val_models(X, y, cv=KFold(n_splits=10,shuffle=True,random_state=42), scoring='neg_mean_squared_error',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val_models(X, y, cv, scoring='r2',n_jobs=-1):\n",
    "    xgb = XGBRegressor()\n",
    "    xgb_scores = cross_val_score(xgb, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for XGBRegressor: \", xgb_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for XGBRegressor: \", np.mean(xgb_scores))\n",
    "    print(\"\\n\\n\")\n",
    "    \n",
    "    #catb = CatBoostRegressor()\n",
    "    #catb_scores = cross_val_score(catb, X, y, cv=cv, scoring=scoring)\n",
    "    #print(\"Cross validation score for CATBoostRegressor: \", catb_scores)\n",
    "    #print()\n",
    "    #print(\"RMSE Cross validation score for CATBoostRegressor: \", np.sqrt(-np.mean(catb_scores)))\n",
    "    #print(\"\\n\\n\")\n",
    "\n",
    "    rf = RandomForestRegressor()\n",
    "    rf_scores = cross_val_score(rf, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for RandomForestRegressor: \", rf_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for RandomForestRegressor: \", np.mean(rf_scores))\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    lr = LinearRegression()\n",
    "    lr_scores = cross_val_score(lr, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for LinearRegression: \", lr_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for LinearRegression: \", np.mean(lr_scores))\n",
    "    print(\"\\n\\n\")\n",
    "\n",
    "    svm = SVR()\n",
    "    svm_scores = cross_val_score(svm, X, y, cv=cv, scoring=scoring)\n",
    "    print(\"Cross validation score for SVR: \", svm_scores)\n",
    "    print()\n",
    "    print(\"RMSE Cross validation score for SVR: \", np.mean(svm_scores))\n",
    "    print(\"\\n\\n\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation score for XGBRegressor:  [0.88292799 0.87197212 0.879852   0.87389737 0.87356113 0.85437029\n",
      " 0.87802676 0.88104084 0.87251033 0.87198539]\n",
      "\n",
      "RMSE Cross validation score for XGBRegressor:  0.8740144210927021\n",
      "\n",
      "\n",
      "\n",
      "Cross validation score for RandomForestRegressor:  [0.86555212 0.85983226 0.867527   0.85933224 0.8703845  0.84213512\n",
      " 0.86248955 0.86456346 0.84792188 0.85383024]\n",
      "\n",
      "RMSE Cross validation score for RandomForestRegressor:  0.8593568366913384\n",
      "\n",
      "\n",
      "\n",
      "Cross validation score for LinearRegression:  [0.33970477 0.34286991 0.33987874 0.33232307 0.30788907 0.31259654\n",
      " 0.36890965 0.3232913  0.31186559 0.35661687]\n",
      "\n",
      "RMSE Cross validation score for LinearRegression:  0.3335945500600425\n",
      "\n",
      "\n",
      "\n",
      "Cross validation score for SVR:  [0.37913341 0.36117868 0.37454711 0.36816662 0.36016921 0.36410246\n",
      " 0.36002718 0.33065597 0.32629487 0.36833575]\n",
      "\n",
      "RMSE Cross validation score for SVR:  0.3592611262425674\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cross_val_models(X, y, cv=KFold(n_splits=10,shuffle=True,random_state=42),n_jobs=-1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning of XGBRegressor using OPTUNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objectives(n_trial):\n",
    "\n",
    "    params ={\n",
    "        'objective': 'reg:squarederror',\n",
    "        # \"n_estimators\" : n_trial.suggest_int(\"n_estimators\",100,400),\n",
    "        \"booster\" : n_trial.suggest_categorical(\"booster\", ['gbtree','gblinear','dart']),\n",
    "        \"learning_rate\" : n_trial.suggest_float(\"learning_rate\",1e-4,0.1,log=True),\n",
    "        \"max_depth\" : n_trial.suggest_int(\"max_depth\",5,15),\n",
    "        \"subsample\" : n_trial.suggest_float('subsample',0.01,1.0,log=True),\n",
    "        'reg_alpha': n_trial.suggest_float('reg_alpha', 1e-8, 1.0,log=True),\n",
    "        'reg_lambda': n_trial.suggest_float('reg_lambda', 1e-8, 1.0,log=True),\n",
    "        'gamma' : n_trial.suggest_float('gamma',1e-8, 1.0,log=True),\n",
    "        'min_child_weight': n_trial.suggest_int('min_child_weight', 1, 10),\n",
    "        \"verbosity\":0\n",
    "        #'use_label_encoder': False\n",
    "\n",
    "    }\n",
    "\n",
    "    dtrain = xgb.DMatrix(data = X_train, label = y_train)\n",
    "    dval = xgb.DMatrix(data = X_test, label = y_test)\n",
    "\n",
    "    \n",
    "    regressor = xgb.train(params, dtrain,verbose_eval=False) \n",
    "    y_pred = regressor.predict(dval)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-01-14 23:46:25,653]\u001b[0m A new study created in memory with name: no-name-703d56ff-ccd9-4c69-9314-b238c176c436\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,745]\u001b[0m Trial 0 finished with value: 63562.1862460887 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0038957137147158146, 'max_depth': 13, 'subsample': 0.23926481117877718, 'reg_alpha': 0.001425653502347148, 'reg_lambda': 0.06242224706874648, 'gamma': 0.9218902300780626, 'min_child_weight': 1}. Best is trial 0 with value: 63562.1862460887.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,788]\u001b[0m Trial 1 finished with value: 63728.434762037985 and parameters: {'booster': 'gbtree', 'learning_rate': 0.003973751370634684, 'max_depth': 7, 'subsample': 0.04978019460726513, 'reg_alpha': 0.007054292467476922, 'reg_lambda': 0.00030306364479278477, 'gamma': 0.003870386981705907, 'min_child_weight': 8}. Best is trial 0 with value: 63562.1862460887.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,818]\u001b[0m Trial 2 finished with value: 55203.10710678233 and parameters: {'booster': 'gbtree', 'learning_rate': 0.012550061155340774, 'max_depth': 7, 'subsample': 0.013992324775523151, 'reg_alpha': 4.804907326276213e-08, 'reg_lambda': 0.00010747742055646746, 'gamma': 4.202854204833814e-07, 'min_child_weight': 1}. Best is trial 2 with value: 55203.10710678233.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,891]\u001b[0m Trial 3 finished with value: 25016.784775419364 and parameters: {'booster': 'dart', 'learning_rate': 0.05748971554553186, 'max_depth': 10, 'subsample': 0.49917322766080013, 'reg_alpha': 0.0025222944271210525, 'reg_lambda': 0.06864133062051647, 'gamma': 6.57104566453023e-05, 'min_child_weight': 3}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,923]\u001b[0m Trial 4 finished with value: 66583.70022163844 and parameters: {'booster': 'dart', 'learning_rate': 0.0014699405114401815, 'max_depth': 5, 'subsample': 0.21664123540564215, 'reg_alpha': 0.04823789623600215, 'reg_lambda': 0.0013277187341777397, 'gamma': 0.00862096682589215, 'min_child_weight': 3}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,957]\u001b[0m Trial 5 finished with value: 60582.10200684832 and parameters: {'booster': 'dart', 'learning_rate': 0.007076890752855721, 'max_depth': 15, 'subsample': 0.023114398330083837, 'reg_alpha': 8.013288625440153e-08, 'reg_lambda': 0.0017991554974210332, 'gamma': 0.00348375948810575, 'min_child_weight': 8}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:25,991]\u001b[0m Trial 6 finished with value: 60793.7060204142 and parameters: {'booster': 'dart', 'learning_rate': 0.00657576659457379, 'max_depth': 7, 'subsample': 0.21684302550787338, 'reg_alpha': 1.6897440366296887e-06, 'reg_lambda': 0.06732825998745774, 'gamma': 0.04757088871027844, 'min_child_weight': 4}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,002]\u001b[0m Trial 7 finished with value: 54311.9890202475 and parameters: {'booster': 'gblinear', 'learning_rate': 0.01622124364521883, 'max_depth': 12, 'subsample': 0.036204085274846275, 'reg_alpha': 1.6882792398556017e-05, 'reg_lambda': 1.0949355857624153e-05, 'gamma': 0.000611776810367004, 'min_child_weight': 3}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,031]\u001b[0m Trial 8 finished with value: 61865.78251572461 and parameters: {'booster': 'dart', 'learning_rate': 0.005815963322491848, 'max_depth': 6, 'subsample': 0.07008887315099215, 'reg_alpha': 3.8276183211587887e-07, 'reg_lambda': 0.45785346458421416, 'gamma': 0.0167948299437046, 'min_child_weight': 10}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,041]\u001b[0m Trial 9 finished with value: 51258.22951619846 and parameters: {'booster': 'gblinear', 'learning_rate': 0.021104593510397323, 'max_depth': 10, 'subsample': 0.050983923258204, 'reg_alpha': 1.6348226422593327e-07, 'reg_lambda': 0.0667247174776906, 'gamma': 1.4820670725342626e-05, 'min_child_weight': 2}. Best is trial 3 with value: 25016.784775419364.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,115]\u001b[0m Trial 10 finished with value: 18751.98328882317 and parameters: {'booster': 'dart', 'learning_rate': 0.07541686374053105, 'max_depth': 10, 'subsample': 0.8903945372175213, 'reg_alpha': 0.812954589753599, 'reg_lambda': 6.404080764691383e-08, 'gamma': 1.898063466253569e-08, 'min_child_weight': 6}. Best is trial 10 with value: 18751.98328882317.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,188]\u001b[0m Trial 11 finished with value: 17845.22865896213 and parameters: {'booster': 'dart', 'learning_rate': 0.07875664930528717, 'max_depth': 10, 'subsample': 0.9625052780011948, 'reg_alpha': 0.5269290185784804, 'reg_lambda': 1.8036051357811906e-08, 'gamma': 1.0184504633286745e-08, 'min_child_weight': 6}. Best is trial 11 with value: 17845.22865896213.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,251]\u001b[0m Trial 12 finished with value: 67929.23380999913 and parameters: {'booster': 'dart', 'learning_rate': 0.00020311973340768024, 'max_depth': 9, 'subsample': 0.9701041157780251, 'reg_alpha': 0.9559237617408692, 'reg_lambda': 1.0871926692450954e-08, 'gamma': 1.4969006236710483e-08, 'min_child_weight': 6}. Best is trial 11 with value: 17845.22865896213.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,354]\u001b[0m Trial 13 finished with value: 13230.042332329092 and parameters: {'booster': 'dart', 'learning_rate': 0.09840131338587978, 'max_depth': 12, 'subsample': 0.920545396983217, 'reg_alpha': 0.8432724773691885, 'reg_lambda': 2.3068193041617585e-08, 'gamma': 1.435412169768991e-08, 'min_child_weight': 6}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,447]\u001b[0m Trial 14 finished with value: 34601.71918479713 and parameters: {'booster': 'dart', 'learning_rate': 0.03789033948195774, 'max_depth': 12, 'subsample': 0.43021687540823333, 'reg_alpha': 0.035692487362810166, 'reg_lambda': 6.624362248682817e-07, 'gamma': 7.029253400103439e-07, 'min_child_weight': 5}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,472]\u001b[0m Trial 15 finished with value: 27527.689541135773 and parameters: {'booster': 'gblinear', 'learning_rate': 0.09614640681883042, 'max_depth': 14, 'subsample': 0.4767582754422448, 'reg_alpha': 0.00024508452762681796, 'reg_lambda': 1.724908994232396e-06, 'gamma': 6.378306561134741e-07, 'min_child_weight': 8}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,640]\u001b[0m Trial 16 finished with value: 67858.50989578004 and parameters: {'booster': 'dart', 'learning_rate': 0.000262558083384848, 'max_depth': 12, 'subsample': 0.1332373629964878, 'reg_alpha': 0.1129666648305327, 'reg_lambda': 1.1104980023216006e-08, 'gamma': 6.6246028470233255e-06, 'min_child_weight': 7}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,698]\u001b[0m Trial 17 finished with value: 67151.70973867437 and parameters: {'booster': 'dart', 'learning_rate': 0.0008518511194667696, 'max_depth': 8, 'subsample': 0.6433209395048582, 'reg_alpha': 5.8312914775987503e-05, 'reg_lambda': 1.9022761622048414e-07, 'gamma': 1.0384017078064129e-07, 'min_child_weight': 10}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,724]\u001b[0m Trial 18 finished with value: 43832.53355025566 and parameters: {'booster': 'gblinear', 'learning_rate': 0.034013345039252664, 'max_depth': 11, 'subsample': 0.11629743260236511, 'reg_alpha': 0.17862195717057827, 'reg_lambda': 8.81663823328307e-06, 'gamma': 1.0389851317948064e-08, 'min_child_weight': 5}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,800]\u001b[0m Trial 19 finished with value: 36835.357510261 and parameters: {'booster': 'gbtree', 'learning_rate': 0.03449449648209468, 'max_depth': 14, 'subsample': 0.3324830322552177, 'reg_alpha': 0.012052479708371346, 'reg_lambda': 8.309631056446281e-08, 'gamma': 1.230031100423759e-07, 'min_child_weight': 7}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,870]\u001b[0m Trial 20 finished with value: 66202.25727432863 and parameters: {'booster': 'dart', 'learning_rate': 0.001624871414226746, 'max_depth': 9, 'subsample': 0.6976832366042993, 'reg_alpha': 0.0006967693023076294, 'reg_lambda': 5.1806620295477995e-06, 'gamma': 1.3518946712070334e-05, 'min_child_weight': 4}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:26,952]\u001b[0m Trial 21 finished with value: 13857.479537963192 and parameters: {'booster': 'dart', 'learning_rate': 0.09646845793779352, 'max_depth': 10, 'subsample': 0.9652200716460357, 'reg_alpha': 0.2537289256412653, 'reg_lambda': 7.644648689593478e-08, 'gamma': 5.778551961921852e-08, 'min_child_weight': 6}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,041]\u001b[0m Trial 22 finished with value: 14249.09935153527 and parameters: {'booster': 'dart', 'learning_rate': 0.09380585351495456, 'max_depth': 11, 'subsample': 0.960225208526203, 'reg_alpha': 0.27494098494068614, 'reg_lambda': 3.172877817276716e-07, 'gamma': 8.150706420966578e-08, 'min_child_weight': 7}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,115]\u001b[0m Trial 23 finished with value: 27747.641105959487 and parameters: {'booster': 'dart', 'learning_rate': 0.051106542744015125, 'max_depth': 11, 'subsample': 0.3380610696192187, 'reg_alpha': 0.16988497457033064, 'reg_lambda': 5.162833833300718e-07, 'gamma': 1.140909162198309e-07, 'min_child_weight': 7}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,209]\u001b[0m Trial 24 finished with value: 43583.29323702618 and parameters: {'booster': 'dart', 'learning_rate': 0.024691130688825114, 'max_depth': 13, 'subsample': 0.6448886225681164, 'reg_alpha': 0.025426425693951198, 'reg_lambda': 8.14950346017482e-08, 'gamma': 1.8838589898205402e-06, 'min_child_weight': 9}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,294]\u001b[0m Trial 25 finished with value: 13675.596567964283 and parameters: {'booster': 'dart', 'learning_rate': 0.09671200897088707, 'max_depth': 11, 'subsample': 0.6081459547706578, 'reg_alpha': 0.16284051680123293, 'reg_lambda': 1.58482139233615e-06, 'gamma': 9.692412925303456e-08, 'min_child_weight': 5}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,358]\u001b[0m Trial 26 finished with value: 54694.151677789225 and parameters: {'booster': 'dart', 'learning_rate': 0.012308105102668164, 'max_depth': 9, 'subsample': 0.3304414138281233, 'reg_alpha': 0.0060369567524422695, 'reg_lambda': 1.7132549023217474e-06, 'gamma': 4.110121905155355e-08, 'min_child_weight': 5}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,430]\u001b[0m Trial 27 finished with value: 26765.61211071237 and parameters: {'booster': 'dart', 'learning_rate': 0.053183307978025327, 'max_depth': 13, 'subsample': 0.16566736014495084, 'reg_alpha': 1.3466995191155582e-08, 'reg_lambda': 4.406084066334038e-08, 'gamma': 2.484257098738953e-06, 'min_child_weight': 4}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,459]\u001b[0m Trial 28 finished with value: 67711.3470758664 and parameters: {'booster': 'gblinear', 'learning_rate': 0.00046248356148943396, 'max_depth': 11, 'subsample': 0.5972792265993856, 'reg_alpha': 0.0827367123753403, 'reg_lambda': 4.300786845267901e-05, 'gamma': 3.38788198534909e-07, 'min_child_weight': 6}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,527]\u001b[0m Trial 29 finished with value: 43169.11103319491 and parameters: {'booster': 'gbtree', 'learning_rate': 0.02539356802833664, 'max_depth': 12, 'subsample': 0.2787874415425819, 'reg_alpha': 0.0013555237590470812, 'reg_lambda': 2.944161480928111e-06, 'gamma': 5.5097974177268494e-05, 'min_child_weight': 5}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,622]\u001b[0m Trial 30 finished with value: 68044.14482113866 and parameters: {'booster': 'gbtree', 'learning_rate': 0.00010746299293677536, 'max_depth': 14, 'subsample': 0.42792928881771175, 'reg_alpha': 1.0167558997278231e-05, 'reg_lambda': 3.170838618139977e-05, 'gamma': 0.7346451333364706, 'min_child_weight': 4}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,710]\u001b[0m Trial 31 finished with value: 13822.433965774839 and parameters: {'booster': 'dart', 'learning_rate': 0.09572552704760788, 'max_depth': 11, 'subsample': 0.7738433464999054, 'reg_alpha': 0.5595392355755495, 'reg_lambda': 2.8383173260693363e-07, 'gamma': 6.459612975764254e-08, 'min_child_weight': 7}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,801]\u001b[0m Trial 32 finished with value: 29105.480848937597 and parameters: {'booster': 'dart', 'learning_rate': 0.04759987247614485, 'max_depth': 13, 'subsample': 0.6921605688157806, 'reg_alpha': 0.34734024170365274, 'reg_lambda': 1.6449724255548188e-07, 'gamma': 6.053383222804025e-08, 'min_child_weight': 8}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,890]\u001b[0m Trial 33 finished with value: 21233.565079498716 and parameters: {'booster': 'dart', 'learning_rate': 0.06678164980360421, 'max_depth': 11, 'subsample': 0.7486043441137067, 'reg_alpha': 0.017511267409434535, 'reg_lambda': 7.446894803606283e-07, 'gamma': 2.772969020519814e-07, 'min_child_weight': 6}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:27,968]\u001b[0m Trial 34 finished with value: 15596.952753087528 and parameters: {'booster': 'dart', 'learning_rate': 0.0881687870843514, 'max_depth': 10, 'subsample': 0.5056454025045065, 'reg_alpha': 0.004480893281786451, 'reg_lambda': 3.6416154077844786e-08, 'gamma': 3.232223516275195e-08, 'min_child_weight': 7}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,023]\u001b[0m Trial 35 finished with value: 56487.08147107766 and parameters: {'booster': 'gbtree', 'learning_rate': 0.01063983033089261, 'max_depth': 8, 'subsample': 0.4394264209234668, 'reg_alpha': 0.06440519694641031, 'reg_lambda': 1.9658555585419677e-07, 'gamma': 1.070066704223303e-06, 'min_child_weight': 9}. Best is trial 13 with value: 13230.042332329092.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,118]\u001b[0m Trial 36 finished with value: 12950.753961761135 and parameters: {'booster': 'dart', 'learning_rate': 0.09951782485791763, 'max_depth': 12, 'subsample': 0.7853311940855063, 'reg_alpha': 0.9714426712372211, 'reg_lambda': 0.0011718907522208348, 'gamma': 1.9242560520918778e-07, 'min_child_weight': 5}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,189]\u001b[0m Trial 37 finished with value: 64898.4000607001 and parameters: {'booster': 'dart', 'learning_rate': 0.0027291686083084307, 'max_depth': 12, 'subsample': 0.17183301834792414, 'reg_alpha': 0.448409989280814, 'reg_lambda': 0.0006154455874452216, 'gamma': 2.3247984880357807e-07, 'min_child_weight': 5}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,293]\u001b[0m Trial 38 finished with value: 29283.219178745978 and parameters: {'booster': 'dart', 'learning_rate': 0.04717301253377956, 'max_depth': 15, 'subsample': 0.26274868464089446, 'reg_alpha': 0.9430519418437507, 'reg_lambda': 0.008827582211447608, 'gamma': 2.0999697995360495e-06, 'min_child_weight': 2}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,345]\u001b[0m Trial 39 finished with value: 50155.20364382488 and parameters: {'booster': 'dart', 'learning_rate': 0.01863485941993082, 'max_depth': 12, 'subsample': 0.017376927515877175, 'reg_alpha': 0.05105302726971314, 'reg_lambda': 0.002514739769940611, 'gamma': 0.0002466413787326071, 'min_child_weight': 4}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,408]\u001b[0m Trial 40 finished with value: 38161.10814266601 and parameters: {'booster': 'gbtree', 'learning_rate': 0.032737779428939545, 'max_depth': 13, 'subsample': 0.08486240888016781, 'reg_alpha': 0.1059160858095964, 'reg_lambda': 0.00016269178762853197, 'gamma': 6.000002674562981e-06, 'min_child_weight': 3}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,496]\u001b[0m Trial 41 finished with value: 23043.70636832918 and parameters: {'booster': 'dart', 'learning_rate': 0.06208443373386495, 'max_depth': 11, 'subsample': 0.5549876812469376, 'reg_alpha': 0.2812170893216236, 'reg_lambda': 0.004525260677450577, 'gamma': 2.9248372341012792e-08, 'min_child_weight': 6}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,570]\u001b[0m Trial 42 finished with value: 21173.159241111018 and parameters: {'booster': 'dart', 'learning_rate': 0.06745372069598038, 'max_depth': 10, 'subsample': 0.7780638318719447, 'reg_alpha': 0.2436261754106998, 'reg_lambda': 0.0004891750091613754, 'gamma': 1.799774060880597e-07, 'min_child_weight': 5}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,662]\u001b[0m Trial 43 finished with value: 15068.533367708505 and parameters: {'booster': 'dart', 'learning_rate': 0.08967370579914427, 'max_depth': 12, 'subsample': 0.8440238443095579, 'reg_alpha': 0.01285060967552624, 'reg_lambda': 0.016587755982446457, 'gamma': 4.090493897592639e-08, 'min_child_weight': 8}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,731]\u001b[0m Trial 44 finished with value: 34226.10719690401 and parameters: {'booster': 'dart', 'learning_rate': 0.039096328761444116, 'max_depth': 9, 'subsample': 0.5581664668531304, 'reg_alpha': 0.9777561713035868, 'reg_lambda': 3.644691488881673e-08, 'gamma': 2.2299005343047286e-08, 'min_child_weight': 6}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,810]\u001b[0m Trial 45 finished with value: 23436.273658943643 and parameters: {'booster': 'dart', 'learning_rate': 0.06105796436640747, 'max_depth': 10, 'subsample': 0.8335710886223429, 'reg_alpha': 0.4225949189689039, 'reg_lambda': 3.262171241190199e-05, 'gamma': 0.001359609534639933, 'min_child_weight': 7}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,837]\u001b[0m Trial 46 finished with value: 61169.859939762435 and parameters: {'booster': 'gblinear', 'learning_rate': 0.008755510281767161, 'max_depth': 11, 'subsample': 0.030403800985823126, 'reg_alpha': 0.039495817204886444, 'reg_lambda': 0.9284333298449496, 'gamma': 5.948480234528176e-07, 'min_child_weight': 6}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,896]\u001b[0m Trial 47 finished with value: 14447.710418904147 and parameters: {'booster': 'dart', 'learning_rate': 0.09766349735402546, 'max_depth': 8, 'subsample': 0.9846884646061878, 'reg_alpha': 0.1254768238955318, 'reg_lambda': 1.8415880002672017e-08, 'gamma': 6.195454408626643e-08, 'min_child_weight': 5}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:28,947]\u001b[0m Trial 48 finished with value: 53184.291170502045 and parameters: {'booster': 'dart', 'learning_rate': 0.015769935967045626, 'max_depth': 5, 'subsample': 0.4045982507421692, 'reg_alpha': 2.0493015914920134e-06, 'reg_lambda': 1.2902678889619994e-06, 'gamma': 1.0217667790186995e-08, 'min_child_weight': 8}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,040]\u001b[0m Trial 49 finished with value: 63313.19000435182 and parameters: {'booster': 'dart', 'learning_rate': 0.004048429701584204, 'max_depth': 12, 'subsample': 0.7646127922974247, 'reg_alpha': 0.5136840860664138, 'reg_lambda': 1.3214134155805157e-07, 'gamma': 2.2454542821756123e-07, 'min_child_weight': 7}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,100]\u001b[0m Trial 50 finished with value: 42601.93210263229 and parameters: {'booster': 'dart', 'learning_rate': 0.02650026563897224, 'max_depth': 10, 'subsample': 0.052827969029483814, 'reg_alpha': 0.06096801798357319, 'reg_lambda': 2.4661828442290774e-08, 'gamma': 2.0826035782802457e-08, 'min_child_weight': 2}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,191]\u001b[0m Trial 51 finished with value: 19068.82619782433 and parameters: {'booster': 'dart', 'learning_rate': 0.07378406454819585, 'max_depth': 11, 'subsample': 0.9181020315220682, 'reg_alpha': 0.27440301580982307, 'reg_lambda': 5.998881814561367e-07, 'gamma': 7.492090552381535e-08, 'min_child_weight': 7}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,287]\u001b[0m Trial 52 finished with value: 14684.447586090184 and parameters: {'booster': 'dart', 'learning_rate': 0.09191953810434803, 'max_depth': 11, 'subsample': 0.9833344648403687, 'reg_alpha': 0.16308426057718547, 'reg_lambda': 2.407771223390134e-07, 'gamma': 7.4956513005592e-08, 'min_child_weight': 6}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,367]\u001b[0m Trial 53 finished with value: 31034.05711042912 and parameters: {'booster': 'dart', 'learning_rate': 0.044355586179756934, 'max_depth': 10, 'subsample': 0.6157986816498808, 'reg_alpha': 0.6401625123862525, 'reg_lambda': 3.9163778511501574e-07, 'gamma': 1.4203679379013712e-07, 'min_child_weight': 6}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,461]\u001b[0m Trial 54 finished with value: 13161.931323478699 and parameters: {'booster': 'dart', 'learning_rate': 0.09919798064421187, 'max_depth': 12, 'subsample': 0.5273159894506126, 'reg_alpha': 0.026338254765791297, 'reg_lambda': 9.266817252119619e-08, 'gamma': 1.185094679521512e-06, 'min_child_weight': 5}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,488]\u001b[0m Trial 55 finished with value: 32062.94255976484 and parameters: {'booster': 'gblinear', 'learning_rate': 0.06899253638398445, 'max_depth': 12, 'subsample': 0.010926864161877561, 'reg_alpha': 0.03507202740694855, 'reg_lambda': 7.056708482047252e-08, 'gamma': 5.87437044504508e-07, 'min_child_weight': 4}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,580]\u001b[0m Trial 56 finished with value: 26305.198991221027 and parameters: {'booster': 'dart', 'learning_rate': 0.053916417984915144, 'max_depth': 13, 'subsample': 0.37790243563261067, 'reg_alpha': 0.003341745881375623, 'reg_lambda': 1.070330698880734e-08, 'gamma': 9.855483307084453e-07, 'min_child_weight': 5}. Best is trial 36 with value: 12950.753961761135.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,694]\u001b[0m Trial 57 finished with value: 12934.179290981116 and parameters: {'booster': 'dart', 'learning_rate': 0.09948082386929613, 'max_depth': 14, 'subsample': 0.6744888237730858, 'reg_alpha': 0.0002199041044908951, 'reg_lambda': 1.22667595442369e-07, 'gamma': 3.4506847489235316e-07, 'min_child_weight': 5}. Best is trial 57 with value: 12934.179290981116.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,807]\u001b[0m Trial 58 finished with value: 18676.749363659026 and parameters: {'booster': 'dart', 'learning_rate': 0.07457326941715425, 'max_depth': 14, 'subsample': 0.5012004149770015, 'reg_alpha': 0.0001476424044183128, 'reg_lambda': 8.718366302664367e-07, 'gamma': 3.942543475863384e-06, 'min_child_weight': 5}. Best is trial 57 with value: 12934.179290981116.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:29,939]\u001b[0m Trial 59 finished with value: 39787.29343188874 and parameters: {'booster': 'dart', 'learning_rate': 0.02939941074214325, 'max_depth': 15, 'subsample': 0.6942764636310094, 'reg_alpha': 0.0008285692247928308, 'reg_lambda': 3.686542513396589e-06, 'gamma': 5.4059942150852165e-05, 'min_child_weight': 4}. Best is trial 57 with value: 12934.179290981116.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,053]\u001b[0m Trial 60 finished with value: 34178.81158485733 and parameters: {'booster': 'dart', 'learning_rate': 0.03824034069934962, 'max_depth': 13, 'subsample': 0.5290200217179148, 'reg_alpha': 1.877886611356292e-05, 'reg_lambda': 8.057042514522709e-06, 'gamma': 1.5123177567743892e-05, 'min_child_weight': 3}. Best is trial 57 with value: 12934.179290981116.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,163]\u001b[0m Trial 61 finished with value: 13503.986578509337 and parameters: {'booster': 'dart', 'learning_rate': 0.09653180047583469, 'max_depth': 12, 'subsample': 0.8279527750812681, 'reg_alpha': 0.009245056025441564, 'reg_lambda': 1.1836017051920967e-07, 'gamma': 4.4337616213661476e-07, 'min_child_weight': 5}. Best is trial 57 with value: 12934.179290981116.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,293]\u001b[0m Trial 62 finished with value: 12916.428781067303 and parameters: {'booster': 'dart', 'learning_rate': 0.09922211029419853, 'max_depth': 14, 'subsample': 0.7853849200738878, 'reg_alpha': 0.007788488418069486, 'reg_lambda': 1.170711485797375e-07, 'gamma': 4.3969659042658095e-07, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,416]\u001b[0m Trial 63 finished with value: 25857.267762447078 and parameters: {'booster': 'dart', 'learning_rate': 0.054214472129740475, 'max_depth': 14, 'subsample': 0.6046319478257357, 'reg_alpha': 0.011012603036980855, 'reg_lambda': 1.1831638705359057e-07, 'gamma': 1.213948401985772e-06, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,533]\u001b[0m Trial 64 finished with value: 18336.156592473515 and parameters: {'booster': 'dart', 'learning_rate': 0.0755881417285258, 'max_depth': 14, 'subsample': 0.4728990636322652, 'reg_alpha': 0.0018060170629451114, 'reg_lambda': 2.3108528180031334e-08, 'gamma': 4.0224993689273426e-07, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,561]\u001b[0m Trial 65 finished with value: 39527.62439498719 and parameters: {'booster': 'gblinear', 'learning_rate': 0.04391270700216654, 'max_depth': 13, 'subsample': 0.3665201910236355, 'reg_alpha': 0.0003883117224014879, 'reg_lambda': 4.8173983120775976e-08, 'gamma': 3.402347315044784e-07, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,701]\u001b[0m Trial 66 finished with value: 22262.151993256182 and parameters: {'booster': 'dart', 'learning_rate': 0.06319996456199861, 'max_depth': 15, 'subsample': 0.6599433237590587, 'reg_alpha': 0.006736493192414329, 'reg_lambda': 1.1206820566117275e-07, 'gamma': 1.474256335793249e-07, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,817]\u001b[0m Trial 67 finished with value: 17476.240312575406 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0784588194124887, 'max_depth': 13, 'subsample': 0.8118965079978198, 'reg_alpha': 0.021540777101762935, 'reg_lambda': 6.813426661013652e-05, 'gamma': 3.5998213163535537e-06, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:30,910]\u001b[0m Trial 68 finished with value: 13283.763644278513 and parameters: {'booster': 'dart', 'learning_rate': 0.09933778489115767, 'max_depth': 12, 'subsample': 0.2868444443197143, 'reg_alpha': 0.002970910953521864, 'reg_lambda': 0.029469873809027512, 'gamma': 2.0317884235115127e-05, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,011]\u001b[0m Trial 69 finished with value: 67028.8147076849 and parameters: {'booster': 'dart', 'learning_rate': 0.0009403513180043903, 'max_depth': 14, 'subsample': 0.19785786933601382, 'reg_alpha': 5.8309738917555175e-05, 'reg_lambda': 0.025606437802567633, 'gamma': 2.0138567925249112e-05, 'min_child_weight': 3}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,108]\u001b[0m Trial 70 finished with value: 25091.934577729942 and parameters: {'booster': 'dart', 'learning_rate': 0.05730472147984214, 'max_depth': 12, 'subsample': 0.29128283285390777, 'reg_alpha': 0.000902110060026961, 'reg_lambda': 0.1290043400261122, 'gamma': 2.8289075006406857e-05, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,219]\u001b[0m Trial 71 finished with value: 17819.00010590437 and parameters: {'booster': 'dart', 'learning_rate': 0.07867951160696739, 'max_depth': 12, 'subsample': 0.6941606800284335, 'reg_alpha': 0.00319697381482127, 'reg_lambda': 0.251882047525814, 'gamma': 1.655678711850053e-06, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,325]\u001b[0m Trial 72 finished with value: 13127.620622474738 and parameters: {'booster': 'dart', 'learning_rate': 0.0988875962615116, 'max_depth': 12, 'subsample': 0.8566837896791011, 'reg_alpha': 0.010347251861585024, 'reg_lambda': 0.0009826572071939027, 'gamma': 0.00014169911725690635, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,453]\u001b[0m Trial 73 finished with value: 13379.716416617872 and parameters: {'booster': 'dart', 'learning_rate': 0.09751254776007008, 'max_depth': 12, 'subsample': 0.7840051727162519, 'reg_alpha': 0.00041229090455314305, 'reg_lambda': 0.0011849184024689539, 'gamma': 0.0002099994907341684, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,556]\u001b[0m Trial 74 finished with value: 32206.37958585916 and parameters: {'booster': 'dart', 'learning_rate': 0.041991065438615166, 'max_depth': 13, 'subsample': 0.45046965071766903, 'reg_alpha': 0.00034234801523433595, 'reg_lambda': 0.0008195659155189047, 'gamma': 0.0004986399348251743, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,668]\u001b[0m Trial 75 finished with value: 17108.325554017163 and parameters: {'booster': 'dart', 'learning_rate': 0.08060818636467958, 'max_depth': 14, 'subsample': 0.5565457960041917, 'reg_alpha': 0.0017613562827097423, 'reg_lambda': 0.00023815626274931807, 'gamma': 0.0003179494545340303, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,781]\u001b[0m Trial 76 finished with value: 23667.128197975853 and parameters: {'booster': 'dart', 'learning_rate': 0.059867221613001655, 'max_depth': 15, 'subsample': 0.7268736047105934, 'reg_alpha': 7.540767167612815e-05, 'reg_lambda': 0.0030270025773214645, 'gamma': 9.411444582604504e-05, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,886]\u001b[0m Trial 77 finished with value: 17092.66040498868 and parameters: {'booster': 'dart', 'learning_rate': 0.08028128747863647, 'max_depth': 12, 'subsample': 0.8730076769606718, 'reg_alpha': 0.0002996428573276585, 'reg_lambda': 0.001717488026096207, 'gamma': 0.00015066416425311277, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:31,960]\u001b[0m Trial 78 finished with value: 13236.6656577301 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09976895453170544, 'max_depth': 13, 'subsample': 0.2302059570144308, 'reg_alpha': 0.0001712571760809132, 'reg_lambda': 0.0011180079256912097, 'gamma': 0.0014865337578905944, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,040]\u001b[0m Trial 79 finished with value: 37763.580054727834 and parameters: {'booster': 'gbtree', 'learning_rate': 0.03291220621827087, 'max_depth': 14, 'subsample': 0.23195485601827096, 'reg_alpha': 0.00013964974087228536, 'reg_lambda': 0.007009621654585382, 'gamma': 0.012315537630912855, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,108]\u001b[0m Trial 80 finished with value: 27879.957464369334 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0516517035334715, 'max_depth': 13, 'subsample': 0.1353829287678348, 'reg_alpha': 3.742094724755243e-05, 'reg_lambda': 0.029767317572013563, 'gamma': 0.0028105204909103032, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,161]\u001b[0m Trial 81 finished with value: 24312.825583713584 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06716750719024173, 'max_depth': 6, 'subsample': 0.6220840954747802, 'reg_alpha': 0.0005462649118776624, 'reg_lambda': 0.0003959715307930792, 'gamma': 0.0026900353323012502, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,223]\u001b[0m Trial 82 finished with value: 13950.86427575409 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09888592097684215, 'max_depth': 13, 'subsample': 0.09449596506325388, 'reg_alpha': 0.00017676181735663254, 'reg_lambda': 0.00015021393348381188, 'gamma': 0.0006764116494191072, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,305]\u001b[0m Trial 83 finished with value: 17008.098461948503 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08215745610901586, 'max_depth': 12, 'subsample': 0.30753764562293423, 'reg_alpha': 0.0012480968130114236, 'reg_lambda': 0.001227570085089527, 'gamma': 0.005787650872463108, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,418]\u001b[0m Trial 84 finished with value: 21115.755089248752 and parameters: {'booster': 'dart', 'learning_rate': 0.06665398354412332, 'max_depth': 13, 'subsample': 0.8567379724090657, 'reg_alpha': 0.0044007519322982955, 'reg_lambda': 0.0010660557975847465, 'gamma': 0.03623204562069788, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,448]\u001b[0m Trial 85 finished with value: 27129.14548046842 and parameters: {'booster': 'gblinear', 'learning_rate': 0.09956425677164647, 'max_depth': 12, 'subsample': 0.19705537000512735, 'reg_alpha': 0.0005272563889198365, 'reg_lambda': 1.9883997706664965e-05, 'gamma': 4.0327465469045335e-05, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,528]\u001b[0m Trial 86 finished with value: 28233.003353638243 and parameters: {'booster': 'dart', 'learning_rate': 0.050243543323680216, 'max_depth': 13, 'subsample': 0.2483089909513273, 'reg_alpha': 0.002441461050291542, 'reg_lambda': 0.004360029872937009, 'gamma': 0.0011566715009888788, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,628]\u001b[0m Trial 87 finished with value: 16563.26655429549 and parameters: {'booster': 'dart', 'learning_rate': 0.0830955630478451, 'max_depth': 14, 'subsample': 0.4049012641993992, 'reg_alpha': 0.015187753342811831, 'reg_lambda': 0.0003224191444436579, 'gamma': 6.888067526236436e-06, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,729]\u001b[0m Trial 88 finished with value: 24628.119604079846 and parameters: {'booster': 'dart', 'learning_rate': 0.057590280545837785, 'max_depth': 11, 'subsample': 0.7374003363600435, 'reg_alpha': 0.026644300958555474, 'reg_lambda': 0.010554183931309588, 'gamma': 0.00013391121178621287, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,830]\u001b[0m Trial 89 finished with value: 20234.458816501712 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06990129481555486, 'max_depth': 12, 'subsample': 0.9024961270154008, 'reg_alpha': 3.8148976159190516e-05, 'reg_lambda': 0.0006433899795771243, 'gamma': 0.0012775630736548001, 'min_child_weight': 7}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:32,943]\u001b[0m Trial 90 finished with value: 65819.07635621929 and parameters: {'booster': 'dart', 'learning_rate': 0.0019186462701532819, 'max_depth': 13, 'subsample': 0.9953785873575237, 'reg_alpha': 4.687208731313904e-06, 'reg_lambda': 8.359524988497094e-05, 'gamma': 9.518640802026665e-06, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,048]\u001b[0m Trial 91 finished with value: 15627.234004605212 and parameters: {'booster': 'dart', 'learning_rate': 0.08636271702985952, 'max_depth': 12, 'subsample': 0.8163937208725776, 'reg_alpha': 0.00820440342568766, 'reg_lambda': 0.002408168663985697, 'gamma': 7.564060252682704e-07, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,153]\u001b[0m Trial 92 finished with value: 13489.901940491689 and parameters: {'booster': 'dart', 'learning_rate': 0.09774046648815363, 'max_depth': 12, 'subsample': 0.5435018828202192, 'reg_alpha': 0.008829178390661338, 'reg_lambda': 5.709666390958911e-08, 'gamma': 0.00020735816374243694, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,257]\u001b[0m Trial 93 finished with value: 13205.786809878618 and parameters: {'booster': 'dart', 'learning_rate': 0.09899264745355128, 'max_depth': 12, 'subsample': 0.5679361578784494, 'reg_alpha': 0.0001934629849579497, 'reg_lambda': 5.286582055754261e-08, 'gamma': 0.00017777376955245198, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,355]\u001b[0m Trial 94 finished with value: 20299.776298790766 and parameters: {'booster': 'dart', 'learning_rate': 0.06980881966266823, 'max_depth': 11, 'subsample': 0.6734289409867232, 'reg_alpha': 0.00022173162246488944, 'reg_lambda': 2.398104614455954e-08, 'gamma': 0.00010375600253212223, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,456]\u001b[0m Trial 95 finished with value: 16629.23045996539 and parameters: {'booster': 'dart', 'learning_rate': 0.082855250025845, 'max_depth': 12, 'subsample': 0.46715196429596495, 'reg_alpha': 0.00010595040025296639, 'reg_lambda': 1.6197990735808178e-08, 'gamma': 0.0004415742582707442, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,563]\u001b[0m Trial 96 finished with value: 28650.695890916882 and parameters: {'booster': 'dart', 'learning_rate': 0.048493881638193896, 'max_depth': 13, 'subsample': 0.5893752470001064, 'reg_alpha': 0.0009889315428164834, 'reg_lambda': 3.334941323108768e-08, 'gamma': 0.0007854673779012909, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,649]\u001b[0m Trial 97 finished with value: 67817.19558528543 and parameters: {'booster': 'dart', 'learning_rate': 0.00029280977802993957, 'max_depth': 11, 'subsample': 0.3748313970701265, 'reg_alpha': 0.0004654387187596518, 'reg_lambda': 0.04452923837988147, 'gamma': 7.59253788427117e-05, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,729]\u001b[0m Trial 98 finished with value: 24304.859037712464 and parameters: {'booster': 'dart', 'learning_rate': 0.05991216953608916, 'max_depth': 12, 'subsample': 0.13919950292245983, 'reg_alpha': 0.004294701741364476, 'reg_lambda': 3.469913112573504e-07, 'gamma': 3.4329267061366866e-05, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,757]\u001b[0m Trial 99 finished with value: 41360.14355042285 and parameters: {'booster': 'gblinear', 'learning_rate': 0.03943579773794203, 'max_depth': 14, 'subsample': 0.7571877247928211, 'reg_alpha': 1.8492489438458358e-05, 'reg_lambda': 0.0016798854943714055, 'gamma': 0.0002652483481628783, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,848]\u001b[0m Trial 100 finished with value: 61052.75477456386 and parameters: {'booster': 'dart', 'learning_rate': 0.006046940956248259, 'max_depth': 11, 'subsample': 0.5226127971161251, 'reg_alpha': 9.430184562287897e-05, 'reg_lambda': 0.004694670298495432, 'gamma': 0.31873016926300185, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:33,952]\u001b[0m Trial 101 finished with value: 13028.631729096944 and parameters: {'booster': 'dart', 'learning_rate': 0.09950369400327526, 'max_depth': 12, 'subsample': 0.6440519752555501, 'reg_alpha': 0.002096931396846769, 'reg_lambda': 6.868048806774063e-08, 'gamma': 0.00021905233402497065, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,057]\u001b[0m Trial 102 finished with value: 12980.451525721192 and parameters: {'booster': 'dart', 'learning_rate': 0.09950442054828103, 'max_depth': 12, 'subsample': 0.6605324322029021, 'reg_alpha': 0.0021962066118857435, 'reg_lambda': 7.80273593657185e-08, 'gamma': 0.0003635348766477924, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,159]\u001b[0m Trial 103 finished with value: 15775.67101262158 and parameters: {'booster': 'dart', 'learning_rate': 0.08557001861670302, 'max_depth': 12, 'subsample': 0.6337470102042452, 'reg_alpha': 0.002280071121572315, 'reg_lambda': 1.955065990623542e-07, 'gamma': 0.0003602480955532199, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,252]\u001b[0m Trial 104 finished with value: 19574.383287588527 and parameters: {'booster': 'dart', 'learning_rate': 0.07197888247205594, 'max_depth': 13, 'subsample': 0.3309821457073707, 'reg_alpha': 0.006116493200594696, 'reg_lambda': 7.303092278355861e-08, 'gamma': 0.0007841685303282246, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,341]\u001b[0m Trial 105 finished with value: 13082.394209095466 and parameters: {'booster': 'dart', 'learning_rate': 0.09996405891109218, 'max_depth': 11, 'subsample': 0.4929562621006787, 'reg_alpha': 0.0921632112697454, 'reg_lambda': 8.900449683500462e-08, 'gamma': 0.00016089767166561117, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,435]\u001b[0m Trial 106 finished with value: 22558.171057236978 and parameters: {'booster': 'dart', 'learning_rate': 0.06335864042808685, 'max_depth': 11, 'subsample': 0.49072868868644476, 'reg_alpha': 0.07487840194497325, 'reg_lambda': 5.246099191692744e-08, 'gamma': 0.0017934408547560783, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,537]\u001b[0m Trial 107 finished with value: 62477.50459447279 and parameters: {'booster': 'dart', 'learning_rate': 0.004775072971794493, 'max_depth': 11, 'subsample': 0.5804500369463871, 'reg_alpha': 0.7056390049369443, 'reg_lambda': 1.4154919003612424e-08, 'gamma': 0.00015408279902445182, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,645]\u001b[0m Trial 108 finished with value: 18713.792423998155 and parameters: {'booster': 'dart', 'learning_rate': 0.07430345969631605, 'max_depth': 12, 'subsample': 0.7038902228626152, 'reg_alpha': 0.034360973793103024, 'reg_lambda': 9.025749701029812e-08, 'gamma': 5.88453663123968e-05, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,777]\u001b[0m Trial 109 finished with value: 15590.123120676744 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08675533554568612, 'max_depth': 13, 'subsample': 0.4183117908926475, 'reg_alpha': 0.11320657947712105, 'reg_lambda': 3.204092771298967e-08, 'gamma': 0.0005269596285430542, 'min_child_weight': 1}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,848]\u001b[0m Trial 110 finished with value: 26881.018671649934 and parameters: {'booster': 'dart', 'learning_rate': 0.05466773828493264, 'max_depth': 12, 'subsample': 0.06073009202911822, 'reg_alpha': 0.01916171061307935, 'reg_lambda': 5.062183448481681e-07, 'gamma': 0.00010488172083580565, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:34,958]\u001b[0m Trial 111 finished with value: 12977.379115562298 and parameters: {'booster': 'dart', 'learning_rate': 0.099592320879375, 'max_depth': 12, 'subsample': 0.6580409381777489, 'reg_alpha': 0.01290358158009614, 'reg_lambda': 1.426766801135476e-07, 'gamma': 0.00019496243142487317, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,060]\u001b[0m Trial 112 finished with value: 16257.483306368043 and parameters: {'booster': 'dart', 'learning_rate': 0.08416387197529464, 'max_depth': 11, 'subsample': 0.9318480483815008, 'reg_alpha': 0.04576757912889963, 'reg_lambda': 1.5481450765819932e-07, 'gamma': 0.0002178051618592414, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,171]\u001b[0m Trial 113 finished with value: 19870.97052620191 and parameters: {'booster': 'dart', 'learning_rate': 0.07066537574874582, 'max_depth': 12, 'subsample': 0.6799930879703628, 'reg_alpha': 0.013321319323117354, 'reg_lambda': 4.5561138148160936e-08, 'gamma': 1.5560019649849942e-08, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,285]\u001b[0m Trial 114 finished with value: 15099.5253342235 and parameters: {'booster': 'dart', 'learning_rate': 0.08844302243818802, 'max_depth': 13, 'subsample': 0.6470096139209658, 'reg_alpha': 0.028401432883437314, 'reg_lambda': 2.5915412746081766e-07, 'gamma': 0.00034003085456535425, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,388]\u001b[0m Trial 115 finished with value: 22762.34704345784 and parameters: {'booster': 'dart', 'learning_rate': 0.06247446933187783, 'max_depth': 12, 'subsample': 0.5256471987076381, 'reg_alpha': 3.483697358287099e-07, 'reg_lambda': 1.011792847449716e-07, 'gamma': 2.0111375074530493e-07, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,483]\u001b[0m Trial 116 finished with value: 18565.714166364378 and parameters: {'booster': 'dart', 'learning_rate': 0.07549466784572804, 'max_depth': 11, 'subsample': 0.8862376074062918, 'reg_alpha': 0.20958642221333823, 'reg_lambda': 1.6503942963092538e-07, 'gamma': 0.000942044399354677, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,615]\u001b[0m Trial 117 finished with value: 29337.984549062014 and parameters: {'booster': 'dart', 'learning_rate': 0.046858475462660934, 'max_depth': 15, 'subsample': 0.8011771901930174, 'reg_alpha': 0.37728113886556125, 'reg_lambda': 6.713511698068025e-08, 'gamma': 0.00017419882084014076, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,723]\u001b[0m Trial 118 finished with value: 13008.729486516562 and parameters: {'booster': 'dart', 'learning_rate': 0.09985315531009063, 'max_depth': 12, 'subsample': 0.6020400695438112, 'reg_alpha': 0.005543009475188591, 'reg_lambda': 3.244116295715471e-08, 'gamma': 0.002053490008620054, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,833]\u001b[0m Trial 119 finished with value: 16544.740730586393 and parameters: {'booster': 'dart', 'learning_rate': 0.08286871206091798, 'max_depth': 12, 'subsample': 0.5818923297158505, 'reg_alpha': 0.004082013065921707, 'reg_lambda': 2.376599910877237e-08, 'gamma': 4.687660303337437e-08, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:35,946]\u001b[0m Trial 120 finished with value: 23592.53749552531 and parameters: {'booster': 'dart', 'learning_rate': 0.06034776160294904, 'max_depth': 12, 'subsample': 0.4559675072115416, 'reg_alpha': 0.0015966455527551642, 'reg_lambda': 3.628812419830639e-08, 'gamma': 8.048134116937336e-05, 'min_child_weight': 3}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,057]\u001b[0m Trial 121 finished with value: 13123.336925045245 and parameters: {'booster': 'dart', 'learning_rate': 0.09912501772903852, 'max_depth': 13, 'subsample': 0.727283953555801, 'reg_alpha': 0.006636412312043978, 'reg_lambda': 1.4601773376832646e-08, 'gamma': 0.0049863855698719945, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,119]\u001b[0m Trial 122 finished with value: 16800.684510733052 and parameters: {'booster': 'dart', 'learning_rate': 0.09033232578963082, 'max_depth': 12, 'subsample': 0.04063999298979639, 'reg_alpha': 0.011263014432756683, 'reg_lambda': 1.0803252646123985e-08, 'gamma': 0.006095319830894964, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,232]\u001b[0m Trial 123 finished with value: 19576.04518517503 and parameters: {'booster': 'dart', 'learning_rate': 0.07141132156492978, 'max_depth': 13, 'subsample': 0.7309632504196304, 'reg_alpha': 0.006799723825548001, 'reg_lambda': 9.16197747452292e-08, 'gamma': 0.0046251173563781394, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,330]\u001b[0m Trial 124 finished with value: 12924.738660402661 and parameters: {'booster': 'dart', 'learning_rate': 0.09990163450553506, 'max_depth': 12, 'subsample': 0.6383737882632476, 'reg_alpha': 0.02021892199380148, 'reg_lambda': 2.202664501283768e-08, 'gamma': 0.02691504921835983, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,428]\u001b[0m Trial 125 finished with value: 13055.427339743872 and parameters: {'booster': 'dart', 'learning_rate': 0.09980198590298502, 'max_depth': 11, 'subsample': 0.6165462518662888, 'reg_alpha': 0.018133990530881137, 'reg_lambda': 1.6464086232853816e-08, 'gamma': 0.10406515861086488, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,511]\u001b[0m Trial 126 finished with value: 18161.40329772272 and parameters: {'booster': 'dart', 'learning_rate': 0.07725020754390216, 'max_depth': 10, 'subsample': 0.6441859941110035, 'reg_alpha': 0.018716454551604006, 'reg_lambda': 1.5391437872574996e-08, 'gamma': 0.13150127234414252, 'min_child_weight': 6}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,609]\u001b[0m Trial 127 finished with value: 15892.614534617956 and parameters: {'booster': 'dart', 'learning_rate': 0.08551989705190204, 'max_depth': 11, 'subsample': 0.7432482857707201, 'reg_alpha': 0.0055768668885615965, 'reg_lambda': 1.8989341514596853e-08, 'gamma': 0.02132320788913012, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,718]\u001b[0m Trial 128 finished with value: 21267.74009049641 and parameters: {'booster': 'dart', 'learning_rate': 0.06632989193110045, 'max_depth': 14, 'subsample': 0.4960345125096894, 'reg_alpha': 0.014167833684206486, 'reg_lambda': 2.8560512276803697e-08, 'gamma': 0.029017442356359283, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,812]\u001b[0m Trial 129 finished with value: 25423.33347347041 and parameters: {'booster': 'dart', 'learning_rate': 0.055625877517230235, 'max_depth': 11, 'subsample': 0.6305483827689077, 'reg_alpha': 0.022225004693312324, 'reg_lambda': 9.399258915454191e-07, 'gamma': 0.10343946382316896, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:36,925]\u001b[0m Trial 130 finished with value: 12947.458081821844 and parameters: {'booster': 'dart', 'learning_rate': 0.09973503613086071, 'max_depth': 12, 'subsample': 0.8478807836653777, 'reg_alpha': 0.05379602744510178, 'reg_lambda': 1.3793345209861691e-08, 'gamma': 0.012351505611722888, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,037]\u001b[0m Trial 131 finished with value: 13010.747717577015 and parameters: {'booster': 'dart', 'learning_rate': 0.09928703033466701, 'max_depth': 12, 'subsample': 0.8260761779591557, 'reg_alpha': 0.0548543187504826, 'reg_lambda': 1.2962879113932381e-08, 'gamma': 0.012252573993793701, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,153]\u001b[0m Trial 132 finished with value: 13011.984496522691 and parameters: {'booster': 'dart', 'learning_rate': 0.09932695974910837, 'max_depth': 12, 'subsample': 0.8619338812377495, 'reg_alpha': 0.05129330722286063, 'reg_lambda': 1.0451633070805135e-08, 'gamma': 0.00902275691096444, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,290]\u001b[0m Trial 133 finished with value: 18132.42414004165 and parameters: {'booster': 'dart', 'learning_rate': 0.07689123458193024, 'max_depth': 12, 'subsample': 0.9980235041146777, 'reg_alpha': 0.06544319218170162, 'reg_lambda': 1.2853869025114808e-08, 'gamma': 0.010786472928640453, 'min_child_weight': 3}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,402]\u001b[0m Trial 134 finished with value: 15565.652128804597 and parameters: {'booster': 'dart', 'learning_rate': 0.08654481858716202, 'max_depth': 12, 'subsample': 0.8044603070828126, 'reg_alpha': 0.04000856649589465, 'reg_lambda': 1.0862883719654172e-08, 'gamma': 0.014854522853682333, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,522]\u001b[0m Trial 135 finished with value: 15234.517038070908 and parameters: {'booster': 'dart', 'learning_rate': 0.08792007312455391, 'max_depth': 13, 'subsample': 0.7123789682778416, 'reg_alpha': 0.12950938108323493, 'reg_lambda': 2.545886899061669e-08, 'gamma': 0.009759634391689467, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,553]\u001b[0m Trial 136 finished with value: 27149.38888267571 and parameters: {'booster': 'gblinear', 'learning_rate': 0.09940454215984733, 'max_depth': 11, 'subsample': 0.9018979248127729, 'reg_alpha': 0.047252105463693524, 'reg_lambda': 1.871764951355364e-08, 'gamma': 0.061645247959021744, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,670]\u001b[0m Trial 137 finished with value: 21052.18878985479 and parameters: {'booster': 'dart', 'learning_rate': 0.06670463455907301, 'max_depth': 12, 'subsample': 0.7645208941957969, 'reg_alpha': 0.07312795491165765, 'reg_lambda': 3.391233367284066e-08, 'gamma': 0.019678876369596712, 'min_child_weight': 3}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,771]\u001b[0m Trial 138 finished with value: 17828.930120265995 and parameters: {'booster': 'dart', 'learning_rate': 0.07780790594235268, 'max_depth': 11, 'subsample': 0.672871502932467, 'reg_alpha': 0.09474842473756251, 'reg_lambda': 1.004434098363443e-08, 'gamma': 0.007422425031125893, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,856]\u001b[0m Trial 139 finished with value: 19120.891447666367 and parameters: {'booster': 'dart', 'learning_rate': 0.07514539017599367, 'max_depth': 9, 'subsample': 0.8635947812800528, 'reg_alpha': 0.0025485555722136294, 'reg_lambda': 4.354779532111576e-08, 'gamma': 0.0037383516624852463, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:37,970]\u001b[0m Trial 140 finished with value: 58269.43297592604 and parameters: {'booster': 'dart', 'learning_rate': 0.008557423898566006, 'max_depth': 13, 'subsample': 0.5763096917655861, 'reg_alpha': 0.017181763549806033, 'reg_lambda': 1.7679696677075504e-08, 'gamma': 0.03866318182784801, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,091]\u001b[0m Trial 141 finished with value: 13138.205621792637 and parameters: {'booster': 'dart', 'learning_rate': 0.09856888657814104, 'max_depth': 12, 'subsample': 0.8165776255848243, 'reg_alpha': 0.009997081431016111, 'reg_lambda': 6.418502746893944e-08, 'gamma': 0.0651523997456652, 'min_child_weight': 3}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,201]\u001b[0m Trial 142 finished with value: 13085.606602707492 and parameters: {'booster': 'dart', 'learning_rate': 0.09906470668957035, 'max_depth': 12, 'subsample': 0.9051813419317422, 'reg_alpha': 0.007854665019797286, 'reg_lambda': 2.214301615364795e-08, 'gamma': 0.0024478955670135197, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,313]\u001b[0m Trial 143 finished with value: 15613.444562635466 and parameters: {'booster': 'dart', 'learning_rate': 0.08632054056274124, 'max_depth': 12, 'subsample': 0.9157088214584158, 'reg_alpha': 0.005737998313580842, 'reg_lambda': 2.086010729973373e-08, 'gamma': 0.0030836657526072324, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,423]\u001b[0m Trial 144 finished with value: 20768.38076772598 and parameters: {'booster': 'dart', 'learning_rate': 0.06765423205416302, 'max_depth': 12, 'subsample': 0.7164842855650093, 'reg_alpha': 0.02867770281209702, 'reg_lambda': 2.8904950196728498e-08, 'gamma': 0.002031131073412333, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,529]\u001b[0m Trial 145 finished with value: 13018.947563961052 and parameters: {'booster': 'dart', 'learning_rate': 0.09935975659073445, 'max_depth': 12, 'subsample': 0.6327373367195203, 'reg_alpha': 0.007367692014290414, 'reg_lambda': 1.6140510197784894e-08, 'gamma': 0.004694751190451703, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,643]\u001b[0m Trial 146 finished with value: 16074.167716234007 and parameters: {'booster': 'dart', 'learning_rate': 0.08435740517893542, 'max_depth': 12, 'subsample': 0.6427161633451718, 'reg_alpha': 0.0505681943567721, 'reg_lambda': 4.340023897051501e-08, 'gamma': 0.016600180412999887, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,736]\u001b[0m Trial 147 finished with value: 23066.270517852437 and parameters: {'booster': 'dart', 'learning_rate': 0.06212388146087142, 'max_depth': 10, 'subsample': 0.6058942283882292, 'reg_alpha': 0.003357893838969345, 'reg_lambda': 1.0048884636391898e-08, 'gamma': 0.008784969217618368, 'min_child_weight': 4}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,846]\u001b[0m Trial 148 finished with value: 18503.807051071548 and parameters: {'booster': 'dart', 'learning_rate': 0.07511426132602836, 'max_depth': 12, 'subsample': 0.7915227403317229, 'reg_alpha': 0.014460692917328972, 'reg_lambda': 1.173205484519201e-07, 'gamma': 0.17500996066719846, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:38,905]\u001b[0m Trial 149 finished with value: 30412.00713243385 and parameters: {'booster': 'dart', 'learning_rate': 0.052794202323208805, 'max_depth': 11, 'subsample': 0.01795089725002691, 'reg_alpha': 0.008216988354802833, 'reg_lambda': 6.396071351685071e-08, 'gamma': 0.027766790832734856, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,012]\u001b[0m Trial 150 finished with value: 67304.99741203668 and parameters: {'booster': 'dart', 'learning_rate': 0.0007018521353635567, 'max_depth': 12, 'subsample': 0.5290754050722906, 'reg_alpha': 1.0756904059282326e-08, 'reg_lambda': 2.2955380127808238e-07, 'gamma': 0.2682342154533937, 'min_child_weight': 5}. Best is trial 62 with value: 12916.428781067303.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,131]\u001b[0m Trial 151 finished with value: 12913.489602297554 and parameters: {'booster': 'dart', 'learning_rate': 0.09982007413696424, 'max_depth': 13, 'subsample': 0.7012382291023969, 'reg_alpha': 0.007156709936386925, 'reg_lambda': 1.4905786919709707e-08, 'gamma': 0.005828159304651638, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,262]\u001b[0m Trial 152 finished with value: 13050.628182686354 and parameters: {'booster': 'dart', 'learning_rate': 0.09919367782171874, 'max_depth': 12, 'subsample': 0.6766506257811838, 'reg_alpha': 0.004906455481552949, 'reg_lambda': 1.855572949103026e-08, 'gamma': 0.00774889263686836, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,402]\u001b[0m Trial 153 finished with value: 16043.555521875094 and parameters: {'booster': 'dart', 'learning_rate': 0.08474442985365382, 'max_depth': 12, 'subsample': 0.6811325063997988, 'reg_alpha': 0.004428384287302967, 'reg_lambda': 1.6091530784588595e-08, 'gamma': 0.007248683603531494, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,530]\u001b[0m Trial 154 finished with value: 15321.016251217601 and parameters: {'booster': 'dart', 'learning_rate': 0.08773976080696598, 'max_depth': 13, 'subsample': 0.5993632866767163, 'reg_alpha': 0.0018483137688543579, 'reg_lambda': 3.680236149613225e-08, 'gamma': 0.012393700354806409, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,663]\u001b[0m Trial 155 finished with value: 18517.725479862973 and parameters: {'booster': 'dart', 'learning_rate': 0.07469373691306057, 'max_depth': 14, 'subsample': 0.7797297534498062, 'reg_alpha': 0.0012051795086518878, 'reg_lambda': 2.7643738452144197e-08, 'gamma': 0.004547755107367678, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,780]\u001b[0m Trial 156 finished with value: 68042.44056342222 and parameters: {'booster': 'dart', 'learning_rate': 0.0001085689516633782, 'max_depth': 12, 'subsample': 0.6634771352312285, 'reg_alpha': 0.02478627426834781, 'reg_lambda': 1.4106119032200582e-08, 'gamma': 0.026293893829019158, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,897]\u001b[0m Trial 157 finished with value: 13193.969238618049 and parameters: {'booster': 'dart', 'learning_rate': 0.09878058834185746, 'max_depth': 12, 'subsample': 0.5705841139284723, 'reg_alpha': 0.03562018116665257, 'reg_lambda': 5.238078707714074e-08, 'gamma': 0.049052654351283115, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:39,933]\u001b[0m Trial 158 finished with value: 65530.54537232049 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0026855393378725327, 'max_depth': 12, 'subsample': 0.4915028555633149, 'reg_alpha': 0.003255189547906586, 'reg_lambda': 1.4596417166741867e-07, 'gamma': 0.007245331676739625, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,033]\u001b[0m Trial 159 finished with value: 15377.75354486098 and parameters: {'booster': 'dart', 'learning_rate': 0.08799792360582241, 'max_depth': 11, 'subsample': 0.62754326987635, 'reg_alpha': 0.012316491835129728, 'reg_lambda': 2.086903619324825e-08, 'gamma': 0.011770600375639631, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,184]\u001b[0m Trial 160 finished with value: 20693.46864734175 and parameters: {'booster': 'dart', 'learning_rate': 0.06751209578152174, 'max_depth': 14, 'subsample': 0.740290640612711, 'reg_alpha': 0.08974500059967469, 'reg_lambda': 8.215855739789011e-08, 'gamma': 0.02032820219293456, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,315]\u001b[0m Trial 161 finished with value: 13411.13427869638 and parameters: {'booster': 'dart', 'learning_rate': 0.09715257767766655, 'max_depth': 12, 'subsample': 0.9067231066494161, 'reg_alpha': 0.019085211221613517, 'reg_lambda': 2.4249400742939394e-08, 'gamma': 0.0026039418986168465, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,437]\u001b[0m Trial 162 finished with value: 13027.567029882366 and parameters: {'booster': 'dart', 'learning_rate': 0.09904696211479685, 'max_depth': 12, 'subsample': 0.8274561810984933, 'reg_alpha': 0.005360296994016585, 'reg_lambda': 1.660573251728112e-08, 'gamma': 0.0031680666249594034, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,551]\u001b[0m Trial 163 finished with value: 17192.406242693753 and parameters: {'booster': 'dart', 'learning_rate': 0.0798169744083284, 'max_depth': 12, 'subsample': 0.8326708948770094, 'reg_alpha': 0.00530850795703067, 'reg_lambda': 1.4638771712407325e-08, 'gamma': 0.004149655705360731, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,674]\u001b[0m Trial 164 finished with value: 13052.638213175436 and parameters: {'booster': 'dart', 'learning_rate': 0.09949294523207079, 'max_depth': 12, 'subsample': 0.6938230357318969, 'reg_alpha': 0.0021016412714663533, 'reg_lambda': 4.030929438242874e-08, 'gamma': 0.7576229142094082, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,791]\u001b[0m Trial 165 finished with value: 18407.596067389994 and parameters: {'booster': 'dart', 'learning_rate': 0.07546281061058763, 'max_depth': 12, 'subsample': 0.6890072504411388, 'reg_alpha': 0.0019789452371359643, 'reg_lambda': 4.045864953336018e-08, 'gamma': 0.7965212468494391, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:40,924]\u001b[0m Trial 166 finished with value: 15873.799911810056 and parameters: {'booster': 'dart', 'learning_rate': 0.08500822182527051, 'max_depth': 13, 'subsample': 0.819040756313391, 'reg_alpha': 0.0026053766382639694, 'reg_lambda': 1.0116898243459937e-08, 'gamma': 0.30525388292633665, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,044]\u001b[0m Trial 167 finished with value: 13004.506204652122 and parameters: {'booster': 'dart', 'learning_rate': 0.0999967826641724, 'max_depth': 12, 'subsample': 0.9788195642616091, 'reg_alpha': 0.0007293292956972822, 'reg_lambda': 3.0850233594975715e-08, 'gamma': 0.009108689253213897, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,162]\u001b[0m Trial 168 finished with value: 24200.183588587923 and parameters: {'booster': 'dart', 'learning_rate': 0.05868638443565959, 'max_depth': 12, 'subsample': 0.953818987425942, 'reg_alpha': 0.0037191187516744956, 'reg_lambda': 3.594379257995591e-08, 'gamma': 0.001680669199816308, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,278]\u001b[0m Trial 169 finished with value: 20582.91007934581 and parameters: {'booster': 'dart', 'learning_rate': 0.06852545081772252, 'max_depth': 12, 'subsample': 0.7493218204935769, 'reg_alpha': 0.0006766164077534199, 'reg_lambda': 4.986553139500614e-08, 'gamma': 0.006125562281730531, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,445]\u001b[0m Trial 170 finished with value: 15455.80563452287 and parameters: {'booster': 'dart', 'learning_rate': 0.08691175346055252, 'max_depth': 15, 'subsample': 0.8758962921047471, 'reg_alpha': 0.0006495586602701344, 'reg_lambda': 3.050167335301957e-08, 'gamma': 0.00898891257723131, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,562]\u001b[0m Trial 171 finished with value: 15175.582158328385 and parameters: {'booster': 'dart', 'learning_rate': 0.088496283228898, 'max_depth': 12, 'subsample': 0.6784471983989108, 'reg_alpha': 0.0012544520718798685, 'reg_lambda': 1.671406017334224e-08, 'gamma': 0.013467114299983032, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,682]\u001b[0m Trial 172 finished with value: 13219.075725642244 and parameters: {'booster': 'dart', 'learning_rate': 0.09874226810721487, 'max_depth': 12, 'subsample': 0.9809566929689263, 'reg_alpha': 0.009247944089163594, 'reg_lambda': 2.1851146060083255e-08, 'gamma': 0.4248019107712241, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,805]\u001b[0m Trial 173 finished with value: 17879.080286872853 and parameters: {'booster': 'dart', 'learning_rate': 0.07730317415948725, 'max_depth': 12, 'subsample': 0.793681688434186, 'reg_alpha': 0.005277539404162814, 'reg_lambda': 1.6192816528358585e-08, 'gamma': 0.005092924945073453, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:41,908]\u001b[0m Trial 174 finished with value: 17229.12946421803 and parameters: {'booster': 'dart', 'learning_rate': 0.08018326463977195, 'max_depth': 12, 'subsample': 0.6225728147140791, 'reg_alpha': 0.0009320038977804998, 'reg_lambda': 6.279789391029069e-08, 'gamma': 0.08051521921090943, 'min_child_weight': 9}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,027]\u001b[0m Trial 175 finished with value: 13259.90159429772 and parameters: {'booster': 'dart', 'learning_rate': 0.09848784305405535, 'max_depth': 13, 'subsample': 0.5603308309332241, 'reg_alpha': 0.0022555015722930086, 'reg_lambda': 1.0103314130392851e-08, 'gamma': 0.003428977970593306, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,149]\u001b[0m Trial 176 finished with value: 15407.071498631916 and parameters: {'booster': 'dart', 'learning_rate': 0.08742106834975304, 'max_depth': 12, 'subsample': 0.7270391992733259, 'reg_alpha': 0.0014372244319810976, 'reg_lambda': 2.6765976768822394e-08, 'gamma': 3.3068689683708705e-07, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,278]\u001b[0m Trial 177 finished with value: 19765.209599255755 and parameters: {'booster': 'dart', 'learning_rate': 0.07039010394581027, 'max_depth': 13, 'subsample': 0.8254060539273729, 'reg_alpha': 0.003934646998998698, 'reg_lambda': 1.4774571929653392e-08, 'gamma': 0.6083157973713142, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,405]\u001b[0m Trial 178 finished with value: 12987.373051434506 and parameters: {'booster': 'dart', 'learning_rate': 0.09949067474347927, 'max_depth': 12, 'subsample': 0.659771310394192, 'reg_alpha': 0.008038597518955026, 'reg_lambda': 3.524411813190528e-08, 'gamma': 0.016390262292969873, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,522]\u001b[0m Trial 179 finished with value: 22742.490417845234 and parameters: {'booster': 'dart', 'learning_rate': 0.062015177560017586, 'max_depth': 12, 'subsample': 0.7598716582670014, 'reg_alpha': 0.008304625220755516, 'reg_lambda': 4.2447834627499144e-08, 'gamma': 1.0791390674887193e-07, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,660]\u001b[0m Trial 180 finished with value: 17502.591275957002 and parameters: {'booster': 'dart', 'learning_rate': 0.07839713802811775, 'max_depth': 12, 'subsample': 0.8580693237473017, 'reg_alpha': 0.0029970708721394315, 'reg_lambda': 1.2277482991806728e-07, 'gamma': 0.017011667705123752, 'min_child_weight': 3}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,780]\u001b[0m Trial 181 finished with value: 14894.259367382854 and parameters: {'booster': 'dart', 'learning_rate': 0.0895031952043042, 'max_depth': 12, 'subsample': 0.6542950287325489, 'reg_alpha': 0.012805246311187617, 'reg_lambda': 2.853707524083016e-08, 'gamma': 0.008333986428984027, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:42,899]\u001b[0m Trial 182 finished with value: 13368.25468972431 and parameters: {'booster': 'dart', 'learning_rate': 0.09765315109933112, 'max_depth': 12, 'subsample': 0.5974718939253473, 'reg_alpha': 0.0065813229144230274, 'reg_lambda': 1.997449410474403e-08, 'gamma': 0.01383674927481042, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,009]\u001b[0m Trial 183 finished with value: 13037.841718886955 and parameters: {'booster': 'dart', 'learning_rate': 0.09992929036407874, 'max_depth': 11, 'subsample': 0.7115947286591918, 'reg_alpha': 4.8770809222846853e-08, 'reg_lambda': 6.490912914158833e-08, 'gamma': 0.04197434154777853, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,132]\u001b[0m Trial 184 finished with value: 15660.051129818443 and parameters: {'booster': 'dart', 'learning_rate': 0.08585113152173693, 'max_depth': 12, 'subsample': 0.7093642905152466, 'reg_alpha': 1.1945571969975767e-06, 'reg_lambda': 8.496149991214045e-08, 'gamma': 0.025691795234926998, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,237]\u001b[0m Trial 185 finished with value: 19125.601003133754 and parameters: {'booster': 'dart', 'learning_rate': 0.07359654908483294, 'max_depth': 12, 'subsample': 0.7626418575684072, 'reg_alpha': 1.555027205903741e-07, 'reg_lambda': 3.887430610667858e-08, 'gamma': 0.040460825489410567, 'min_child_weight': 10}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,359]\u001b[0m Trial 186 finished with value: 13068.985916751819 and parameters: {'booster': 'dart', 'learning_rate': 0.0996008260287076, 'max_depth': 12, 'subsample': 0.9824015636983814, 'reg_alpha': 0.00889151046513992, 'reg_lambda': 6.241525031973076e-08, 'gamma': 0.009806505219356533, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,482]\u001b[0m Trial 187 finished with value: 16821.336508542092 and parameters: {'booster': 'dart', 'learning_rate': 0.08123444108599717, 'max_depth': 13, 'subsample': 0.6834492335875508, 'reg_alpha': 0.005431562171406048, 'reg_lambda': 2.108943686492857e-07, 'gamma': 0.005228281725932607, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,608]\u001b[0m Trial 188 finished with value: 12953.066281236654 and parameters: {'booster': 'dart', 'learning_rate': 0.09991932378401532, 'max_depth': 12, 'subsample': 0.8537076429749321, 'reg_alpha': 0.0017972648655680639, 'reg_lambda': 5.290320274801274e-08, 'gamma': 0.019168678826964863, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,717]\u001b[0m Trial 189 finished with value: 20598.205777147807 and parameters: {'booster': 'dart', 'learning_rate': 0.06863268663264727, 'max_depth': 11, 'subsample': 0.8656955308682114, 'reg_alpha': 0.00422539895888296, 'reg_lambda': 7.517198918855314e-08, 'gamma': 0.032270846783285956, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,753]\u001b[0m Trial 190 finished with value: 28866.80869739483 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0862809898188976, 'max_depth': 12, 'subsample': 0.8295591471708589, 'reg_alpha': 0.0015459486154794928, 'reg_lambda': 2.8344515504892733e-08, 'gamma': 0.017771502126715143, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,875]\u001b[0m Trial 191 finished with value: 15437.289588392525 and parameters: {'booster': 'dart', 'learning_rate': 0.08742049182628515, 'max_depth': 12, 'subsample': 0.7539677499735536, 'reg_alpha': 0.002702229465890588, 'reg_lambda': 4.8269861110575294e-08, 'gamma': 0.006807639045523282, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:43,998]\u001b[0m Trial 192 finished with value: 13003.14832281852 and parameters: {'booster': 'dart', 'learning_rate': 0.099359904226053, 'max_depth': 12, 'subsample': 0.9181824276164255, 'reg_alpha': 0.0009678267852079908, 'reg_lambda': 1.6999620447611646e-07, 'gamma': 4.718539657052584e-07, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,136]\u001b[0m Trial 193 finished with value: 18521.2742303994 and parameters: {'booster': 'dart', 'learning_rate': 0.07494893475644772, 'max_depth': 14, 'subsample': 0.9210895859413888, 'reg_alpha': 0.006868976492181339, 'reg_lambda': 1.4008815887690456e-07, 'gamma': 2.298008136340275e-07, 'min_child_weight': 6}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,262]\u001b[0m Trial 194 finished with value: 13132.078059066997 and parameters: {'booster': 'dart', 'learning_rate': 0.09898389960766728, 'max_depth': 12, 'subsample': 0.996793784696242, 'reg_alpha': 0.0002990672435820794, 'reg_lambda': 3.394156808721934e-07, 'gamma': 4.113005034248751e-07, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,397]\u001b[0m Trial 195 finished with value: 15351.659021609777 and parameters: {'booster': 'dart', 'learning_rate': 0.08729635519656069, 'max_depth': 12, 'subsample': 0.8748924508788165, 'reg_alpha': 0.0010566871720310538, 'reg_lambda': 9.476178081587157e-08, 'gamma': 5.845082465497151e-07, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,473]\u001b[0m Trial 196 finished with value: 19499.376443326855 and parameters: {'booster': 'dart', 'learning_rate': 0.07884716882488192, 'max_depth': 7, 'subsample': 0.7837227417347082, 'reg_alpha': 2.5205148899162105e-08, 'reg_lambda': 1.651533845145708e-07, 'gamma': 1.3945110957735655e-07, 'min_child_weight': 5}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,595]\u001b[0m Trial 197 finished with value: 15656.893021924709 and parameters: {'booster': 'dart', 'learning_rate': 0.08705917628174635, 'max_depth': 12, 'subsample': 0.553664110073745, 'reg_alpha': 0.0006849604940524604, 'reg_lambda': 1.2546876370569597e-08, 'gamma': 0.04766126518906914, 'min_child_weight': 7}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,712]\u001b[0m Trial 198 finished with value: 21727.813608165536 and parameters: {'booster': 'dart', 'learning_rate': 0.06516850777527014, 'max_depth': 11, 'subsample': 0.8411502573243517, 'reg_alpha': 0.013402751678427664, 'reg_lambda': 2.1111735508701445e-08, 'gamma': 0.019828998903924026, 'min_child_weight': 4}. Best is trial 151 with value: 12913.489602297554.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,852]\u001b[0m Trial 199 finished with value: 12870.063485850671 and parameters: {'booster': 'dart', 'learning_rate': 0.09978944491726524, 'max_depth': 13, 'subsample': 0.644558372448844, 'reg_alpha': 0.0040295858882043204, 'reg_lambda': 6.625703647395222e-08, 'gamma': 0.011032014873335638, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:44,993]\u001b[0m Trial 200 finished with value: 17519.096996853405 and parameters: {'booster': 'dart', 'learning_rate': 0.07849736974024019, 'max_depth': 13, 'subsample': 0.6261674650235842, 'reg_alpha': 0.0004451628166917929, 'reg_lambda': 6.919114855603365e-08, 'gamma': 0.013273739897547496, 'min_child_weight': 6}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,148]\u001b[0m Trial 201 finished with value: 14878.73212713046 and parameters: {'booster': 'dart', 'learning_rate': 0.08949470766468193, 'max_depth': 14, 'subsample': 0.6944746569605641, 'reg_alpha': 0.0038953988061816813, 'reg_lambda': 5.549903282330606e-08, 'gamma': 0.010225720600320594, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,286]\u001b[0m Trial 202 finished with value: 13150.485114790608 and parameters: {'booster': 'dart', 'learning_rate': 0.09871120855785305, 'max_depth': 12, 'subsample': 0.7677603779636231, 'reg_alpha': 0.0018477186420744027, 'reg_lambda': 1.0174562750087434e-07, 'gamma': 0.003925743517002766, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,424]\u001b[0m Trial 203 finished with value: 13163.012882731951 and parameters: {'booster': 'dart', 'learning_rate': 0.09862102205338778, 'max_depth': 12, 'subsample': 0.6506606027095143, 'reg_alpha': 0.0029942273927870944, 'reg_lambda': 2.7317375288623842e-08, 'gamma': 0.020553118215708332, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,557]\u001b[0m Trial 204 finished with value: 18182.929013480203 and parameters: {'booster': 'dart', 'learning_rate': 0.0760853578286803, 'max_depth': 12, 'subsample': 0.9117109538361833, 'reg_alpha': 0.005038554865928814, 'reg_lambda': 1.232870856065356e-07, 'gamma': 0.007129782227157485, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,703]\u001b[0m Trial 205 finished with value: 53141.636279937506 and parameters: {'booster': 'dart', 'learning_rate': 0.013603336667846182, 'max_depth': 13, 'subsample': 0.8037837341501617, 'reg_alpha': 0.008597643849439115, 'reg_lambda': 1.3113698824374509e-08, 'gamma': 2.8791725047042434e-07, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,831]\u001b[0m Trial 206 finished with value: 15385.077989416717 and parameters: {'booster': 'dart', 'learning_rate': 0.08729014857593438, 'max_depth': 12, 'subsample': 0.7194116647900048, 'reg_alpha': 0.000922046848314119, 'reg_lambda': 3.589892531084626e-08, 'gamma': 0.030751490410012672, 'min_child_weight': 5}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:45,983]\u001b[0m Trial 207 finished with value: 12923.359702180398 and parameters: {'booster': 'dart', 'learning_rate': 0.09954955689222936, 'max_depth': 14, 'subsample': 0.5822826857600936, 'reg_alpha': 0.15850418868433616, 'reg_lambda': 2.1153355149334603e-07, 'gamma': 0.012037152918521976, 'min_child_weight': 4}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:46,132]\u001b[0m Trial 208 finished with value: 12889.533941839969 and parameters: {'booster': 'dart', 'learning_rate': 0.09992420775324391, 'max_depth': 14, 'subsample': 0.5352129044943014, 'reg_alpha': 0.20474360314581005, 'reg_lambda': 2.433580728914871e-07, 'gamma': 0.013616971623019484, 'min_child_weight': 4}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:46,282]\u001b[0m Trial 209 finished with value: 18988.84742985902 and parameters: {'booster': 'dart', 'learning_rate': 0.07317096843224288, 'max_depth': 14, 'subsample': 0.5291601967886578, 'reg_alpha': 0.06002521642799151, 'reg_lambda': 2.3445702541168176e-07, 'gamma': 0.011958266143687429, 'min_child_weight': 4}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:46,432]\u001b[0m Trial 210 finished with value: 15757.970687897296 and parameters: {'booster': 'dart', 'learning_rate': 0.0857003177377275, 'max_depth': 14, 'subsample': 0.5761517230375511, 'reg_alpha': 0.14409139757850983, 'reg_lambda': 2.829907395395586e-07, 'gamma': 7.433253712631574e-07, 'min_child_weight': 4}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:46,579]\u001b[0m Trial 211 finished with value: 14979.792410043114 and parameters: {'booster': 'dart', 'learning_rate': 0.08913120260603093, 'max_depth': 14, 'subsample': 0.6293732970652164, 'reg_alpha': 0.3044781331164392, 'reg_lambda': 4.0902894496749616e-07, 'gamma': 0.016549271283840747, 'min_child_weight': 4}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:46,726]\u001b[0m Trial 212 finished with value: 15075.53363174307 and parameters: {'booster': 'dart', 'learning_rate': 0.08857898283682276, 'max_depth': 14, 'subsample': 0.5532663642126362, 'reg_alpha': 0.5101722802754848, 'reg_lambda': 1.878973029186218e-07, 'gamma': 0.010149925495396315, 'min_child_weight': 4}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:46,900]\u001b[0m Trial 213 finished with value: 13374.005195609425 and parameters: {'booster': 'dart', 'learning_rate': 0.09737258478329554, 'max_depth': 14, 'subsample': 0.599309566366543, 'reg_alpha': 0.23182187238653912, 'reg_lambda': 1.448159572322105e-07, 'gamma': 0.00032564665080681254, 'min_child_weight': 3}. Best is trial 199 with value: 12870.063485850671.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:47,070]\u001b[0m Trial 214 finished with value: 12828.98127084494 and parameters: {'booster': 'dart', 'learning_rate': 0.0995493084021986, 'max_depth': 14, 'subsample': 0.7330061844547614, 'reg_alpha': 0.15117116761750046, 'reg_lambda': 9.98583166157106e-08, 'gamma': 0.02921411643593191, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:47,252]\u001b[0m Trial 215 finished with value: 18099.846211904034 and parameters: {'booster': 'dart', 'learning_rate': 0.07614688991513929, 'max_depth': 15, 'subsample': 0.902043455440127, 'reg_alpha': 0.1757279875850302, 'reg_lambda': 1.3410450697065442e-05, 'gamma': 0.024043473917414213, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:47,344]\u001b[0m Trial 216 finished with value: 18617.776089690986 and parameters: {'booster': 'dart', 'learning_rate': 0.0799868878314001, 'max_depth': 14, 'subsample': 0.07581219013429322, 'reg_alpha': 0.10797041813108142, 'reg_lambda': 1.8544842786162446e-07, 'gamma': 0.01308567888750817, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:47,506]\u001b[0m Trial 217 finished with value: 12848.605072008702 and parameters: {'booster': 'dart', 'learning_rate': 0.0994698454793699, 'max_depth': 14, 'subsample': 0.806685174508179, 'reg_alpha': 0.7945297269154717, 'reg_lambda': 5.486875645815828e-07, 'gamma': 0.0030145264845215735, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:47,670]\u001b[0m Trial 218 finished with value: 21617.956149114136 and parameters: {'booster': 'dart', 'learning_rate': 0.06490270621520192, 'max_depth': 14, 'subsample': 0.8244787610186572, 'reg_alpha': 0.4479155183804096, 'reg_lambda': 3.614535383618781e-07, 'gamma': 0.002053400156695965, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:47,835]\u001b[0m Trial 219 finished with value: 15105.8579224274 and parameters: {'booster': 'dart', 'learning_rate': 0.08787103581645689, 'max_depth': 14, 'subsample': 0.7651868230644414, 'reg_alpha': 0.2213090879950048, 'reg_lambda': 7.50180836267995e-07, 'gamma': 0.005168332531243275, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,014]\u001b[0m Trial 220 finished with value: 47569.35408863232 and parameters: {'booster': 'dart', 'learning_rate': 0.01961231444165564, 'max_depth': 14, 'subsample': 0.9292494770038447, 'reg_alpha': 0.048584443508801076, 'reg_lambda': 2.725814453574453e-07, 'gamma': 0.003030325308060565, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,169]\u001b[0m Trial 221 finished with value: 13158.87140232791 and parameters: {'booster': 'dart', 'learning_rate': 0.09792393091092882, 'max_depth': 14, 'subsample': 0.6873780276112814, 'reg_alpha': 0.5385575383143699, 'reg_lambda': 1.0603975521990403e-07, 'gamma': 0.0012472637303215697, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,356]\u001b[0m Trial 222 finished with value: 12866.395646090008 and parameters: {'booster': 'dart', 'learning_rate': 0.09982093681355478, 'max_depth': 15, 'subsample': 0.9985956799184253, 'reg_alpha': 0.14531335759110314, 'reg_lambda': 1.3355284248250847e-07, 'gamma': 0.006771899526497646, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,531]\u001b[0m Trial 223 finished with value: 16420.575280010948 and parameters: {'booster': 'dart', 'learning_rate': 0.08244468825710075, 'max_depth': 15, 'subsample': 0.9934801519554233, 'reg_alpha': 0.16611653215090755, 'reg_lambda': 1.6744540145877872e-07, 'gamma': 0.0036389614596517954, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,706]\u001b[0m Trial 224 finished with value: 13080.773169984517 and parameters: {'booster': 'dart', 'learning_rate': 0.09828684887349225, 'max_depth': 15, 'subsample': 0.8617374918535805, 'reg_alpha': 0.30279934899637756, 'reg_lambda': 1.0061858424633102e-07, 'gamma': 0.00580149592038882, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,799]\u001b[0m Trial 225 finished with value: 15356.16919769799 and parameters: {'booster': 'dart', 'learning_rate': 0.09934585689500476, 'max_depth': 15, 'subsample': 0.029297920453866443, 'reg_alpha': 0.9198904607245024, 'reg_lambda': 2.320116581335111e-07, 'gamma': 0.008154060852140842, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:48,974]\u001b[0m Trial 226 finished with value: 17303.59536740369 and parameters: {'booster': 'dart', 'learning_rate': 0.0785125347683394, 'max_depth': 15, 'subsample': 0.8237153568674968, 'reg_alpha': 0.8283129210335204, 'reg_lambda': 1.1509862453595601e-07, 'gamma': 0.01605611224743875, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,015]\u001b[0m Trial 227 finished with value: 28755.768335514604 and parameters: {'booster': 'gblinear', 'learning_rate': 0.08707055756152696, 'max_depth': 14, 'subsample': 0.11782735134631246, 'reg_alpha': 0.13023781415484223, 'reg_lambda': 1.5458945995203007e-07, 'gamma': 0.00997286933995014, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,189]\u001b[0m Trial 228 finished with value: 12872.47273090723 and parameters: {'booster': 'dart', 'learning_rate': 0.09947900482256287, 'max_depth': 14, 'subsample': 0.9251524864251863, 'reg_alpha': 0.06514666429361439, 'reg_lambda': 4.5622182192343964e-07, 'gamma': 0.021506279759752427, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,365]\u001b[0m Trial 229 finished with value: 19926.5301311784 and parameters: {'booster': 'dart', 'learning_rate': 0.07011803817152276, 'max_depth': 14, 'subsample': 0.97900672521404, 'reg_alpha': 0.07262213105437262, 'reg_lambda': 1.4937942344037256e-06, 'gamma': 0.025559566419925975, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,526]\u001b[0m Trial 230 finished with value: 16010.0180238585 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0842235656611656, 'max_depth': 14, 'subsample': 0.9191659501640544, 'reg_alpha': 0.0841961599489373, 'reg_lambda': 4.909263145937223e-07, 'gamma': 0.01626708055650725, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,698]\u001b[0m Trial 231 finished with value: 14917.710015692923 and parameters: {'booster': 'dart', 'learning_rate': 0.08876403978185843, 'max_depth': 14, 'subsample': 0.763223325026429, 'reg_alpha': 0.031822739868658044, 'reg_lambda': 4.970289888024777e-07, 'gamma': 5.040968997796223e-07, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,769]\u001b[0m Trial 232 finished with value: 16277.200966318154 and parameters: {'booster': 'dart', 'learning_rate': 0.09913626056861585, 'max_depth': 6, 'subsample': 0.8748057122799304, 'reg_alpha': 0.05491815159910971, 'reg_lambda': 2.998268230034341e-07, 'gamma': 0.005764163333639747, 'min_child_weight': 3}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:49,938]\u001b[0m Trial 233 finished with value: 15170.393899400891 and parameters: {'booster': 'dart', 'learning_rate': 0.08763446717587908, 'max_depth': 14, 'subsample': 0.799063256457576, 'reg_alpha': 0.12926343985928707, 'reg_lambda': 2.1365203817070823e-07, 'gamma': 0.010865822813509193, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:50,120]\u001b[0m Trial 234 finished with value: 12871.31474372556 and parameters: {'booster': 'dart', 'learning_rate': 0.0994565576354027, 'max_depth': 15, 'subsample': 0.7178686794138163, 'reg_alpha': 0.09829359086529686, 'reg_lambda': 8.248383210925556e-08, 'gamma': 0.02250432093051337, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:50,298]\u001b[0m Trial 235 finished with value: 17331.660884518864 and parameters: {'booster': 'dart', 'learning_rate': 0.07858730704882452, 'max_depth': 15, 'subsample': 0.7334075620858366, 'reg_alpha': 0.09392047799541989, 'reg_lambda': 1.3485959862832587e-07, 'gamma': 0.0231242722701033, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:50,478]\u001b[0m Trial 236 finished with value: 15186.961544150274 and parameters: {'booster': 'dart', 'learning_rate': 0.08782904344065937, 'max_depth': 15, 'subsample': 0.6451026229615324, 'reg_alpha': 0.2069931829722533, 'reg_lambda': 5.3251175664923297e-08, 'gamma': 0.03860854527299352, 'min_child_weight': 4}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:50,733]\u001b[0m Trial 237 finished with value: 13367.06481326637 and parameters: {'booster': 'dart', 'learning_rate': 0.09920804588643341, 'max_depth': 15, 'subsample': 0.9979645336158997, 'reg_alpha': 0.04245803084778625, 'reg_lambda': 9.058457807187373e-08, 'gamma': 0.01308670525208125, 'min_child_weight': 2}. Best is trial 214 with value: 12828.98127084494.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:50,891]\u001b[0m Trial 238 finished with value: 12809.393638349062 and parameters: {'booster': 'dart', 'learning_rate': 0.09987153887032081, 'max_depth': 14, 'subsample': 0.7219755633566101, 'reg_alpha': 0.3577754110246665, 'reg_lambda': 8.638083451840758e-08, 'gamma': 1.8276860451917874e-07, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:51,051]\u001b[0m Trial 239 finished with value: 18542.103583899636 and parameters: {'booster': 'dart', 'learning_rate': 0.07469490439992803, 'max_depth': 14, 'subsample': 0.7183251873822051, 'reg_alpha': 0.39379351004949986, 'reg_lambda': 9.214110459882553e-08, 'gamma': 1.7643051806660458e-07, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:51,227]\u001b[0m Trial 240 finished with value: 17038.30656220982 and parameters: {'booster': 'dart', 'learning_rate': 0.08026024236378407, 'max_depth': 14, 'subsample': 0.8886729967719497, 'reg_alpha': 0.5958343829881044, 'reg_lambda': 7.019743264100924e-08, 'gamma': 3.109891653650743e-07, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:51,379]\u001b[0m Trial 241 finished with value: 13130.13433720052 and parameters: {'booster': 'dart', 'learning_rate': 0.0984456495603442, 'max_depth': 14, 'subsample': 0.621928405319475, 'reg_alpha': 0.289622666336883, 'reg_lambda': 1.4172959859188787e-07, 'gamma': 0.020659761331890934, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:51,541]\u001b[0m Trial 242 finished with value: 14954.372053261808 and parameters: {'booster': 'dart', 'learning_rate': 0.08879819392586635, 'max_depth': 14, 'subsample': 0.6833745069390553, 'reg_alpha': 0.1539865516703494, 'reg_lambda': 7.492947906955559e-08, 'gamma': 1.453163847340204e-07, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:51,713]\u001b[0m Trial 243 finished with value: 12946.6969325755 and parameters: {'booster': 'dart', 'learning_rate': 0.09879395324532522, 'max_depth': 15, 'subsample': 0.7685212404191615, 'reg_alpha': 0.9441156682871623, 'reg_lambda': 2.0432122954281307e-07, 'gamma': 0.00767940573936701, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:51,890]\u001b[0m Trial 244 finished with value: 16144.230319457643 and parameters: {'booster': 'dart', 'learning_rate': 0.08324574388126067, 'max_depth': 15, 'subsample': 0.775065697929877, 'reg_alpha': 0.6431973168347582, 'reg_lambda': 3.2284836589032534e-07, 'gamma': 2.052801267609092e-07, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:52,064]\u001b[0m Trial 245 finished with value: 12843.316526300849 and parameters: {'booster': 'dart', 'learning_rate': 0.09906099370336376, 'max_depth': 15, 'subsample': 0.8370158936286577, 'reg_alpha': 0.913717296072701, 'reg_lambda': 1.83649365912579e-07, 'gamma': 0.017313042252419135, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:52,242]\u001b[0m Trial 246 finished with value: 12825.750982192132 and parameters: {'booster': 'dart', 'learning_rate': 0.09928339074602605, 'max_depth': 15, 'subsample': 0.7607599314142607, 'reg_alpha': 0.7650882832228083, 'reg_lambda': 1.7960796351404593e-07, 'gamma': 0.025239647320630818, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:52,413]\u001b[0m Trial 247 finished with value: 19667.992188825458 and parameters: {'booster': 'dart', 'learning_rate': 0.07060909290000061, 'max_depth': 15, 'subsample': 0.7095810029541735, 'reg_alpha': 0.7392528885902012, 'reg_lambda': 2.1535761619268234e-07, 'gamma': 0.034988414826872975, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:52,592]\u001b[0m Trial 248 finished with value: 15434.7185898349 and parameters: {'booster': 'dart', 'learning_rate': 0.08613142869160093, 'max_depth': 15, 'subsample': 0.7578294519832576, 'reg_alpha': 0.9711102612485595, 'reg_lambda': 6.276575106606901e-07, 'gamma': 0.026850505382112304, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:52,775]\u001b[0m Trial 249 finished with value: 15051.639565512247 and parameters: {'booster': 'dart', 'learning_rate': 0.08793226504745905, 'max_depth': 15, 'subsample': 0.7889074596656988, 'reg_alpha': 0.5714023857497808, 'reg_lambda': 1.9795181295137426e-07, 'gamma': 0.06668368393349043, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:52,956]\u001b[0m Trial 250 finished with value: 17790.3025678907 and parameters: {'booster': 'dart', 'learning_rate': 0.07712934248260485, 'max_depth': 15, 'subsample': 0.6683139011951393, 'reg_alpha': 0.9683612259691767, 'reg_lambda': 3.8294802480390653e-07, 'gamma': 0.01842551092968481, 'min_child_weight': 3}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:53,119]\u001b[0m Trial 251 finished with value: 12837.18723967757 and parameters: {'booster': 'dart', 'learning_rate': 0.0999499179111503, 'max_depth': 15, 'subsample': 0.5810201977332338, 'reg_alpha': 0.6984724917439896, 'reg_lambda': 1.3224720512565944e-07, 'gamma': 0.032101471176951696, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:53,308]\u001b[0m Trial 252 finished with value: 12926.78799951725 and parameters: {'booster': 'dart', 'learning_rate': 0.09969887541831522, 'max_depth': 15, 'subsample': 0.889709583755195, 'reg_alpha': 0.47691874854647975, 'reg_lambda': 1.369708770695854e-07, 'gamma': 0.03198250306865606, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:53,483]\u001b[0m Trial 253 finished with value: 15466.375918961698 and parameters: {'booster': 'dart', 'learning_rate': 0.08596781453023786, 'max_depth': 15, 'subsample': 0.8534874059821517, 'reg_alpha': 0.3820485514678169, 'reg_lambda': 1.410844800992886e-07, 'gamma': 0.04978576152428073, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:53,666]\u001b[0m Trial 254 finished with value: 12870.72497908672 and parameters: {'booster': 'dart', 'learning_rate': 0.09959526052263003, 'max_depth': 15, 'subsample': 0.7285274622445715, 'reg_alpha': 0.595527434021093, 'reg_lambda': 1.7340643384538123e-07, 'gamma': 0.03561110281834398, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:53,836]\u001b[0m Trial 255 finished with value: 19984.36817048359 and parameters: {'booster': 'dart', 'learning_rate': 0.06973239333081736, 'max_depth': 15, 'subsample': 0.7345795474609493, 'reg_alpha': 0.7178686049646601, 'reg_lambda': 1.0855879618235377e-07, 'gamma': 0.03391238407037411, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,003]\u001b[0m Trial 256 finished with value: 17446.671679242074 and parameters: {'booster': 'dart', 'learning_rate': 0.07849569046102364, 'max_depth': 15, 'subsample': 0.5853067195918399, 'reg_alpha': 0.41886303879987, 'reg_lambda': 2.763331680805686e-07, 'gamma': 0.05610972018759884, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,172]\u001b[0m Trial 257 finished with value: 12935.703223022156 and parameters: {'booster': 'dart', 'learning_rate': 0.09963669517530392, 'max_depth': 15, 'subsample': 0.6667983314001616, 'reg_alpha': 0.5907114887481112, 'reg_lambda': 2.0250639505758133e-07, 'gamma': 0.03561529784241028, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,348]\u001b[0m Trial 258 finished with value: 15272.70340685163 and parameters: {'booster': 'dart', 'learning_rate': 0.08689716521985996, 'max_depth': 15, 'subsample': 0.7375693032437356, 'reg_alpha': 0.6041502945686309, 'reg_lambda': 2.5450582979278253e-07, 'gamma': 0.08027934800491883, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,528]\u001b[0m Trial 259 finished with value: 66665.06426091446 and parameters: {'booster': 'dart', 'learning_rate': 0.0012184257940996864, 'max_depth': 15, 'subsample': 0.5293686366930449, 'reg_alpha': 0.4603205974374532, 'reg_lambda': 1.7521251457992702e-07, 'gamma': 0.0322020884289244, 'min_child_weight': 3}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,570]\u001b[0m Trial 260 finished with value: 30965.982960091424 and parameters: {'booster': 'gblinear', 'learning_rate': 0.07448221479653247, 'max_depth': 15, 'subsample': 0.6710685514740861, 'reg_alpha': 0.7698759205687752, 'reg_lambda': 1.2617325261448218e-07, 'gamma': 0.04802636062755353, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,729]\u001b[0m Trial 261 finished with value: 15284.38407029696 and parameters: {'booster': 'dart', 'learning_rate': 0.08720380560201815, 'max_depth': 15, 'subsample': 0.5866620167699488, 'reg_alpha': 0.31059139536178904, 'reg_lambda': 3.47270324347826e-07, 'gamma': 0.023347746492796778, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:54,913]\u001b[0m Trial 262 finished with value: 15222.18697386995 and parameters: {'booster': 'dart', 'learning_rate': 0.0873225783385063, 'max_depth': 15, 'subsample': 0.8086049092699722, 'reg_alpha': 0.4852001969041176, 'reg_lambda': 1.9886270277085393e-07, 'gamma': 0.0321895254536716, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:55,088]\u001b[0m Trial 263 finished with value: 19324.171542051485 and parameters: {'booster': 'dart', 'learning_rate': 0.07192683111963658, 'max_depth': 15, 'subsample': 0.7109892665520436, 'reg_alpha': 0.983682020667402, 'reg_lambda': 9.714417357410467e-07, 'gamma': 0.02408134878476262, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:55,247]\u001b[0m Trial 264 finished with value: 22156.73270238943 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06344268286547557, 'max_depth': 15, 'subsample': 0.6339299973096931, 'reg_alpha': 0.26874356349773765, 'reg_lambda': 1.1475493792668345e-07, 'gamma': 0.04163690054884162, 'min_child_weight': 4}. Best is trial 238 with value: 12809.393638349062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:55,426]\u001b[0m Trial 265 finished with value: 12687.266327568548 and parameters: {'booster': 'dart', 'learning_rate': 0.09989614341410454, 'max_depth': 15, 'subsample': 0.7663770665205047, 'reg_alpha': 0.5855910957270605, 'reg_lambda': 4.295962702200428e-07, 'gamma': 8.515017741440044e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:55,595]\u001b[0m Trial 266 finished with value: 67576.08905923371 and parameters: {'booster': 'dart', 'learning_rate': 0.00048026999277765665, 'max_depth': 15, 'subsample': 0.7848177348305424, 'reg_alpha': 0.6428747938076785, 'reg_lambda': 5.67427779769643e-07, 'gamma': 8.518487219572564e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:55,769]\u001b[0m Trial 267 finished with value: 12768.311647341865 and parameters: {'booster': 'dart', 'learning_rate': 0.09929576664089763, 'max_depth': 15, 'subsample': 0.8409863061975049, 'reg_alpha': 0.3872123768377931, 'reg_lambda': 2.233094083440303e-06, 'gamma': 1.0022523849060514e-07, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:55,970]\u001b[0m Trial 268 finished with value: 13020.198261039639 and parameters: {'booster': 'dart', 'learning_rate': 0.09917163558019368, 'max_depth': 15, 'subsample': 0.8684500193848115, 'reg_alpha': 0.37371054232527484, 'reg_lambda': 4.320358215802306e-07, 'gamma': 4.3834036573031684e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:56,149]\u001b[0m Trial 269 finished with value: 12752.615794765026 and parameters: {'booster': 'dart', 'learning_rate': 0.09980085636979583, 'max_depth': 15, 'subsample': 0.804020608471309, 'reg_alpha': 0.23393452671678625, 'reg_lambda': 2.7537163566079413e-07, 'gamma': 7.065732914391942e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:56,328]\u001b[0m Trial 270 finished with value: 16580.151083709887 and parameters: {'booster': 'dart', 'learning_rate': 0.0817100789680979, 'max_depth': 15, 'subsample': 0.7596650248643042, 'reg_alpha': 0.27104226407327126, 'reg_lambda': 8.971762535295385e-07, 'gamma': 5.7247014729977475e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:56,503]\u001b[0m Trial 271 finished with value: 17081.22185519024 and parameters: {'booster': 'dart', 'learning_rate': 0.0795551883029113, 'max_depth': 15, 'subsample': 0.799531634353039, 'reg_alpha': 0.20252429953790813, 'reg_lambda': 4.1984367260683834e-07, 'gamma': 9.409448463251787e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:56,681]\u001b[0m Trial 272 finished with value: 15090.660722649933 and parameters: {'booster': 'dart', 'learning_rate': 0.0879623192153913, 'max_depth': 15, 'subsample': 0.7255935036808882, 'reg_alpha': 0.4498165362837495, 'reg_lambda': 2.8747822255477163e-07, 'gamma': 6.706388262638976e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:56,863]\u001b[0m Trial 273 finished with value: 12856.367949057743 and parameters: {'booster': 'dart', 'learning_rate': 0.09960073335811077, 'max_depth': 15, 'subsample': 0.9075434549337694, 'reg_alpha': 0.9863361369715175, 'reg_lambda': 2.2673545309064727e-06, 'gamma': 3.431948461146554e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,049]\u001b[0m Trial 274 finished with value: 12841.560952058895 and parameters: {'booster': 'dart', 'learning_rate': 0.09966775126749915, 'max_depth': 15, 'subsample': 0.9034730939664758, 'reg_alpha': 0.6715737255589342, 'reg_lambda': 3.2264289205887163e-06, 'gamma': 3.416070005115309e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,233]\u001b[0m Trial 275 finished with value: 19946.147138771696 and parameters: {'booster': 'dart', 'learning_rate': 0.07005736557197913, 'max_depth': 15, 'subsample': 0.9292889620118494, 'reg_alpha': 0.6606867438172174, 'reg_lambda': 2.199267421861875e-06, 'gamma': 3.180518035926997e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,432]\u001b[0m Trial 276 finished with value: 15163.97503950587 and parameters: {'booster': 'dart', 'learning_rate': 0.08785339858697354, 'max_depth': 15, 'subsample': 0.9034741706230462, 'reg_alpha': 0.35244694173559876, 'reg_lambda': 4.836446516873956e-06, 'gamma': 3.5309774107246955e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,610]\u001b[0m Trial 277 finished with value: 17421.342260116784 and parameters: {'booster': 'dart', 'learning_rate': 0.07853612095962638, 'max_depth': 15, 'subsample': 0.9934292549144395, 'reg_alpha': 0.9872285799894285, 'reg_lambda': 2.6508736427633463e-06, 'gamma': 5.850384657819824e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,713]\u001b[0m Trial 278 finished with value: 15517.600002166917 and parameters: {'booster': 'dart', 'learning_rate': 0.08865500031206924, 'max_depth': 15, 'subsample': 0.1671868505574291, 'reg_alpha': 0.6145055155158189, 'reg_lambda': 2.2129052748176734e-06, 'gamma': 2.3239691161697963e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,788]\u001b[0m Trial 279 finished with value: 18194.09049114963 and parameters: {'booster': 'dart', 'learning_rate': 0.0997890129580132, 'max_depth': 15, 'subsample': 0.011304462298223425, 'reg_alpha': 0.49676026970145476, 'reg_lambda': 5.87822452951398e-06, 'gamma': 1.2186305331599843e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:57,970]\u001b[0m Trial 280 finished with value: 21637.763132386357 and parameters: {'booster': 'dart', 'learning_rate': 0.06459368458672712, 'max_depth': 15, 'subsample': 0.8404009671529798, 'reg_alpha': 0.21658489530925867, 'reg_lambda': 6.550166807552923e-07, 'gamma': 2.5141827984015048e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:58,147]\u001b[0m Trial 281 finished with value: 15204.844649533808 and parameters: {'booster': 'dart', 'learning_rate': 0.08750007193635835, 'max_depth': 15, 'subsample': 0.7947371918747329, 'reg_alpha': 0.3473335048017049, 'reg_lambda': 1.2213326269477107e-06, 'gamma': 8.654639175524172e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:58,326]\u001b[0m Trial 282 finished with value: 17660.304468054 and parameters: {'booster': 'dart', 'learning_rate': 0.07777268011802299, 'max_depth': 15, 'subsample': 0.9061217080459729, 'reg_alpha': 0.7035179401895248, 'reg_lambda': 4.372897036595526e-06, 'gamma': 1.282386331781137e-07, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:58,510]\u001b[0m Trial 283 finished with value: 12940.429531598296 and parameters: {'booster': 'dart', 'learning_rate': 0.0990284319579042, 'max_depth': 15, 'subsample': 0.7142278537943743, 'reg_alpha': 0.9905160782009211, 'reg_lambda': 2.3572346272943625e-07, 'gamma': 1.7945341317355564e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:58,699]\u001b[0m Trial 284 finished with value: 12861.473961047943 and parameters: {'booster': 'dart', 'learning_rate': 0.0994781608317401, 'max_depth': 15, 'subsample': 0.6957979767648735, 'reg_alpha': 0.4478686355053495, 'reg_lambda': 2.698794051996905e-07, 'gamma': 1.9264327248573523e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:58,872]\u001b[0m Trial 285 finished with value: 17820.9882521069 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07649702648190634, 'max_depth': 14, 'subsample': 0.5938678631866011, 'reg_alpha': 0.1910447494916582, 'reg_lambda': 5.627109246150414e-07, 'gamma': 4.063204302315757e-08, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:58,917]\u001b[0m Trial 286 finished with value: 28765.986797592308 and parameters: {'booster': 'gblinear', 'learning_rate': 0.08708552803951312, 'max_depth': 15, 'subsample': 0.6700787516444676, 'reg_alpha': 0.32505227474333537, 'reg_lambda': 1.7563656992256026e-06, 'gamma': 2.2509111614864147e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:59,074]\u001b[0m Trial 287 finished with value: 24422.297475362688 and parameters: {'booster': 'dart', 'learning_rate': 0.05778629464150125, 'max_depth': 14, 'subsample': 0.4741165007316173, 'reg_alpha': 0.4901408865189181, 'reg_lambda': 3.5391874808868657e-07, 'gamma': 5.342103932822235e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:59,253]\u001b[0m Trial 288 finished with value: 19772.113223861044 and parameters: {'booster': 'dart', 'learning_rate': 0.07022552063376263, 'max_depth': 15, 'subsample': 0.8579044517506913, 'reg_alpha': 0.25676414680426785, 'reg_lambda': 3.2037454064973507e-06, 'gamma': 3.253248879104308e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:59,420]\u001b[0m Trial 289 finished with value: 12947.23043328519 and parameters: {'booster': 'dart', 'learning_rate': 0.0996790256334496, 'max_depth': 15, 'subsample': 0.5398327978094997, 'reg_alpha': 0.4201154667408743, 'reg_lambda': 7.558577393425496e-06, 'gamma': 0.06155711175747801, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:59,601]\u001b[0m Trial 290 finished with value: 15596.727602582743 and parameters: {'booster': 'dart', 'learning_rate': 0.08601611803427796, 'max_depth': 14, 'subsample': 0.7177328079902541, 'reg_alpha': 0.15128514756381548, 'reg_lambda': 1.184980126342764e-06, 'gamma': 1.8944198873028694e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:59,770]\u001b[0m Trial 291 finished with value: 64485.55580500531 and parameters: {'booster': 'dart', 'learning_rate': 0.003017783494738662, 'max_depth': 15, 'subsample': 0.6191906276691272, 'reg_alpha': 0.5867743055822565, 'reg_lambda': 3.201596773098308e-07, 'gamma': 0.10030118677297109, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:46:59,955]\u001b[0m Trial 292 finished with value: 17453.697264121587 and parameters: {'booster': 'dart', 'learning_rate': 0.07804241333804066, 'max_depth': 15, 'subsample': 0.82612282901651, 'reg_alpha': 0.3418792469587211, 'reg_lambda': 1.8379770290063424e-07, 'gamma': 8.063118319585254e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:00,125]\u001b[0m Trial 293 finished with value: 15153.390307438765 and parameters: {'booster': 'dart', 'learning_rate': 0.08782591156131636, 'max_depth': 14, 'subsample': 0.6862047961029745, 'reg_alpha': 0.2394283978542525, 'reg_lambda': 4.350385901121794e-07, 'gamma': 1.2589719507638368e-07, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:00,373]\u001b[0m Trial 294 finished with value: 12957.176340084901 and parameters: {'booster': 'dart', 'learning_rate': 0.09928388494423986, 'max_depth': 15, 'subsample': 0.7487406134451342, 'reg_alpha': 0.6830840673968912, 'reg_lambda': 2.700624945646343e-07, 'gamma': 0.026832428063306782, 'min_child_weight': 1}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:00,550]\u001b[0m Trial 295 finished with value: 15143.474333231865 and parameters: {'booster': 'dart', 'learning_rate': 0.08810805482174125, 'max_depth': 14, 'subsample': 0.9981288794569968, 'reg_alpha': 0.49784806814866206, 'reg_lambda': 4.9194905116550025e-05, 'gamma': 4.6086696894291175e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:00,733]\u001b[0m Trial 296 finished with value: 20548.241394163346 and parameters: {'booster': 'dart', 'learning_rate': 0.06817364638110648, 'max_depth': 15, 'subsample': 0.9113470841463972, 'reg_alpha': 0.14874764622861528, 'reg_lambda': 1.3292506511205415e-07, 'gamma': 0.03790619070487328, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:00,905]\u001b[0m Trial 297 finished with value: 12860.090640526441 and parameters: {'booster': 'dart', 'learning_rate': 0.09982626987406489, 'max_depth': 14, 'subsample': 0.8154173967094849, 'reg_alpha': 1.102125074908442e-05, 'reg_lambda': 7.010445802087055e-07, 'gamma': 1.2890757023892807e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:01,088]\u001b[0m Trial 298 finished with value: 67966.39333597575 and parameters: {'booster': 'dart', 'learning_rate': 0.0001685014633527803, 'max_depth': 14, 'subsample': 0.9031969173860561, 'reg_alpha': 5.443960854035567e-06, 'reg_lambda': 5.833690169073409e-07, 'gamma': 1.9125458578792745e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:01,266]\u001b[0m Trial 299 finished with value: 17543.502099747682 and parameters: {'booster': 'dart', 'learning_rate': 0.07805123947956882, 'max_depth': 14, 'subsample': 0.7975130153778957, 'reg_alpha': 0.10571619748856388, 'reg_lambda': 9.230330102040341e-07, 'gamma': 1.5297621866326238e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:01,450]\u001b[0m Trial 300 finished with value: 15148.859646150837 and parameters: {'booster': 'dart', 'learning_rate': 0.08785639481802408, 'max_depth': 14, 'subsample': 0.8292083977937429, 'reg_alpha': 9.848992381155726e-07, 'reg_lambda': 8.392476986795425e-07, 'gamma': 1.0183680462132162e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:01,633]\u001b[0m Trial 301 finished with value: 12834.583236428887 and parameters: {'booster': 'dart', 'learning_rate': 0.09982438013689354, 'max_depth': 14, 'subsample': 0.9257155876562021, 'reg_alpha': 1.552293861201791e-05, 'reg_lambda': 4.5681591471892576e-07, 'gamma': 2.7503577473904168e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:01,809]\u001b[0m Trial 302 finished with value: 17779.095235707166 and parameters: {'booster': 'dart', 'learning_rate': 0.077395449483399, 'max_depth': 14, 'subsample': 0.9310495940750052, 'reg_alpha': 2.8581019312545445e-05, 'reg_lambda': 6.446471347183818e-07, 'gamma': 1.411783880515221e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:01,990]\u001b[0m Trial 303 finished with value: 58593.25901892089 and parameters: {'booster': 'dart', 'learning_rate': 0.008193240988597205, 'max_depth': 14, 'subsample': 0.9957574478677891, 'reg_alpha': 0.32519299116537204, 'reg_lambda': 4.461069619344377e-07, 'gamma': 3.9701230380170244e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:02,177]\u001b[0m Trial 304 finished with value: 15480.701590211182 and parameters: {'booster': 'dart', 'learning_rate': 0.08622019703713882, 'max_depth': 15, 'subsample': 0.884565984390107, 'reg_alpha': 1.5072139992480101e-05, 'reg_lambda': 2.9528065729757144e-07, 'gamma': 2.6626117635826325e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:02,351]\u001b[0m Trial 305 finished with value: 22431.550378334257 and parameters: {'booster': 'dart', 'learning_rate': 0.06259299384586732, 'max_depth': 14, 'subsample': 0.7897111485488024, 'reg_alpha': 1.2482882614903927e-05, 'reg_lambda': 5.00891694831785e-07, 'gamma': 2.955977066907933e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:02,531]\u001b[0m Trial 306 finished with value: 15087.774877706963 and parameters: {'booster': 'dart', 'learning_rate': 0.08810145193683866, 'max_depth': 15, 'subsample': 0.8600015256409699, 'reg_alpha': 4.526434441301135e-06, 'reg_lambda': 1.5150919716357112e-06, 'gamma': 1.3252453690918274e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:02,706]\u001b[0m Trial 307 finished with value: 19208.741203804424 and parameters: {'booster': 'dart', 'learning_rate': 0.07253776478849112, 'max_depth': 14, 'subsample': 0.9272072762009504, 'reg_alpha': 0.1993146623643755, 'reg_lambda': 1.7355585945953327e-05, 'gamma': 6.158165190000401e-08, 'min_child_weight': 4}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:02,898]\u001b[0m Trial 308 finished with value: 12792.51406599031 and parameters: {'booster': 'dart', 'learning_rate': 0.09984351274756847, 'max_depth': 15, 'subsample': 0.738477888980295, 'reg_alpha': 0.4387010903471926, 'reg_lambda': 7.050550017543041e-07, 'gamma': 1.0117470686173891e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:02,941]\u001b[0m Trial 309 finished with value: 28666.95476280341 and parameters: {'booster': 'gblinear', 'learning_rate': 0.08792852849390582, 'max_depth': 15, 'subsample': 0.7456727172137324, 'reg_alpha': 0.7257740251302878, 'reg_lambda': 7.751253048806791e-07, 'gamma': 1.1057432929250263e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:03,119]\u001b[0m Trial 310 finished with value: 17877.80188034748 and parameters: {'booster': 'dart', 'learning_rate': 0.0771048964725433, 'max_depth': 14, 'subsample': 0.7252400345277831, 'reg_alpha': 0.14013063551404223, 'reg_lambda': 9.934917063905627e-07, 'gamma': 2.8539139132945748e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:03,308]\u001b[0m Trial 311 finished with value: 12774.65727747406 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09968411188816786, 'max_depth': 15, 'subsample': 0.7980960432657049, 'reg_alpha': 2.394822162777442e-05, 'reg_lambda': 4.10114675206002e-07, 'gamma': 1.8084402899554814e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:03,488]\u001b[0m Trial 312 finished with value: 12813.09723792619 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09947407008026508, 'max_depth': 15, 'subsample': 0.8148943112314072, 'reg_alpha': 7.455099342421703e-06, 'reg_lambda': 4.25832419317116e-07, 'gamma': 2.0640453412092256e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:03,715]\u001b[0m Trial 313 finished with value: 43775.01799383257 and parameters: {'booster': 'gbtree', 'learning_rate': 0.024122949614340323, 'max_depth': 15, 'subsample': 0.8167993012759738, 'reg_alpha': 5.415836949267666e-06, 'reg_lambda': 6.426057373512144e-07, 'gamma': 1.5786924199585735e-08, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:03,891]\u001b[0m Trial 314 finished with value: 20773.804662957536 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06726277651664413, 'max_depth': 15, 'subsample': 0.7829529894101273, 'reg_alpha': 1.0678814486079804e-05, 'reg_lambda': 4.1470082206342354e-07, 'gamma': 1.8187872112658786e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:04,079]\u001b[0m Trial 315 finished with value: 62776.21164524786 and parameters: {'booster': 'gbtree', 'learning_rate': 0.004470796147073417, 'max_depth': 15, 'subsample': 0.8443129785488573, 'reg_alpha': 8.038212054365114e-06, 'reg_lambda': 1.2978811468955918e-06, 'gamma': 1.9970831661647905e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:04,260]\u001b[0m Trial 316 finished with value: 65579.84818885222 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0020996719605149055, 'max_depth': 15, 'subsample': 0.7649930226590707, 'reg_alpha': 1.834121649093185e-05, 'reg_lambda': 4.798186388793534e-07, 'gamma': 1.1344308615683119e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:04,463]\u001b[0m Trial 317 finished with value: 15154.118674617679 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08821161376971987, 'max_depth': 15, 'subsample': 0.9406541716735856, 'reg_alpha': 8.361845311906462e-06, 'reg_lambda': 6.771254836107662e-07, 'gamma': 3.0262502275467216e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:04,649]\u001b[0m Trial 318 finished with value: 17230.36550448742 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07929803944322561, 'max_depth': 15, 'subsample': 0.8339006636168798, 'reg_alpha': 2.7383556870065643e-05, 'reg_lambda': 2.0285256704534585e-06, 'gamma': 1.42419463735292e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:04,862]\u001b[0m Trial 319 finished with value: 13049.080172378517 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0997845066363024, 'max_depth': 15, 'subsample': 0.9957715303804776, 'reg_alpha': 2.5827136041967644e-05, 'reg_lambda': 3.446498987859675e-07, 'gamma': 2.4095616894917532e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:05,049]\u001b[0m Trial 320 finished with value: 15538.06345101572 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08608098742815595, 'max_depth': 15, 'subsample': 0.7518060842177038, 'reg_alpha': 1.055819510656992e-05, 'reg_lambda': 3.076394643210876e-07, 'gamma': 4.411538421553187e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:05,227]\u001b[0m Trial 321 finished with value: 18589.26460859276 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07396115655481052, 'max_depth': 15, 'subsample': 0.6976268794098637, 'reg_alpha': 5.0148540686175196e-05, 'reg_lambda': 3.118056837182606e-06, 'gamma': 1.0098130725156401e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:05,311]\u001b[0m Trial 322 finished with value: 14622.380962929114 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0991851052206653, 'max_depth': 15, 'subsample': 0.043325961319296176, 'reg_alpha': 6.518558812508794e-06, 'reg_lambda': 7.889940944824258e-07, 'gamma': 2.02643726211572e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:05,516]\u001b[0m Trial 323 finished with value: 12842.79139609695 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0998141537761878, 'max_depth': 15, 'subsample': 0.8611743924264454, 'reg_alpha': 1.6777165297066168e-05, 'reg_lambda': 4.2450393030805766e-07, 'gamma': 5.939744090595194e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:05,751]\u001b[0m Trial 324 finished with value: 24611.64338642157 and parameters: {'booster': 'gbtree', 'learning_rate': 0.05711976011673299, 'max_depth': 15, 'subsample': 0.8698831593419217, 'reg_alpha': 3.522746722545491e-05, 'reg_lambda': 4.493705956037804e-07, 'gamma': 8.864159414552282e-08, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:05,942]\u001b[0m Trial 325 finished with value: 15022.301685303035 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0886213778950141, 'max_depth': 15, 'subsample': 0.917229802378829, 'reg_alpha': 2.1677819474930198e-05, 'reg_lambda': 1.119650330608791e-06, 'gamma': 4.210957879598895e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:06,130]\u001b[0m Trial 326 finished with value: 17305.8458372744 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07868671305318341, 'max_depth': 15, 'subsample': 0.8124921584003861, 'reg_alpha': 3.511924780279596e-06, 'reg_lambda': 3.7429649412267244e-07, 'gamma': 6.078186752968797e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:06,313]\u001b[0m Trial 327 finished with value: 15005.432549420731 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0880946444842838, 'max_depth': 15, 'subsample': 0.7327809033523428, 'reg_alpha': 1.3776488560163147e-05, 'reg_lambda': 5.359015767166635e-07, 'gamma': 3.083751998228736e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:06,492]\u001b[0m Trial 328 finished with value: 20661.258266889734 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06786971419244214, 'max_depth': 15, 'subsample': 0.8295354312134092, 'reg_alpha': 0.3778977288779322, 'reg_lambda': 2.6099369384355933e-07, 'gamma': 1.5768515616700583e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:06,718]\u001b[0m Trial 329 finished with value: 56057.76177341358 and parameters: {'booster': 'gbtree', 'learning_rate': 0.01064271491215337, 'max_depth': 15, 'subsample': 0.9149489789993127, 'reg_alpha': 1.6188637103082863e-05, 'reg_lambda': 7.642721983441124e-07, 'gamma': 2.3865679054899875e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:06,917]\u001b[0m Trial 330 finished with value: 12845.268097002501 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09923415301620118, 'max_depth': 15, 'subsample': 0.68989764700777, 'reg_alpha': 8.870730312937796e-06, 'reg_lambda': 1.5059607189580323e-06, 'gamma': 6.530182883889308e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:07,099]\u001b[0m Trial 331 finished with value: 12823.525678547949 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09968344577530239, 'max_depth': 15, 'subsample': 0.7778809072892284, 'reg_alpha': 7.045223671930612e-06, 'reg_lambda': 1.7933215046992633e-06, 'gamma': 6.37108130165619e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:07,276]\u001b[0m Trial 332 finished with value: 17326.561894569026 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0789141352795119, 'max_depth': 15, 'subsample': 0.7631486474463725, 'reg_alpha': 8.625533474572583e-06, 'reg_lambda': 1.680395408098937e-06, 'gamma': 6.954639142568187e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:07,473]\u001b[0m Trial 333 finished with value: 14920.171070246099 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08896689685771589, 'max_depth': 15, 'subsample': 0.8505413113405825, 'reg_alpha': 2.8097590004044863e-06, 'reg_lambda': 2.8372385903675968e-06, 'gamma': 1.1885444660176249e-07, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:07,563]\u001b[0m Trial 334 finished with value: 14273.816027681272 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09998284817675797, 'max_depth': 15, 'subsample': 0.05940691332995024, 'reg_alpha': 8.265543164957676e-06, 'reg_lambda': 1.9403127542554293e-06, 'gamma': 5.2132235547131944e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:07,800]\u001b[0m Trial 335 finished with value: 20425.17489543295 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06917366163022724, 'max_depth': 15, 'subsample': 0.9858962071372545, 'reg_alpha': 1.261373138119813e-05, 'reg_lambda': 1.5244986617024958e-06, 'gamma': 3.631418720346467e-08, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:07,986]\u001b[0m Trial 336 finished with value: 16259.018274226806 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08310493396256352, 'max_depth': 15, 'subsample': 0.7918638653263267, 'reg_alpha': 3.670648364557981e-06, 'reg_lambda': 1.0129278231036627e-06, 'gamma': 8.261897941014369e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:08,159]\u001b[0m Trial 337 finished with value: 15141.02825530012 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08735644364482893, 'max_depth': 15, 'subsample': 0.7008139238094281, 'reg_alpha': 6.924735018112949e-06, 'reg_lambda': 4.095141724409903e-06, 'gamma': 5.674990842596858e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:08,356]\u001b[0m Trial 338 finished with value: 50256.860292938705 and parameters: {'booster': 'gbtree', 'learning_rate': 0.01659811382315508, 'max_depth': 15, 'subsample': 0.9076326625378051, 'reg_alpha': 1.7535912857884854e-05, 'reg_lambda': 1.3736325514142068e-06, 'gamma': 9.415742557881321e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:08,538]\u001b[0m Trial 339 finished with value: 12902.565952610128 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09953547536645002, 'max_depth': 15, 'subsample': 0.7862654139925067, 'reg_alpha': 9.755729326029817e-06, 'reg_lambda': 2.659079063885369e-06, 'gamma': 4.354078842868251e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:08,726]\u001b[0m Trial 340 finished with value: 17861.35758736998 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0769236090874085, 'max_depth': 15, 'subsample': 0.8720420750255683, 'reg_alpha': 0.7116619373336693, 'reg_lambda': 7.193598227119946e-07, 'gamma': 3.1120366944087155e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:08,902]\u001b[0m Trial 341 finished with value: 22841.924759228423 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06147633818299984, 'max_depth': 15, 'subsample': 0.6658250881817382, 'reg_alpha': 0.7278046317476449, 'reg_lambda': 1.1767387050301254e-06, 'gamma': 2.2837458396738895e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:09,072]\u001b[0m Trial 342 finished with value: 12792.05024905185 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09998316874634844, 'max_depth': 15, 'subsample': 0.7348158747609245, 'reg_alpha': 0.9507739274035893, 'reg_lambda': 3.6821376905792954e-07, 'gamma': 7.143739388490463e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:09,273]\u001b[0m Trial 343 finished with value: 15206.122943992674 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0870411134378951, 'max_depth': 15, 'subsample': 0.7302771385364486, 'reg_alpha': 0.9950758798966443, 'reg_lambda': 1.8676209080809094e-06, 'gamma': 6.792886800246672e-08, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:09,373]\u001b[0m Trial 344 finished with value: 13648.33504776703 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09983542170617404, 'max_depth': 15, 'subsample': 0.09412568641058029, 'reg_alpha': 0.46450147976310946, 'reg_lambda': 5.844198397876531e-07, 'gamma': 1.545441631695791e-07, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:09,543]\u001b[0m Trial 345 finished with value: 18015.14290726494 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07631902580453404, 'max_depth': 15, 'subsample': 0.6745268389344078, 'reg_alpha': 2.4934062424173193e-06, 'reg_lambda': 3.5093989239339563e-07, 'gamma': 4.5779492597920485e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:09,659]\u001b[0m Trial 346 finished with value: 15639.971181884857 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08698601944613424, 'max_depth': 15, 'subsample': 0.20900193056779645, 'reg_alpha': 0.9994569770944993, 'reg_lambda': 9.547129979576766e-07, 'gamma': 1.5146554928328953e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:09,833]\u001b[0m Trial 347 finished with value: 20093.240246835783 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06944566113951102, 'max_depth': 15, 'subsample': 0.749768979764561, 'reg_alpha': 0.5709538935738918, 'reg_lambda': 1.6470094925341102e-07, 'gamma': 1.2467531768655788e-07, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:10,020]\u001b[0m Trial 348 finished with value: 12765.652350520355 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09980866418072375, 'max_depth': 15, 'subsample': 0.805217611448117, 'reg_alpha': 5.6394352067730884e-06, 'reg_lambda': 2.424363907349595e-07, 'gamma': 7.85833832137765e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:10,201]\u001b[0m Trial 349 finished with value: 17069.70955690494 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07968845384930424, 'max_depth': 15, 'subsample': 0.8107665057943575, 'reg_alpha': 4.68246217867901e-06, 'reg_lambda': 2.4081094832046353e-07, 'gamma': 7.3110457870548e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:10,430]\u001b[0m Trial 350 finished with value: 14974.472016817996 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0884789093734141, 'max_depth': 15, 'subsample': 0.8219266319782617, 'reg_alpha': 5.677740544723877e-06, 'reg_lambda': 5.243597224406192e-07, 'gamma': 1.0342825819216423e-07, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:10,612]\u001b[0m Trial 351 finished with value: 17324.259787313214 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0785693370202661, 'max_depth': 15, 'subsample': 0.6436158720001429, 'reg_alpha': 1.2160406434303844e-05, 'reg_lambda': 3.837755426940715e-07, 'gamma': 3.5654784413641666e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:10,803]\u001b[0m Trial 352 finished with value: 15269.799674477112 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08727658243716854, 'max_depth': 15, 'subsample': 0.8679183532985701, 'reg_alpha': 7.260331817609087e-06, 'reg_lambda': 2.6032641555691513e-07, 'gamma': 5.7873921471568207e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:10,983]\u001b[0m Trial 353 finished with value: 12847.334336444208 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09933279746320814, 'max_depth': 15, 'subsample': 0.7680419802566245, 'reg_alpha': 6.988708355255155e-05, 'reg_lambda': 7.082493357794823e-07, 'gamma': 6.768600670913337e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:11,067]\u001b[0m Trial 354 finished with value: 19997.421440463335 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07374446212621118, 'max_depth': 8, 'subsample': 0.7863556147417103, 'reg_alpha': 1.74757517725661e-05, 'reg_lambda': 7.254421707625627e-07, 'gamma': 9.090666917376485e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:11,275]\u001b[0m Trial 355 finished with value: 15207.001898618038 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08757208934124065, 'max_depth': 15, 'subsample': 0.9154241275322711, 'reg_alpha': 7.921655609323032e-05, 'reg_lambda': 1.2365050041551653e-06, 'gamma': 1.6741762711808176e-07, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:11,480]\u001b[0m Trial 356 finished with value: 21656.697865847163 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0651970829045373, 'max_depth': 15, 'subsample': 0.9879812274979987, 'reg_alpha': 4.9082344433846606e-05, 'reg_lambda': 5.814638364363527e-07, 'gamma': 5.070276524038874e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:11,691]\u001b[0m Trial 357 finished with value: 15084.248964840504 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08855451873170021, 'max_depth': 15, 'subsample': 0.8416878182975746, 'reg_alpha': 0.0001046507370900734, 'reg_lambda': 8.845567188793347e-07, 'gamma': 7.296342429218508e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:11,871]\u001b[0m Trial 358 finished with value: 17480.80308222669 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07826477160881061, 'max_depth': 15, 'subsample': 0.7689340655356199, 'reg_alpha': 6.393068699529345e-06, 'reg_lambda': 4.0587983054344363e-07, 'gamma': 2.705807695406145e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,051]\u001b[0m Trial 359 finished with value: 15043.748294435096 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08826610356826357, 'max_depth': 15, 'subsample': 0.6866535953388708, 'reg_alpha': 1.9038182484172107e-05, 'reg_lambda': 2.5067464544821233e-06, 'gamma': 1.1950825957275063e-07, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,227]\u001b[0m Trial 360 finished with value: 25739.01585113637 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0545285958460999, 'max_depth': 15, 'subsample': 0.8394372441958189, 'reg_alpha': 1.2021094157459108e-05, 'reg_lambda': 6.025282543503015e-07, 'gamma': 3.3286350624935135e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,327]\u001b[0m Trial 361 finished with value: 20507.449428332508 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06998939975211503, 'max_depth': 15, 'subsample': 0.1534950399287957, 'reg_alpha': 2.902369904304835e-05, 'reg_lambda': 3.7398333046840215e-07, 'gamma': 3.0489933561966294e-06, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,539]\u001b[0m Trial 362 finished with value: 14676.075542084236 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08999008927832575, 'max_depth': 15, 'subsample': 0.7490684694609949, 'reg_alpha': 3.9298060463240936e-05, 'reg_lambda': 6.450042394853134e-06, 'gamma': 4.8417814758733466e-08, 'min_child_weight': 2}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,739]\u001b[0m Trial 363 finished with value: 13250.195275644235 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09842727114623995, 'max_depth': 15, 'subsample': 0.9968218054635949, 'reg_alpha': 3.416763244560972e-06, 'reg_lambda': 3.766847378285435e-06, 'gamma': 1.7839426561798373e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,785]\u001b[0m Trial 364 finished with value: 30059.721424215328 and parameters: {'booster': 'gblinear', 'learning_rate': 0.07903124747975722, 'max_depth': 15, 'subsample': 0.6497445045689529, 'reg_alpha': 1.8954587034021438e-06, 'reg_lambda': 1.5310933153262784e-06, 'gamma': 1.0051160789126868e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:12,968]\u001b[0m Trial 365 finished with value: 14859.360145834811 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08944063989611435, 'max_depth': 15, 'subsample': 0.8790314590088936, 'reg_alpha': 0.4445162025348997, 'reg_lambda': 8.919536598386973e-07, 'gamma': 7.11531727147424e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:13,126]\u001b[0m Trial 366 finished with value: 12858.274200587843 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09886039961733371, 'max_depth': 15, 'subsample': 0.7099106218689768, 'reg_alpha': 0.6950242885437662, 'reg_lambda': 2.7760901838502274e-07, 'gamma': 3.958080635802826e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:13,295]\u001b[0m Trial 367 finished with value: 61722.80348932597 and parameters: {'booster': 'gbtree', 'learning_rate': 0.005389157926796478, 'max_depth': 15, 'subsample': 0.781868080767624, 'reg_alpha': 0.7742694247790295, 'reg_lambda': 2.4665778620787335e-07, 'gamma': 3.6527959447968956e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:13,455]\u001b[0m Trial 368 finished with value: 12709.866138274063 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09975519025218837, 'max_depth': 15, 'subsample': 0.7129651550818779, 'reg_alpha': 0.7136890835317454, 'reg_lambda': 3.617610928993317e-07, 'gamma': 2.0461503689221787e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:13,526]\u001b[0m Trial 369 finished with value: 16436.81054225538 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09985733870468472, 'max_depth': 15, 'subsample': 0.019825765154034274, 'reg_alpha': 0.7333693129764567, 'reg_lambda': 4.891434755496717e-07, 'gamma': 2.3190396817651517e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:13,692]\u001b[0m Trial 370 finished with value: 18843.91048136874 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07295655009853201, 'max_depth': 15, 'subsample': 0.7062819367639664, 'reg_alpha': 0.5397784099826548, 'reg_lambda': 3.1093953543063087e-07, 'gamma': 1.654778938572731e-08, 'min_child_weight': 3}. Best is trial 265 with value: 12687.266327568548.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:13,849]\u001b[0m Trial 371 finished with value: 12676.840424706446 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09992200905693721, 'max_depth': 15, 'subsample': 0.7104949545169242, 'reg_alpha': 0.9442625402419876, 'reg_lambda': 5.880595548858073e-07, 'gamma': 2.566641839532314e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,006]\u001b[0m Trial 372 finished with value: 22626.35105645643 and parameters: {'booster': 'gbtree', 'learning_rate': 0.062065788664158426, 'max_depth': 15, 'subsample': 0.7449775709469062, 'reg_alpha': 0.8341343857983666, 'reg_lambda': 6.746405032670771e-07, 'gamma': 4.530497137060389e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,162]\u001b[0m Trial 373 finished with value: 17006.073840977155 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0800663618632801, 'max_depth': 15, 'subsample': 0.6205603679548753, 'reg_alpha': 0.8887261617723058, 'reg_lambda': 9.71841715803403e-07, 'gamma': 3.100185585120156e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,356]\u001b[0m Trial 374 finished with value: 15660.638521669205 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0854517014976754, 'max_depth': 15, 'subsample': 0.792159644767492, 'reg_alpha': 0.6008060843836527, 'reg_lambda': 4.731731610440395e-07, 'gamma': 7.727222977784793e-08, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,521]\u001b[0m Trial 375 finished with value: 12705.268520808968 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09978479987242818, 'max_depth': 15, 'subsample': 0.7049557572290437, 'reg_alpha': 0.9471803918521994, 'reg_lambda': 1.3708163691573754e-06, 'gamma': 2.0115443051244387e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,676]\u001b[0m Trial 376 finished with value: 67334.50399454082 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0006731976504253611, 'max_depth': 15, 'subsample': 0.68424424954203, 'reg_alpha': 0.7976193996365063, 'reg_lambda': 1.6908348060424188e-06, 'gamma': 1.9987821358525126e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,833]\u001b[0m Trial 377 finished with value: 17152.93192296368 and parameters: {'booster': 'gbtree', 'learning_rate': 0.079620965775917, 'max_depth': 15, 'subsample': 0.6148714520725949, 'reg_alpha': 0.3548839020820629, 'reg_lambda': 2.298593984620721e-06, 'gamma': 1.4998060519825015e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:14,999]\u001b[0m Trial 378 finished with value: 20600.409008559072 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06735986888311518, 'max_depth': 15, 'subsample': 0.7056910478461301, 'reg_alpha': 0.9942947617447376, 'reg_lambda': 3.5877604405427886e-07, 'gamma': 1.0206814270223245e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:15,153]\u001b[0m Trial 379 finished with value: 13500.554182527258 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0998210525480631, 'max_depth': 15, 'subsample': 0.7455286104576678, 'reg_alpha': 0.6041042379939834, 'reg_lambda': 0.6591269024794908, 'gamma': 2.459990472346882e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:15,318]\u001b[0m Trial 380 finished with value: 15395.879990728941 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08629380087279899, 'max_depth': 15, 'subsample': 0.6441215591272439, 'reg_alpha': 0.9899644893414772, 'reg_lambda': 9.206725337334373e-07, 'gamma': 5.677999565694541e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:15,520]\u001b[0m Trial 381 finished with value: 18803.988799389394 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07288137435599829, 'max_depth': 15, 'subsample': 0.7262446188953362, 'reg_alpha': 0.462766311420194, 'reg_lambda': 2.056582371893873e-06, 'gamma': 1.0321083244390465e-07, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:15,719]\u001b[0m Trial 382 finished with value: 16038.93734847487 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08425578171972095, 'max_depth': 15, 'subsample': 0.8527578448322084, 'reg_alpha': 0.6742700346107471, 'reg_lambda': 1.278703891372314e-06, 'gamma': 4.786837671690005e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:15,911]\u001b[0m Trial 383 finished with value: 15349.834494094683 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08687499081100064, 'max_depth': 15, 'subsample': 0.76960846666564, 'reg_alpha': 0.3221183092431183, 'reg_lambda': 3.268678299541104e-06, 'gamma': 3.443164365358912e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:16,079]\u001b[0m Trial 384 finished with value: 12851.941597640272 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09987010766954027, 'max_depth': 15, 'subsample': 0.6036178445740762, 'reg_alpha': 0.5418676788392172, 'reg_lambda': 1.2286823309076124e-06, 'gamma': 8.047571151766535e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:16,162]\u001b[0m Trial 385 finished with value: 20433.382885515093 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07800816437449899, 'max_depth': 15, 'subsample': 0.030262683469355383, 'reg_alpha': 0.43516711223174853, 'reg_lambda': 1.481729880444391e-06, 'gamma': 1.7972348416667772e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:16,329]\u001b[0m Trial 386 finished with value: 15043.440725447934 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08824261001595278, 'max_depth': 15, 'subsample': 0.6008587236416306, 'reg_alpha': 0.2970517306039527, 'reg_lambda': 1.3328048059765152e-06, 'gamma': 1.0451572558037908e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:16,493]\u001b[0m Trial 387 finished with value: 22759.533876845453 and parameters: {'booster': 'gbtree', 'learning_rate': 0.061392153002533215, 'max_depth': 15, 'subsample': 0.6515900869282228, 'reg_alpha': 0.5282583324624289, 'reg_lambda': 1.0037548103165324e-06, 'gamma': 7.104743597284368e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:16,662]\u001b[0m Trial 388 finished with value: 12876.76943004091 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09951053902205857, 'max_depth': 15, 'subsample': 0.5883199380606049, 'reg_alpha': 0.675574403665388, 'reg_lambda': 6.301711147011494e-07, 'gamma': 1.3153295870845176e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:16,835]\u001b[0m Trial 389 finished with value: 12850.475083981637 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09992445274034709, 'max_depth': 15, 'subsample': 0.8474910314364366, 'reg_alpha': 0.9702354802625098, 'reg_lambda': 2.472006050548421e-06, 'gamma': 6.825121429260793e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:17,012]\u001b[0m Trial 390 finished with value: 19125.096346444196 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07241979241506903, 'max_depth': 15, 'subsample': 0.8070671448291229, 'reg_alpha': 0.41507442649010545, 'reg_lambda': 7.762159419199984e-07, 'gamma': 7.757274723939936e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:17,185]\u001b[0m Trial 391 finished with value: 15213.280445687858 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08729642263298683, 'max_depth': 15, 'subsample': 0.6708672374586014, 'reg_alpha': 0.5616495233995064, 'reg_lambda': 5.173868118125628e-07, 'gamma': 2.2936026139862512e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:17,384]\u001b[0m Trial 392 finished with value: 17435.615907255215 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07862932909142659, 'max_depth': 15, 'subsample': 0.8263680369882466, 'reg_alpha': 0.9878359103318959, 'reg_lambda': 1.1951286754452608e-06, 'gamma': 1.0314181105707672e-07, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:17,545]\u001b[0m Trial 393 finished with value: 15222.835505604853 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08764930528835853, 'max_depth': 15, 'subsample': 0.5594908739462732, 'reg_alpha': 0.30885126846134936, 'reg_lambda': 4.775598194134348e-06, 'gamma': 5.909589279386332e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:17,715]\u001b[0m Trial 394 finished with value: 12894.925846719849 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09951141498451915, 'max_depth': 15, 'subsample': 0.7498201270290963, 'reg_alpha': 2.278943849206583e-05, 'reg_lambda': 4.4330533743226414e-07, 'gamma': 1.1293417466020625e-05, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:17,891]\u001b[0m Trial 395 finished with value: 20369.93445315053 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06869778546288453, 'max_depth': 15, 'subsample': 0.8652240544864982, 'reg_alpha': 0.6785152832146558, 'reg_lambda': 1.7019680448958406e-06, 'gamma': 4.972863525960909e-06, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,055]\u001b[0m Trial 396 finished with value: 17023.390383042784 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07974246928422699, 'max_depth': 15, 'subsample': 0.6636825687318121, 'reg_alpha': 6.856900928993875e-05, 'reg_lambda': 2.9349291446065056e-06, 'gamma': 1.38712661827865e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,191]\u001b[0m Trial 397 finished with value: 15089.740954922467 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08867771431432189, 'max_depth': 15, 'subsample': 0.2636086657312803, 'reg_alpha': 0.4262385649360566, 'reg_lambda': 6.740763329393731e-07, 'gamma': 8.025370126236332e-08, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,366]\u001b[0m Trial 398 finished with value: 15346.296748770174 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08675416104670605, 'max_depth': 15, 'subsample': 0.7654805559674781, 'reg_alpha': 0.00014602807836358318, 'reg_lambda': 0.00013651636477857808, 'gamma': 5.6279407631106353e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,435]\u001b[0m Trial 399 finished with value: 24861.426374392802 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07133853156265035, 'max_depth': 5, 'subsample': 0.713613933962642, 'reg_alpha': 4.526065413938126e-06, 'reg_lambda': 1.910254917303812e-07, 'gamma': 2.417789981807562e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,607]\u001b[0m Trial 400 finished with value: 15399.272403647628 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08737639437872304, 'max_depth': 15, 'subsample': 0.8990179575274863, 'reg_alpha': 0.7133221201822932, 'reg_lambda': 3.4657991308989214e-07, 'gamma': 1.7041273005671188e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,766]\u001b[0m Trial 401 finished with value: 12885.044786805405 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09948577501120245, 'max_depth': 14, 'subsample': 0.6152443666129112, 'reg_alpha': 7.926605047592391e-06, 'reg_lambda': 1.1005911998869657e-06, 'gamma': 9.13171631423681e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:18,939]\u001b[0m Trial 402 finished with value: 17900.005282785576 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07667462565759728, 'max_depth': 15, 'subsample': 0.7822609628506532, 'reg_alpha': 0.25400681411973736, 'reg_lambda': 4.1691960492689545e-07, 'gamma': 5.2880261530331214e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,123]\u001b[0m Trial 403 finished with value: 23179.888727644207 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06069456260928242, 'max_depth': 15, 'subsample': 0.8308131152724405, 'reg_alpha': 0.49972089251004786, 'reg_lambda': 1.9788476787861334e-06, 'gamma': 2.389775011913983e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,169]\u001b[0m Trial 404 finished with value: 28580.53435948212 and parameters: {'booster': 'gblinear', 'learning_rate': 0.08865143964464837, 'max_depth': 15, 'subsample': 0.6765682782191454, 'reg_alpha': 0.9737406479888848, 'reg_lambda': 7.364343521354332e-07, 'gamma': 1.1386285773389901e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,248]\u001b[0m Trial 405 finished with value: 15850.444655955967 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09968165912926469, 'max_depth': 15, 'subsample': 0.026435588430402324, 'reg_alpha': 0.38810829893637233, 'reg_lambda': 1.897180441066189e-07, 'gamma': 0.1681910826589035, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,340]\u001b[0m Trial 406 finished with value: 66581.87886536008 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0013138591658449653, 'max_depth': 9, 'subsample': 0.9030455015071155, 'reg_alpha': 0.6067813133398636, 'reg_lambda': 5.366834804560086e-07, 'gamma': 6.244197028191536e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,547]\u001b[0m Trial 407 finished with value: 27033.373766134766 and parameters: {'booster': 'gbtree', 'learning_rate': 0.05131022219990885, 'max_depth': 14, 'subsample': 0.7402543437117125, 'reg_alpha': 0.9937209294383315, 'reg_lambda': 2.909050069419562e-07, 'gamma': 4.015601993274651e-08, 'min_child_weight': 1}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,723]\u001b[0m Trial 408 finished with value: 12818.883827404708 and parameters: {'booster': 'gbtree', 'learning_rate': 0.099688276437962, 'max_depth': 15, 'subsample': 0.8214993769455723, 'reg_alpha': 0.25836764671559537, 'reg_lambda': 9.959418698435493e-06, 'gamma': 7.911219909930296e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:19,909]\u001b[0m Trial 409 finished with value: 17516.48201847246 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07832367831911333, 'max_depth': 15, 'subsample': 0.8386531648092143, 'reg_alpha': 9.42697805698222e-06, 'reg_lambda': 6.981530470880953e-06, 'gamma': 2.616121121193591e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:20,015]\u001b[0m Trial 410 finished with value: 13427.925018194619 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09986123861622707, 'max_depth': 15, 'subsample': 0.11806758374643526, 'reg_alpha': 0.283480699213148, 'reg_lambda': 3.907209832630774e-06, 'gamma': 2.7779674860539744e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:20,225]\u001b[0m Trial 411 finished with value: 21530.5769567469 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06559881836822111, 'max_depth': 14, 'subsample': 0.920141739541334, 'reg_alpha': 5.776986246107411e-06, 'reg_lambda': 2.9041142828787283e-06, 'gamma': 4.353221705907028e-08, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:20,405]\u001b[0m Trial 412 finished with value: 17057.042386717763 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07937979658301046, 'max_depth': 15, 'subsample': 0.8000202724268498, 'reg_alpha': 3.7437007197231446e-05, 'reg_lambda': 2.2313214388075595e-07, 'gamma': 2.266620481300294e-05, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:20,581]\u001b[0m Trial 413 finished with value: 59859.620030154656 and parameters: {'booster': 'gbtree', 'learning_rate': 0.007042182867024349, 'max_depth': 15, 'subsample': 0.8671895002056698, 'reg_alpha': 0.2882507555378471, 'reg_lambda': 4.0872959975762614e-07, 'gamma': 1.5306314341669125e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:20,758]\u001b[0m Trial 414 finished with value: 15243.914994372157 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08711761025242627, 'max_depth': 14, 'subsample': 0.7671873499621383, 'reg_alpha': 1.7591417618028475e-05, 'reg_lambda': 1.5011703261818349e-05, 'gamma': 1.3319711613970008e-07, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:20,821]\u001b[0m Trial 415 finished with value: 31374.122216528038 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0721933885741135, 'max_depth': 15, 'subsample': 0.7136251491549973, 'reg_alpha': 1.3471379326177315e-05, 'reg_lambda': 3.367976278399934e-05, 'gamma': 6.578389160384619e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:21,017]\u001b[0m Trial 416 finished with value: 15034.213431769396 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0884006677356144, 'max_depth': 15, 'subsample': 0.8138255836740522, 'reg_alpha': 0.368548555164487, 'reg_lambda': 5.481339391506458e-07, 'gamma': 3.1488770907228e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:21,231]\u001b[0m Trial 417 finished with value: 15029.10765731808 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08863700321564243, 'max_depth': 15, 'subsample': 0.9141651592331824, 'reg_alpha': 0.23333865236093415, 'reg_lambda': 3.1112764591326403e-07, 'gamma': 9.735177446714437e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:21,411]\u001b[0m Trial 418 finished with value: 17374.197755890335 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07809813990347007, 'max_depth': 15, 'subsample': 0.7160525620581486, 'reg_alpha': 0.7556980654897709, 'reg_lambda': 1.129387270207575e-05, 'gamma': 1.9371874426569057e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:21,593]\u001b[0m Trial 419 finished with value: 15037.892760637304 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08842532636094537, 'max_depth': 14, 'subsample': 0.8088185044855866, 'reg_alpha': 0.48920955419970036, 'reg_lambda': 1.5310610793793794e-07, 'gamma': 4.807135903845656e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:21,830]\u001b[0m Trial 420 finished with value: 21151.595393650714 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06634377953525401, 'max_depth': 15, 'subsample': 0.9135297305192263, 'reg_alpha': 0.9900058009677795, 'reg_lambda': 1.021395193379234e-05, 'gamma': 1.8292954954368077e-07, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,014]\u001b[0m Trial 421 finished with value: 67888.80646437132 and parameters: {'booster': 'gbtree', 'learning_rate': 0.00022996049330972413, 'max_depth': 15, 'subsample': 0.7677041316098113, 'reg_alpha': 3.167961235270148e-06, 'reg_lambda': 8.281751370462894e-07, 'gamma': 7.319319956432149e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,193]\u001b[0m Trial 422 finished with value: 14732.374481545849 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08934510909382123, 'max_depth': 15, 'subsample': 0.668400858028572, 'reg_alpha': 0.6797475500518249, 'reg_lambda': 1.7098527420079475e-06, 'gamma': 3.629939873048205e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,377]\u001b[0m Trial 423 finished with value: 12935.065631996491 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09947266940785163, 'max_depth': 15, 'subsample': 0.8633493945931072, 'reg_alpha': 2.563596177586762e-05, 'reg_lambda': 4.365196598164372e-06, 'gamma': 1.0470616744646627e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,560]\u001b[0m Trial 424 finished with value: 17817.012371975416 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07664869759538419, 'max_depth': 14, 'subsample': 0.7076103889851262, 'reg_alpha': 1.294494685802102e-06, 'reg_lambda': 2.0298482574152776e-07, 'gamma': 2.536607892555237e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,715]\u001b[0m Trial 425 finished with value: 17303.582467265394 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07972205766572309, 'max_depth': 15, 'subsample': 0.9404167367155938, 'reg_alpha': 0.3533458954930859, 'reg_lambda': 5.178885439749395e-07, 'gamma': 1.2832522112415262e-07, 'min_child_weight': 8}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,906]\u001b[0m Trial 426 finished with value: 12783.373287387281 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09998332089130335, 'max_depth': 15, 'subsample': 0.7929692019070634, 'reg_alpha': 7.974719632482038e-06, 'reg_lambda': 3.2769178298618103e-07, 'gamma': 5.647015631890966e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:22,960]\u001b[0m Trial 427 finished with value: 27120.88992708622 and parameters: {'booster': 'gblinear', 'learning_rate': 0.09963717822312967, 'max_depth': 15, 'subsample': 0.7651341498433807, 'reg_alpha': 9.965537913286773e-06, 'reg_lambda': 3.152730529257799e-07, 'gamma': 1.350390420851622e-06, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:23,135]\u001b[0m Trial 428 finished with value: 67649.41546233495 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0004211628288629221, 'max_depth': 15, 'subsample': 0.647820542321375, 'reg_alpha': 7.165955450537318e-06, 'reg_lambda': 1.0605951253622649e-07, 'gamma': 4.894956002441677e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:23,308]\u001b[0m Trial 429 finished with value: 63902.318723344215 and parameters: {'booster': 'gbtree', 'learning_rate': 0.003506288924157934, 'max_depth': 14, 'subsample': 0.7301832935763933, 'reg_alpha': 4.749088294181725e-06, 'reg_lambda': 2.532945487092119e-07, 'gamma': 1.745601534740894e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:23,483]\u001b[0m Trial 430 finished with value: 15291.259352946197 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08721943261140049, 'max_depth': 15, 'subsample': 0.7799829843878787, 'reg_alpha': 1.2096002196983912e-05, 'reg_lambda': 3.962313573448481e-07, 'gamma': 3.466097154576966e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:23,651]\u001b[0m Trial 431 finished with value: 24594.641567948962 and parameters: {'booster': 'gbtree', 'learning_rate': 0.056903362133148006, 'max_depth': 15, 'subsample': 0.6610518999025647, 'reg_alpha': 4.5369508209400675e-06, 'reg_lambda': 1.7336695205436147e-07, 'gamma': 1.0006298059828055e-07, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:23,730]\u001b[0m Trial 432 finished with value: 23589.556603490393 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06957835366501328, 'max_depth': 6, 'subsample': 0.8421201624806796, 'reg_alpha': 1.783508509741941e-05, 'reg_lambda': 7.741692892670462e-07, 'gamma': 2.260025843890962e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:23,909]\u001b[0m Trial 433 finished with value: 17084.843432124602 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07927285930922379, 'max_depth': 15, 'subsample': 0.7214724581071694, 'reg_alpha': 8.1356501018935e-06, 'reg_lambda': 4.986940801837484e-07, 'gamma': 5.356752997470084e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:24,088]\u001b[0m Trial 434 finished with value: 38816.48017617994 and parameters: {'booster': 'gbtree', 'learning_rate': 0.030829584184643172, 'max_depth': 15, 'subsample': 0.7680042081448136, 'reg_alpha': 1.3800091701491102e-05, 'reg_lambda': 3.0593998014730086e-07, 'gamma': 2.1309298050701814e-07, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:24,267]\u001b[0m Trial 435 finished with value: 12993.276394934994 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09961915153416898, 'max_depth': 14, 'subsample': 0.9916215374887268, 'reg_alpha': 5.588881560786755e-05, 'reg_lambda': 0.00022495210740288807, 'gamma': 7.361014870691291e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:24,454]\u001b[0m Trial 436 finished with value: 15105.652745859501 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0885919593432288, 'max_depth': 15, 'subsample': 0.841374784146837, 'reg_alpha': 0.21488688802648112, 'reg_lambda': 2.2809752908217074e-07, 'gamma': 1.4160963065081958e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:24,546]\u001b[0m Trial 437 finished with value: 21672.10516291166 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07106778137205173, 'max_depth': 7, 'subsample': 0.6142321833824153, 'reg_alpha': 6.906266374925749e-06, 'reg_lambda': 5.996248655669697e-07, 'gamma': 2.9324415127192107e-08, 'min_child_weight': 4}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:24,731]\u001b[0m Trial 438 finished with value: 12772.978338011193 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0998875728042355, 'max_depth': 15, 'subsample': 0.686884304598316, 'reg_alpha': 0.453834539205907, 'reg_lambda': 2.1917764205233122e-05, 'gamma': 4.514289346007422e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:24,910]\u001b[0m Trial 439 finished with value: 15228.041722478592 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08714388401701637, 'max_depth': 15, 'subsample': 0.6572062677901015, 'reg_alpha': 0.2663906093652898, 'reg_lambda': 2.062468013042447e-05, 'gamma': 3.393374277031232e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:25,054]\u001b[0m Trial 440 finished with value: 18201.423882470368 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07634336620592803, 'max_depth': 15, 'subsample': 0.6966695445685192, 'reg_alpha': 0.3717737611172928, 'reg_lambda': 1.2351526251060464e-07, 'gamma': 4.326373938631769e-08, 'min_child_weight': 9}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:25,245]\u001b[0m Trial 441 finished with value: 14662.925412302784 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08938403689026272, 'max_depth': 15, 'subsample': 0.573850049171755, 'reg_alpha': 0.48289572177511303, 'reg_lambda': 5.3595290301331686e-06, 'gamma': 5.724702806193237e-08, 'min_child_weight': 3}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:25,459]\u001b[0m Trial 442 finished with value: 12861.336236384139 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09931348138937254, 'max_depth': 15, 'subsample': 0.6340285804652093, 'reg_alpha': 2.056523017153307e-06, 'reg_lambda': 7.177552865432573e-05, 'gamma': 9.098213421670079e-08, 'min_child_weight': 2}. Best is trial 371 with value: 12676.840424706446.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:25,646]\u001b[0m Trial 443 finished with value: 12659.137449852062 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09980493345232212, 'max_depth': 15, 'subsample': 0.7049092270967089, 'reg_alpha': 3.676229748257749e-06, 'reg_lambda': 1.8976482954362074e-07, 'gamma': 2.2920724510577443e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:25,835]\u001b[0m Trial 444 finished with value: 16953.937763955375 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08033699478636223, 'max_depth': 15, 'subsample': 0.6865661659663692, 'reg_alpha': 3.4466356006003208e-06, 'reg_lambda': 9.8856090398665e-06, 'gamma': 2.0791089828119862e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:26,030]\u001b[0m Trial 445 finished with value: 22440.201626091337 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06268210276113208, 'max_depth': 15, 'subsample': 0.519678496321067, 'reg_alpha': 6.7738243588907076e-06, 'reg_lambda': 1.6062655817739362e-07, 'gamma': 1.3302756313038458e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:26,215]\u001b[0m Trial 446 finished with value: 15545.4501788286 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08601240962336863, 'max_depth': 15, 'subsample': 0.5774000012881556, 'reg_alpha': 8.895168393309607e-06, 'reg_lambda': 1.0244444073893841e-07, 'gamma': 2.4277026233381454e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:26,275]\u001b[0m Trial 447 finished with value: 31446.887133331547 and parameters: {'booster': 'gblinear', 'learning_rate': 0.07183659304468229, 'max_depth': 15, 'subsample': 0.7071705439229201, 'reg_alpha': 3.328532392215042e-06, 'reg_lambda': 1.9485485240269668e-05, 'gamma': 1.9835163924462086e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:26,472]\u001b[0m Trial 448 finished with value: 15249.938913063512 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08752415574133594, 'max_depth': 15, 'subsample': 0.6359751696281113, 'reg_alpha': 2.2915281388250027e-06, 'reg_lambda': 1.8143237804134762e-07, 'gamma': 1.0301218690597613e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:26,668]\u001b[0m Trial 449 finished with value: 12902.436689478396 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09921512898377444, 'max_depth': 15, 'subsample': 0.7508602580620574, 'reg_alpha': 4.9180482997557196e-06, 'reg_lambda': 4.162658798425258e-05, 'gamma': 3.780479955385376e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:26,889]\u001b[0m Trial 450 finished with value: 17164.440592057384 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07955471061853987, 'max_depth': 15, 'subsample': 0.9082510021404925, 'reg_alpha': 0.19947202440049605, 'reg_lambda': 2.246903603565046e-05, 'gamma': 1.5110115717664005e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:27,097]\u001b[0m Trial 451 finished with value: 14795.743059681776 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08890936574771885, 'max_depth': 15, 'subsample': 0.8151649455150121, 'reg_alpha': 0.5176073743318029, 'reg_lambda': 2.267965743468003e-07, 'gamma': 3.040987953192643e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:27,294]\u001b[0m Trial 452 finished with value: 17331.69410102967 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07881469782441955, 'max_depth': 15, 'subsample': 0.6867179491243383, 'reg_alpha': 5.6972111759473876e-06, 'reg_lambda': 1.4283816921266208e-07, 'gamma': 4.610279768378065e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:27,402]\u001b[0m Trial 453 finished with value: 13678.14976636642 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09970219764331952, 'max_depth': 15, 'subsample': 0.07431036501452376, 'reg_alpha': 7.659580045167684e-07, 'reg_lambda': 2.762141802052673e-05, 'gamma': 2.5154868657389467e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:27,643]\u001b[0m Trial 454 finished with value: 12855.562925209137 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09983289846930583, 'max_depth': 15, 'subsample': 0.8576300127293737, 'reg_alpha': 3.883889885748463e-06, 'reg_lambda': 3.2268296043114143e-07, 'gamma': 1.4599345242387742e-07, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:27,730]\u001b[0m Trial 455 finished with value: 24867.440025831755 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06584391366636248, 'max_depth': 15, 'subsample': 0.01530346856382696, 'reg_alpha': 0.31467997056706504, 'reg_lambda': 9.067728703992513e-08, 'gamma': 1.7665828727107798e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:27,913]\u001b[0m Trial 456 finished with value: 15468.30659926917 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08657151461837796, 'max_depth': 15, 'subsample': 0.6144162389461001, 'reg_alpha': 0.5869093261051317, 'reg_lambda': 2.7660217818063045e-07, 'gamma': 4.385131839487708e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:28,095]\u001b[0m Trial 457 finished with value: 19294.97049228242 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07210664430695819, 'max_depth': 15, 'subsample': 0.7731656551214048, 'reg_alpha': 1.1090431556347128e-05, 'reg_lambda': 3.863694583706114e-07, 'gamma': 5.931026519626091e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:28,271]\u001b[0m Trial 458 finished with value: 15200.832465828666 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08671956016408634, 'max_depth': 15, 'subsample': 0.7162216453890349, 'reg_alpha': 0.4022185674473977, 'reg_lambda': 1.372503138693307e-07, 'gamma': 1.0507576930881208e-07, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:28,474]\u001b[0m Trial 459 finished with value: 13031.707884161882 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09939477373815102, 'max_depth': 15, 'subsample': 0.9305669074842868, 'reg_alpha': 0.7034920070995537, 'reg_lambda': 6.806684839349306e-06, 'gamma': 2.8181665131988e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:28,537]\u001b[0m Trial 460 finished with value: 30095.502617680675 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0788199032952089, 'max_depth': 15, 'subsample': 0.7824293570696506, 'reg_alpha': 2.4905028451484613e-06, 'reg_lambda': 2.0316023456878122e-07, 'gamma': 2.976836503262225e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:28,670]\u001b[0m Trial 461 finished with value: 15946.107193091499 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0870530569776877, 'max_depth': 15, 'subsample': 0.5632221823508062, 'reg_alpha': 0.4035426245154099, 'reg_lambda': 0.27474163590986206, 'gamma': 1.6361135475898543e-08, 'min_child_weight': 10}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:28,847]\u001b[0m Trial 462 finished with value: 12732.069534781802 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09968370640508184, 'max_depth': 15, 'subsample': 0.6694904396801921, 'reg_alpha': 0.2175237774550768, 'reg_lambda': 3.8194245329704916e-07, 'gamma': 3.5454297340903616e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,031]\u001b[0m Trial 463 finished with value: 23140.57475013778 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06085769821055836, 'max_depth': 15, 'subsample': 0.8618787305433552, 'reg_alpha': 0.1801485861936726, 'reg_lambda': 3.648266496558135e-07, 'gamma': 2.3439993386946115e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,204]\u001b[0m Trial 464 finished with value: 19356.763449302845 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07166586731468685, 'max_depth': 15, 'subsample': 0.6306994333482017, 'reg_alpha': 0.18956600311655167, 'reg_lambda': 2.5142415904919785e-07, 'gamma': 3.4065264425617445e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,383]\u001b[0m Trial 465 finished with value: 29315.435314955437 and parameters: {'booster': 'gbtree', 'learning_rate': 0.04669996043288605, 'max_depth': 15, 'subsample': 0.723163384219513, 'reg_alpha': 0.2961889034855633, 'reg_lambda': 4.4702406604350014e-07, 'gamma': 1.2287393609277895e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,571]\u001b[0m Trial 466 finished with value: 15200.411464885517 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08753543071923052, 'max_depth': 15, 'subsample': 0.8109540921730639, 'reg_alpha': 0.2889780829734441, 'reg_lambda': 1.7552183870855968e-07, 'gamma': 7.767845084818993e-06, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,691]\u001b[0m Trial 467 finished with value: 65679.52131686552 and parameters: {'booster': 'dart', 'learning_rate': 0.0020571975355129766, 'max_depth': 10, 'subsample': 0.33340160984012707, 'reg_alpha': 0.22366588998260575, 'reg_lambda': 3.09222640001689e-07, 'gamma': 4.535094728531183e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,783]\u001b[0m Trial 468 finished with value: 20093.270529154703 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07930258199884617, 'max_depth': 14, 'subsample': 0.03449021122341632, 'reg_alpha': 0.5470554755046759, 'reg_lambda': 2.4859032048913725e-07, 'gamma': 1.9465494057524047e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:29,887]\u001b[0m Trial 469 finished with value: 16419.33631296347 and parameters: {'booster': 'dart', 'learning_rate': 0.0882601108190306, 'max_depth': 8, 'subsample': 0.9272353313131493, 'reg_alpha': 0.41666615796476214, 'reg_lambda': 1.338717699948996e-07, 'gamma': 2.8812308240504357e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:30,071]\u001b[0m Trial 470 finished with value: 53913.7967024567 and parameters: {'booster': 'gbtree', 'learning_rate': 0.012739393664390709, 'max_depth': 15, 'subsample': 0.651304554241815, 'reg_alpha': 0.6925356340634354, 'reg_lambda': 4.106167555335368e-07, 'gamma': 4.371489053685735e-05, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:30,207]\u001b[0m Trial 471 finished with value: 68004.51105003207 and parameters: {'booster': 'gbtree', 'learning_rate': 0.00014001325252447413, 'max_depth': 15, 'subsample': 0.19139789987673259, 'reg_alpha': 0.1254874202687418, 'reg_lambda': 8.834543088391905e-08, 'gamma': 4.127046756988629e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:30,405]\u001b[0m Trial 472 finished with value: 12847.335480906137 and parameters: {'booster': 'dart', 'learning_rate': 0.09906863095756858, 'max_depth': 15, 'subsample': 0.7516953703427574, 'reg_alpha': 1.526269643916872e-06, 'reg_lambda': 1.9612859213630952e-07, 'gamma': 8.986784634355677e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:30,612]\u001b[0m Trial 473 finished with value: 19340.37221301861 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07186452073517023, 'max_depth': 15, 'subsample': 0.9967591715651896, 'reg_alpha': 0.5451276304697061, 'reg_lambda': 5.081571930633601e-07, 'gamma': 2.073795956866909e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:30,844]\u001b[0m Trial 474 finished with value: 16976.64155509574 and parameters: {'booster': 'dart', 'learning_rate': 0.079736687855308, 'max_depth': 15, 'subsample': 0.846307978160969, 'reg_alpha': 0.21776041885682695, 'reg_lambda': 3.0966990842396607e-07, 'gamma': 1.6696073379491733e-07, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,012]\u001b[0m Trial 475 finished with value: 15046.505085971768 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08810055331403285, 'max_depth': 14, 'subsample': 0.6731402353409978, 'reg_alpha': 0.3758241979617152, 'reg_lambda': 1.0177672741031049e-07, 'gamma': 6.224170796063002e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,212]\u001b[0m Trial 476 finished with value: 14995.534029750546 and parameters: {'booster': 'dart', 'learning_rate': 0.08831324502511446, 'max_depth': 15, 'subsample': 0.7727359932593215, 'reg_alpha': 0.7112908800432988, 'reg_lambda': 3.788721996992936e-07, 'gamma': 3.0297399503867125e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,395]\u001b[0m Trial 477 finished with value: 20661.73064380594 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0677059786154586, 'max_depth': 15, 'subsample': 0.8640922947006325, 'reg_alpha': 0.2853798926023077, 'reg_lambda': 9.430193664318486e-07, 'gamma': 1.0308157369268718e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,607]\u001b[0m Trial 478 finished with value: 12924.49709165307 and parameters: {'booster': 'dart', 'learning_rate': 0.09948152392424087, 'max_depth': 15, 'subsample': 0.5887307384604376, 'reg_alpha': 0.7129493373987223, 'reg_lambda': 2.362633181095452e-07, 'gamma': 1.1483235346239872e-07, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,667]\u001b[0m Trial 479 finished with value: 30164.876796351502 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0786249762732435, 'max_depth': 15, 'subsample': 0.7031486252484168, 'reg_alpha': 0.42198964280756746, 'reg_lambda': 5.323451317250758e-05, 'gamma': 5.01210451679766e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,839]\u001b[0m Trial 480 finished with value: 12880.650340768456 and parameters: {'booster': 'gbtree', 'learning_rate': 0.099787002483338, 'max_depth': 14, 'subsample': 0.7841604618670255, 'reg_alpha': 0.582425447334225, 'reg_lambda': 6.511712015612217e-07, 'gamma': 1.6957416694852187e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:31,995]\u001b[0m Trial 481 finished with value: 24554.110906145674 and parameters: {'booster': 'dart', 'learning_rate': 0.05801830852652515, 'max_depth': 15, 'subsample': 0.5171865635676952, 'reg_alpha': 0.960363647543518, 'reg_lambda': 1.6047034253400044e-07, 'gamma': 3.532922203069521e-08, 'min_child_weight': 8}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:32,198]\u001b[0m Trial 482 finished with value: 15134.420921060419 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08801796539683211, 'max_depth': 15, 'subsample': 0.8881084627516849, 'reg_alpha': 0.1669254609424982, 'reg_lambda': 1.3321647200403704e-05, 'gamma': 7.335418450657136e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:32,394]\u001b[0m Trial 483 finished with value: 12832.888274816993 and parameters: {'booster': 'dart', 'learning_rate': 0.0999706420947809, 'max_depth': 15, 'subsample': 0.7116364445562928, 'reg_alpha': 2.157114778048462e-05, 'reg_lambda': 4.4152446706547644e-07, 'gamma': 2.515218892633092e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:32,592]\u001b[0m Trial 484 finished with value: 17371.840030512503 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07880262033546723, 'max_depth': 15, 'subsample': 0.6078195633374763, 'reg_alpha': 2.0172108327435678e-05, 'reg_lambda': 5.29425642121461e-07, 'gamma': 2.25659371033613e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:32,798]\u001b[0m Trial 485 finished with value: 15573.016120904706 and parameters: {'booster': 'dart', 'learning_rate': 0.0861939757677031, 'max_depth': 15, 'subsample': 0.6527214358474593, 'reg_alpha': 2.6387842902786553e-05, 'reg_lambda': 7.80355828853682e-07, 'gamma': 1.3458346177751646e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:32,977]\u001b[0m Trial 486 finished with value: 12972.928088811332 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0992644331831651, 'max_depth': 15, 'subsample': 0.5519894057361739, 'reg_alpha': 3.806531546234578e-05, 'reg_lambda': 4.3779420011860546e-07, 'gamma': 2.8785522671854517e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:33,089]\u001b[0m Trial 487 finished with value: 21250.53140297369 and parameters: {'booster': 'dart', 'learning_rate': 0.0679833010328097, 'max_depth': 9, 'subsample': 0.6972944449942103, 'reg_alpha': 1.6475085360072574e-05, 'reg_lambda': 3.4460397805569626e-07, 'gamma': 4.27764013159645e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:33,272]\u001b[0m Trial 488 finished with value: 17895.615559492817 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07671585793013087, 'max_depth': 14, 'subsample': 0.7292882394046798, 'reg_alpha': 1.4984631007299368e-05, 'reg_lambda': 5.996932312761234e-07, 'gamma': 1.561306281831395e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:33,472]\u001b[0m Trial 489 finished with value: 67782.5652761041 and parameters: {'booster': 'dart', 'learning_rate': 0.00031512294023627386, 'max_depth': 15, 'subsample': 0.6349703315724834, 'reg_alpha': 2.330699281088848e-05, 'reg_lambda': 1.164713659063835e-06, 'gamma': 2.357367981435427e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:33,673]\u001b[0m Trial 490 finished with value: 14967.263652558328 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08819984940728207, 'max_depth': 15, 'subsample': 0.7374971824456363, 'reg_alpha': 5.505165912223016e-06, 'reg_lambda': 8.283739418218553e-07, 'gamma': 5.5299138552139865e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:33,868]\u001b[0m Trial 491 finished with value: 13039.983055628005 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09928872114561994, 'max_depth': 15, 'subsample': 0.7986733146909898, 'reg_alpha': 1.3322641735999924e-05, 'reg_lambda': 0.06692736576973274, 'gamma': 3.92209224581791e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:34,056]\u001b[0m Trial 492 finished with value: 12782.438691544095 and parameters: {'booster': 'dart', 'learning_rate': 0.09988285498930445, 'max_depth': 15, 'subsample': 0.4525651910503802, 'reg_alpha': 9.90350388770098e-06, 'reg_lambda': 8.693104270773203e-06, 'gamma': 9.542014934307021e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:34,249]\u001b[0m Trial 493 finished with value: 17612.235191187076 and parameters: {'booster': 'dart', 'learning_rate': 0.07837444744968146, 'max_depth': 15, 'subsample': 0.43595206784856244, 'reg_alpha': 6.754898986087372e-06, 'reg_lambda': 2.391625190600861e-05, 'gamma': 2.2887185703582314e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:34,435]\u001b[0m Trial 494 finished with value: 15436.894469529821 and parameters: {'booster': 'dart', 'learning_rate': 0.08703381293074829, 'max_depth': 14, 'subsample': 0.5400807622419587, 'reg_alpha': 3.7196099556601264e-06, 'reg_lambda': 9.211298894905276e-06, 'gamma': 1.3980572582835556e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:34,600]\u001b[0m Trial 495 finished with value: 20272.176819345634 and parameters: {'booster': 'dart', 'learning_rate': 0.06897001565917027, 'max_depth': 15, 'subsample': 0.3724942930622409, 'reg_alpha': 1.0588197377092727e-05, 'reg_lambda': 7.444569792607569e-06, 'gamma': 3.712416134569374e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:34,806]\u001b[0m Trial 496 finished with value: 12902.925349004923 and parameters: {'booster': 'dart', 'learning_rate': 0.09962177040250675, 'max_depth': 15, 'subsample': 0.5859981060258755, 'reg_alpha': 0.2693241955460864, 'reg_lambda': 6.056828897423418e-06, 'gamma': 9.122234099039516e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:35,032]\u001b[0m Trial 497 finished with value: 15409.82700067777 and parameters: {'booster': 'dart', 'learning_rate': 0.08702243111020332, 'max_depth': 15, 'subsample': 0.6316542743300834, 'reg_alpha': 0.3938434698270109, 'reg_lambda': 3.4287791939864485e-06, 'gamma': 1.4034612612490397e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:35,221]\u001b[0m Trial 498 finished with value: 12863.618054726574 and parameters: {'booster': 'dart', 'learning_rate': 0.09967154911465978, 'max_depth': 15, 'subsample': 0.4466511796480864, 'reg_alpha': 5.616285536517562e-06, 'reg_lambda': 1.0213781848647345e-05, 'gamma': 1.769622480892026e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:35,434]\u001b[0m Trial 499 finished with value: 17826.28353109754 and parameters: {'booster': 'dart', 'learning_rate': 0.07706629187084771, 'max_depth': 14, 'subsample': 0.6831367086132192, 'reg_alpha': 0.24752723876701288, 'reg_lambda': 5.055842781967465e-06, 'gamma': 9.663201108587241e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:35,633]\u001b[0m Trial 500 finished with value: 66916.98246361503 and parameters: {'booster': 'dart', 'learning_rate': 0.0010110527490809177, 'max_depth': 15, 'subsample': 0.5737058534339456, 'reg_alpha': 0.1290148863693413, 'reg_lambda': 4.027420328281332e-06, 'gamma': 2.8037918974434253e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:35,832]\u001b[0m Trial 501 finished with value: 12861.099418319 and parameters: {'booster': 'dart', 'learning_rate': 0.09991312357285301, 'max_depth': 15, 'subsample': 0.6709900052896568, 'reg_alpha': 2.5766057861696836e-06, 'reg_lambda': 2.5770762319416214e-07, 'gamma': 1.0637716111363643e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:36,021]\u001b[0m Trial 502 finished with value: 26474.5572348831 and parameters: {'booster': 'dart', 'learning_rate': 0.05299348576053095, 'max_depth': 15, 'subsample': 0.4986086940724421, 'reg_alpha': 8.696185005296025e-06, 'reg_lambda': 1.2778452952345828e-05, 'gamma': 7.566944872040403e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:36,232]\u001b[0m Trial 503 finished with value: 15419.606393825015 and parameters: {'booster': 'dart', 'learning_rate': 0.08689147930458316, 'max_depth': 15, 'subsample': 0.7339487947670598, 'reg_alpha': 1.5104400334146833e-07, 'reg_lambda': 1.5461422513117263e-05, 'gamma': 1.8542886044605275e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:36,414]\u001b[0m Trial 504 finished with value: 18823.313645858143 and parameters: {'booster': 'dart', 'learning_rate': 0.07379730323125075, 'max_depth': 14, 'subsample': 0.6291271683878711, 'reg_alpha': 0.5011950510928959, 'reg_lambda': 3.0247178731980137e-07, 'gamma': 3.8005629953741967e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:36,615]\u001b[0m Trial 505 finished with value: 15376.569755205792 and parameters: {'booster': 'dart', 'learning_rate': 0.08700377212352456, 'max_depth': 15, 'subsample': 0.7341499057735571, 'reg_alpha': 4.256103711565391e-06, 'reg_lambda': 5.47340680146e-07, 'gamma': 2.0946064663399975e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:36,855]\u001b[0m Trial 506 finished with value: 42967.44220472156 and parameters: {'booster': 'dart', 'learning_rate': 0.025146322807755014, 'max_depth': 15, 'subsample': 0.6817581771273671, 'reg_alpha': 0.35379590166239344, 'reg_lambda': 2.4856544389568714e-06, 'gamma': 5.562330892122218e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:37,051]\u001b[0m Trial 507 finished with value: 22672.201730320106 and parameters: {'booster': 'dart', 'learning_rate': 0.06192813888150349, 'max_depth': 15, 'subsample': 0.7925799429802147, 'reg_alpha': 0.9933274244022167, 'reg_lambda': 1.1652116402893461e-07, 'gamma': 1.4506877476493273e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:37,111]\u001b[0m Trial 508 finished with value: 27111.114207442835 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0999698745681358, 'max_depth': 15, 'subsample': 0.5677557266547507, 'reg_alpha': 0.5295261921950062, 'reg_lambda': 2.1437684187271162e-07, 'gamma': 1.2102448136817733e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:37,285]\u001b[0m Trial 509 finished with value: 65134.63230663972 and parameters: {'booster': 'dart', 'learning_rate': 0.00247895979010926, 'max_depth': 14, 'subsample': 0.6127440438090559, 'reg_alpha': 0.20270349542005675, 'reg_lambda': 3.35687287212787e-05, 'gamma': 2.9314969767060042e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:37,443]\u001b[0m Trial 510 finished with value: 17664.901264455624 and parameters: {'booster': 'dart', 'learning_rate': 0.07791882469520474, 'max_depth': 15, 'subsample': 0.29382016638919756, 'reg_alpha': 7.78233395990151e-06, 'reg_lambda': 7.975522833493717e-06, 'gamma': 8.528607896920513e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:37,632]\u001b[0m Trial 511 finished with value: 35264.260321369926 and parameters: {'booster': 'dart', 'learning_rate': 0.036299591556118366, 'max_depth': 15, 'subsample': 0.48394359458271685, 'reg_alpha': 0.6571017551851899, 'reg_lambda': 1.014846130841988e-06, 'gamma': 4.733188885924747e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:37,826]\u001b[0m Trial 512 finished with value: 15320.211554131112 and parameters: {'booster': 'gbtree', 'learning_rate': 0.086999996539207, 'max_depth': 15, 'subsample': 0.7937031004895353, 'reg_alpha': 0.32455292599884134, 'reg_lambda': 6.765172970004871e-08, 'gamma': 2.5977879782175466e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:38,033]\u001b[0m Trial 513 finished with value: 20420.159691888766 and parameters: {'booster': 'dart', 'learning_rate': 0.06836123646671388, 'max_depth': 15, 'subsample': 0.7270517302672643, 'reg_alpha': 0.4219521430488191, 'reg_lambda': 4.3044129694397923e-07, 'gamma': 7.070389950502325e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:38,203]\u001b[0m Trial 514 finished with value: 15115.875495363463 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08748479687473742, 'max_depth': 14, 'subsample': 0.6534612116844073, 'reg_alpha': 1.1190911654018187e-05, 'reg_lambda': 1.7315818234999833e-06, 'gamma': 3.814129733562582e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:38,422]\u001b[0m Trial 515 finished with value: 17740.94444356689 and parameters: {'booster': 'dart', 'learning_rate': 0.07782534314751599, 'max_depth': 15, 'subsample': 0.9190002453702771, 'reg_alpha': 0.668885143063326, 'reg_lambda': 0.016823682689892934, 'gamma': 2.028753130968451e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:38,530]\u001b[0m Trial 516 finished with value: 16165.35623444806 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08786100440411271, 'max_depth': 15, 'subsample': 0.08638141244270267, 'reg_alpha': 0.17134658608421952, 'reg_lambda': 1.4616690508808816e-07, 'gamma': 1.1924543145880393e-07, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:38,749]\u001b[0m Trial 517 finished with value: 14990.753000538109 and parameters: {'booster': 'dart', 'learning_rate': 0.08863731640467028, 'max_depth': 15, 'subsample': 0.7988184012951339, 'reg_alpha': 0.26210071743912006, 'reg_lambda': 6.429884368738437e-07, 'gamma': 1.0200343119779022e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:38,931]\u001b[0m Trial 518 finished with value: 19441.598376892107 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07110359638443078, 'max_depth': 15, 'subsample': 0.7257818939668312, 'reg_alpha': 0.4735909547381088, 'reg_lambda': 3.3375252100411597e-07, 'gamma': 8.171329806736188e-07, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:39,042]\u001b[0m Trial 519 finished with value: 56802.14658888885 and parameters: {'booster': 'dart', 'learning_rate': 0.010242039777451438, 'max_depth': 14, 'subsample': 0.05352003650611823, 'reg_alpha': 6.341520563647539e-06, 'reg_lambda': 9.80816534354944e-05, 'gamma': 2.0206242728751479e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:39,233]\u001b[0m Trial 520 finished with value: 66071.07003717759 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0016992380011637816, 'max_depth': 15, 'subsample': 0.8408457521568637, 'reg_alpha': 3.118094859678929e-05, 'reg_lambda': 2.3811451315631598e-07, 'gamma': 1.5410661054512998e-08, 'min_child_weight': 4}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:39,425]\u001b[0m Trial 521 finished with value: 12747.726437056916 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09974228539724597, 'max_depth': 15, 'subsample': 0.6808890075780609, 'reg_alpha': 0.6808645204315084, 'reg_lambda': 1.1147383426735825e-06, 'gamma': 5.682725376436532e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:39,610]\u001b[0m Trial 522 finished with value: 17045.56324496731 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08028005375600042, 'max_depth': 15, 'subsample': 0.5332344256110477, 'reg_alpha': 3.0943681293852313e-06, 'reg_lambda': 1.065947145246513e-06, 'gamma': 7.161993689082449e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:39,807]\u001b[0m Trial 523 finished with value: 12800.026255220731 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09983677022872788, 'max_depth': 15, 'subsample': 0.6154199906246035, 'reg_alpha': 0.7261890484064545, 'reg_lambda': 7.925875088735714e-07, 'gamma': 1.0427232046609346e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:40,009]\u001b[0m Trial 524 finished with value: 12774.153953910298 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09967289947582247, 'max_depth': 15, 'subsample': 0.6329930067768953, 'reg_alpha': 6.712703764753688e-07, 'reg_lambda': 8.523000578454792e-07, 'gamma': 2.2994265504600917e-06, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:40,197]\u001b[0m Trial 525 finished with value: 12806.305440322987 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09988819005359706, 'max_depth': 15, 'subsample': 0.6158459020650546, 'reg_alpha': 3.8417722509110605e-07, 'reg_lambda': 1.241206726961583e-06, 'gamma': 1.6663917459914633e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:40,397]\u001b[0m Trial 526 finished with value: 21656.772997079643 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06474948607675098, 'max_depth': 15, 'subsample': 0.5161010332874432, 'reg_alpha': 2.919969681397278e-07, 'reg_lambda': 1.277142662833614e-06, 'gamma': 3.857556213547954e-07, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:40,596]\u001b[0m Trial 527 finished with value: 16559.00428880149 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08152964422910805, 'max_depth': 15, 'subsample': 0.5914792264215498, 'reg_alpha': 2.4573219067780743e-07, 'reg_lambda': 1.3383992938462256e-06, 'gamma': 2.230239060909842e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:40,790]\u001b[0m Trial 528 finished with value: 15161.574136072559 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08793259878207112, 'max_depth': 15, 'subsample': 0.5637211017936661, 'reg_alpha': 2.5986136948076985e-07, 'reg_lambda': 1.7385836030146644e-06, 'gamma': 1.5451801605786894e-06, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:40,980]\u001b[0m Trial 529 finished with value: 18686.510901488193 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07395509248245298, 'max_depth': 15, 'subsample': 0.6134003932712498, 'reg_alpha': 4.2435001561712683e-07, 'reg_lambda': 1.0119224896462675e-06, 'gamma': 5.781702767731693e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:41,148]\u001b[0m Trial 530 finished with value: 15287.48736041297 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0878048506795942, 'max_depth': 15, 'subsample': 0.4026040521089648, 'reg_alpha': 1.627415582965705e-06, 'reg_lambda': 7.206731981388662e-07, 'gamma': 1.447859563359708e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:41,337]\u001b[0m Trial 531 finished with value: 14946.731622338724 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08823516295524189, 'max_depth': 15, 'subsample': 0.6497871969519287, 'reg_alpha': 1.1926349677857463e-07, 'reg_lambda': 8.266825902788709e-07, 'gamma': 2.8297388949951997e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:41,530]\u001b[0m Trial 532 finished with value: 17775.49838297235 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07741374418093294, 'max_depth': 15, 'subsample': 0.6120630860397027, 'reg_alpha': 4.5279833620197694e-07, 'reg_lambda': 1.4210968829688957e-06, 'gamma': 1.6079899448124546e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:41,731]\u001b[0m Trial 533 finished with value: 24135.273393606025 and parameters: {'booster': 'gbtree', 'learning_rate': 0.058409263013007175, 'max_depth': 15, 'subsample': 0.5449218204501961, 'reg_alpha': 9.78901598436082e-07, 'reg_lambda': 1.1168993237466903e-06, 'gamma': 1.0006747224177025e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:41,916]\u001b[0m Trial 534 finished with value: 14713.108343403945 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0895273932770497, 'max_depth': 15, 'subsample': 0.6818017180165009, 'reg_alpha': 8.049601694052576e-07, 'reg_lambda': 2.248776682340561e-06, 'gamma': 2.2723327424820865e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:42,102]\u001b[0m Trial 535 finished with value: 19670.57570429781 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07083626981434249, 'max_depth': 15, 'subsample': 0.6251901174196081, 'reg_alpha': 4.4964210236454905e-07, 'reg_lambda': 8.216779112453327e-07, 'gamma': 1.703946386548713e-05, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:42,336]\u001b[0m Trial 536 finished with value: 12837.617074394519 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0995278249525591, 'max_depth': 15, 'subsample': 0.6645344369305689, 'reg_alpha': 4.913213007242009e-07, 'reg_lambda': 7.2376669524261e-07, 'gamma': 1.4579036816464528e-07, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:42,513]\u001b[0m Trial 537 finished with value: 12835.561548583055 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09975139465683883, 'max_depth': 15, 'subsample': 0.48142819049595525, 'reg_alpha': 2.9655495778968387e-07, 'reg_lambda': 1.0273972145340324e-06, 'gamma': 1.8462770435211373e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:42,704]\u001b[0m Trial 538 finished with value: 17273.66851974197 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07833791103653547, 'max_depth': 15, 'subsample': 0.5790097308838402, 'reg_alpha': 8.648205786863991e-07, 'reg_lambda': 1.4410308361781877e-06, 'gamma': 1.0746875930489406e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:42,924]\u001b[0m Trial 539 finished with value: 12694.415207036676 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0999689935991001, 'max_depth': 15, 'subsample': 0.6631570905623829, 'reg_alpha': 6.077020045817909e-07, 'reg_lambda': 2.0467836337965124e-06, 'gamma': 7.895522116910568e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:43,067]\u001b[0m Trial 540 finished with value: 15390.286421607472 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08737062091208075, 'max_depth': 15, 'subsample': 0.23990032604860967, 'reg_alpha': 6.138671602776857e-07, 'reg_lambda': 2.2172582061643872e-06, 'gamma': 3.2811882810059197e-06, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:43,249]\u001b[0m Trial 541 finished with value: 17043.478591878673 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07983039663027529, 'max_depth': 15, 'subsample': 0.5164959301780866, 'reg_alpha': 6.082312649514102e-07, 'reg_lambda': 1.6891079380573743e-06, 'gamma': 9.318412492110879e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:43,450]\u001b[0m Trial 542 finished with value: 20786.35219242747 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0670537727775628, 'max_depth': 15, 'subsample': 0.6437584567804551, 'reg_alpha': 3.196870978708508e-07, 'reg_lambda': 1.8013726467665925e-06, 'gamma': 3.0619714366023353e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:43,637]\u001b[0m Trial 543 finished with value: 15048.06581572857 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08862784974598237, 'max_depth': 15, 'subsample': 0.5967118479612896, 'reg_alpha': 7.573812805940664e-08, 'reg_lambda': 2.579451141041787e-06, 'gamma': 6.535864183608817e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:43,836]\u001b[0m Trial 544 finished with value: 14730.28071238749 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08922139661608784, 'max_depth': 15, 'subsample': 0.6713214245813505, 'reg_alpha': 6.228773155927054e-07, 'reg_lambda': 8.848317600886693e-07, 'gamma': 9.20562526871199e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:44,031]\u001b[0m Trial 545 finished with value: 17375.200869009845 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07872677049408752, 'max_depth': 15, 'subsample': 0.6872615725608695, 'reg_alpha': 1.3615950708423026e-06, 'reg_lambda': 1.3250728637799032e-06, 'gamma': 1.3232364659587873e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:44,215]\u001b[0m Trial 546 finished with value: 12882.7112141071 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09976131160874847, 'max_depth': 15, 'subsample': 0.5472263701931752, 'reg_alpha': 0.7870105955136477, 'reg_lambda': 6.09788508227144e-07, 'gamma': 1.2509041935725558e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:44,448]\u001b[0m Trial 547 finished with value: 12797.981422401568 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0993264115291802, 'max_depth': 15, 'subsample': 0.7258308886743029, 'reg_alpha': 2.194991522237118e-07, 'reg_lambda': 3.109747428812942e-06, 'gamma': 7.842417294818434e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:44,646]\u001b[0m Trial 548 finished with value: 12745.306998621949 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09979508821168001, 'max_depth': 15, 'subsample': 0.6356389625983278, 'reg_alpha': 1.8231401202669998e-07, 'reg_lambda': 4.734265710380863e-06, 'gamma': 7.763299772008925e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:44,868]\u001b[0m Trial 549 finished with value: 21978.06200787812 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06371059513800478, 'max_depth': 15, 'subsample': 0.5742658705302882, 'reg_alpha': 1.970091545137604e-07, 'reg_lambda': 6.63396062121896e-06, 'gamma': 1.839071189741277e-07, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:44,967]\u001b[0m Trial 550 finished with value: 19438.369664868773 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07582192419080917, 'max_depth': 8, 'subsample': 0.4890106147061425, 'reg_alpha': 1.3017192046662842e-07, 'reg_lambda': 4.491327811732088e-06, 'gamma': 8.599996675041056e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:45,205]\u001b[0m Trial 551 finished with value: 14949.800291147545 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08871870584856172, 'max_depth': 15, 'subsample': 0.6273274623210555, 'reg_alpha': 8.294682407968757e-08, 'reg_lambda': 4.587678152754156e-06, 'gamma': 9.721561879524512e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:45,447]\u001b[0m Trial 552 finished with value: 17137.71550335109 and parameters: {'booster': 'gbtree', 'learning_rate': 0.079872453143399, 'max_depth': 15, 'subsample': 0.604749269060657, 'reg_alpha': 2.412892971374046e-07, 'reg_lambda': 2.9175969795039206e-06, 'gamma': 6.328843485829028e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:45,712]\u001b[0m Trial 553 finished with value: 12834.442111113174 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09962833046760068, 'max_depth': 15, 'subsample': 0.5328109425819091, 'reg_alpha': 1.5768376368174067e-07, 'reg_lambda': 5.797846182276336e-06, 'gamma': 2.569412092735437e-07, 'min_child_weight': 1}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:45,939]\u001b[0m Trial 554 finished with value: 14875.687524941128 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0884848112747352, 'max_depth': 15, 'subsample': 0.6727287934886228, 'reg_alpha': 3.778465087915646e-07, 'reg_lambda': 2.7748878529019998e-06, 'gamma': 1.389438083907954e-07, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:46,066]\u001b[0m Trial 555 finished with value: 19695.32010294811 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07196450127585177, 'max_depth': 15, 'subsample': 0.10632822236818341, 'reg_alpha': 2.1118921042568164e-07, 'reg_lambda': 3.62855175128753e-06, 'gamma': 2.3654627275761514e-06, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:46,261]\u001b[0m Trial 556 finished with value: 15366.496701241751 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08724061568248516, 'max_depth': 15, 'subsample': 0.6233339066824992, 'reg_alpha': 5.1534245883995e-07, 'reg_lambda': 9.722048507212322e-06, 'gamma': 7.879488187100795e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:46,449]\u001b[0m Trial 557 finished with value: 12763.012370695778 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09956911320247616, 'max_depth': 15, 'subsample': 0.7040923909066255, 'reg_alpha': 2.970623929927954e-07, 'reg_lambda': 2.936629423135244e-06, 'gamma': 5.318645379191392e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:46,597]\u001b[0m Trial 558 finished with value: 17361.321580079315 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07910358214058191, 'max_depth': 15, 'subsample': 0.5752732897650037, 'reg_alpha': 3.684955701597276e-07, 'reg_lambda': 2.468934223787658e-06, 'gamma': 4.961238840508298e-08, 'min_child_weight': 7}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:46,801]\u001b[0m Trial 559 finished with value: 12754.073429018954 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09988673104329768, 'max_depth': 15, 'subsample': 0.6749885100392248, 'reg_alpha': 7.59056775215828e-08, 'reg_lambda': 3.859304398322282e-06, 'gamma': 5.431185056561486e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:47,060]\u001b[0m Trial 560 finished with value: 20882.24679650852 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06741111796429734, 'max_depth': 15, 'subsample': 0.46516926711665824, 'reg_alpha': 2.0601928051943395e-07, 'reg_lambda': 3.6778846131789937e-06, 'gamma': 4.5292018260231796e-07, 'min_child_weight': 1}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:47,383]\u001b[0m Trial 561 finished with value: 15093.084591125786 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08775875862643331, 'max_depth': 15, 'subsample': 0.6379953198022518, 'reg_alpha': 5.261437959904025e-08, 'reg_lambda': 2.9241290240130895e-06, 'gamma': 6.527829290105274e-08, 'min_child_weight': 1}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:47,572]\u001b[0m Trial 562 finished with value: 12714.30006785134 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09988060361074659, 'max_depth': 15, 'subsample': 0.57347214339752, 'reg_alpha': 1.4542564388920166e-07, 'reg_lambda': 3.652122556528606e-06, 'gamma': 1.1122800322789212e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:47,755]\u001b[0m Trial 563 finished with value: 25398.24280489844 and parameters: {'booster': 'gbtree', 'learning_rate': 0.055351436636141685, 'max_depth': 15, 'subsample': 0.5232971435360649, 'reg_alpha': 1.2092422157018876e-07, 'reg_lambda': 5.050002365226215e-06, 'gamma': 5.028462719224255e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:47,942]\u001b[0m Trial 564 finished with value: 17220.243190014517 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07949859028108738, 'max_depth': 15, 'subsample': 0.5451359737429059, 'reg_alpha': 1.5481457870804115e-07, 'reg_lambda': 4.895215933231867e-06, 'gamma': 1.1433476886401785e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:48,134]\u001b[0m Trial 565 finished with value: 12710.759041098247 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09982411377242732, 'max_depth': 15, 'subsample': 0.5791762058645494, 'reg_alpha': 7.443785714748387e-08, 'reg_lambda': 3.4783268407961053e-06, 'gamma': 5.551969575169635e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:48,216]\u001b[0m Trial 566 finished with value: 20630.96772966432 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08674210584652191, 'max_depth': 5, 'subsample': 0.5732262755079415, 'reg_alpha': 2.84726773865856e-08, 'reg_lambda': 3.768416275455347e-06, 'gamma': 6.051453375323042e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:48,394]\u001b[0m Trial 567 finished with value: 19977.957980444276 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0701885803297793, 'max_depth': 15, 'subsample': 0.43062610627974657, 'reg_alpha': 4.4580025320960435e-08, 'reg_lambda': 3.5173434875655364e-06, 'gamma': 5.3102875936008774e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:48,574]\u001b[0m Trial 568 finished with value: 15319.920810129966 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08715774499818055, 'max_depth': 15, 'subsample': 0.5304528165074907, 'reg_alpha': 1.1740167020965608e-07, 'reg_lambda': 6.479045108303885e-06, 'gamma': 4.3047034621961264e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:48,759]\u001b[0m Trial 569 finished with value: 12803.952359236802 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09987224390608186, 'max_depth': 15, 'subsample': 0.5059182763235588, 'reg_alpha': 8.257441885061661e-08, 'reg_lambda': 3.517312603275414e-06, 'gamma': 9.342378159741632e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:48,954]\u001b[0m Trial 570 finished with value: 31269.44617001195 and parameters: {'booster': 'gbtree', 'learning_rate': 0.04303132710096618, 'max_depth': 15, 'subsample': 0.5799951770897499, 'reg_alpha': 2.3021231844148306e-08, 'reg_lambda': 4.247602341242045e-06, 'gamma': 7.020559730545754e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:49,162]\u001b[0m Trial 571 finished with value: 17658.943372196103 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07738253877249364, 'max_depth': 15, 'subsample': 0.6664994808668292, 'reg_alpha': 1.8396486288637251e-07, 'reg_lambda': 2.17089917421786e-06, 'gamma': 4.9712954399407615e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:49,355]\u001b[0m Trial 572 finished with value: 15284.89718351738 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08732428398916511, 'max_depth': 15, 'subsample': 0.6102071437899552, 'reg_alpha': 1.0856680521194313e-07, 'reg_lambda': 2.209042805699759e-06, 'gamma': 8.094947998558248e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:49,555]\u001b[0m Trial 573 finished with value: 21621.154597558994 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06455221835747738, 'max_depth': 15, 'subsample': 0.48258403154195273, 'reg_alpha': 9.409043235117254e-08, 'reg_lambda': 5.248259343168062e-06, 'gamma': 4.1458007100062145e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:49,750]\u001b[0m Trial 574 finished with value: 49762.76112088339 and parameters: {'booster': 'gbtree', 'learning_rate': 0.017087495247188077, 'max_depth': 15, 'subsample': 0.6753186816879732, 'reg_alpha': 2.434165355391894e-07, 'reg_lambda': 2.855217839338802e-06, 'gamma': 5.809269833445378e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:49,835]\u001b[0m Trial 575 finished with value: 18105.241595003467 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09994565341412386, 'max_depth': 15, 'subsample': 0.012420537861249947, 'reg_alpha': 5.873388314279156e-08, 'reg_lambda': 7.831049497088579e-06, 'gamma': 1.0359879889354845e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:50,020]\u001b[0m Trial 576 finished with value: 17269.723295105792 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07869204784358544, 'max_depth': 15, 'subsample': 0.5663892786782678, 'reg_alpha': 1.076149986607114e-07, 'reg_lambda': 3.6533485889709025e-06, 'gamma': 7.504953670376835e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:50,240]\u001b[0m Trial 577 finished with value: 14952.27263251777 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08838158746529061, 'max_depth': 15, 'subsample': 0.6942540020638217, 'reg_alpha': 8.520415028307176e-08, 'reg_lambda': 2.076083758322463e-06, 'gamma': 4.3798259067751915e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:50,441]\u001b[0m Trial 578 finished with value: 12743.247856180897 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09992716233693763, 'max_depth': 15, 'subsample': 0.6437649870614218, 'reg_alpha': 6.054460524453988e-08, 'reg_lambda': 5.755638942689691e-06, 'gamma': 1.1838834105706401e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:50,641]\u001b[0m Trial 579 finished with value: 19528.905373660917 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07098039408151124, 'max_depth': 15, 'subsample': 0.6923425059534515, 'reg_alpha': 3.408229573619491e-08, 'reg_lambda': 6.549445850982824e-06, 'gamma': 6.129333003245731e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:50,871]\u001b[0m Trial 580 finished with value: 12840.206662897708 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09999922265532413, 'max_depth': 15, 'subsample': 0.6249951639459653, 'reg_alpha': 6.063723009317205e-08, 'reg_lambda': 4.773005476084007e-06, 'gamma': 3.569127935825771e-08, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:51,064]\u001b[0m Trial 581 finished with value: 16867.109586563525 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0802284111851474, 'max_depth': 15, 'subsample': 0.6997379158241904, 'reg_alpha': 4.1630384362581095e-08, 'reg_lambda': 6.161951780829076e-06, 'gamma': 1.2817888025899908e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:51,269]\u001b[0m Trial 582 finished with value: 15414.918967997832 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0862240940805894, 'max_depth': 15, 'subsample': 0.6439146654985963, 'reg_alpha': 6.396553251635405e-08, 'reg_lambda': 2.696248251667489e-06, 'gamma': 7.500839580145571e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:51,476]\u001b[0m Trial 583 finished with value: 15048.469893550457 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08800203379319164, 'max_depth': 15, 'subsample': 0.7356188934124752, 'reg_alpha': 1.7145573145362667e-07, 'reg_lambda': 8.001563799569153e-06, 'gamma': 5.823500151870239e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:51,682]\u001b[0m Trial 584 finished with value: 19651.30868114217 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07084996176946269, 'max_depth': 15, 'subsample': 0.5546968374125504, 'reg_alpha': 7.288975514288528e-08, 'reg_lambda': 3.1835402960270277e-06, 'gamma': 3.777500949015442e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:52,034]\u001b[0m Trial 585 finished with value: 15070.924835839729 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08798754211007992, 'max_depth': 15, 'subsample': 0.6573939887000335, 'reg_alpha': 1.4459847537092896e-08, 'reg_lambda': 4.4634753039272855e-06, 'gamma': 1.0447239627449878e-07, 'min_child_weight': 1}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:52,166]\u001b[0m Trial 586 finished with value: 23996.7480606019 and parameters: {'booster': 'gbtree', 'learning_rate': 0.05990838113118409, 'max_depth': 15, 'subsample': 0.14939431075771856, 'reg_alpha': 1.0838767402940231e-07, 'reg_lambda': 1.241233948334283e-05, 'gamma': 1.0123849193016164e-06, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:52,409]\u001b[0m Trial 587 finished with value: 17370.92048120031 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07782927414367993, 'max_depth': 15, 'subsample': 0.7256865004128074, 'reg_alpha': 4.077541460642808e-08, 'reg_lambda': 5.297551501483651e-06, 'gamma': 5.431891315387332e-06, 'min_child_weight': 2}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:52,616]\u001b[0m Trial 588 finished with value: 14905.332110523508 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08919993375452155, 'max_depth': 15, 'subsample': 0.6030299595724332, 'reg_alpha': 1.6360773282926932e-07, 'reg_lambda': 2.1841051936754144e-06, 'gamma': 4.818528939642264e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:52,818]\u001b[0m Trial 589 finished with value: 12756.042613692203 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09987052950181395, 'max_depth': 15, 'subsample': 0.7255192893276556, 'reg_alpha': 6.486261010330544e-08, 'reg_lambda': 3.738628817655929e-06, 'gamma': 7.909493294041822e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:52,882]\u001b[0m Trial 590 finished with value: 27090.083258134946 and parameters: {'booster': 'gblinear', 'learning_rate': 0.0999132285554627, 'max_depth': 15, 'subsample': 0.660971076835727, 'reg_alpha': 5.732132226215953e-08, 'reg_lambda': 8.66887684664492e-06, 'gamma': 1.4083391299563008e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:53,094]\u001b[0m Trial 591 finished with value: 46214.48992562885 and parameters: {'booster': 'gbtree', 'learning_rate': 0.02110867998608526, 'max_depth': 15, 'subsample': 0.7422889223390795, 'reg_alpha': 7.995359663112186e-08, 'reg_lambda': 6.089083980781291e-06, 'gamma': 3.420492693004911e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:53,287]\u001b[0m Trial 592 finished with value: 67348.08296499783 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0006614789622695925, 'max_depth': 15, 'subsample': 0.5944651726097131, 'reg_alpha': 8.984702931269075e-08, 'reg_lambda': 3.8044901563571125e-06, 'gamma': 1.019802363249265e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:53,401]\u001b[0m Trial 593 finished with value: 18846.503647415775 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07740300514936939, 'max_depth': 15, 'subsample': 0.06454733954846953, 'reg_alpha': 1.281121741543316e-07, 'reg_lambda': 1.7549556788978825e-06, 'gamma': 5.686302163384009e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:53,596]\u001b[0m Trial 594 finished with value: 12780.758388525801 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09988461345385305, 'max_depth': 15, 'subsample': 0.6692095893022264, 'reg_alpha': 2.669758414343947e-08, 'reg_lambda': 4.14871103194869e-06, 'gamma': 3.645352864448787e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:53,782]\u001b[0m Trial 595 finished with value: 15503.884111973555 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08659640274177884, 'max_depth': 15, 'subsample': 0.512758740502861, 'reg_alpha': 1.0946922270761283e-08, 'reg_lambda': 4.746433445181295e-06, 'gamma': 3.7521729116846924e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:53,990]\u001b[0m Trial 596 finished with value: 19254.355796183718 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07196055735896306, 'max_depth': 15, 'subsample': 0.564235113177098, 'reg_alpha': 4.846228997921737e-08, 'reg_lambda': 6.569141829111251e-06, 'gamma': 5.0832176195980056e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:54,184]\u001b[0m Trial 597 finished with value: 15332.213784991884 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08671294368308788, 'max_depth': 15, 'subsample': 0.6418180767526832, 'reg_alpha': 3.317955028000005e-08, 'reg_lambda': 3.381510092602217e-06, 'gamma': 7.883560074332928e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:54,367]\u001b[0m Trial 598 finished with value: 22270.49951352217 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0634154176029939, 'max_depth': 15, 'subsample': 0.4635374959841403, 'reg_alpha': 8.740690380332847e-08, 'reg_lambda': 9.973945973029396e-06, 'gamma': 4.261676563805531e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:54,568]\u001b[0m Trial 599 finished with value: 12729.243706391895 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09975771668687118, 'max_depth': 15, 'subsample': 0.6586612708864977, 'reg_alpha': 2.925668957445319e-08, 'reg_lambda': 3.018884278451488e-06, 'gamma': 6.783271343612877e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:54,753]\u001b[0m Trial 600 finished with value: 17538.69919674327 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07783141039282496, 'max_depth': 15, 'subsample': 0.5957275865685581, 'reg_alpha': 2.624505324128915e-08, 'reg_lambda': 2.872000853011751e-06, 'gamma': 3.4029461371343954e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:54,953]\u001b[0m Trial 601 finished with value: 12822.479079974508 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09993402436675997, 'max_depth': 15, 'subsample': 0.5434411639318092, 'reg_alpha': 1.722894096789451e-08, 'reg_lambda': 4.512877610642826e-06, 'gamma': 1.1887845165828653e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:55,165]\u001b[0m Trial 602 finished with value: 15060.649933560673 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0881170799589421, 'max_depth': 15, 'subsample': 0.6758008746898486, 'reg_alpha': 1.7152731783406005e-08, 'reg_lambda': 2.514090023599885e-06, 'gamma': 5.732169369944076e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:55,364]\u001b[0m Trial 603 finished with value: 17232.46268404637 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07919744509130967, 'max_depth': 15, 'subsample': 0.633773768370967, 'reg_alpha': 4.0571740985118545e-08, 'reg_lambda': 7.478695372001999e-06, 'gamma': 1.7258533356554764e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:55,560]\u001b[0m Trial 604 finished with value: 14731.306605808926 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08926008900363884, 'max_depth': 15, 'subsample': 0.5813931002304895, 'reg_alpha': 5.0918064270606467e-08, 'reg_lambda': 4.9249129411955175e-06, 'gamma': 8.203313112982781e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:55,750]\u001b[0m Trial 605 finished with value: 19603.94425577046 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07061569123421925, 'max_depth': 15, 'subsample': 0.6695806756514812, 'reg_alpha': 3.162454015430313e-08, 'reg_lambda': 2.086622492777081e-06, 'gamma': 3.012713620249943e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:55,948]\u001b[0m Trial 606 finished with value: 14719.53204047712 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08974690714086268, 'max_depth': 15, 'subsample': 0.6856578872021264, 'reg_alpha': 2.71402125630616e-08, 'reg_lambda': 3.5382637171551642e-06, 'gamma': 5.185996940269836e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:56,142]\u001b[0m Trial 607 finished with value: 17234.03418859362 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07936782680519269, 'max_depth': 15, 'subsample': 0.5314148443168384, 'reg_alpha': 6.036928651193252e-08, 'reg_lambda': 3.1239609362881587e-06, 'gamma': 1.1870603919557075e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:56,338]\u001b[0m Trial 608 finished with value: 14886.331806320575 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08883141247526513, 'max_depth': 15, 'subsample': 0.634113173204876, 'reg_alpha': 2.0679105379459613e-08, 'reg_lambda': 2.053119724410866e-06, 'gamma': 7.385948602363328e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:56,436]\u001b[0m Trial 609 finished with value: 14917.882205947419 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09962221180217387, 'max_depth': 7, 'subsample': 0.5885868321298999, 'reg_alpha': 3.216264136885251e-08, 'reg_lambda': 1.4635312052802328e-05, 'gamma': 3.5026002974795254e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:56,616]\u001b[0m Trial 610 finished with value: 22633.925830072796 and parameters: {'booster': 'gbtree', 'learning_rate': 0.062287321307882934, 'max_depth': 15, 'subsample': 0.4158641149557923, 'reg_alpha': 5.957573876560388e-08, 'reg_lambda': 6.918332649808989e-06, 'gamma': 5.407099917029508e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:56,821]\u001b[0m Trial 611 finished with value: 26290.369402230688 and parameters: {'booster': 'gbtree', 'learning_rate': 0.05281253857039853, 'max_depth': 15, 'subsample': 0.7331137563802071, 'reg_alpha': 3.7072904238115275e-08, 'reg_lambda': 4.568811626882568e-06, 'gamma': 8.634483647284927e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:57,018]\u001b[0m Trial 612 finished with value: 12691.873490834003 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09989266135850189, 'max_depth': 15, 'subsample': 0.6968707936876634, 'reg_alpha': 2.3446206979937892e-08, 'reg_lambda': 2.426202277649364e-06, 'gamma': 1.5189951238357597e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:57,212]\u001b[0m Trial 613 finished with value: 19269.827757990723 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07218209784945881, 'max_depth': 15, 'subsample': 0.5046396499179036, 'reg_alpha': 4.248942981585576e-08, 'reg_lambda': 2.4112787075208813e-06, 'gamma': 2.2149643977439115e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:57,407]\u001b[0m Trial 614 finished with value: 16676.669412166153 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08127527300211299, 'max_depth': 15, 'subsample': 0.6270799340401696, 'reg_alpha': 2.0944623052561007e-08, 'reg_lambda': 3.504495835896921e-06, 'gamma': 1.6498266692559175e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:57,600]\u001b[0m Trial 615 finished with value: 63318.43022121478 and parameters: {'booster': 'gbtree', 'learning_rate': 0.003990077383763606, 'max_depth': 15, 'subsample': 0.6704699685772249, 'reg_alpha': 1.2327802133518953e-08, 'reg_lambda': 1.7671153452101727e-06, 'gamma': 2.304837914758272e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:57,791]\u001b[0m Trial 616 finished with value: 15149.205344752907 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08757947405988832, 'max_depth': 15, 'subsample': 0.5735738911545151, 'reg_alpha': 2.4593092527954326e-08, 'reg_lambda': 2.65845443888008e-06, 'gamma': 1.3925344504894165e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:57,858]\u001b[0m Trial 617 finished with value: 27154.23258775912 and parameters: {'booster': 'gblinear', 'learning_rate': 0.09934066562272566, 'max_depth': 15, 'subsample': 0.6994547464150148, 'reg_alpha': 1.3902927022698134e-08, 'reg_lambda': 5.598491903841923e-06, 'gamma': 1.1589723242610398e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:58,061]\u001b[0m Trial 618 finished with value: 12838.049224925173 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09960653938044968, 'max_depth': 15, 'subsample': 0.615682316226151, 'reg_alpha': 2.3252249216907786e-08, 'reg_lambda': 1.6415049717043935e-06, 'gamma': 1.0191027450869125e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:58,253]\u001b[0m Trial 619 finished with value: 17444.69652139636 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07807586526948768, 'max_depth': 14, 'subsample': 0.7392459646232328, 'reg_alpha': 1.6873350651589337e-08, 'reg_lambda': 3.9622158800142945e-06, 'gamma': 2.5838790011565084e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:58,448]\u001b[0m Trial 620 finished with value: 19924.653937066232 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07018171599895953, 'max_depth': 15, 'subsample': 0.5444198282751206, 'reg_alpha': 6.623272225450344e-08, 'reg_lambda': 9.343460729392861e-06, 'gamma': 7.542605096665131e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:58,642]\u001b[0m Trial 621 finished with value: 15410.430574775253 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08622307454648862, 'max_depth': 15, 'subsample': 0.660461813232269, 'reg_alpha': 4.440746576348823e-08, 'reg_lambda': 2.536257687428656e-06, 'gamma': 2.814628028720012e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:58,873]\u001b[0m Trial 622 finished with value: 14834.027310620311 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08892486726372562, 'max_depth': 15, 'subsample': 0.7390101676153757, 'reg_alpha': 1.4425278378742816e-07, 'reg_lambda': 1.8738150340472194e-06, 'gamma': 1.5497888382384664e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:59,053]\u001b[0m Trial 623 finished with value: 17164.589033972978 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07931432488453319, 'max_depth': 15, 'subsample': 0.46455949343567954, 'reg_alpha': 3.234518780744172e-08, 'reg_lambda': 3.53912619549933e-06, 'gamma': 3.258142307116471e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:59,186]\u001b[0m Trial 624 finished with value: 15672.39421620163 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08750354299393052, 'max_depth': 15, 'subsample': 0.18265225291280288, 'reg_alpha': 1.0062442895981268e-07, 'reg_lambda': 5.736401931316769e-06, 'gamma': 4.26447663232465e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:59,380]\u001b[0m Trial 625 finished with value: 20876.400160878013 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06707479072467291, 'max_depth': 15, 'subsample': 0.6096260752051529, 'reg_alpha': 5.891424407296103e-08, 'reg_lambda': 0.0005613457754983387, 'gamma': 1.749047709352883e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:59,581]\u001b[0m Trial 626 finished with value: 14812.335604418417 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08917672404008348, 'max_depth': 15, 'subsample': 0.6762435293846806, 'reg_alpha': 3.085470187315811e-07, 'reg_lambda': 1.4672136144247698e-06, 'gamma': 8.530264794353538e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:59,685]\u001b[0m Trial 627 finished with value: 62114.287879388976 and parameters: {'booster': 'gbtree', 'learning_rate': 0.005447899699393852, 'max_depth': 14, 'subsample': 0.022634467711261496, 'reg_alpha': 7.678857928863587e-08, 'reg_lambda': 2.824284205937466e-06, 'gamma': 1.2090378438592117e-07, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:47:59,890]\u001b[0m Trial 628 finished with value: 12878.059887294405 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0994607203667735, 'max_depth': 15, 'subsample': 0.7598183735823313, 'reg_alpha': 1.080054669472461e-06, 'reg_lambda': 4.542433397502969e-06, 'gamma': 6.100162542896481e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:00,086]\u001b[0m Trial 629 finished with value: 17852.73420533689 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07716507125117952, 'max_depth': 15, 'subsample': 0.5602659967833025, 'reg_alpha': 1.7582783560146775e-08, 'reg_lambda': 2.1665190354187376e-06, 'gamma': 3.0397418652001777e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:00,291]\u001b[0m Trial 630 finished with value: 12721.622496857395 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09989002969429427, 'max_depth': 15, 'subsample': 0.6994896897166089, 'reg_alpha': 1.7225235583244033e-07, 'reg_lambda': 9.441646139278084e-06, 'gamma': 4.230618228252075e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:00,497]\u001b[0m Trial 631 finished with value: 16988.698682385057 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07995223900916543, 'max_depth': 15, 'subsample': 0.693459116002847, 'reg_alpha': 1.7758506003990734e-07, 'reg_lambda': 1.319036045354473e-05, 'gamma': 2.296633348426565e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:00,707]\u001b[0m Trial 632 finished with value: 14821.724869345277 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08887492757513918, 'max_depth': 15, 'subsample': 0.7604148410277368, 'reg_alpha': 1.1769533445599958e-07, 'reg_lambda': 6.080343370050256e-06, 'gamma': 4.153797838071371e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:00,905]\u001b[0m Trial 633 finished with value: 21595.60758118044 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06472470766060555, 'max_depth': 15, 'subsample': 0.646062361338566, 'reg_alpha': 2.85925145685493e-07, 'reg_lambda': 3.304611132384619e-06, 'gamma': 2.590696501711354e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:01,098]\u001b[0m Trial 634 finished with value: 52361.75257820769 and parameters: {'booster': 'gbtree', 'learning_rate': 0.014326801417888863, 'max_depth': 14, 'subsample': 0.701939466244877, 'reg_alpha': 6.297078012022451e-07, 'reg_lambda': 1.4669208076504824e-06, 'gamma': 3.9444199581141655e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:01,299]\u001b[0m Trial 635 finished with value: 18974.306913409044 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0729689163271278, 'max_depth': 15, 'subsample': 0.7668783960533126, 'reg_alpha': 1.988905911711488e-07, 'reg_lambda': 3.806010853134025e-06, 'gamma': 5.4562977296628304e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:01,507]\u001b[0m Trial 636 finished with value: 15004.430145708746 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08871241682375698, 'max_depth': 15, 'subsample': 0.6234333041203151, 'reg_alpha': 1.0914572670651482e-07, 'reg_lambda': 2.0163818630475514e-06, 'gamma': 3.1872468722672324e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:01,712]\u001b[0m Trial 637 finished with value: 15121.520293677486 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08798780101620454, 'max_depth': 15, 'subsample': 0.6862222323119737, 'reg_alpha': 4.1469771946305516e-08, 'reg_lambda': 8.91003792683519e-06, 'gamma': 2.2598014348417546e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:01,932]\u001b[0m Trial 638 finished with value: 17742.914484125824 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07747365596734689, 'max_depth': 15, 'subsample': 0.750843705755871, 'reg_alpha': 1.8282064509608562e-07, 'reg_lambda': 4.893493463198003e-06, 'gamma': 4.751252637552991e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:02,129]\u001b[0m Trial 639 finished with value: 13026.680259370294 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0988937014867719, 'max_depth': 15, 'subsample': 0.6094317451272218, 'reg_alpha': 0.00023578943852875799, 'reg_lambda': 2.627675186414342e-06, 'gamma': 7.117703671009632e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:02,343]\u001b[0m Trial 640 finished with value: 68051.17528065146 and parameters: {'booster': 'gbtree', 'learning_rate': 0.00010085105041870816, 'max_depth': 15, 'subsample': 0.786753583909224, 'reg_alpha': 9.269312258068912e-08, 'reg_lambda': 6.826685663985404e-06, 'gamma': 2.0034863989312657e-06, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:02,530]\u001b[0m Trial 641 finished with value: 23752.996479385598 and parameters: {'booster': 'gbtree', 'learning_rate': 0.059168333047992516, 'max_depth': 14, 'subsample': 0.6923865731637575, 'reg_alpha': 3.9068232978463074e-07, 'reg_lambda': 1.4100260366712493e-06, 'gamma': 3.723183520940017e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:02,730]\u001b[0m Trial 642 finished with value: 12962.697746517375 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0989851139223864, 'max_depth': 15, 'subsample': 0.6300842647605113, 'reg_alpha': 1.3520035469280668e-07, 'reg_lambda': 1.8841325904244836e-05, 'gamma': 1.9616422949723116e-08, 'min_child_weight': 3}. Best is trial 443 with value: 12659.137449852062.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:02,926]\u001b[0m Trial 643 finished with value: 12658.644519053976 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09984791075452347, 'max_depth': 15, 'subsample': 0.7233143572628361, 'reg_alpha': 2.811310098532195e-08, 'reg_lambda': 3.751983853204647e-06, 'gamma': 5.421418320235589e-08, 'min_child_weight': 3}. Best is trial 643 with value: 12658.644519053976.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:02,989]\u001b[0m Trial 644 finished with value: 29807.76768430599 and parameters: {'booster': 'gblinear', 'learning_rate': 0.08045868199360758, 'max_depth': 15, 'subsample': 0.7900155349787252, 'reg_alpha': 6.310938419117142e-08, 'reg_lambda': 2.3676979518023554e-06, 'gamma': 8.455031016617986e-08, 'min_child_weight': 3}. Best is trial 643 with value: 12658.644519053976.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:03,122]\u001b[0m Trial 645 finished with value: 13162.719730546898 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09997035535433585, 'max_depth': 10, 'subsample': 0.7283562063377114, 'reg_alpha': 8.15254486149901e-07, 'reg_lambda': 1.201186897413222e-06, 'gamma': 6.392305624107864e-08, 'min_child_weight': 3}. Best is trial 643 with value: 12658.644519053976.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:03,333]\u001b[0m Trial 646 finished with value: 19501.3168546213 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07112626251365897, 'max_depth': 15, 'subsample': 0.802843821848766, 'reg_alpha': 4.341073943682246e-08, 'reg_lambda': 1.7523717205608354e-06, 'gamma': 1.2383778006050875e-07, 'min_child_weight': 3}. Best is trial 643 with value: 12658.644519053976.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:03,529]\u001b[0m Trial 647 finished with value: 12645.370995169798 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09986616613534406, 'max_depth': 15, 'subsample': 0.7162417007411497, 'reg_alpha': 2.684952999686149e-07, 'reg_lambda': 3.2602785886197124e-06, 'gamma': 5.3604883119526475e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:03,724]\u001b[0m Trial 648 finished with value: 15580.672793014983 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08637211993003949, 'max_depth': 14, 'subsample': 0.5892440462892233, 'reg_alpha': 2.0387335457736034e-07, 'reg_lambda': 3.009420447373175e-06, 'gamma': 5.528996313629581e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:03,928]\u001b[0m Trial 649 finished with value: 16742.553665466083 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08068876521951875, 'max_depth': 15, 'subsample': 0.701876108643894, 'reg_alpha': 2.6257813150539243e-07, 'reg_lambda': 5.1691138721111025e-06, 'gamma': 6.760442783441523e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:04,162]\u001b[0m Trial 650 finished with value: 14956.4627513311 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08830780428808425, 'max_depth': 15, 'subsample': 0.6494964216892113, 'reg_alpha': 3.1761013517942033e-07, 'reg_lambda': 3.568466827882596e-06, 'gamma': 8.63354566740943e-08, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:04,360]\u001b[0m Trial 651 finished with value: 19496.180005404232 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07096509376433502, 'max_depth': 15, 'subsample': 0.7232684782095338, 'reg_alpha': 1.0023633994271048e-08, 'reg_lambda': 2.63079718125946e-06, 'gamma': 1.1183025652886483e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:04,476]\u001b[0m Trial 652 finished with value: 17443.339001579894 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08631778265661834, 'max_depth': 15, 'subsample': 0.048030308378181466, 'reg_alpha': 5.023470822174237e-07, 'reg_lambda': 6.804374717859536e-06, 'gamma': 1.6782152255918756e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:04,665]\u001b[0m Trial 653 finished with value: 12874.943339564146 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09919223521323739, 'max_depth': 15, 'subsample': 0.5724465660671201, 'reg_alpha': 1.5443075342785566e-07, 'reg_lambda': 4.201987139884757e-06, 'gamma': 4.897655288507757e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:04,816]\u001b[0m Trial 654 finished with value: 17974.635495038638 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07746973775854754, 'max_depth': 15, 'subsample': 0.6424847955517804, 'reg_alpha': 2.878264050161892e-07, 'reg_lambda': 1.8984174410362216e-06, 'gamma': 6.839407930061607e-08, 'min_child_weight': 9}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:05,051]\u001b[0m Trial 655 finished with value: 22401.819443754535 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06256908776876711, 'max_depth': 15, 'subsample': 0.7188388661884193, 'reg_alpha': 1.492766679448166e-07, 'reg_lambda': 1.102212671272974e-05, 'gamma': 9.172621711563067e-08, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:05,270]\u001b[0m Trial 656 finished with value: 27938.9603321052 and parameters: {'booster': 'gbtree', 'learning_rate': 0.04960113938612707, 'max_depth': 15, 'subsample': 0.7878972896877309, 'reg_alpha': 8.869262079257383e-08, 'reg_lambda': 3.026997849779972e-06, 'gamma': 4.8842781267129306e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:05,466]\u001b[0m Trial 657 finished with value: 12784.245923967484 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09947191448191016, 'max_depth': 15, 'subsample': 0.6594895966359231, 'reg_alpha': 4.2452644971928e-07, 'reg_lambda': 5.256374521352626e-06, 'gamma': 1.7307460661800705e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:05,657]\u001b[0m Trial 658 finished with value: 15229.865108474734 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08771195263917896, 'max_depth': 14, 'subsample': 0.5333182997056131, 'reg_alpha': 2.311587359566084e-07, 'reg_lambda': 2.0930547992030575e-06, 'gamma': 1.1375571571106162e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:05,853]\u001b[0m Trial 659 finished with value: 17463.048643365244 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07822388784279694, 'max_depth': 15, 'subsample': 0.6005562884030947, 'reg_alpha': 5.546603709512892e-08, 'reg_lambda': 1.4021988585730864e-06, 'gamma': 6.943121964520175e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:05,952]\u001b[0m Trial 660 finished with value: 18094.153473326147 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08855448102359902, 'max_depth': 6, 'subsample': 0.7170178303268244, 'reg_alpha': 3.6331879957991155e-08, 'reg_lambda': 3.1060343487910577e-06, 'gamma': 4.5900858023086276e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:06,115]\u001b[0m Trial 661 finished with value: 12961.425829432175 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09963459169268978, 'max_depth': 15, 'subsample': 0.8001732151817912, 'reg_alpha': 2.0644854789124207e-08, 'reg_lambda': 8.871490374979718e-06, 'gamma': 8.054940255218127e-08, 'min_child_weight': 7}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:06,179]\u001b[0m Trial 662 finished with value: 31588.437524100053 and parameters: {'booster': 'gblinear', 'learning_rate': 0.07116396249062766, 'max_depth': 15, 'subsample': 0.6617563014742657, 'reg_alpha': 7.391269829329762e-07, 'reg_lambda': 4.112570627480231e-06, 'gamma': 2.4123452225754e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:06,394]\u001b[0m Trial 663 finished with value: 12748.219260372203 and parameters: {'booster': 'gbtree', 'learning_rate': 0.099957559268395, 'max_depth': 15, 'subsample': 0.5958392190455746, 'reg_alpha': 1.1700740029780798e-07, 'reg_lambda': 2.1963256418641593e-06, 'gamma': 3.5185655552022166e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:06,533]\u001b[0m Trial 664 finished with value: 15861.996953753134 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0868821740165943, 'max_depth': 15, 'subsample': 0.12618592025105474, 'reg_alpha': 1.2415813962704049e-07, 'reg_lambda': 2.25428872477765e-06, 'gamma': 3.41787582836099e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:06,747]\u001b[0m Trial 665 finished with value: 17135.840183764034 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07956753569787119, 'max_depth': 15, 'subsample': 0.5059890192382864, 'reg_alpha': 7.017829928714048e-08, 'reg_lambda': 5.7616990553767025e-06, 'gamma': 3.158619410855075e-08, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:06,965]\u001b[0m Trial 666 finished with value: 15074.872539950951 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08791812894323343, 'max_depth': 15, 'subsample': 0.5706754599933727, 'reg_alpha': 7.324702343057058e-08, 'reg_lambda': 3.2692115398978515e-06, 'gamma': 4.906270629153848e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:07,067]\u001b[0m Trial 667 finished with value: 22934.33524206933 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06747716555980725, 'max_depth': 14, 'subsample': 0.035738672547345876, 'reg_alpha': 1.1269490872658714e-07, 'reg_lambda': 1.9030614342651534e-06, 'gamma': 2.8673551358087342e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:07,263]\u001b[0m Trial 668 finished with value: 17514.612800161067 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07823576319809127, 'max_depth': 15, 'subsample': 0.7484645236005838, 'reg_alpha': 1.426287707975549e-07, 'reg_lambda': 4.313531430059556e-06, 'gamma': 4.446641633941689e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:07,503]\u001b[0m Trial 669 finished with value: 15120.28808409577 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08792404874749091, 'max_depth': 15, 'subsample': 0.586138794830626, 'reg_alpha': 1.7912872543995198e-07, 'reg_lambda': 7.414653691316025e-06, 'gamma': 6.269098731185434e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:07,710]\u001b[0m Trial 670 finished with value: 15121.918110413864 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08792943086630496, 'max_depth': 15, 'subsample': 0.686895528359164, 'reg_alpha': 7.448885468000434e-08, 'reg_lambda': 2.8850760974494888e-06, 'gamma': 1.0542827839569505e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:07,914]\u001b[0m Trial 671 finished with value: 19642.42104778047 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07083304047551829, 'max_depth': 15, 'subsample': 0.8289280400328591, 'reg_alpha': 2.823066170004092e-08, 'reg_lambda': 1.7372172209686343e-06, 'gamma': 4.4942776696513626e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:08,112]\u001b[0m Trial 672 finished with value: 24631.630597648684 and parameters: {'booster': 'gbtree', 'learning_rate': 0.056781238120108504, 'max_depth': 15, 'subsample': 0.7447427682364685, 'reg_alpha': 5.128351227710851e-08, 'reg_lambda': 1.4282519414936307e-05, 'gamma': 2.603028598727773e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:08,291]\u001b[0m Trial 673 finished with value: 13038.002205895567 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09892443893734353, 'max_depth': 14, 'subsample': 0.6110810073303415, 'reg_alpha': 1.994171469063143e-07, 'reg_lambda': 2.6210605548376445e-06, 'gamma': 7.588910822945758e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:08,486]\u001b[0m Trial 674 finished with value: 67162.64707461151 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0008144190971860686, 'max_depth': 15, 'subsample': 0.527877099283806, 'reg_alpha': 1.0673057243337228e-07, 'reg_lambda': 4.929232683967965e-06, 'gamma': 6.067794997080678e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:08,730]\u001b[0m Trial 675 finished with value: 12683.04458058072 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09970175858480125, 'max_depth': 15, 'subsample': 0.6732936510930875, 'reg_alpha': 8.852361787334904e-08, 'reg_lambda': 1.3865711669813904e-06, 'gamma': 1.2540737772549118e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:09,045]\u001b[0m Trial 676 finished with value: 17167.109067430567 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07931797997460012, 'max_depth': 15, 'subsample': 0.6243452922179374, 'reg_alpha': 8.266903102326605e-08, 'reg_lambda': 1.268384099322276e-06, 'gamma': 1.3170507297368308e-07, 'min_child_weight': 1}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:09,294]\u001b[0m Trial 677 finished with value: 15102.245113021634 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08821425527237989, 'max_depth': 15, 'subsample': 0.8271590060728632, 'reg_alpha': 1.0584986599952241e-07, 'reg_lambda': 1.5513970382410623e-06, 'gamma': 1.7386585674307295e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:09,509]\u001b[0m Trial 678 finished with value: 19426.158150027364 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07167599792565453, 'max_depth': 15, 'subsample': 0.5592751181090508, 'reg_alpha': 4.7219651637845945e-08, 'reg_lambda': 1.3294440870795185e-06, 'gamma': 1.45007354319006e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:09,708]\u001b[0m Trial 679 finished with value: 15592.646059596205 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08738015317432539, 'max_depth': 15, 'subsample': 0.6946606775894358, 'reg_alpha': 1.4915286538986939e-07, 'reg_lambda': 0.14174147031930304, 'gamma': 1.0640898398756154e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:09,902]\u001b[0m Trial 680 finished with value: 17554.132255461063 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07822880029613662, 'max_depth': 15, 'subsample': 0.7515121293495998, 'reg_alpha': 8.387549774523897e-08, 'reg_lambda': 2.2191142784245414e-06, 'gamma': 3.3382480665248004e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:09,964]\u001b[0m Trial 681 finished with value: 33405.72685625645 and parameters: {'booster': 'gblinear', 'learning_rate': 0.06332166657698135, 'max_depth': 14, 'subsample': 0.6563209923475939, 'reg_alpha': 3.5510527757749247e-08, 'reg_lambda': 1.1476843915761614e-06, 'gamma': 2.1848148015662066e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:10,213]\u001b[0m Trial 682 finished with value: 12941.251505860346 and parameters: {'booster': 'gbtree', 'learning_rate': 0.0990693697793461, 'max_depth': 15, 'subsample': 0.7807229284640858, 'reg_alpha': 2.3587842288804484e-07, 'reg_lambda': 1.8847824495915298e-06, 'gamma': 1.1497113364020693e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:10,433]\u001b[0m Trial 683 finished with value: 12972.431857393238 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09930700238230071, 'max_depth': 15, 'subsample': 0.5008071053650428, 'reg_alpha': 5.4625338087921685e-08, 'reg_lambda': 2.8057599078498856e-06, 'gamma': 8.518467444608203e-08, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:10,649]\u001b[0m Trial 684 finished with value: 15056.155617117422 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08790487166717234, 'max_depth': 15, 'subsample': 0.5999076835870619, 'reg_alpha': 1.2002641208163733e-07, 'reg_lambda': 3.672655526262564e-06, 'gamma': 1.5831954494081407e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:11,034]\u001b[0m Trial 685 finished with value: 67565.12312515068 and parameters: {'booster': 'gbtree', 'learning_rate': 0.00048443215781649335, 'max_depth': 15, 'subsample': 0.7169622745892964, 'reg_alpha': 2.888582882925095e-07, 'reg_lambda': 2.1536521954833997e-06, 'gamma': 8.621180399148149e-08, 'min_child_weight': 1}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:11,197]\u001b[0m Trial 686 finished with value: 17571.926127655177 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07876323033668914, 'max_depth': 15, 'subsample': 0.22317364112385832, 'reg_alpha': 2.015243883158931e-08, 'reg_lambda': 1.5054853837423177e-06, 'gamma': 6.191402450019662e-08, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:11,411]\u001b[0m Trial 687 finished with value: 58821.51518122895 and parameters: {'booster': 'gbtree', 'learning_rate': 0.008008513297022684, 'max_depth': 15, 'subsample': 0.843938000435286, 'reg_alpha': 6.618864506369845e-08, 'reg_lambda': 1.0318306603132587e-06, 'gamma': 1.2383960233826082e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:11,622]\u001b[0m Trial 688 finished with value: 12892.890925724249 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09925303227407872, 'max_depth': 15, 'subsample': 0.6454430938369529, 'reg_alpha': 1.6739151016670944e-07, 'reg_lambda': 3.3393294201966492e-06, 'gamma': 3.496344234150739e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:11,826]\u001b[0m Trial 689 finished with value: 12882.205682405236 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09947306851122278, 'max_depth': 15, 'subsample': 0.558760248702289, 'reg_alpha': 3.056908661770095e-08, 'reg_lambda': 2.341658877974205e-06, 'gamma': 2.0078277290172015e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:12,018]\u001b[0m Trial 690 finished with value: 16690.631004557985 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08077872659708343, 'max_depth': 14, 'subsample': 0.7049135699329607, 'reg_alpha': 7.756271733580551e-08, 'reg_lambda': 1.4474370859470426e-06, 'gamma': 6.398664588880537e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:12,243]\u001b[0m Trial 691 finished with value: 19893.261099425566 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07003559979174168, 'max_depth': 15, 'subsample': 0.7645814810869908, 'reg_alpha': 1.3666368320099205e-07, 'reg_lambda': 1.8780022222701286e-06, 'gamma': 9.08842831565128e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:12,471]\u001b[0m Trial 692 finished with value: 12797.954490769413 and parameters: {'booster': 'gbtree', 'learning_rate': 0.099791176865972, 'max_depth': 15, 'subsample': 0.6515686954881401, 'reg_alpha': 4.808205264949004e-08, 'reg_lambda': 4.339438228014008e-06, 'gamma': 3.890147453999654e-08, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:12,646]\u001b[0m Trial 693 finished with value: 15253.691561097234 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08801277181681415, 'max_depth': 14, 'subsample': 0.838361321451529, 'reg_alpha': 9.488243239109056e-08, 'reg_lambda': 2.7080344083798295e-06, 'gamma': 5.820155794425542e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:12,816]\u001b[0m Trial 694 finished with value: 16964.05199912872 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08000837085787812, 'max_depth': 15, 'subsample': 0.5911798799789865, 'reg_alpha': 3.735627306772428e-08, 'reg_lambda': 1.0279836253215171e-06, 'gamma': 2.590922177343212e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:13,025]\u001b[0m Trial 695 finished with value: 15108.070436053444 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08798282901358682, 'max_depth': 15, 'subsample': 0.7017038597318482, 'reg_alpha': 2.2628460879731852e-07, 'reg_lambda': 3.727476237481809e-06, 'gamma': 1.1979535827704716e-07, 'min_child_weight': 2}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:13,163]\u001b[0m Trial 696 finished with value: 22638.385997524794 and parameters: {'booster': 'gbtree', 'learning_rate': 0.06292789182075409, 'max_depth': 15, 'subsample': 0.7811687856414372, 'reg_alpha': 1.5828885105954043e-08, 'reg_lambda': 5.909103389165734e-06, 'gamma': 8.290463925197444e-08, 'min_child_weight': 10}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:13,334]\u001b[0m Trial 697 finished with value: 12792.449049300825 and parameters: {'booster': 'gbtree', 'learning_rate': 0.09967308040932271, 'max_depth': 15, 'subsample': 0.6393460716811661, 'reg_alpha': 3.7666736017155493e-07, 'reg_lambda': 1.8744389005405873e-06, 'gamma': 4.4340604487034964e-08, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:13,500]\u001b[0m Trial 698 finished with value: 19155.217880059583 and parameters: {'booster': 'gbtree', 'learning_rate': 0.07259747594818479, 'max_depth': 15, 'subsample': 0.5418558849857236, 'reg_alpha': 1.7217497299929868e-07, 'reg_lambda': 1.1384370675888398e-06, 'gamma': 2.4176113455437004e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n",
      "\u001b[32m[I 2023-01-14 23:48:13,687]\u001b[0m Trial 699 finished with value: 15288.228669141112 and parameters: {'booster': 'gbtree', 'learning_rate': 0.08684461679391117, 'max_depth': 15, 'subsample': 0.7204920399324041, 'reg_alpha': 6.118107736226438e-08, 'reg_lambda': 2.8852596999623804e-06, 'gamma': 1.4426470128385025e-07, 'min_child_weight': 3}. Best is trial 647 with value: 12645.370995169798.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study(direction=\"minimize\",pruner=optuna.pruners.MedianPruner(n_startup_trials=100, n_warmup_steps=25))\n",
    "study.optimize(objectives, n_trials=700)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12645.370995169798"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "study.best_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized MSE: 12645.3710\n",
      "\n",
      "Optimized RMSE: 112.4516\n",
      "\n",
      "Best params:\n",
      "\tbooster: gbtree\n",
      "\tlearning_rate: 0.09986616613534406\n",
      "\tmax_depth: 15\n",
      "\tsubsample: 0.7162417007411497\n",
      "\treg_alpha: 2.684952999686149e-07\n",
      "\treg_lambda: 3.2602785886197124e-06\n",
      "\tgamma: 5.3604883119526475e-08\n",
      "\tmin_child_weight: 3\n"
     ]
    }
   ],
   "source": [
    "print(f\"Optimized MSE: {study.best_value:.4f}\")\n",
    "print()\n",
    "print(f\"Optimized RMSE: {np.sqrt(study.best_value):.4f}\")\n",
    "print()\n",
    "print(\"Best params:\")\n",
    "for key, value in study.best_params.items():\n",
    "    print(f\"\\t{key}: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optuna.visualization import plot_param_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_12180\\199994575.py:2: ExperimentalWarning: plot_optimization_history is experimental (supported from v2.2.0). The interface can change in the future.\n",
      "  plot_optimization_history(study)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Optimization History Plot'}, xlabel='Trial', ylabel='Objective Value'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optuna.visualization.matplotlib import plot_optimization_history\n",
    "plot_optimization_history(study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "cliponaxis": false,
         "hovertemplate": [
          "gamma (FloatDistribution): 6.354143959452783e-07<extra></extra>",
          "reg_lambda (FloatDistribution): 1.5132160343543595e-06<extra></extra>",
          "reg_alpha (FloatDistribution): 2.7955850506770628e-05<extra></extra>",
          "min_child_weight (IntDistribution): 6.649306188245008e-05<extra></extra>",
          "subsample (FloatDistribution): 0.00021608390244197073<extra></extra>",
          "max_depth (IntDistribution): 0.0002199788516515439<extra></extra>",
          "booster (CategoricalDistribution): 0.042261856589498055<extra></extra>",
          "learning_rate (FloatDistribution): 0.9572054831135889<extra></extra>"
         ],
         "marker": {
          "color": "rgb(66,146,198)"
         },
         "orientation": "h",
         "text": [
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "0.04",
          "0.96"
         ],
         "textposition": "outside",
         "type": "bar",
         "x": [
          6.354143959452783e-7,
          0.0000015132160343543595,
          0.000027955850506770628,
          0.00006649306188245008,
          0.00021608390244197073,
          0.0002199788516515439,
          0.042261856589498055,
          0.9572054831135889
         ],
         "y": [
          "gamma",
          "reg_lambda",
          "reg_alpha",
          "min_child_weight",
          "subsample",
          "max_depth",
          "booster",
          "learning_rate"
         ]
        }
       ],
       "layout": {
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
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            "gridcolor": "white",
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            "ticks": ""
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            "gridcolor": "white",
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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            "standoff": 15
           },
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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        "title": {
         "text": "Hyperparameter Importances"
        },
        "xaxis": {
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          "text": "Importance for Objective Value"
         }
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        "yaxis": {
         "title": {
          "text": "Hyperparameter"
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     },
     "metadata": {},
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   ],
   "source": [
    "plot_param_importances(study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = study.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'booster': 'gbtree',\n",
       " 'learning_rate': 0.09986616613534406,\n",
       " 'max_depth': 15,\n",
       " 'subsample': 0.7162417007411497,\n",
       " 'reg_alpha': 2.684952999686149e-07,\n",
       " 'reg_lambda': 3.2602785886197124e-06,\n",
       " 'gamma': 5.3604883119526475e-08,\n",
       " 'min_child_weight': 3}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=5.3604883119526475e-08, gpu_id=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.09986616613534406, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=5.3604883119526475e-08, gpu_id=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.09986616613534406, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=5.3604883119526475e-08, gpu_id=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.09986616613534406, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=15, max_leaves=None,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor(**params)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8593993177327326\n",
      "67.9257623419359\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_test, model.predict(X_test)))\n",
    "print(np.sqrt(mean_squared_error(y_test, model.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9983586864711618\n",
      "7.352324059661258\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_train, model.predict(X_train)))\n",
    "print(np.sqrt(mean_squared_error(y_train, model.predict(X_train))))"
   ]
  }
 ],
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