Snowfall Predicter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "178d9b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from wwo_hist import retrieve_hist_data\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "import math\n",
    "from datetime import timedelta\n",
    "from sklearn import metrics\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "from xgboost import XGBClassifier\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41227f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# frequency = 3\n",
    "# start_date = '01-JAN-2009'\n",
    "# end_date = '31-DEC-2021'\n",
    "# api_key = '06199218b89440389fc112619220501'\n",
    "# location = ['Pithoragarh']\n",
    "\n",
    "# hist_weather_data = retrieve_hist_data(api_key,\n",
    "#                                       location,\n",
    "#                                       start_date,\n",
    "#                                       end_date,\n",
    "#                                       frequency,\n",
    "#                                       location_label= False,\n",
    "#                                       export_csv= True,\n",
    "#                                       store_df = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041df04",
   "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>date_time</th>\n",
       "      <th>maxtempC</th>\n",
       "      <th>mintempC</th>\n",
       "      <th>totalSnow_cm</th>\n",
       "      <th>sunHour</th>\n",
       "      <th>uvIndex</th>\n",
       "      <th>moon_illumination</th>\n",
       "      <th>moonrise</th>\n",
       "      <th>moonset</th>\n",
       "      <th>sunrise</th>\n",
       "      <th>...</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>humidity</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>pressure</th>\n",
       "      <th>tempC</th>\n",
       "      <th>visibility</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>windspeedKmph</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01 00:00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>09:59 AM</td>\n",
       "      <td>09:44 PM</td>\n",
       "      <td>07:04 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1018</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>Pithoragarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01 03:00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>09:59 AM</td>\n",
       "      <td>09:44 PM</td>\n",
       "      <td>07:04 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>Pithoragarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-01 06:00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>09:59 AM</td>\n",
       "      <td>09:44 PM</td>\n",
       "      <td>07:04 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>Pithoragarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-01 09:00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>09:59 AM</td>\n",
       "      <td>09:44 PM</td>\n",
       "      <td>07:04 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>204</td>\n",
       "      <td>11</td>\n",
       "      <td>Pithoragarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-01 12:00:00</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>09:59 AM</td>\n",
       "      <td>09:44 PM</td>\n",
       "      <td>07:04 AM</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>226</td>\n",
       "      <td>12</td>\n",
       "      <td>Pithoragarh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time  maxtempC  mintempC  totalSnow_cm  sunHour  uvIndex  \\\n",
       "0  2009-01-01 00:00:00        19         4           0.0      8.7        4   \n",
       "1  2009-01-01 03:00:00        19         4           0.0      8.7        4   \n",
       "2  2009-01-01 06:00:00        19         4           0.0      8.7        4   \n",
       "3  2009-01-01 09:00:00        19         4           0.0      8.7        4   \n",
       "4  2009-01-01 12:00:00        19         4           0.0      8.7        4   \n",
       "\n",
       "   moon_illumination  moonrise   moonset   sunrise  ... WindGustKmph  \\\n",
       "0                 32  09:59 AM  09:44 PM  07:04 AM  ...           27   \n",
       "1                 32  09:59 AM  09:44 PM  07:04 AM  ...           33   \n",
       "2                 32  09:59 AM  09:44 PM  07:04 AM  ...           21   \n",
       "3                 32  09:59 AM  09:44 PM  07:04 AM  ...           12   \n",
       "4                 32  09:59 AM  09:44 PM  07:04 AM  ...           14   \n",
       "\n",
       "   cloudcover  humidity  precipMM  pressure  tempC  visibility  winddirDegree  \\\n",
       "0           0        26       0.0      1018      4          10             23   \n",
       "1           0        24       0.0      1019      8          10             25   \n",
       "2           0        21       0.0      1019     12          10             35   \n",
       "3           0        15       0.0      1016     19          10            204   \n",
       "4           0        17       0.0      1015     19          10            226   \n",
       "\n",
       "   windspeedKmph     location  \n",
       "0             13  Pithoragarh  \n",
       "1             16  Pithoragarh  \n",
       "2             12  Pithoragarh  \n",
       "3             11  Pithoragarh  \n",
       "4             12  Pithoragarh  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df = pd.read_csv(\"Pithoragarh.csv\")\n",
    "weather_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b66d6051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time            0\n",
       "maxtempC             0\n",
       "mintempC             0\n",
       "totalSnow_cm         0\n",
       "sunHour              0\n",
       "uvIndex              0\n",
       "moon_illumination    0\n",
       "moonrise             0\n",
       "moonset              0\n",
       "sunrise              0\n",
       "sunset               0\n",
       "DewPointC            0\n",
       "FeelsLikeC           0\n",
       "HeatIndexC           0\n",
       "WindChillC           0\n",
       "WindGustKmph         0\n",
       "cloudcover           0\n",
       "humidity             0\n",
       "precipMM             0\n",
       "pressure             0\n",
       "tempC                0\n",
       "visibility           0\n",
       "winddirDegree        0\n",
       "windspeedKmph        0\n",
       "location             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fb61faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37984 entries, 0 to 37983\n",
      "Data columns (total 25 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   date_time          37984 non-null  object \n",
      " 1   maxtempC           37984 non-null  int64  \n",
      " 2   mintempC           37984 non-null  int64  \n",
      " 3   totalSnow_cm       37984 non-null  float64\n",
      " 4   sunHour            37984 non-null  float64\n",
      " 5   uvIndex            37984 non-null  int64  \n",
      " 6   moon_illumination  37984 non-null  int64  \n",
      " 7   moonrise           37984 non-null  object \n",
      " 8   moonset            37984 non-null  object \n",
      " 9   sunrise            37984 non-null  object \n",
      " 10  sunset             37984 non-null  object \n",
      " 11  DewPointC          37984 non-null  int64  \n",
      " 12  FeelsLikeC         37984 non-null  int64  \n",
      " 13  HeatIndexC         37984 non-null  int64  \n",
      " 14  WindChillC         37984 non-null  int64  \n",
      " 15  WindGustKmph       37984 non-null  int64  \n",
      " 16  cloudcover         37984 non-null  int64  \n",
      " 17  humidity           37984 non-null  int64  \n",
      " 18  precipMM           37984 non-null  float64\n",
      " 19  pressure           37984 non-null  int64  \n",
      " 20  tempC              37984 non-null  int64  \n",
      " 21  visibility         37984 non-null  int64  \n",
      " 22  winddirDegree      37984 non-null  int64  \n",
      " 23  windspeedKmph      37984 non-null  int64  \n",
      " 24  location           37984 non-null  object \n",
      "dtypes: float64(3), int64(16), object(6)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "weather_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e3a982f",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df[\"moonrise\"] = weather_df[\"moonrise\"].replace({\"No moonrise\":math.nan})\n",
    "weather_df[\"moonset\"] = weather_df[\"moonset\"].replace({\"No moonset\":math.nan})\n",
    "weather_df[\"sunrise\"] = weather_df[\"sunrise\"].replace({\"No sunrise\":math.nan})\n",
    "weather_df[\"sunset\"] = weather_df[\"sunset\"].replace({\"No sunset\":math.nan})\n",
    "\n",
    "weather_df[\"moonrise\"] = weather_df[\"moonrise\"].fillna(weather_df[\"moonrise\"].mode()[0])\n",
    "weather_df[\"moonset\"] = weather_df[\"moonset\"].fillna(weather_df[\"moonset\"].mode()[0])\n",
    "weather_df[\"sunrise\"] = weather_df[\"sunrise\"].fillna(weather_df[\"sunrise\"].mode()[0])\n",
    "weather_df[\"sunset\"] = weather_df[\"sunset\"].fillna(weather_df[\"sunset\"].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f88d402",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df[\"moonrise\"] = pd.to_datetime(weather_df[\"moonrise\"], format='%I:%M %p')\n",
    "weather_df[\"moonset\"] = pd.to_datetime(weather_df[\"moonset\"], format='%I:%M %p')\n",
    "weather_df[\"sunrise\"] = pd.to_datetime(weather_df[\"sunrise\"], format='%I:%M %p')\n",
    "weather_df[\"sunset\"] = pd.to_datetime(weather_df[\"sunset\"], format='%I:%M %p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4645ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df[\"nightHours\"] = (weather_df[\"moonset\"] - weather_df[\"moonrise\"])/timedelta(hours =1)\n",
    "weather_df[\"dayHours\"] = (weather_df[\"sunset\"] - weather_df[\"sunrise\"])/timedelta(hours =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43e78969",
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_df[\"snowfall\"] = weather_df[\"totalSnow_cm\"].map(lambda x:1 if (x > 0.0) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "073e959a",
   "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>maxtempC</th>\n",
       "      <th>mintempC</th>\n",
       "      <th>sunHour</th>\n",
       "      <th>uvIndex</th>\n",
       "      <th>moon_illumination</th>\n",
       "      <th>DewPointC</th>\n",
       "      <th>FeelsLikeC</th>\n",
       "      <th>HeatIndexC</th>\n",
       "      <th>WindChillC</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>...</th>\n",
       "      <th>humidity</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>pressure</th>\n",
       "      <th>tempC</th>\n",
       "      <th>visibility</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>windspeedKmph</th>\n",
       "      <th>nightHours</th>\n",
       "      <th>dayHours</th>\n",
       "      <th>snowfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-13</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1018</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-11</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-8</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>204</td>\n",
       "      <td>11</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-7</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>226</td>\n",
       "      <td>12</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>215</td>\n",
       "      <td>8</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>-11</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>-14</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>-12</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>-11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>-7</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1018</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>198</td>\n",
       "      <td>10</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>-5</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>215</td>\n",
       "      <td>10</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1018</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>240</td>\n",
       "      <td>7</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>-6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    maxtempC  mintempC  sunHour  uvIndex  moon_illumination  DewPointC  \\\n",
       "0         19         4      8.7        4                 32        -13   \n",
       "1         19         4      8.7        4                 32        -11   \n",
       "2         19         4      8.7        4                 32        -10   \n",
       "3         19         4      8.7        4                 32         -8   \n",
       "4         19         4      8.7        4                 32         -7   \n",
       "5         19         4      8.7        4                 32          0   \n",
       "6         19         4      8.7        4                 32         -9   \n",
       "7         19         4      8.7        4                 32        -11   \n",
       "8         19         4      8.7        4                 39        -14   \n",
       "9         19         4      8.7        4                 39        -12   \n",
       "10        19         4      8.7        4                 39        -11   \n",
       "11        19         4      8.7        4                 39         -7   \n",
       "12        19         4      8.7        4                 39         -5   \n",
       "13        19         4      8.7        4                 39          2   \n",
       "14        19         4      8.7        4                 39         -6   \n",
       "\n",
       "    FeelsLikeC  HeatIndexC  WindChillC  WindGustKmph  ...  humidity  precipMM  \\\n",
       "0            1           4           1            27  ...        26       0.0   \n",
       "1            6           8           6            33  ...        24       0.0   \n",
       "2           11          12          11            21  ...        21       0.0   \n",
       "3           19          19          19            12  ...        15       0.0   \n",
       "4           19          19          19            14  ...        17       0.0   \n",
       "5            8           9           8            17  ...        54       0.0   \n",
       "6            2           4           2            20  ...        39       0.0   \n",
       "7            2           5           2            26  ...        30       0.0   \n",
       "8            1           4           1            26  ...        25       0.0   \n",
       "9            6           8           6            19  ...        22       0.0   \n",
       "10          11          12          11            16  ...        19       0.0   \n",
       "11          18          18          18            12  ...        18       0.0   \n",
       "12          19          19          19            12  ...        19       0.0   \n",
       "13           8           8           8            14  ...        66       0.0   \n",
       "14           3           5           3            19  ...        43       0.0   \n",
       "\n",
       "    pressure  tempC  visibility  winddirDegree  windspeedKmph  nightHours  \\\n",
       "0       1018      4          10             23             13   11.750000   \n",
       "1       1019      8          10             25             16   11.750000   \n",
       "2       1019     12          10             35             12   11.750000   \n",
       "3       1016     19          10            204             11   11.750000   \n",
       "4       1015     19          10            226             12   11.750000   \n",
       "5       1017      9          10            215              8   11.750000   \n",
       "6       1019      4          10             34              9   11.750000   \n",
       "7       1020      5          10             24             12   11.750000   \n",
       "8       1020      4          10             27             13   12.183333   \n",
       "9       1020      8          10             51              9   12.183333   \n",
       "10      1021     12          10             30              9   12.183333   \n",
       "11      1018     18          10            198             10   12.183333   \n",
       "12      1017     19          10            215             10   12.183333   \n",
       "13      1018      8          10            240              7   12.183333   \n",
       "14      1020      5          10             15              9   12.183333   \n",
       "\n",
       "    dayHours  snowfall  \n",
       "0       10.3         0  \n",
       "1       10.3         0  \n",
       "2       10.3         0  \n",
       "3       10.3         0  \n",
       "4       10.3         0  \n",
       "5       10.3         0  \n",
       "6       10.3         0  \n",
       "7       10.3         0  \n",
       "8       10.3         0  \n",
       "9       10.3         0  \n",
       "10      10.3         0  \n",
       "11      10.3         0  \n",
       "12      10.3         0  \n",
       "13      10.3         0  \n",
       "14      10.3         0  \n",
       "\n",
       "[15 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df = weather_df.drop(columns=[\"moonrise\", \"moonset\", \"sunrise\", \"sunset\", \"location\", \"date_time\", \"totalSnow_cm\"])\n",
    "clean_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e841f2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = clean_df.corr()\n",
    "plt.figure(figsize= (20,10))\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns, yticklabels=corr.columns, linewidths=2, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7bfdc955",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanDf = clean_df.drop(columns=[\"dayHours\", \"pressure\", \"uvIndex\", \"DewPointC\", \"mintempC\", \"WindGustKmph\", \"tempC\", \"WindChillC\", \"HeatIndexC\", \"moon_illumination\", \"sunHour\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7b53c549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = cleanDf.corr()\n",
    "plt.figure(figsize= (20,10))\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns, yticklabels=corr.columns, linewidths=2, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98b78c5c",
   "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>maxtempC</th>\n",
       "      <th>FeelsLikeC</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>humidity</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>visibility</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>windspeedKmph</th>\n",
       "      <th>nightHours</th>\n",
       "      <th>snowfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.063889</td>\n",
       "      <td>13</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>16</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.097222</td>\n",
       "      <td>12</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>11</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.627778</td>\n",
       "      <td>12</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0</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>37979</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>4</td>\n",
       "      <td>10.733333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37980</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.663889</td>\n",
       "      <td>4</td>\n",
       "      <td>10.733333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37981</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>6</td>\n",
       "      <td>10.733333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37982</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.338889</td>\n",
       "      <td>2</td>\n",
       "      <td>10.733333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37983</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.258333</td>\n",
       "      <td>4</td>\n",
       "      <td>10.733333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37984 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       maxtempC  FeelsLikeC  cloudcover  humidity  precipMM  visibility  \\\n",
       "0            19           1           0        26       0.0          10   \n",
       "1            19           6           0        24       0.0          10   \n",
       "2            19          11           0        21       0.0          10   \n",
       "3            19          19           0        15       0.0          10   \n",
       "4            19          19           0        17       0.0          10   \n",
       "...         ...         ...         ...       ...       ...         ...   \n",
       "37979        16          11          44        44       0.0          10   \n",
       "37980        16          16          27        31       0.0          10   \n",
       "37981        16          15          21        37       0.0          10   \n",
       "37982        16           8           8        75       0.0          10   \n",
       "37983        16           4           5        76       0.0          10   \n",
       "\n",
       "       winddirDegree  windspeedKmph  nightHours  snowfall  \n",
       "0           0.063889             13   11.750000         0  \n",
       "1           0.069444             16   11.750000         0  \n",
       "2           0.097222             12   11.750000         0  \n",
       "3           0.566667             11   11.750000         0  \n",
       "4           0.627778             12   11.750000         0  \n",
       "...              ...            ...         ...       ...  \n",
       "37979       0.075000              4   10.733333         0  \n",
       "37980       0.663889              4   10.733333         0  \n",
       "37981       0.633333              6   10.733333         0  \n",
       "37982       0.338889              2   10.733333         0  \n",
       "37983       0.258333              4   10.733333         0  \n",
       "\n",
       "[37984 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data sacaling (normalizing)\n",
    "def normalize_data(df):\n",
    "    min_max_scaler = sklearn.preprocessing.MinMaxScaler()\n",
    "#     df[\"pressure\"] = min_max_scaler.fit_transform(df[\"pressure\"].values.reshape(-1,1))\n",
    "    df[\"winddirDegree\"] = min_max_scaler.fit_transform(df[\"winddirDegree\"].values.reshape(-1,1))\n",
    "    return df\n",
    "normalize_data(cleanDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8531447",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37984, 9)\n",
      "(37984,)\n"
     ]
    }
   ],
   "source": [
    "X = cleanDf.drop(columns = ['snowfall']).copy()\n",
    "y = cleanDf['snowfall']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90424381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26588, 9)\n",
      "(26588,)\n",
      "(11396, 9)\n",
      "(11396,)\n",
      "(5698, 9)\n",
      "(5698,)\n",
      "(5698, 9)\n",
      "(5698,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_rem, y_train, y_rem = train_test_split(X,y, train_size=0.7, random_state = 100, stratify=y)\n",
    "X_valid, X_test, y_valid, y_test = train_test_split(X_rem,y_rem, test_size=0.5, random_state = 100, stratify=y_rem)\n",
    "print(X_train.shape), print(y_train.shape)\n",
    "print(X_rem.shape), print(y_rem.shape)\n",
    "print(X_valid.shape), print(y_valid.shape)\n",
    "print(X_test.shape), print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e862d2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding best hyperparameters using GridSearchCV\n",
    "#This will take alot of time in low end PC.\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')\n",
    "# xgboost = XGBClassifier()\n",
    "\n",
    "# booster = [\"gbtree\", \"gblinear\", \"dart\"]\n",
    "# eta = np.arange(0.0, 1.1, 0.1)\n",
    "# gamma = np.arange(0, 11, 1)\n",
    "# maxDepth = np.arange(0, 11, 1)\n",
    "# samplingMethod = [\"uniform\", \"gradient_based\"]\n",
    "# treeMethod = [\"auto\", \"exact\", \"approx\", \"hist\", \"gpu_hist\"]\n",
    "\n",
    "# grid={\n",
    "#     \"booster\": booster,\n",
    "#     \"learning_rate\": eta,\n",
    "#     \"gamma\":gamma,\n",
    "#     \"max_depth\": maxDepth,\n",
    "#     \"sampling_method\":samplingMethod,\n",
    "#     \"tree_method\":treeMethod\n",
    "#      }\n",
    "# clf = GridSearchCV(xgboost, grid,cv=10)\n",
    "# clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba210d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"tuned hpyerparameters :(best parameters) \",clf.best_params_)\n",
    "# print(\"accuracy :\",clf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef7bab85",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_fpr, valid_tpr, valid_thresholds = metrics.roc_curve(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fdefa53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(valid_fpr, valid_tpr, 'b')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "432b0329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:22:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:22:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:22:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:23:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:23:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:23:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:23:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:24:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:24:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:24:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:24:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:24:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:25:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:25:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:25:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:25:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:26:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:26:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:26:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:26:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:26:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:26:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:27:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:26] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:27:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:53] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:27:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:04] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:28:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:16] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:17] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:18] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:19] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:20] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:21] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:22] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:23] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:24] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:25] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:27] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:29] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:30] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:28:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:31] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:32] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:34] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:35] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:36] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:37] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:38] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:39] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:40] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:41] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:42] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:43] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:44] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:45] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:46] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:47] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:48] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:49] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:50] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:52] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:54] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:55] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:56] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:57] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:28:58] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:28:59] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:00] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:01] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:02] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:03] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:05] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:06] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:07] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:08] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:09] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:29:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:10] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:11] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:12] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:13] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:14] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[08:29:15] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None,\n",
       "                                     enable_categorical=False, gamma=None,\n",
       "                                     gpu_id=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, predictor=None,\n",
       "                                     random_state=None, reg_alpha=None,\n",
       "                                     reg_lambda=None, scale_pos_weight=None,\n",
       "                                     subsample=None, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=None),\n",
       "             param_grid={'gamma': [0.01, 0.1, 10],\n",
       "                         'learning_rate': [0.01, 0.1, 0.5, 1, 10],\n",
       "                         'max_depth': [1, 3, 5, 10, 15]})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "xgboost = XGBClassifier()\n",
    "grid={\n",
    "    \"learning_rate\": [0.01, 0.1, 0.5, 1, 10],\n",
    "    \"gamma\":[0.01, 0.1,10],\n",
    "    \"max_depth\": [1, 3, 5,10,15]\n",
    "}\n",
    "clf = GridSearchCV(xgboost, grid,cv=10)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "938a6afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'gamma': 0.01, 'learning_rate': 0.5, 'max_depth': 3}\n",
      "accuracy : 0.9987588470351133\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",clf.best_params_)\n",
    "print(\"accuracy :\",clf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5249da4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:31:51] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "model = XGBClassifier(gamma= 0.01, learning_rate= 0.5, max_depth=3)\n",
    "model.fit(X_train, y_train)\n",
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6fec39aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4cb1074d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[26510     0]\n",
      " [    0    78]]\n"
     ]
    }
   ],
   "source": [
    "train_confusion = metrics.confusion_matrix(y_train, y_train_pred)\n",
    "train_TP = train_confusion[1, 1]\n",
    "train_TN = train_confusion[0, 0]\n",
    "train_FP = train_confusion[0, 1]\n",
    "train_FN = train_confusion[1, 0]\n",
    "print(train_confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c480699b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5680    1]\n",
      " [   7   10]]\n"
     ]
    }
   ],
   "source": [
    "test_confusion = metrics.confusion_matrix(y_test, y_test_pred)\n",
    "test_TP = test_confusion[1, 1]\n",
    "test_TN = test_confusion[0, 0]\n",
    "test_FP = test_confusion[0, 1]\n",
    "test_FN = test_confusion[1, 0]\n",
    "print(test_confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ace36d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5681\n",
      "           1       0.91      0.59      0.71        17\n",
      "\n",
      "    accuracy                           1.00      5698\n",
      "   macro avg       0.95      0.79      0.86      5698\n",
      "weighted avg       1.00      1.00      1.00      5698\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "903c953c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:\n",
      "0.9985959985959986\n",
      "ROC AUC Score:\n",
      "0.7940296343849986\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\")\n",
    "print(metrics.accuracy_score(y_test, y_test_pred))\n",
    "print(\"ROC AUC Score:\")\n",
    "print(metrics.roc_auc_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8477380e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_fpr, test_tpr, test_thresholds = metrics.roc_curve(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a5edf5a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA3N0lEQVR4nO3dd3hUZfbA8e8hVENTLCiIoCAKSpFIUUEQsK3KuvaGbRexYS8rthXbrmXFLqKLq/50V9eCHVGaIFKkFxGpEVSKIgERSM7vj3PHGWIyGUImd8r5PM88mbn3ztwzN8mcue973/OKquKcc86VpkrYATjnnEttniicc87F5YnCOedcXJ4onHPOxeWJwjnnXFyeKJxzzsXlicJtFxGZIyLdw44jVYjILSIyNKR9DxORu8PYd0UTkXNEZEQ5n+t/k0nmiSKNicgSEflFRApE5Lvgg6N2Mvepqq1VdXQy9xEhIjVE5D4RWRa8z69F5AYRkcrYfwnxdBeR/Nhlqnqvqv45SfsTERkgIrNFZIOI5IvIayJycDL2V14icqeIvLQjr6GqL6vq0Qns63fJsTL/JrOVJ4r0d6Kq1gbaAe2Bv4YbzvYTkaqlrHoN6AkcD9QBzgP6AYOTEIOISKr9PwwGrgIGALsA+wNvAX+o6B3F+R0kXZj7dglSVb+l6Q1YAvSKefwP4L2Yx52BCcBPwAyge8y6XYB/ASuAH4G3YtadAEwPnjcBaFN8n8BewC/ALjHr2gOrgWrB44uAecHrfwTsE7OtApcDXwOLS3hvPYFNwN7FlncCCoHmwePRwH3AJGAd8HaxmOIdg9HAPcD44L00By4MYl4PLAIuCbbNDbYpAgqC217AncBLwTZNg/d1PrAsOBYDY/ZXC3ghOB7zgBuB/FJ+ty2C99kxzu9/GPAE8F4Q7xfAfjHrBwPLgZ+BqUDXmHV3Aq8DLwXr/wx0BD4PjtVK4HGgesxzWgMfA2uB74FbgGOBzcCW4JjMCLatBzwXvM63wN1ATrDuguCY/zN4rbuDZZ8F6yVY90PwO50JHIR9SdgS7K8AeKf4/wGQE8T1TXBMplLsb8hv5fisCTsAv+3AL2/bf5DGwCxgcPC4EbAG+zZeBegdPN4tWP8e8B9gZ6AacGSw/JDgH7RT8E93frCfGiXs81PgLzHxPAA8Hdz/I7AQOBCoCtwKTIjZVoMPnV2AWiW8t/uBMaW876VEP8BHBx9EB2Ef5v8j+sFd1jEYjX2gtw5irIZ9W98v+LA6EtgIHBJs351iH+yUnCiexZJCW+BX4MDY9xQc88bYB2BpiaI/sLSM3/8w7IO2YxD/y8CrMevPBRoE664DvgNqxsS9Jfg9VQni7YAl1qrBe5kHXB1sXwf70L8OqBk87lT8GMTs+y3gmeB3sjuWyCO/swuArcCVwb5qsW2iOAb7gK8f/B4OBPaMec93x/k/uAH7P2gZPLct0CDs/9V0v4UegN924Jdn/yAF2DcnBT4B6gfrbgJeLLb9R9gH/57YN+OdS3jNp4BBxZZ9RTSRxP5T/hn4NLgv2LfXbsHjD4CLY16jCvahu0/wWIGj4ry3obEfesXWTST4po592N8fs64V9o0zJ94xiHnuXWUc47eAq4L73UksUTSOWT8JODO4vwg4Jmbdn4u/Xsy6gcDEMmIbBgyNeXw8MD/O9j8CbWPiHlvG618NvBncPwuYVsp2vx2D4PEeWIKsFbPsLGBUcP8CYFmx17iAaKI4CliAJa0qJbzneIniK6DPjv5v+W3bW6q1ybrt90dVrYN9iB0A7Bos3wc4TUR+ityAI7AksTewVlV/LOH19gGuK/a8vbFmluJeB7qIyF5AN+xDclzM6wyOeY21WDJpFPP85XHe1+og1pLsGawv6XWWYmcGuxL/GJQYg4gcJyITRWRtsP3xRI9por6Lub8RiFxgsFex/cV7/2so/f0nsi9E5DoRmSci64L3Uo9t30vx976/iLwbXBjxM3BvzPZ7Y805idgH+x2sjDnuz2BnFiXuO5aqfoo1ez0BfC8iQ0SkboL73p44XYI8UWQIVR2Dfdt6MFi0HPs2XT/mlquq9wfrdhGR+iW81HLgnmLP20lVXylhnz8BI4DTgbOBVzT4Whe8ziXFXqeWqk6IfYk4b2kk0ElE9o5dKCIdsQ+DT2MWx27TBGtSWV3GMfhdDCJSA2u6ehDYQ1XrA+9jCa6seBOxEmtyKinu4j4BGotIXnl2JCJdsTOq07Ezx/pYe3/sFWPF389TwHygharWxdr6I9svx5rkSlL8dZZjZxS7xhz3uqraOs5ztn1B1UdVtQPWLLg/1qRU5vPKiNOVkyeKzPII0FtE2mGdlCeKyDEikiMiNYPLOxur6kqsaehJEdlZRKqJSLfgNZ4F+otIp+BKoFwR+YOI1Clln/8H9AVOCe5HPA38VURaA4hIPRE5LdE3oqojsQ/L/4lI6+A9dMba4Z9S1a9jNj9XRFqJyE7AXcDrqloY7xiUstvqQA1gFbBVRI4DYi/Z/B5oICL1En0fxfwXOyY7i0gj4IrSNgze35PAK0HM1YP4zxSRmxPYVx2sH2AVUFVEbgfK+lZeB+vYLhCRA4BLY9a9CzQUkauDy5briEinYN33QNPIVWPB39cI4CERqSsiVURkPxE5MoG4EZFDg7+/asAG7KKGwph97Rvn6UOBQSLSIvj7bSMiDRLZryudJ4oMoqqrgH8Dt6nqcqAP9q1wFfZN6waiv/PzsG/e87HO66uD15gC/AU79f8R65C+IM5uh2NX6HyvqjNiYnkT+DvwatCMMRs4bjvf0inAKOBDrC/mJexKmiuLbfcidjb1HdbROiCIoaxjsA1VXR8897/Yez87eH+R9fOBV4BFQZNKSc1x8dwF5AOLsTOm17Fv3qUZQLQJ5iesSeVk4J0E9vUR9mVgAdYct4n4TV0A12PveT32heE/kRXBsekNnIgd56+BHsHq14Kfa0Tky+B+XyzxzsWO5esk1pQGltCeDZ63FGuGi5wpPwe0Co7/WyU892Hs9zcCS3rPYZ3lbgdItKXAufQjIqOxjtRQRkfvCBG5FOvoTuibtnNh8TMK5yqJiOwpIocHTTEtsUtN3ww7LufKkrREISLPi8gPIjK7lPUiIo+KyEIRmSkihyQrFudSRHXs6p/1WGf821g/hHMpLWlNT0HnaAHwb1U9qIT1x2Ntzcdjg7sGq2qn4ts555wLV9LOKFR1LHbtfGn6YElEVXUiUF9EEu3scs45V0nCLMbViG2vwsgPlq0svqGI9MPqvJCbm9vhgAMOqJQAnXMu3ajCxo3w8892q1OwkoZ8xzSKVqvqbuV5zTATRUmloktsB1PVIcAQgLy8PJ0yZUoy43LOubShCl9/DSNHwscfw6hRsG4dgNK+vTCg6XCOKhzBPsOfWFrefYSZKPLZdmRqY6ySqXPOuThWrYJPPrHEMHIkLFtmy/fZB84/6UeuWn49e3TZl9x7BwIn2U2eKPf+wkwUw4ErRORVrDN7XTCi0znnXIyNG+Gzz6KJYfp0W16/Phx1FNx8M/TuDfvNfBO5/DLLJEfeWmH7T1qiEJFXsEJ1u4rNCnYHVigMVX0aq6FzPDbydyM2D4BzzmW9wkKYNi2aGD77DDZvhmrV4PDD4e67LTF06AA5OcD338OVV8Jrr0G7dvDee3BIxY04SFqiUNWzyliv2MQ1zjmX9RYtiiaGTz+FtcE1o23aWA7o1Qu6doXc3BKevHy5JYd77oEbbrCMUoF8CkLnnAvBmjWWECKd0IsX2/LGjaFPH0sMPXvCHnuU8gJLl8I778AVV0BennVUNEhO/UNPFM45Vwk2bYLx46NnDV9+aVcs1a0LPXrAtddac9L++4OUdE1oRFERPPWUdUwAnHIK7Lln0pIEeKJwzrmkKCqCGTOiiWHcOEsWVatCly7wt7/ZWcOhh9qyhHz1Ffz5z9Zpccwx8MwzliSSzBOFc85VkCVLLCmMHGmXr64O5mFs3Rr697fEcOSRULt23Jcp2caNcMQR1tM9bBj07VvGqUfF8UThnHPl9OOPNsAtctawcKEt33NPOP54Swy9eu3gl/4FC6BFC9hpJ3jxRbuqqWHDigg/YZ4onHMuQb/+Cp9/Hk0MU6ZYE1Pt2tC9u/Ur9+4NBx5YAV/2N22CQYPg73+3M4hzz4Vjj62Ad7H9PFE451wpVGHWrGhiGDvWWoBycqBTJ7j1VksMnTpV8BWp48fDxRdbn8SFF8If/lCBL779PFE451yM/PxoYhg5En74wZYfcABcdJElhiOPhHrlnTm9LIMGwR13QJMm8NFHcPTRZT8nyTxROOey2rp1MHp0dDzDV1/Z8t13t/6F3r1tPMPee8d9mR2nau1V7drZCLt77ilnr3fF80ThnMsqW7bAxInRxDBpkl1ItNNOdqbQr58lh4MOqqSLitauhWuugebN4bbb4MQT7ZZCPFE45zKaKsydG00MY8ZAQQFUqWJjGCIF9Tp3hho1Kjm411+Hyy+3ZHHbbZW888R5onDOZZwVK6J9DCNHwsqgLnWLFnDeeZYYuneHnXcOKcCVK+0SqTfesMp+I0ZA27YhBVM2TxTOubS3fr2dKUTOGubOteW77hody9Crl83XkBJWrLCO6r//3Wp3JDw0OxypHZ1zzpVgyxaYPDmaGCZOhK1boWZN6NYNLrjAzhratLEmppSwZIkV8bvySjuLWL48xFOa7eOJwjmX8lTtaqTIZaujRtlZhIh95l5/vSWGww6zZJFSCgvhiSfgllssa512mo2sTpMkAZ4onHMp6vvvt+1nyM+35fvuC2edZYmhR4+kFk3dcfPmWRG/CRNsVPUzz1R6+Y2K4InCOZcSNmywkc+R5qRZs2z5zjvbOIbeva2fYd99w40zYRs3WjtYURH8+99WgqOSivhVNE8UzrlQFBZaraRIYpgwwfoeatSwIqn33WeJoX37YLrPdDF/PrRsaQMzXn7ZrmYqdfah9OCJwjlXKVStumokMYwaBT/9ZOvat4err7azhsMPt8/YtPPLL3DnnfDgg/DCC3YGkQLlNyqCJwrnXNKsWmXTfUY6oZcuteVNmtjEbL17w1FHwW67hRvnDhs71voivv7afp5wQtgRVShPFM65CvPLLzaTW6QDeto0W16vniWEm26y5qTmzdO2uf73/vY3O5No1szedM+eYUdU4TxROOfKrbDQkkGkOWn8eJuzoVo1u1T17rstMXTokPJjyrZfpIhfXp7Vaho0CHJzw44qKTLtV+ecS7JFi6KJ4dNPrUwR2OC2yy+3xNCtW8Z+Ztr8ptdcY/VAbr/d5ooIeb6IZPNE4ZyLa82abaf7XLTIljdqBCedFO1nSMPhAdtHFV57zWo0/fijzRmRJTxROOe2sWmTNSFFzhq+/NI+I+vUsQFu11xjZw0tW2ZQP0NZVqyAyy6Dt9+2pqaRI+0UKkt4onAuyxUVwYwZ0cQwbpwli6pVrfT2nXdaYujYMQP7GRL13XfWzvbAA3Ydb5YdiOx6t845wC5TjSSGTz6xZneA1q3hkkusOalbNzuLyFqLFsHw4ZYYDjkEli2D+vXDjioUniicywI//bRtP8PXX9vyPfeE446LTve5116hhpkaCgvh0Udh4EC7fOvMM60DJkuTBHiicC4j/forfP55dDzD5MnWxJSbaxP2RK5OatUqi/oZEjFnDlx8MXzxhV3J9PTTWdBLXzZPFM5lAFUrohdpTho71mrS5eRY38Ktt1pi6NQJqlcPO9oUtXGjTZotAv/3f3Ym4VkU8EThXNrKz9+2n+H77215y5Zw0UWWGLp3t1HRLo65c+HAA63A1KuvWhG/tK8pUrE8UTiXJn7+GUaPjvYzzJ9vy3fffdvpPvfeO9Qw08fGjTYW4uGHYdgwm0y7V6+wo0pJniicS1FbtlhTeSQxfPGF9bPWqmUtJH/5i32uHXRQCk33mS5Gj7YDuHChXeZ10klhR5TSPFE4lyJUbUK0SGIYPRoKCiwJ5OXBzTdbYujSxeZscOV0xx1w112w3342NqJHj7AjSnmeKJwL0YoV1r8QSQ4rV9ry5s2jLSE9eqTV9MqpK1LEr2NHuO46SxZpOfFF5UtqohCRY4HBQA4wVFXvL7a+HvAS0CSI5UFV/VcyY3IuTOvX2xVJkcQwZ44t33VXG8cQ6Wdo2jTUMDPLqlVw1VXWy3/HHVlRxK+iJS1RiEgO8ATQG8gHJovIcFWdG7PZ5cBcVT1RRHYDvhKRl1V1c7Licq4ybd1qYxgiieHzz21ZzZrQtSucf74lhrZtvZ+hwqnCK6/AgAF2JcDf/hZ2RGkrmWcUHYGFqroIQEReBfoAsYlCgToiIkBtYC2wNYkxOZdUqrBgQTQxjBpln1EiVgXi+ustMRx+uCULlyT5+XDppfDuuzZ45LnnrD6JK5dkJopGwPKYx/lAp2LbPA4MB1YAdYAzVLWo+AuJSD+gH0CTJk2SEqxz5fX999v2M+Tn2/JmzWzMVq9eVoa7QYNw48wqq1ZZG9/DD9sZRU5O2BGltWQmipKGNGqxx8cA04GjgP2Aj0VknKr+vM2TVIcAQwDy8vKKv4ZzlWrDBquwGkkMM2fa8p13jvYz9O4N++4bbpxZZ+FCeOcdq4Pevj0sXw5164YdVUZIZqLIB2KH/jTGzhxiXQjcr6oKLBSRxcABwKQkxuXcdikshKlTo4lhwgTYvNlKYRxxBNx7ryWG9u39i2sotm6FRx6B226z64bPPhv22MOTRAVKZqKYDLQQkWbAt8CZwNnFtlkG9ATGicgeQEtgURJjcq5MqvDNN9HE8OmnVn0VoF07u4CmVy9LEn51ZchmzbIifpMn26C5J5+0JOEqVNIShapuFZErgI+wy2OfV9U5ItI/WP80MAgYJiKzsKaqm1R1dbJicq40q1dbP0OkdtLSpba8SRM45RRLDD17egmglLJxow0yqVLFajSdfroX8UsSsVaf9JGXl6dTpkwJOwyX5n75BT77LJoYpk2z5fXqWcdzpJ+heXP/7Ek5s2fbFUwilt3btrWBKC4uEZmqqnnlea6PzHZZoajIkkGkOemzz2zOhmrV4LDDYNAgSwwdOmTdLJfpY8MG64d45BF44QUbut6zZ9hRZQX/l3AZa/HiaGL45BNYu9aWH3wwXHaZJYauXaF27XDjdAn45BMr4rd4sf3y+vQJO6Ks4onCZYy1a63jOdKctCi4LKJRI+vnjPQz+IRlaea22+Duu6FFCxgzxibzdpXKE4VLW5s22aWqkcQwdapdsVSnjvVxXn21nTW0bOn9DGmpqMg6qg87DG68Ee6802qsu0rnicKljaIiG9wWaU4aN846patWhc6drd5b795w6KHW9+DS1A8/2Gjqli2tPtNxx9nNhcYThUtpy5Zt28+wapUtb9XKmqx797ZJfOrUCTdOVwFU4eWXbaBKQYGVAXcpwROFSyk//WSF9CLNSV9/bcsbNoRjj42W4d5rr1DDdBVt+XLo3x/ef99mZho61L4NuJTgicKFavNmK70dSQyTJ1sTU24udO8evTqpVSvvZ8hoa9bA+PEweDBcfrnXQkkxnihcpVK18VKRxDBmjA2wzcmxiccGDrTE0KmT1VJyGWzBAhg+3Gqvt2tnZxXehpiSPFG4pPv222hiGDnSynKD9VVeeKElhu7dbVS0ywJbt8JDD9nVB7Vq2cC5PfbwJJHCPFG4CvfzzzB6tCWFkSNh3jxbvttu0dIYPXtaHSWXZWbMgIsugi+/hJNPhiee8CJ+acAThdthW7bAF19Ezxq++MJKc9eqZWOjLr7YEsTBB/t0n1lt40b7hlC1Krz+ulVbdGnBE4Xbbqp2lhBJDKNH29WMVapAXh7cdJOdNXTpYtMDuCw3c6Z9S9hpJ3jtNSvit8suYUfltoMnCpeQlSu3ne5zRTAFVfPmcO65lhh69LBZ3pwD7NvDwIHw2GMwbBj07Wt/JC7teKJwJSoosCuSImcNc+bY8gYNrPWgd29rTmraNNQwXar6+GPo1w+WLIErrrD+CJe2PFE4wC5EmTw5mhg+/9yW1axpFVb79rXk0Lat9zO4MgwcaPPDtmxpdVaOOCLsiNwOSjhRiEiuqm5IZjCu8qjaZeyRxDBqlF2tJAKHHALXXWeJ4bDDvA6bS1CkiN8RR8Bf/wq3327fNFzaKzNRiMhhwFCgNtBERNoCl6jqZckOzlWsH37Ytp9h+XJb3rQpnHFGtJ/BJwtz2+W776x5qVUrq8/kRfwyTiJnFP8EjgGGA6jqDBHxgvBpYONGO/OPJIYZM2z5zjvbdJ8DB1o/w377hRunS1OqNtPctdfaH1vnzmFH5JIkoaYnVV0u2xbaKUxOOG5HFBbanAyRgW7jx1stperV4fDDrdm4Vy9rWvJSOm6HLF1qndUjRlhT09Ch1ifhMlIiiWJ50PykIlIdGADMS25YLhGq8M030X6GTz+16qtgnc4DBlhi6NrVLmF3rsL89JNd/fD443DppX6FQ4ZLJFH0BwYDjYB8YATg/RMhWb3aEkKkOWnJElu+997wpz9ZP8NRR8Huu4capstEX31lRfxuuMG+iSxb5hOOZ4lEEkVLVT0ndoGIHA6MT05ILtYvv1gTUiQxTJtmZxL16lnH8w032FlDixZehtslyZYt8OCDNttcbi6cf759E/EkkTUSSRSPAYcksMxVgKIimD592+k+f/3Vpvbs0sUuKunVy0plVPVRMC7Zpk2zYl3TpsGpp1pTk5+uZp1SP2pEpAtwGLCbiFwbs6ou4F2hFWjx4m37GdasseUHHWQT9/TqZcX1/Aucq1QbN1pbZrVq8L//Wdumy0rxvpNWx8ZOVAViC8X/DJyazKAy3dq1NsAtctbwzTe2fK+94IQTomW4GzYMN06XpaZNs4mEdtrJqry2betFvLJcqYlCVccAY0RkmKourcSYMs6vv8KECdHEMGWK9TPUqWMT9lx1lZ01HHCA9zO4EK1fbyOqn3jCxkf07Wt/oC7rJdLKvVFEHgBaA7+Nx1fVo5IWVZorKoJZs6KJYexY65TOybExSXfcYYmhY0c7q3cudB9+CJdcYsP1r7rKm5ncNhJJFC8D/wFOwC6VPR9Ylcyg0tGyZdGBbiNHwqrgCB14IPzlL5YYjjwS6tYNN07nfuevf4X777c/1vHj7aoJ52IkkigaqOpzInJVTHPUmGQHlup++skm7ImcNSxYYMsbNoRjjrHE0KsXNGoUZpTOxVFYaKe53bvbJXS33uozTbkSJZIotgQ/V4rIH4AVQOPkhZSaNm+GiROjiWHSJGtiys21M4VLL7XE0Lq19zO4FLdyJVx+uf2xDhpk32yOOSbsqFwKSyRR3C0i9YDrsPETdYGrkxlUKlC1yXoiiWHMGNiwwSoVdOwYLajXubPVUnIu5anaTHPXXgubNvk8ES5hZSYKVX03uLsO6AG/jczOON9+u20/w3ff2fL994cLLrDE0L071K8fYpDOlceSJdZZNnKkFf8aOtT+sJ1LQLwBdznA6ViNpw9VdbaInADcAtQC2ldOiMmzfr31M0QGu80LSh3utlu0j6FXL2jSJNQwndtx69bBl1/Ck0/a1U1exM9th3hnFM8BewOTgEdFZCnQBbhZVd9K5MVF5FisoGAOMFRV7y9hm+7AI0A1YLWqHpl4+NtnyxbrW4g0J33xhU33WauWjXy+6CIb7Hbwwf5/5DLA3LlWxO/mm6NF/HJzw47KpaF4iSIPaKOqRSJSE1gNNFfV7xJ54eCM5AmgN1Z1drKIDFfVuTHb1AeeBI5V1WUiUqFFZFRh/vxoYhg92s4iRKxW0o032hlDly4+Y6PLIJs3wz/+YR3VderYN6Ddd/ck4cotXqLYrKpFAKq6SUQWJJokAh2Bhaq6CEBEXgX6AHNjtjkbeENVlwX7+WG7oi/Bd99t28/w7be2fL/94JxzLDH06AG77LKje3IuBU2ZYkX8Zs6EM8+EwYO9iJ/bYfESxQEiMjO4L8B+wWMBVFXblPHajYDlMY/zgU7FttkfqCYio7F6UoNV9d/FX0hE+gH9AJoU6zAoKLCRz5GzhtmzbXmDBlYvKdLP0KxZGdE6l+42bLDLXGvWhLffhpNOCjsilyHiJYoDd/C1SxpNoCXsvwPQE+sg/1xEJqrqgm2epDoEGALQoUOexo5n+Pxz63uoUcMu5jj3XOtnaNfO+xlclvjyS/uDz82FN9+ENm380jxXoeIVBdzRQoD5WGd4RGNssF7xbVar6gZgg4iMBdoCCyjFjBnWpyAC7dvbJeG9etmc0LVq7WDEzqWTn3+2juqnnooW8evWLeyoXAZK5tQ3k4EWItIM+BY4E+uTiPU28LiIVMXKmncC/hnvRQsL4dVXrVlp112TELVz6eD99+0y1xUr7NvSKaeEHZHLYElLFKq6VUSuAD7CLo99XlXniEj/YP3TqjpPRD4EZgJF2CW0s8t67dNP9zIZLovddJNd1dSqlc0X0al4159zFUtUi3cblLCRSC2giap+lfyQyoolT4uKpniicNlF1YqL5eTAiBFW5fWWW7yIn0uYiExV1bzyPLfM7l4RORGYDnwYPG4nIsPLszPnXDl8+y388Y82kQnA0UfD3/7mScJVmkSuC7oTGxPxE4CqTgeaJisg51xAFZ591pqYRozwTjkXmkT6KLaq6jrxth7nKs/ixTZwbtQoq0T57LPQvHnYUbkslUiimC0iZwM5ItICGABMSG5YzmW5ggIbXf3MM/DnP/ugIBeqRP76rsTmy/4V+D+s3PjVSYzJuew0ezbce6/dP/hgK+LXr58nCRe6Mq96EpH2qjqtkuIpk1/15DLO5s1w331wzz1Qr57NmOX1mVwFS+pVT8DDIjJfRAaJSOvy7KSieZJwGWPyZOjQAe68E047zUqDe5JwKSaRGe56iEhDbBKjISJSF/iPqt6d9Oicy2QbNsCxx1rtmeHD4cQTw47IuRIlNODut41FDgZuBM5Q1VBmihbJU9UpYezauYoxZQoccoj1PXz2mfVH1KsXdlQuwyV7wN2BInKniMwGHseueGpcnp05l9XWrbP6TIceCi+9ZMuOOMKThEt5iVwe+y/gFeBoVS1e/dU5l4h33oH+/W1mreuvh1NPDTsi5xKWSB9F58oIxLmMdcMN8OCD1sT01lt2RuFcGik1UYjIf1X1dBGZxbYTDiU6w51z2UvVauJXrWq1merWtaqv1UPp2nNuh5TamS0ie6rqShHZp6T1FTCxUbl4Z7ZLefn5cOmlNtPcPfeEHY1zQJI6s1V1ZXD3MlVdGnsDLivPzpzLaEVFVnKjVSv49FNo2DDsiJyrEIkMuOtdwrLjKjoQ59LaokVw1FHWYd2xI8yaBVdeGXZUzlWIeH0Ul2JnDvuKyMyYVXWA8ckOzLm0smGDjaoeOhQuusjLB7iMEq+Poh6wM3AfcHPMqvWqurYSYiuR91G4lDFrFrz9Ntx6qz3+5RcbZe1cCkrWgDtV1SXA5cD6mBsiskt5duZcRvj1V7j9dhtd/eij8MMPttyThMtQ8cZR/B9wAjAVuzw29lxagX2TGJdzqWniRJtQaO5cOO88+Oc/oUGDsKNyLqlKTRSqekLws1nlheNcCtuwAf7wB8jNhfffh+P8mg6XHRKp9XS4iOQG988VkYdFpEnyQ3MuRXzxhV36mptrpTjmzPEk4bJKIpfHPgVsFJG2WOXYpcCLSY3KuVTw0082DWnnztEifocdBnXqhBqWc5UtkUSxVe3SqD7AYFUdjF0i61zmeustGzg3bJiV3jjttLAjci40iVSPXS8ifwXOA7qKSA5QLblhOReia6+1Tuq2ba2pqUOHsCNyLlSJJIozgLOBi1T1u6B/4oHkhuVcJYst4nf88XYl0403QjX/TuRcQjPcicgeQKQ28iRV/SGpUcWNxQfcuQq2bJmV3mjf3ov4uYyV7BnuTgcmAadh82Z/ISI+64pLf0VF8OST0Lo1jBkDe+0VdkTOpaREmp4GAodGziJEZDdgJPB6MgNzLqkWLrSaTOPGQe/eMGQING0adlTOpaREEkWVYk1Na0jsainnUtemTbBgAfzrX3D++V7Ez7k4EkkUH4rIR9i82WCd2+8nLyTnkmT6dCvid8cdcNBBsGQJ1KwZdlTOpbwyzwxU9QbgGaAN0BYYoqo3JTsw5yrMpk0wcCDk5cFTT0WL+HmScC4h8eajaAE8COwHzAKuV9VvKysw5yrEhAlWxG/+fGtievhh2MWLHzu3PeKdUTwPvAucglWQfaxSInKuomzYACeeCBs3wocf2ihrTxLObbd4fRR1VPXZ4P5XIvJlZQTk3A77/HPo1MmK+L37rvVHeH0m58ot3hlFTRFpLyKHiMghQK1ij8skIseKyFcislBEbo6z3aEiUujjM9wO+fFHu+T1sMPgxaBuZZcuniSc20HxzihWAg/HPP4u5rECR8V74aAm1BNAbyAfmCwiw1V1bgnb/R34aPtCdy7GG2/A5ZfDqlXw17/CGWeEHZFzGSPexEU9dvC1OwILVXURgIi8ilWgnVtsuyuB/xEtEeLc9rnmGnjkEWjXziYUat8+7IicyyiJjKMor0bA8pjH+UCn2A1EpBFwMnZ2UmqiEJF+QD975JU8HdsW8TvhBNh9d7j+ei/i51wSJHOEdUlDXYtXIHwEuElVC+O9kKoOUdW88ha0chlmyRI49li47TZ73LOnNTd5knAuKZKZKPKBvWMeNwZWFNsmD3hVRJYApwJPisgfkxiTS2dFRfDYY3YV04QJsM8+YUfkXFYos+lJRAQ4B9hXVe8K5qNoqKqTynjqZKCFiDQDvgXOxOa1+I2qNovZzzDgXVV9a7vegcsOX38NF14I48fb2cTTT3uicK6SJHJG8STQBTgreLweu5opLlXdClyBXc00D/ivqs4Rkf4i0r+c8bpstXkzfPMN/Pvf1mHtScK5SlPmxEUi8qWqHiIi01S1fbBshqq2rZQIfxePT1yUNaZNsyJ+d95pj3/9FWrUCDUk59JVUicuArYEYx002NluQFF5duZcQjZtss7pQw+FZ56xsRHgScK5kCSSKB4F3gR2F5F7gM+Ae5Malcten30GbdvC/fdD374wdy7stlvYUTmX1crszFbVl0VkKtATu+T1j6o6L+mRuexTUAB9+kDdujBihM0855wLXSJXPTUBNgLvxC5T1WXJDMxlkc8+s/pMtWvDe+/Z5a+1a4cdlXMukEjT03tYufH3gE+ARcAHyQzKZYk1a6x5qWvXaBG/zp09STiXYhJpejo49nFQOfaSpEXkMp8qvP46XHEFrF1rI6zPPDPsqJxzpdjuWk+q+qWIeAE/V37XXAODB0OHDtYX0TaUK62dcwlKpI/i2piHVYBDgFVJi8hlJlXYutXqMZ10Euy1F1x7rRX1c86ltET6KOrE3GpgfRV9khmUyzCLF8PRR0eL+B11FNx4oycJ59JE3P/UYKBdbVW9oZLicZmksBAefxxuuQVycuC008KOyDlXDqUmChGpqqpbE5321LltLFgAF1xg81cfd5yNsN577zKf5pxLPfHOKCZh/RHTRWQ48BqwIbJSVd9IcmwunW3dCkuXwksvwdlng5Q0PYlzLh0k0ki8C7AGm4VOsdHZCniicNuaMsWK+A0aBK1awaJFXp/JuQwQL1HsHlzxNJtogoiIX3I2ifyLaQr65Re44w546CFo2BAGDLD6TJ4knMsI8a56ygFqB7c6MfcjN+dgzBho0wYeeAAuvhjmzPEifs5lmHhnFCtV9a5Ki8Sln4IC+NOfoH59+OQTu+zVOZdx4iUKb+RxJRs3Dg4/3GoyffABtG4NublhR+WcS5J4TU89Ky0Klx5Wr4Zzz4Vu3aJF/Dp29CThXIYr9YxCVddWZiAuhanCf/8LV14JP/5oHddexM+5rOE1FFzZrroKHnvMpib95BM4+OCyn+OcyxieKFzJVGHLFqheHU4+GfbZB66+2kpxOOeySiJFAV22+eYb6NkTbr3VHvfoAddd50nCuSzlicJFFRbCww9b09LUqdCyZdgROedSgDc9OTN/Ppx/PkyaBCeeCE89BY0ahR2Vcy4FeKJwpqgIVqyAV16BM87wWinOud94oshmkyZZEb977rEift98Y53XzjkXw/sostHGjXD99dClC7zwAqwKZrb1JOGcK4EnimwzapR1Vj/0EPzlL17EzzlXJm96yiYFBTYdaf36ljC6dw87IudcGvAzimwwerR1VkeK+M2c6UnCOZcwTxSZbNUqOOssGzD30ku27NBDYaedwo3LOZdWvOkpE6naZa4DBsD69TY1qRfxc86VkyeKTHTllfDEE9C5Mzz3nF366pxz5eSJIlMUFcHWrXaJ66mnQvPmljC8PpNzbgcltY9CRI4Vka9EZKGI3FzC+nNEZGZwmyAibZMZT8b6+mubhnTgQHvcvbtXenXOVZikJQoRyQGeAI4DWgFniUjxNpDFwJGq2gYYBAxJVjwZaetWePBBaNMGpk+HAw8MOyLnXAZKZtNTR2Chqi4CEJFXgT7A3MgGqjohZvuJQOMkxpNZ5s2Dvn1hyhTo0weefBL22ivsqJxzGSiZTU+NgOUxj/ODZaW5GPigpBUi0k9EpojIFFWtwBDT3Pffw3/+A2++6UnCOZc0yTyjKKn8aImf8iLSA0sUR5S0XlWHEDRLVamSl72ZYuJEK+J3333WzPTNN1CtWthROecyXDLPKPKBvWMeNwZWFN9IRNoAQ4E+qromifGkrw0b4Jpr4LDD4OWXo0X8PEk45ypBMhPFZKCFiDQTkerAmcDw2A1EpAnwBnCeqi5IYizpa+RIOOggeOQRuOwyL+LnnKt0SWt6UtWtInIF8BGQAzyvqnNEpH+w/mngdqAB8KTYRDlbVTUvWTGlnYICG1G9yy4wdix07Rp2RM65LCTp1jlcpUqeFhVNCTuM5Pr0UzjySBsHMXWqjayuVSvsqJxzaUxEppb3i7gXBUwl338Pp58OPXtGi/h16OBJwjkXKk8UqUAVXnzRzhwiU5OefXbYUTnnHOC1nlLD5ZfDU0/Z1KTPPecjrJ1zKcUTRViKimDLFqhRA844w5LDZZd5fSbnXMrxpqcwfPWVdVZHivgdeaRXenXOpSxPFJVpyxa4/35o2xZmz4aDDw47IuecK5M3PVWWOXPgvPNg2jT4059sYqGGDcOOyjnnyuSJorLk5MDatfD663DKKWFH45xzCfOmp2SaMAFuusnuH3AALFzoScI5l3Y8USRDQQEMGABHHGFlwFevtuVV/QTOOZd+PFFUtBEjrIjf44/DFVdYp/Wuu4YdlXPOlZt/xa1IBQVwzjnQoAGMGweHHx52RM45t8P8jKIifPwxFBZC7dp2RjF9uicJ51zGSLtEISXNmxeWlSutc/roo21CIYD27aFmzXDjcs65CpR2iSIlqMKwYVbE7733bBCdF/FzzmUo76Moj0svhWeesauahg6Fli3Djsg555LGE0WiYov4nX02tGkD/ftDFT8pc85lNv+US8S8eTYN6S232ONu3azSqycJ51wW8E+6eLZsgXvvhXbtYP5866h2zrks401PpZkzB8491y51Pe00eOwx2GOPsKNyzrlK54miNFWrwrp18MYbcPLJYUfjnHOh8aanWOPGwfXX2/2WLWHBAk8Szrms54kCYP16m7e6Wzc7g/Aifs459xtPFB98AK1bw1NPwdVXw6xZXsTPOediZPdX5vXroW9f2H13mzuic+ewI3LOuZSTfYlCFT76CHr3hjp1YORIm1SoRo2wI3POuYRs2bKF/Px8Nm3a9Lt1NWvWpHHjxlSrVq3C9pddiWLlShso99Zb8MILdjbRtm3YUTnn3HbJz8+nTp06NG3aFImplKqqrFmzhvz8fJo1a1Zh+8uOPgpVeP55OPBA+PBD+Mc/vIifcy5tbdq0iQYNGmyTJABEhAYNGpR4prEjsuOMon9/GDLErmoaOhRatAg7Iuec2yHFk0RZy3dE5iaKwkIrwVGzpo2wbt8e+vXz+kzOObedMvNTc84cm2EuUsSva1ev9Oqcc+WUWZ+cmzfDoEF29rBwIRx6aNgROedcUqjqdi3fEZnT9DRrFpxzjv0880x49FHYbbewo3LOuQpXs2ZN1qxZ87sO7chVTzUreDrmzEkU1avDxo3w9ttw0klhR+Occ0nTuHFj8vPzWbVq1e/WRcZRVCRJxmlKMuXk5Glh4RR7MGYMDB8ODz1kjwsLIScnvOCccy5FichUVc0rz3OT2kchIseKyFcislBEbi5hvYjIo8H6mSJySEIv/PPPNm919+42eC5SxM+ThHPOVbikJQoRyQGeAI4DWgFniUirYpsdB7QIbv2Ap8p63bq6zor4DRkC117rRfyccy7JknlG0RFYqKqLVHUz8CrQp9g2fYB/q5kI1BeRPeO96D66BOrVsyJ+Dz0EO+2UlOCdc86ZZHZmNwKWxzzOBzolsE0jYGXsRiLSDzvjAPhV5syZ7ZVeAdgVWB12ECnCj0WUH4soPxZRLcv7xGQmipLGkRfvOU9kG1R1CDAEQESmlLdDJtP4sYjyYxHlxyLKj0WUiEwp73OT2fSUD+wd87gxsKIc2zjnnAtRMhPFZKCFiDQTkerAmcDwYtsMB/oGVz91Btap6sriL+Sccy48SWt6UtWtInIF8BGQAzyvqnNEpH+w/mngfeB4YCGwEbgwgZcekqSQ05Efiyg/FlF+LKL8WESV+1ik3YA755xzlSuzigI655yrcJ4onHPOxZWyiSJp5T/SUALH4pzgGMwUkQkikrETgZd1LGK2O1RECkXk1MqMrzIlcixEpLuITBeROSIyprJjrCwJ/I/UE5F3RGRGcCwS6Q9NOyLyvIj8ICKzS1lfvs9NVU25G9b5/Q2wL1AdmAG0KrbN8cAH2FiMzsAXYccd4rE4DNg5uH9cNh+LmO0+xS6WODXsuEP8u6gPzAWaBI93DzvuEI/FLcDfg/u7AWuB6mHHnoRj0Q04BJhdyvpyfW6m6hlFUsp/pKkyj4WqTlDVH4OHE7HxKJkokb8LgCuB/wE/VGZwlSyRY3E28IaqLgNQ1Uw9HokcCwXqiE3eUBtLFFsrN8zkU9Wx2HsrTbk+N1M1UZRW2mN7t8kE2/s+L8a+MWSiMo+FiDQCTgaersS4wpDI38X+wM4iMlpEpopI30qLrnIlciweBw7EBvTOAq5S1aLKCS+llOtzM1UnLqqw8h8ZIOH3KSI9sERxRFIjCk8ix+IR4CZVLYyd+SsDJXIsqgIdgJ5ALeBzEZmoqguSHVwlS+RYHANMB44C9gM+FpFxqvpzkmNLNeX63EzVROHlP6ISep8i0gYYChynqmsqKbbKlsixyANeDZLErsDxIrJVVd+qlAgrT6L/I6tVdQOwQUTGAm2BTEsUiRyLC4H71RrqF4rIYuAAYFLlhJgyyvW5mapNT17+I6rMYyEiTYA3gPMy8NtirDKPhao2U9WmqtoUeB24LAOTBCT2P/I20FVEqorITlj15nmVHGdlSORYLMPOrBCRPbBKqosqNcrUUK7PzZQ8o9Dklf9IOwkei9uBBsCTwTfprZqBFTMTPBZZIZFjoarzRORDYCZQBAxV1RIvm0xnCf5dDAKGicgsrPnlJlXNuPLjIvIK0B3YVUTygTuAarBjn5tewsM551xcqdr05JxzLkV4onDOOReXJwrnnHNxeaJwzjkXlycK55xzcXmicCkpqPw6PebWNM62BRWwv2EisjjY15ci0qUcrzFURFoF928ptm7CjsYYvE7kuMwOqqHWL2P7diJyfEXs22UvvzzWpSQRKVDV2hW9bZzXGAa8q6qvi8jRwIOq2mYHXm+HYyrrdUXkBWCBqt4TZ/sLgDxVvaKiY3HZw88oXFoQkdoi8knwbX+WiPyuaqyI7CkiY2O+cXcNlh8tIp8Hz31NRMr6AB8LNA+ee23wWrNF5OpgWa6IvBfMbTBbRM4Ilo8WkTwRuR+oFcTxcrCuIPj5n9hv+MGZzCkikiMiD4jIZLF5Ai5J4LB8TlDQTUQ6is1FMi342TIYpXwXcEYQyxlB7M8H+5lW0nF07nfCrp/uN7+VdAMKsSJu04E3sSoCdYN1u2IjSyNnxAXBz+uAgcH9HKBOsO1YIDdYfhNwewn7G0YwdwVwGvAFVlBvFpCLlaaeA7QHTgGejXluveDnaOzb+28xxWwTifFk4IXgfnWskmctoB9wa7C8BjAFaFZCnAUx7+814NjgcV2ganC/F/C/4P4FwOMxz78XODe4Xx+r+5Qb9u/bb6l9S8kSHs4Bv6hqu8gDEakG3Csi3bByFI2APYDvYp4zGXg+2PYtVZ0uIkcCrYDxQXmT6tg38ZI8ICK3AquwKrw9gTfViuohIm8AXYEPgQdF5O9Yc9W47XhfHwCPikgN4FhgrKr+EjR3tZHojHz1gBbA4mLPryUi04GmwFTg45jtXxCRFlg10Gql7P9o4CQRuT54XBNoQmbWgHIVxBOFSxfnYDOTdVDVLSKyBPuQ+42qjg0SyR+AF0XkAeBH4GNVPSuBfdygqq9HHohIr5I2UtUFItIBq5lzn4iMUNW7EnkTqrpJREZjZa/PAF6J7A64UlU/KuMlflHVdiJSD3gXuBx4FKtlNEpVTw46/keX8nwBTlHVrxKJ1znwPgqXPuoBPwRJogewT/ENRGSfYJtngeewKSEnAoeLSKTPYScR2T/BfY4F/hg8JxdrNhonInsBG1X1JeDBYD/FbQnObEryKlaMrStWyI7g56WR54jI/sE+S6Sq64ABwPXBc+oB3warL4jZdD3WBBfxEXClBKdXItK+tH04F+GJwqWLl4E8EZmCnV3ML2Gb7sB0EZmG9SMMVtVV2AfnKyIyE0scBySyQ1X9Euu7mIT1WQxV1WnAwcCkoAloIHB3CU8fAsyMdGYXMwKb23ik2tSdYHOJzAW+FJHZwDOUccYfxDIDK6v9D+zsZjzWfxExCmgV6czGzjyqBbHNDh47F5dfHuuccy4uP6NwzjkXlycK55xzcXmicM45F5cnCuecc3F5onDOOReXJwrnnHNxeaJwzjkX1/8DKhHr46Ek/i4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(test_fpr, test_tpr, 'b')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7deb5ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_valid_pred = model.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78bdcb15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5679    2]\n",
      " [   6   11]]\n"
     ]
    }
   ],
   "source": [
    "test_confusion = metrics.confusion_matrix(y_valid, y_valid_pred)\n",
    "test_TP = test_confusion[1, 1]\n",
    "test_TN = test_confusion[0, 0]\n",
    "test_FP = test_confusion[0, 1]\n",
    "test_FN = test_confusion[1, 0]\n",
    "print(test_confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be2da760",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5681\n",
      "           1       0.85      0.65      0.73        17\n",
      "\n",
      "    accuracy                           1.00      5698\n",
      "   macro avg       0.92      0.82      0.87      5698\n",
      "weighted avg       1.00      1.00      1.00      5698\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid,y_valid_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "396551da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:\n",
      "0.9985959985959986\n",
      "ROC AUC Score:\n",
      "0.8233533864170559\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\")\n",
    "print(metrics.accuracy_score(y_valid, y_valid_pred))\n",
    "print(\"ROC AUC Score:\")\n",
    "print(metrics.roc_auc_score(y_valid, y_valid_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c0700c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_fpr, valid_tpr, valid_thresholds = metrics.roc_curve(y_valid, y_valid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6cd12304",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(valid_fpr, valid_tpr, 'b')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3f2984f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model to disk\n",
    "filename = 'xgboost.pkl'\n",
    "pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "efb0c9f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['maxtempC', 'FeelsLikeC', 'cloudcover', 'humidity', 'precipMM',\n",
       "       'visibility', 'winddirDegree', 'windspeedKmph', 'nightHours',\n",
       "       'snowfall'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanDf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f478181e",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"columns_required.txt\", \"w\")\n",
    "file.write(str(cleanDf.columns.values))\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4e492b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}