Truegene_Nucleotide classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c93b5842",
   "metadata": {},
   "source": [
    "# Import statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6df13f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb \n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from xgboost import plot_importance\n",
    "import scipy\n",
    "from scipy.stats import pearsonr\n",
    "import skopt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a3f2926",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ee2c8e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45220, 16)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"ntd_table.csv\", sep='\\t')\n",
    "#to view the loaded data\n",
    "data\n",
    "#To get the number of rows and columns\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4b7db7e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Length</th>\n",
       "      <th>GC Content</th>\n",
       "      <th>GC Skew_Stdev</th>\n",
       "      <th>GC Skew_median</th>\n",
       "      <th>GC Skew_mean</th>\n",
       "      <th>GC Position 1</th>\n",
       "      <th>GC Position 2</th>\n",
       "      <th>GC Postion 3</th>\n",
       "      <th>Melting temperature</th>\n",
       "      <th>Molecular weight</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Zlib compression ratio</th>\n",
       "      <th>CAI</th>\n",
       "      <th>Intergenic region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>396</td>\n",
       "      <td>44.191919</td>\n",
       "      <td>0.123351</td>\n",
       "      <td>0.170290</td>\n",
       "      <td>0.133493</td>\n",
       "      <td>55.303030</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>40.909091</td>\n",
       "      <td>79.408282</td>\n",
       "      <td>122886.4961</td>\n",
       "      <td>5.866457</td>\n",
       "      <td>0.447552</td>\n",
       "      <td>0.197978</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>396</td>\n",
       "      <td>44.191919</td>\n",
       "      <td>0.123351</td>\n",
       "      <td>0.170290</td>\n",
       "      <td>0.133493</td>\n",
       "      <td>55.303030</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>40.909091</td>\n",
       "      <td>79.408282</td>\n",
       "      <td>122886.4961</td>\n",
       "      <td>5.866457</td>\n",
       "      <td>0.447552</td>\n",
       "      <td>0.197978</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1143</td>\n",
       "      <td>42.957130</td>\n",
       "      <td>0.233937</td>\n",
       "      <td>0.116883</td>\n",
       "      <td>0.081073</td>\n",
       "      <td>48.818898</td>\n",
       "      <td>39.895013</td>\n",
       "      <td>40.157480</td>\n",
       "      <td>79.163389</td>\n",
       "      <td>353481.5123</td>\n",
       "      <td>6.934628</td>\n",
       "      <td>0.368197</td>\n",
       "      <td>0.215192</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>651</td>\n",
       "      <td>45.007680</td>\n",
       "      <td>0.123106</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.208360</td>\n",
       "      <td>50.691244</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>41.474654</td>\n",
       "      <td>80.001978</td>\n",
       "      <td>202544.3158</td>\n",
       "      <td>6.367186</td>\n",
       "      <td>0.402047</td>\n",
       "      <td>0.190523</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>777</td>\n",
       "      <td>34.877735</td>\n",
       "      <td>0.147628</td>\n",
       "      <td>0.320574</td>\n",
       "      <td>0.319266</td>\n",
       "      <td>38.223938</td>\n",
       "      <td>29.343629</td>\n",
       "      <td>37.065637</td>\n",
       "      <td>75.591492</td>\n",
       "      <td>241883.9497</td>\n",
       "      <td>6.539303</td>\n",
       "      <td>0.382716</td>\n",
       "      <td>0.205294</td>\n",
       "      <td>895</td>\n",
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       "    <tr>\n",
       "      <th>45215</th>\n",
       "      <td>2643</td>\n",
       "      <td>36.360197</td>\n",
       "      <td>0.185318</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.157952</td>\n",
       "      <td>41.997730</td>\n",
       "      <td>34.279228</td>\n",
       "      <td>32.803632</td>\n",
       "      <td>76.906870</td>\n",
       "      <td>819468.4661</td>\n",
       "      <td>7.763098</td>\n",
       "      <td>0.332212</td>\n",
       "      <td>0.185867</td>\n",
       "      <td>18263</td>\n",
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       "    <tr>\n",
       "      <th>45216</th>\n",
       "      <td>444</td>\n",
       "      <td>40.315315</td>\n",
       "      <td>0.179418</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.041099</td>\n",
       "      <td>52.027027</td>\n",
       "      <td>31.756757</td>\n",
       "      <td>37.162162</td>\n",
       "      <td>77.592156</td>\n",
       "      <td>137251.7441</td>\n",
       "      <td>5.978426</td>\n",
       "      <td>0.440252</td>\n",
       "      <td>0.233469</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>45217</th>\n",
       "      <td>1764</td>\n",
       "      <td>41.213152</td>\n",
       "      <td>0.179125</td>\n",
       "      <td>0.103833</td>\n",
       "      <td>0.085499</td>\n",
       "      <td>45.578231</td>\n",
       "      <td>44.047619</td>\n",
       "      <td>34.013605</td>\n",
       "      <td>78.791894</td>\n",
       "      <td>546423.3003</td>\n",
       "      <td>7.358203</td>\n",
       "      <td>0.348915</td>\n",
       "      <td>0.159023</td>\n",
       "      <td>272</td>\n",
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       "      <td>41.666667</td>\n",
       "      <td>0.407731</td>\n",
       "      <td>0.198439</td>\n",
       "      <td>0.325410</td>\n",
       "      <td>52.884615</td>\n",
       "      <td>34.615385</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>77.363064</td>\n",
       "      <td>96725.7946</td>\n",
       "      <td>5.630104</td>\n",
       "      <td>0.481159</td>\n",
       "      <td>0.225985</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45219</th>\n",
       "      <td>1533</td>\n",
       "      <td>41.682975</td>\n",
       "      <td>0.208049</td>\n",
       "      <td>0.159664</td>\n",
       "      <td>0.119421</td>\n",
       "      <td>45.988258</td>\n",
       "      <td>44.422701</td>\n",
       "      <td>34.637965</td>\n",
       "      <td>79.012952</td>\n",
       "      <td>475400.9763</td>\n",
       "      <td>7.216107</td>\n",
       "      <td>0.355683</td>\n",
       "      <td>0.162251</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45220 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Length  GC Content  GC Skew_Stdev  GC Skew_median  GC Skew_mean  \\\n",
       "0         396   44.191919       0.123351        0.170290      0.133493   \n",
       "1         396   44.191919       0.123351        0.170290      0.133493   \n",
       "2        1143   42.957130       0.233937        0.116883      0.081073   \n",
       "3         651   45.007680       0.123106        0.227273      0.208360   \n",
       "4         777   34.877735       0.147628        0.320574      0.319266   \n",
       "...       ...         ...            ...             ...           ...   \n",
       "45215    2643   36.360197       0.185318        0.125000      0.157952   \n",
       "45216     444   40.315315       0.179418        0.100000      0.041099   \n",
       "45217    1764   41.213152       0.179125        0.103833      0.085499   \n",
       "45218     312   41.666667       0.407731        0.198439      0.325410   \n",
       "45219    1533   41.682975       0.208049        0.159664      0.119421   \n",
       "\n",
       "       GC Position 1  GC Position 2  GC Postion 3  Melting temperature  \\\n",
       "0          55.303030      36.363636     40.909091            79.408282   \n",
       "1          55.303030      36.363636     40.909091            79.408282   \n",
       "2          48.818898      39.895013     40.157480            79.163389   \n",
       "3          50.691244      42.857143     41.474654            80.001978   \n",
       "4          38.223938      29.343629     37.065637            75.591492   \n",
       "...              ...            ...           ...                  ...   \n",
       "45215      41.997730      34.279228     32.803632            76.906870   \n",
       "45216      52.027027      31.756757     37.162162            77.592156   \n",
       "45217      45.578231      44.047619     34.013605            78.791894   \n",
       "45218      52.884615      34.615385     37.500000            77.363064   \n",
       "45219      45.988258      44.422701     34.637965            79.012952   \n",
       "\n",
       "       Molecular weight   Entropy  Zlib compression ratio       CAI  \\\n",
       "0           122886.4961  5.866457                0.447552  0.197978   \n",
       "1           122886.4961  5.866457                0.447552  0.197978   \n",
       "2           353481.5123  6.934628                0.368197  0.215192   \n",
       "3           202544.3158  6.367186                0.402047  0.190523   \n",
       "4           241883.9497  6.539303                0.382716  0.205294   \n",
       "...                 ...       ...                     ...       ...   \n",
       "45215       819468.4661  7.763098                0.332212  0.185867   \n",
       "45216       137251.7441  5.978426                0.440252  0.233469   \n",
       "45217       546423.3003  7.358203                0.348915  0.159023   \n",
       "45218        96725.7946  5.630104                0.481159  0.225985   \n",
       "45219       475400.9763  7.216107                0.355683  0.162251   \n",
       "\n",
       "       Intergenic region  \n",
       "0                    247  \n",
       "1                    131  \n",
       "2                   1142  \n",
       "3                    208  \n",
       "4                    895  \n",
       "...                  ...  \n",
       "45215              18263  \n",
       "45216                  0  \n",
       "45217                272  \n",
       "45218                 72  \n",
       "45219                364  \n",
       "\n",
       "[45220 rows x 14 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare Data\n",
    "Y = data.iloc[:,-1]\n",
    "X = data.iloc[:, 1:15]\n",
    "#to view the spiltting \n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ef0e3975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9999790394044235, 0.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Length'],data['Molecular weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0d5f075c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.16722699063661817, 7.114311411293348e-281)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Length'],data['Melting temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8dd79180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.10977156516866324, 3.1467848784331784e-121)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Entropy'],data['GC Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "769ce5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8310570198430475, 0.0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Length'],data['Entropy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68dc75c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.06293864713561007, 6.311615833524462e-41)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Length'],data['GC Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60aaf8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9258610153092581, 0.0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['Melting temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27f2a872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4037768256149038, 0.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Length'],data['Intergenic region'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "533e15bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.06296898004065557, 5.784746414909375e-41)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Molecular weight'],data['GC Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd0638f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.831029772712627, 0.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Entropy'],data['Molecular weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2bb37d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.03299883638166419, 2.235926129841803e-12)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['CAI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dc115ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0219170984404549, 3.1452259819332343e-06)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['CAI'],data['Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4cddac0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.009810249683541345, 0.036965798102476716)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['CAI'],data['Intergenic region'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "954032f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.022323940056752825, 2.057988027850407e-06)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['CAI'],data['Molecular weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "582ed6cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.07263947259196055, 5.813711545116708e-54)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['GC Skew_Stdev'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c6079185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.16711076737035577, 1.7577085517339071e-280)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Melting temperature'],data['Molecular weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "96a4f68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7256530052356985, 0.0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['GC Postion 3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "11130fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7098949015668433, 0.0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['GC Position 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0f73c2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5799543221524908, 0.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['GC Position 1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "93bb0e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.09398304405604668, 3.0604966285641983e-89)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['GC Skew_median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1880e95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.08785575865216959, 3.5179200941756794e-78)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['GC Skew_mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c7270511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.08188810240932046, 3.937787062623953e-68)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Skew_mean'],data['GC Skew_Stdev'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7a898b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.06931503466398402, 2.7660107915535896e-49)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Skew_median'],data['GC Skew_Stdev'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4eaccd5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1125714157539141, 1.9856032184971123e-127)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['GC Content'],data['Zlib compression ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bad420e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.6852685087094584, 0.0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Length'],data['Zlib compression ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "44ccd5c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.30160922299692544, 0.0)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(data['Intergenic region'],data['Zlib compression ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc8afb8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "66a754a2",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3a37d7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train and test data split up to 80% training and 20% test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58c362da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shriprabha/anaconda3/envs/ML_env/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:30:02] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', use_label_encoder=True,\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build classifier\n",
    "model = XGBClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e39d319",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.43%\n"
     ]
    }
   ],
   "source": [
    "#predictions and accuracy\n",
    "y_pred = model.predict(X_test)\n",
    "predictions = [round(value) for value in y_pred]\n",
    "\n",
    "\n",
    "# evaluate predictions\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6978eff3",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0753ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the feature importance plots\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "\n",
    "plot_importance(model, importance_type = 'cover')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89d39b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(model, importance_type = 'gain')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5e76da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(model, importance_type = 'weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7930a6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#explanation plots for feaure importance\n",
    "import shap\n",
    "explainer = shap.Explainer(model)\n",
    "shap_values = explainer(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04bdf802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the first prediction's explanation\n",
    "shap.plots.waterfall(shap_values[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a66becc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x511.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.beeswarm(shap_values, max_display=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86abe704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.bar(shap_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77666fc7",
   "metadata": {},
   "source": [
    "# Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "febf4c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## import statements\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import skopt\n",
    "from skopt.searchcv import BayesSearchCV\n",
    "from skopt.space import Real, Categorical, Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2617bef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the parameter space\n",
    "space ={'learning_rate': Real(0.01, 1.0, 'log-uniform'),\n",
    "        'min_child_weight': Integer(0, 10),\n",
    "        'max_depth': Integer(0, 50),\n",
    "        'max_delta_step': Integer(0, 20),\n",
    "        'subsample': Real(0.01, 1.0, 'uniform'),\n",
    "        'colsample_bytree': Real(0.01, 1.0, 'uniform'),\n",
    "        'colsample_bylevel': Real(0.01, 1.0, 'uniform'),\n",
    "        'reg_lambda': Real(1e-9, 1000, 'log-uniform'),\n",
    "        'reg_alpha': Real(1e-9, 1.0, 'log-uniform'),\n",
    "        'gamma': Real(1e-9, 0.5, 'log-uniform'),\n",
    "        'min_child_weight': Integer(0, 5),\n",
    "        'n_estimators': Integer(50, 200),\n",
    "        'scale_pos_weight': Real(1e-6, 500, 'log-uniform')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "944cedca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#callback function to be used later which stops parameter search if it goes above 98% to save time\n",
    "def on_step(optim_result):\n",
    "    \"\"\"\n",
    "    Callback meant to view scores after\n",
    "    each iteration while performing Bayesian\n",
    "    Optimization in Skopt\"\"\"\n",
    "    score = xgb_bayes_search.best_score_\n",
    "    print(\"best score: %s\" % score)\n",
    "    if score >= 0.98:\n",
    "        print('Interrupting!')\n",
    "        return True\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd2fc45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
      "[CV] colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216 \n",
      "[11:06:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216, score=0.703, total=   1.2s\n",
      "[CV] colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216 \n",
      "[11:06:49] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216, score=0.713, total=   1.2s\n",
      "[CV] colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216 \n",
      "[11:06:50] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    2.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216, score=0.712, total=   1.2s\n",
      "[CV] colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216 \n",
      "[11:06:51] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216, score=0.707, total=   1.2s\n",
      "[CV] colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216 \n",
      "[11:06:52] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.4160029192647807, colsample_bytree=0.7304484857455519, gamma=0.13031389926541354, learning_rate=0.042815319280763466, max_delta_step=13, max_depth=21, min_child_weight=2, n_estimators=161, reg_alpha=5.497557739289786e-07, reg_lambda=0.05936070635912049, scale_pos_weight=0.060830282487222144, subsample=0.13556548021189216, score=0.722, total=   1.0s\n",
      "[CV] colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859 \n",
      "[11:06:53] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859, score=0.851, total=   8.9s\n",
      "[CV] colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859 \n",
      "[11:07:02] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859, score=0.847, total=   8.8s\n",
      "[CV] colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859 \n",
      "[11:07:11] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859, score=0.851, total=   8.6s\n",
      "[CV] colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859 \n",
      "[11:07:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859, score=0.855, total=   8.8s\n",
      "[CV] colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859 \n",
      "[11:07:28] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.18630307921210337, colsample_bytree=0.6357746718688816, gamma=4.759632988007942e-09, learning_rate=0.21560448660617149, max_delta_step=14, max_depth=39, min_child_weight=0, n_estimators=198, reg_alpha=1.307103676304351e-05, reg_lambda=1.1326211354138217e-09, scale_pos_weight=22.356393886375308, subsample=0.7121579996266859, score=0.852, total=   8.6s\n",
      "[CV] colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597 \n",
      "[11:07:37] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597, score=0.848, total=   2.2s\n",
      "[CV] colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597 \n",
      "[11:07:39] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597, score=0.853, total=   2.2s\n",
      "[CV] colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597 \n",
      "[11:07:41] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597, score=0.847, total=   2.2s\n",
      "[CV] colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597 \n",
      "[11:07:43] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597, score=0.854, total=   2.3s\n",
      "[CV] colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597 \n",
      "[11:07:46] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.25617325301227906, colsample_bytree=0.7083937150495909, gamma=2.41812432168581e-07, learning_rate=0.13965555720269418, max_delta_step=10, max_depth=27, min_child_weight=1, n_estimators=76, reg_alpha=3.178148842971562e-08, reg_lambda=0.005381781269387993, scale_pos_weight=0.23835043249575294, subsample=0.9559763235078597, score=0.855, total=   2.0s\n",
      "[CV] colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827 \n",
      "[11:07:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827 \n",
      "[11:07:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827 \n",
      "[11:07:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827 \n",
      "[11:07:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827 \n",
      "[11:07:49] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.7711308526006485, colsample_bytree=0.07988300914246868, gamma=1.3877597085692663e-08, learning_rate=0.15021004353467043, max_delta_step=6, max_depth=3, min_child_weight=3, n_estimators=77, reg_alpha=0.006097622112520179, reg_lambda=1.9368851099909265e-09, scale_pos_weight=6.97020795804701e-06, subsample=0.46717487628832827, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085 \n",
      "[11:07:49] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085, score=0.238, total=   1.0s\n",
      "[CV] colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085 \n",
      "[11:07:50] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085, score=0.238, total=   1.0s\n",
      "[CV] colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085 \n",
      "[11:07:51] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085, score=0.238, total=   0.9s\n",
      "[CV] colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085 \n",
      "[11:07:52] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085, score=0.238, total=   1.2s\n",
      "[CV] colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085 \n",
      "[11:07:53] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9132100359267209, colsample_bytree=0.7679711803126804, gamma=2.428808912219129e-07, learning_rate=0.03511167856740689, max_delta_step=3, max_depth=8, min_child_weight=4, n_estimators=197, reg_alpha=0.023358352781682152, reg_lambda=2.1396512525873352e-07, scale_pos_weight=0.001760608876847004, subsample=0.15990388740095085, score=0.238, total=   0.9s\n",
      "[CV] colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985 \n",
      "[11:07:54] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985, score=0.729, total=   0.3s\n",
      "[CV] colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985 \n",
      "[11:07:54] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985, score=0.757, total=   0.3s\n",
      "[CV] colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985 \n",
      "[11:07:55] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985, score=0.731, total=   0.3s\n",
      "[CV] colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985 \n",
      "[11:07:55] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985, score=0.731, total=   0.3s\n",
      "[CV] colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985 \n",
      "[11:07:55] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3569079180734289, colsample_bytree=0.1120289995668169, gamma=0.05034432042804318, learning_rate=0.7467518194835729, max_delta_step=7, max_depth=47, min_child_weight=1, n_estimators=67, reg_alpha=0.005500281359785164, reg_lambda=6.3166999939833564e-06, scale_pos_weight=0.4420166988445819, subsample=0.037978567417966985, score=0.735, total=   0.3s\n",
      "[CV] colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774 \n",
      "[11:07:56] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774, score=0.532, total=   0.7s\n",
      "[CV] colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774 \n",
      "[11:07:56] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774, score=0.540, total=   0.8s\n",
      "[CV] colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774 \n",
      "[11:07:57] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774, score=0.539, total=   3.4s\n",
      "[CV] colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774 \n",
      "[11:08:00] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774, score=0.534, total=   0.7s\n",
      "[CV] colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774 \n",
      "[11:08:01] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3173884684879576, colsample_bytree=0.9154083808190769, gamma=0.37957777315906627, learning_rate=0.046564465205051725, max_delta_step=5, max_depth=25, min_child_weight=4, n_estimators=98, reg_alpha=1.8932210348902655e-05, reg_lambda=10.393593598030753, scale_pos_weight=0.019183460268621746, subsample=0.6299477217301774, score=0.538, total=   0.7s\n",
      "[CV] colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868 \n",
      "[11:08:02] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868 \n",
      "[11:08:02] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868 \n",
      "[11:08:02] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868 \n",
      "[11:08:03] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868 \n",
      "[11:08:03] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.19358622710388942, colsample_bytree=0.9236239290551462, gamma=6.242737149649543e-09, learning_rate=0.5839290998374393, max_delta_step=9, max_depth=2, min_child_weight=1, n_estimators=157, reg_alpha=0.32247446903004606, reg_lambda=5.7439751766594195e-06, scale_pos_weight=1.6287615199535114e-06, subsample=0.9872483677632868, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377 \n",
      "[11:08:03] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377, score=0.783, total=   2.0s\n",
      "[CV] colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377 \n",
      "[11:08:05] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377, score=0.786, total=   1.9s\n",
      "[CV] colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377 \n",
      "[11:08:07] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377, score=0.784, total=   1.8s\n",
      "[CV] colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377 \n",
      "[11:08:09] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377, score=0.785, total=   1.8s\n",
      "[CV] colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377 \n",
      "[11:08:11] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.06936552953088004, colsample_bytree=0.5708703891954323, gamma=1.9956389638103137e-08, learning_rate=0.09010297955022104, max_delta_step=9, max_depth=25, min_child_weight=2, n_estimators=139, reg_alpha=0.9824119669778386, reg_lambda=0.00017577858139476563, scale_pos_weight=261.4450653749918, subsample=0.14242164035537377, score=0.788, total=   1.9s\n",
      "[CV] colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144 \n",
      "[11:08:13] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144, score=0.555, total=   1.3s\n",
      "[CV] colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144 \n",
      "[11:08:14] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144, score=0.577, total=   1.4s\n",
      "[CV] colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144 \n",
      "[11:08:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144, score=0.570, total=   1.2s\n",
      "[CV] colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144 \n",
      "[11:08:17] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144, score=0.569, total=   1.3s\n",
      "[CV] colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144 \n",
      "[11:08:18] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.1798648261515349, colsample_bytree=0.1536154650973617, gamma=2.692171045057089e-07, learning_rate=0.031045751168128115, max_delta_step=12, max_depth=24, min_child_weight=3, n_estimators=113, reg_alpha=0.0011518554964803237, reg_lambda=3.9270934206560135e-05, scale_pos_weight=0.036328353682153036, subsample=0.5123744729843144, score=0.577, total=   1.3s\n",
      "[CV] colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086 \n",
      "[11:08:19] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086 \n",
      "[11:08:19] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086 \n",
      "[11:08:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086, score=0.238, total=   0.3s\n",
      "[CV] colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086 \n",
      "[11:08:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086 \n",
      "[11:08:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.8477171831834894, colsample_bytree=0.9590782028716157, gamma=0.4419705200245942, learning_rate=0.010068534554171981, max_delta_step=18, max_depth=46, min_child_weight=5, n_estimators=66, reg_alpha=1.3396471230171212e-09, reg_lambda=0.1502689667792386, scale_pos_weight=5.68219069233494e-06, subsample=0.3682037565911086, score=0.238, total=   0.2s\n",
      "[CV] colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889 \n",
      "[11:08:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889, score=0.784, total=   1.3s\n",
      "[CV] colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889 \n",
      "[11:08:22] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889, score=0.787, total=   1.5s\n",
      "[CV] colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889 \n",
      "[11:08:23] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889, score=0.784, total=   1.3s\n",
      "[CV] colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889 \n",
      "[11:08:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889, score=0.787, total=   1.5s\n",
      "[CV] colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889 \n",
      "[11:08:26] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.88542798001491, colsample_bytree=0.15107725561517718, gamma=0.09274068410384222, learning_rate=0.11437510345794359, max_delta_step=0, max_depth=6, min_child_weight=5, n_estimators=200, reg_alpha=1.2952579279714691e-08, reg_lambda=0.45707493844512304, scale_pos_weight=10.561837873121673, subsample=0.9105119208713889, score=0.788, total=   1.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  60 out of  60 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score: 0.8515037593984962\n",
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0 \n",
      "[11:08:35] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0, score=0.854, total=   3.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0 \n",
      "[11:08:39] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0, score=0.858, total=   3.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0 \n",
      "[11:08:42] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    7.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0, score=0.858, total=   3.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0 \n",
      "[11:08:46] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0, score=0.860, total=   3.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0 \n",
      "[11:08:49] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.013696642996184748, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=5.8033717468910575e-06, scale_pos_weight=1.2005347096752574, subsample=1.0, score=0.859, total=   3.4s\n",
      "[CV] colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0 \n",
      "[11:08:53] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0, score=0.848, total=   3.0s\n",
      "[CV] colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0 \n",
      "[11:08:56] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0, score=0.859, total=   3.1s\n",
      "[CV] colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0 \n",
      "[11:08:59] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0, score=0.854, total=   2.9s\n",
      "[CV] colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0 \n",
      "[11:09:02] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0, score=0.860, total=   3.0s\n",
      "[CV] colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0 \n",
      "[11:09:05] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.0851536319012147, colsample_bytree=0.8377589216080676, gamma=1e-09, learning_rate=0.09832434568073845, max_delta_step=14, max_depth=34, min_child_weight=0, n_estimators=50, reg_alpha=7.694097623488464e-09, reg_lambda=0.000123453356537238, scale_pos_weight=0.4502469955009597, subsample=1.0, score=0.856, total=   5.1s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:09:10] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.831, total=   2.6s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:09:13] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.832, total=   2.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:09:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.830, total=   2.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:09:18] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.839, total=   2.9s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:09:21] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=1.0, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.836, total=   2.8s\n",
      "[CV] colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913 \n",
      "[11:09:23] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913, score=0.762, total=   0.2s\n",
      "[CV] colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913 \n",
      "[11:09:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913, score=0.762, total=   0.2s\n",
      "[CV] colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913 \n",
      "[11:09:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913, score=0.762, total=   0.2s\n",
      "[CV] colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913 \n",
      "[11:09:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913, score=0.762, total=   0.2s\n",
      "[CV] colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913 \n",
      "[11:09:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.3431586808864059, colsample_bytree=0.04312607138788329, gamma=1e-09, learning_rate=0.01, max_delta_step=14, max_depth=49, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=3.8954515813196213, scale_pos_weight=78.84174294842636, subsample=0.7421532664169913, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0 \n",
      "[11:09:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0, score=0.854, total=   2.5s\n",
      "[CV] colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0 \n",
      "[11:09:27] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0, score=0.860, total=   2.2s\n",
      "[CV] colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0 \n",
      "[11:09:29] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0, score=0.855, total=   2.2s\n",
      "[CV] colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0 \n",
      "[11:09:31] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0, score=0.860, total=   2.2s\n",
      "[CV] colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0 \n",
      "[11:09:34] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.30119622128128176, colsample_bytree=0.7620317744063955, gamma=1.05618601392427e-05, learning_rate=0.2039500668731053, max_delta_step=8, max_depth=24, min_child_weight=1, n_estimators=81, reg_alpha=1.9555518361939537e-09, reg_lambda=0.2257391035280488, scale_pos_weight=0.5045628850143017, subsample=1.0, score=0.861, total=   2.5s\n",
      "[CV] colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0 \n",
      "[11:09:36] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0, score=0.845, total=   2.9s\n",
      "[CV] colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0 \n",
      "[11:09:39] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0, score=0.850, total=   2.8s\n",
      "[CV] colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0 \n",
      "[11:09:42] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0, score=0.847, total=   2.8s\n",
      "[CV] colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0 \n",
      "[11:09:45] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0, score=0.851, total=   3.4s\n",
      "[CV] colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0 \n",
      "[11:09:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.42091421584304095, colsample_bytree=0.48269065290819324, gamma=6.116092503929188e-06, learning_rate=0.5125687185303802, max_delta_step=16, max_depth=40, min_child_weight=0, n_estimators=76, reg_alpha=1e-09, reg_lambda=0.09204487399576025, scale_pos_weight=0.42393181700817767, subsample=1.0, score=0.853, total=   3.0s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0 \n",
      "[11:09:51] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0, score=0.850, total=  16.0s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0 \n",
      "[11:10:07] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0, score=0.850, total=  15.8s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0 \n",
      "[11:10:23] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0, score=0.853, total=  15.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0 \n",
      "[11:10:38] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0, score=0.857, total=  15.4s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0 \n",
      "[11:10:54] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=16, max_depth=44, min_child_weight=0, n_estimators=179, reg_alpha=3.386156528135081e-07, reg_lambda=1e-09, scale_pos_weight=103.01684294548575, subsample=1.0, score=0.857, total=  15.1s\n",
      "[CV] colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618 \n",
      "[11:11:09] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618, score=0.778, total=   0.5s\n",
      "[CV] colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618 \n",
      "[11:11:10] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618, score=0.786, total=   0.5s\n",
      "[CV] colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618 \n",
      "[11:11:10] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618, score=0.779, total=   0.5s\n",
      "[CV] colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618 \n",
      "[11:11:10] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618, score=0.782, total=   0.5s\n",
      "[CV] colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618 \n",
      "[11:11:11] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.08727443954581468, colsample_bytree=0.01, gamma=1e-09, learning_rate=0.07861005867580347, max_delta_step=13, max_depth=2, min_child_weight=5, n_estimators=168, reg_alpha=1e-09, reg_lambda=1.5979553859349523e-06, scale_pos_weight=0.33017916732211994, subsample=0.5223270885080618, score=0.794, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01 \n",
      "[11:11:11] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01, score=0.769, total=   0.4s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01 \n",
      "[11:11:12] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01, score=0.766, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01 \n",
      "[11:11:12] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01, score=0.765, total=   0.4s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01 \n",
      "[11:11:13] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01, score=0.766, total=   1.9s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01 \n",
      "[11:11:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.01, gamma=0.49999999999999994, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=1e-09, scale_pos_weight=499.99999999999994, subsample=0.01, score=0.766, total=   0.4s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019 \n",
      "[11:11:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019, score=0.788, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019 \n",
      "[11:11:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019, score=0.801, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019 \n",
      "[11:11:16] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019, score=0.793, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019 \n",
      "[11:11:16] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019, score=0.799, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019 \n",
      "[11:11:17] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.46418074654022323, gamma=0.4337029728490312, learning_rate=0.06894223624111642, max_delta_step=18, max_depth=8, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.0067867486618005245, scale_pos_weight=0.30841932115677756, subsample=0.9033516545749019, score=0.800, total=   0.5s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0 \n",
      "[11:11:17] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0, score=0.848, total=   2.8s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0 \n",
      "[11:11:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0, score=0.852, total=   2.8s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0 \n",
      "[11:11:23] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0, score=0.852, total=   3.0s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0 \n",
      "[11:11:26] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0, score=0.859, total=   2.8s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0 \n",
      "[11:11:29] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=0.962234083225859, gamma=8.552705517701013e-07, learning_rate=0.13113339067886315, max_delta_step=0, max_depth=50, min_child_weight=0, n_estimators=50, reg_alpha=1e-09, reg_lambda=0.004163687156382224, scale_pos_weight=23.519043372736856, subsample=1.0, score=0.857, total=   2.7s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:11:31] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:11:32] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:11:32] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:11:32] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.762, total=   0.4s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0 \n",
      "[11:11:33] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.01, gamma=1e-09, learning_rate=1.0, max_delta_step=19, max_depth=25, min_child_weight=0, n_estimators=50, reg_alpha=1.2191924136513715e-05, reg_lambda=1000.0, scale_pos_weight=499.99999999999994, subsample=1.0, score=0.762, total=   0.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  60 out of  60 | elapsed:  3.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score: 0.8580827067669173\n",
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n",
      "[CV] colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149 \n",
      "[11:11:39] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149, score=0.854, total=  11.6s\n",
      "[CV] colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149 \n",
      "[11:11:51] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   11.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149, score=0.859, total=  12.2s\n",
      "[CV] colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149 \n",
      "[11:12:03] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   23.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149, score=0.856, total=  12.1s\n",
      "[CV] colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149 \n",
      "[11:12:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149, score=0.861, total=  12.2s\n",
      "[CV] colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149 \n",
      "[11:12:28] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.9122152721762351, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.14426554399281882, max_delta_step=9, max_depth=36, min_child_weight=0, n_estimators=152, reg_alpha=1e-09, reg_lambda=16.19042158442415, scale_pos_weight=0.3060244467502831, subsample=0.9247961160246149, score=0.860, total=  12.3s\n",
      "[CV] colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0 \n",
      "[11:12:40] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0, score=0.835, total=   1.9s\n",
      "[CV] colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0 \n",
      "[11:12:42] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0, score=0.840, total=   1.9s\n",
      "[CV] colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0 \n",
      "[11:12:44] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0, score=0.836, total=   1.9s\n",
      "[CV] colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0 \n",
      "[11:12:46] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0, score=0.840, total=   2.1s\n",
      "[CV] colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0 \n",
      "[11:12:48] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01857782493727273, colsample_bytree=1.0, gamma=5.190965027719857e-09, learning_rate=1.0, max_delta_step=19, max_depth=50, min_child_weight=5, n_estimators=143, reg_alpha=1e-09, reg_lambda=2.0595856798492014e-09, scale_pos_weight=1.9954123706694802, subsample=1.0, score=0.842, total=   2.0s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893 \n",
      "[11:12:50] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893, score=0.768, total=   0.7s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893 \n",
      "[11:12:50] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893, score=0.769, total=   0.7s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893 \n",
      "[11:12:51] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893, score=0.769, total=   0.7s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893 \n",
      "[11:12:52] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893, score=0.767, total=   0.7s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893 \n",
      "[11:12:53] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=1.0, gamma=0.004676108599199854, learning_rate=0.01, max_delta_step=0, max_depth=50, min_child_weight=2, n_estimators=65, reg_alpha=1.087017550744327e-06, reg_lambda=1000.0, scale_pos_weight=2.4281288078813343, subsample=0.2385905237427893, score=0.769, total=   0.7s\n",
      "[CV] colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0 \n",
      "[11:12:53] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0, score=0.854, total=   4.8s\n",
      "[CV] colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0 \n",
      "[11:12:58] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0, score=0.861, total=   4.7s\n",
      "[CV] colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0 \n",
      "[11:13:03] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0, score=0.857, total=   4.8s\n",
      "[CV] colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0 \n",
      "[11:13:08] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0, score=0.862, total=   4.6s\n",
      "[CV] colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0 \n",
      "[11:13:12] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.6282504252278241, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45, min_child_weight=1, n_estimators=119, reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441, scale_pos_weight=0.5355364766388139, subsample=1.0, score=0.860, total=   4.7s\n",
      "[CV] colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447 \n",
      "[11:13:17] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447, score=0.837, total=   3.3s\n",
      "[CV] colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447 \n",
      "[11:13:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447, score=0.841, total=   3.7s\n",
      "[CV] colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447 \n",
      "[11:13:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447, score=0.844, total=   3.4s\n",
      "[CV] colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447 \n",
      "[11:13:27] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447, score=0.845, total=   5.3s\n",
      "[CV] colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447 \n",
      "[11:13:33] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.4019737321218408, colsample_bytree=1.0, gamma=1.8941795397039795e-08, learning_rate=1.0, max_delta_step=0, max_depth=43, min_child_weight=3, n_estimators=200, reg_alpha=1.9099670727042163e-06, reg_lambda=1e-09, scale_pos_weight=14.061197770368228, subsample=0.7135108429622447, score=0.837, total=   3.9s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985 \n",
      "[11:13:37] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985 \n",
      "[11:13:37] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985 \n",
      "[11:13:37] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985 \n",
      "[11:13:37] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985 \n",
      "[11:13:38] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=0.01, colsample_bytree=1.0, gamma=1e-09, learning_rate=0.01, max_delta_step=0, max_depth=0, min_child_weight=0, n_estimators=200, reg_alpha=0.22069746917838395, reg_lambda=1000.0, scale_pos_weight=8.919648320105123, subsample=0.8423112591198985, score=0.762, total=   0.3s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0 \n",
      "[11:13:38] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0, score=0.821, total=   6.4s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0 \n",
      "[11:13:44] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0, score=0.833, total=   6.3s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0 \n",
      "[11:13:50] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0, score=0.830, total=   6.2s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0 \n",
      "[11:13:57] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0, score=0.832, total=   6.2s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0 \n",
      "[11:14:03] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.6372445567928446, gamma=1e-09, learning_rate=0.0729277692600135, max_delta_step=20, max_depth=38, min_child_weight=0, n_estimators=200, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=0.5363537193160919, subsample=1.0, score=0.832, total=   7.0s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013 \n",
      "[11:14:10] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013, score=0.763, total=   2.6s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013 \n",
      "[11:14:12] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013, score=0.763, total=   2.6s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013 \n",
      "[11:14:15] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013, score=0.764, total=   2.6s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013 \n",
      "[11:14:18] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013, score=0.763, total=   2.5s\n",
      "[CV] colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013 \n",
      "[11:14:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV]  colsample_bylevel=1.0, colsample_bytree=0.7937127672755004, gamma=1e-09, learning_rate=0.01, max_delta_step=20, max_depth=50, min_child_weight=0, n_estimators=148, reg_alpha=1e-09, reg_lambda=1000.0, scale_pos_weight=8.883614837300197, subsample=0.8207105024131013, score=0.764, total=   2.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:  2.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score: 0.8588567005749669\n",
      "[11:14:24] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": "stderr",
     "output_type": "stream",
     "text": [
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('colsample_bylevel', 0.6282504252278241),\n",
       "             ('colsample_bytree', 1.0),\n",
       "             ('gamma', 1e-09),\n",
       "             ('learning_rate', 0.42226475952791975),\n",
       "             ('max_delta_step', 5),\n",
       "             ('max_depth', 45),\n",
       "             ('min_child_weight', 1),\n",
       "             ('n_estimators', 119),\n",
       "             ('reg_alpha', 4.549402233131108e-09),\n",
       "             ('reg_lambda', 0.1373161505648441),\n",
       "             ('scale_pos_weight', 0.5355364766388139),\n",
       "             ('subsample', 1.0)])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Search for best parameters (could take like an hour or so, depending on number of inputs)\n",
    "xgbcl = xgb.XGBClassifier()\n",
    "\n",
    "xgb_bayes_search = BayesSearchCV(xgbcl, space, n_iter=32, # specify how many iterations\n",
    "                                    scoring=None, n_jobs=1, cv=5, verbose=3, random_state=42, n_points=12,\n",
    "                                 refit=True)\n",
    "\n",
    "xgb_bayes_search.fit(X_train, y_train.ravel(), callback = on_step)\n",
    "\n",
    "#prints best parameters\n",
    "xgb_bayes_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c93e06a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xgb_bayes_search' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_16996/1445858835.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#initialise new model with the best parameters, can fit model to data now with this model :)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mxgb_bayes_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'xgb_bayes_search' is not defined"
     ]
    }
   ],
   "source": [
    "#initialise new model with the best parameters, can fit model to data now with this model :)\n",
    "model = xgb.XGBClassifier(**xgb_bayes_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0a99cd5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if not in the same session\n",
    "#have to define ordered dict to avoid name error\n",
    "from collections import OrderedDict \n",
    "params = OrderedDict([('colsample_bylevel', 0.6282504252278241),\n",
    "             ('colsample_bytree', 1.0),\n",
    "             ('gamma', 1e-09),\n",
    "             ('learning_rate', 0.42226475952791975),\n",
    "             ('max_delta_step', 5),\n",
    "             ('max_depth', 45),\n",
    "             ('min_child_weight', 1),\n",
    "             ('n_estimators', 119),\n",
    "             ('reg_alpha', 4.549402233131108e-09),\n",
    "             ('reg_lambda', 0.1373161505648441),\n",
    "             ('scale_pos_weight', 0.5355364766388139),\n",
    "             ('subsample', 1.0)])\n",
    "model = xgb.XGBClassifier(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d1fdf64a",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3749871935.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_16996/3749871935.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    params = {base_score=0.5,booster='gbtree',\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#if not in the same session, (not required anymore)\n",
    "params = {base_score=0.5,booster='gbtree',\n",
    "              colsample_bylevel=0.6282504252278241, colsample_bynode=1,\n",
    "              colsample_bytree=1.0, gamma=1e-09, gpu_id=-1,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45,\n",
    "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
    "              n_estimators=119, n_jobs=4, num_parallel_tree=1,\n",
    "              objective='binary:logistic', random_state=0,\n",
    "              reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441,\n",
    "              scale_pos_weight=0.5355364766388139, subsample=1.0,\n",
    "              tree_method='exact', use_label_encoder=True,\n",
    "              validate_parameters=1, verbosity=None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1f7a8a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:16:11] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.80274214949138\n",
      "[11:16:19] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.97965501990271\n",
      "[11:16:27] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.4046881910659\n",
      "[11:16:35] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "84.98452012383902\n",
      "[11:16:42] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.36045997346307\n",
      "[11:16:50] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.20566121185317\n",
      "[11:16:57] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "86.24502432551968\n",
      "[11:17:04] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.91331269349845\n",
      "[11:17:12] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.38257408226448\n",
      "[11:17:20] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "85.05086245024326\n",
      "Training Testing Accuracy: 85.53% (0.40%)\n"
     ]
    }
   ],
   "source": [
    "#cross validation works as a way of evaluating performance of the model across different data splits\n",
    "#eg. Training the model on samples 1-80 and testing on samples 81-100 might give 90% accuracy\n",
    "# but training the model on samples 21-100 and testing on samples 1-20 might give 80% accuracy\n",
    "# in this scenario it could leave up to 10% of accuracy up to chance\n",
    "\n",
    "\n",
    "results = []\n",
    "\n",
    "#can change n_splits\n",
    "cv = StratifiedKFold(n_splits=10,shuffle=False)\n",
    "\n",
    "#use all your data here, it does the train_test_split for you!\n",
    "for train,test in cv.split(X,Y):\n",
    "    model.fit(X.iloc[train],Y.iloc[train].ravel())\n",
    "    y_pred = model.predict(X.iloc[test])\n",
    "    predictions = [round(value) for value in y_pred]\n",
    "    #sees how accurate the model was when testing the test set\n",
    "    accuracy = accuracy_score(Y.iloc[test], predictions)\n",
    "    pcent = accuracy * 100.0\n",
    "    print(pcent)\n",
    "    results.append(pcent)\n",
    "\n",
    "print(\"Training Testing Accuracy: %.2f%% (%.2f%%)\" % (np.mean(results), np.std(results)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275336fb",
   "metadata": {},
   "source": [
    "# Explanation for best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a533841",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xgb_bayes_search' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_16996/3788332797.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxgb_best\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mxgb_bayes_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mxgb_best\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'xgb_bayes_search' is not defined"
     ]
    }
   ],
   "source": [
    "xgb_best = xgb.XGBClassifier(**xgb_bayes_search.best_params_)\n",
    "xgb_best.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1f7227d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if not in the same session\n",
    "xgb_best = xgb.XGBClassifier(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "630b5e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shriprabha/anaconda3/envs/ML_env/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:37:26] WARNING: /tmp/build/80754af9/xgboost-split_1619724447847/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with 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": [
       "XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "              colsample_bylevel=0.6282504252278241, colsample_bynode=1,\n",
       "              colsample_bytree=1.0, gamma=1e-09, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.42226475952791975, max_delta_step=5, max_depth=45,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=119, n_jobs=4, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=4.549402233131108e-09, reg_lambda=0.1373161505648441,\n",
       "              scale_pos_weight=0.5355364766388139, subsample=1.0,\n",
       "              tree_method='exact', use_label_encoder=True,\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if not in the same session\n",
    "xgb_best.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f59f162c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.72%\n"
     ]
    }
   ],
   "source": [
    "y_preds = xgb_best.predict(X_test)\n",
    "predictions = [round(value) for value in y_preds]\n",
    "\n",
    "\n",
    "# evaluate predictions\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb48632",
   "metadata": {},
   "source": [
    "# Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "36ccfc73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9132914591004261\n",
      "0.895068272700562\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, average_precision_score\n",
    "\n",
    "f1 = f1_score(y_test, y_preds)\n",
    "print(f1)\n",
    "\n",
    "precision = average_precision_score(y_test, y_preds)\n",
    "print(precision)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "99018577",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = xgb_best.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4b9cc388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10143,   650],\n",
       "       [  551, 33876]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(Y, all_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0f35f413",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9a25a24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cm = confusion_matrix(Y, all_preds)\n",
    "ax = sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "ax.set_title('Seaborn Confusion matrix with labels\\n\\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values');\n",
    "\n",
    "## Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['Non-gene','Gene'])\n",
    "ax.yaxis.set_ticklabels(['Non-gene','Gene'])\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (11,7)\n",
    "plt.savefig('cm_ntd.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "690cfea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC: 0.9267054836989276\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import matthews_corrcoef\n",
    "print('MCC: %s'%(matthews_corrcoef(Y, all_preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2c984214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.922\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from matplotlib import pyplot\n",
    "probs = xgb_best.predict_proba(X_test)\n",
    "probs = probs[:, 1]\n",
    "\n",
    "auc = roc_auc_score(y_test, probs)\n",
    "print('AUC: %.3f' % auc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c7df7c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pea_fpr, pea_tpr, pea_thresholds = roc_curve(y_test, probs, \n",
    "                                 drop_intermediate = False)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (4, 4)\n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(pea_fpr, pea_tpr, color='red',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "71923d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ac3fd0e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.70      0.72      2168\n",
      "           1       0.91      0.92      0.91      6876\n",
      "\n",
      "    accuracy                           0.87      9044\n",
      "   macro avg       0.82      0.81      0.81      9044\n",
      "weighted avg       0.87      0.87      0.87      9044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(y_test, y_preds)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ac24928",
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.Explainer(xgb_best)\n",
    "shap_values = explainer(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "484c7a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x511.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.beeswarm(shap_values, max_display=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6e17ba2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.waterfall(shap_values[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5653d91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.bar(shap_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "827fd718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the feature importance plots\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "\n",
    "plot_importance(xgb_best, importance_type = 'cover', show_values = False, height=0.4, max_num_features = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b54612c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(xgb_best, importance_type = 'cover', max_num_features = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "248de285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(xgb_best, importance_type = 'gain', show_values = False, height=0.4, max_num_features = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "89223137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(xgb_best, importance_type = 'gain', max_num_features = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ebba111e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(xgb_best, importance_type = 'weight', show_values = False, height=0.4, max_num_features = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4ed7c46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(xgb_best, importance_type = 'weight', max_num_features = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "366210ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}