ml_12_GradientBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "compact-islam",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "\n",
    "from sklearn.model_selection import validation_curve\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.datasets import make_classification\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from scipy.sparse import vstack\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15, 8)\n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaningful-geography",
   "metadata": {},
   "source": [
    "## Теоретическое обоснование:\n",
    "$$f(x) == argmin_f{E[L(f(x), y)]}$$\n",
    "\n",
    "Параметризуем функцию $f(x) = f(x, \\theta)$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "economic-following",
   "metadata": {},
   "source": [
    "Задача сводится к подбору параметров функции: $$\\hat{\\theta} = argmin_\\theta E[L(f(x, \\theta), y)]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "listed-rubber",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adwiz\\anaconda3\\lib\\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": [
      "[08:38:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import loadtxt\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_tree\n",
    "# from lightgbm import LGBMClassifier\n",
    "# from lightgbm import plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataset = sklearn.datasets.load_iris()\n",
    "X = dataset.data\n",
    "y = dataset.target\n",
    "\n",
    "model = XGBClassifier() # XGBoost \n",
    "# model = LGBMClassifier() # Lightgbm\n",
    "model.fit(X, y)\n",
    "\n",
    "plot_tree(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-builder",
   "metadata": {},
   "source": [
    "# Практическое занятие по градиентному бустингу"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatty-terminal",
   "metadata": {},
   "source": [
    "**Sklearn**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "first-toolbox",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11934047, 0.88065953])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.datasets import make_hastie_10_2\n",
    "\n",
    "n_estimators = 200\n",
    "\n",
    "X, y = make_hastie_10_2(n_samples=10000)\n",
    "X_test, y_test = make_hastie_10_2(n_samples=10000)\n",
    "est = GradientBoostingClassifier(n_estimators=200, max_depth=3)\n",
    "est.fit(X, y)\n",
    "\n",
    "pred = est.predict(X)\n",
    "est.predict_proba(X)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "premier-prague",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x232834e3fa0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "alternative-adaptation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2328353f4f0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_score = np.empty(len(est.estimators_))\n",
    "for i, pred in enumerate(est.staged_predict(X_test)):\n",
    "    test_score[i] = est.loss_(y_test, pred)\n",
    "plt.plot(np.arange(n_estimators) + 1, test_score, label='Test')\n",
    "plt.plot(np.arange(n_estimators) + 1, est.train_score_, label='Train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "amended-jurisdiction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:38:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:38:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:38:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:38:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:38:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:38:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:38:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:38:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:39:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:39:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:39:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:39:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:39:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:40:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:40:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:40:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:40:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:40:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:40:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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": [
      "[08:41:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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",
      "[08:41:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:541: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[08:41:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/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"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "xgb_params1 = {'booster': 'gbtree', 'max_depth': 3, 'eta': 0.1, \n",
    "    'silent': 1, 'objective': 'binary:logistic', 'nthread': 1}\n",
    "xgb_params2 = {'booster': 'gbtree', 'max_depth': 3, 'eta': 0.1, \n",
    "    'silent': 1, 'objective': 'binary:logistic', 'nthread': 4}\n",
    "sklearn_params = {'n_estimators': 100, 'max_depth': 3}\n",
    "\n",
    "xgb_time1 = list()\n",
    "xgb_time2 = list()\n",
    "sklearn_time = list()\n",
    "\n",
    "n_runs = 50\n",
    "\n",
    "for i in range(n_runs):\n",
    "    X, y = make_classification(n_samples=1000, n_features=50, \n",
    "                               n_informative=20)\n",
    "    t = time.time()\n",
    "    bst = xgb.train(xgb_params1, xgb.DMatrix(X, label=y), \n",
    "                    num_boost_round=100)\n",
    "    elapsed = time.time() - t\n",
    "    xgb_time1.append(elapsed)\n",
    "    t = time.time()\n",
    "    bst = xgb.train(xgb_params2, xgb.DMatrix(X, label=y), \n",
    "                    num_boost_round=100)\n",
    "    elapsed = time.time() - t\n",
    "    xgb_time2.append(elapsed)\n",
    "    t = time.time()\n",
    "    clf = GradientBoostingClassifier(**sklearn_params).fit(X, y)\n",
    "    elapsed = time.time() - t\n",
    "    sklearn_time.append(elapsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "deluxe-diana",
   "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": [
    "%matplotlib  inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xgb_mean1 = sum(xgb_time1) / n_runs\n",
    "xgb_mean2 = sum(xgb_time2) / n_runs\n",
    "sklearn_mean = sum(sklearn_time) / n_runs\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(xgb_time1, label='XGBooster (nthread = 1)', lw=2)\n",
    "plt.plot(xgb_time2, label='XGBooster (nthread = 4)', lw=2)\n",
    "plt.plot(sklearn_time, label='GradientBoostingClassifier', lw=2)\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Running time, sec')\n",
    "plt.title('XGBoost vs. GradientBoostingClassifier comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instrumental-prince",
   "metadata": {},
   "source": [
    "# Bias Variance tradeoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "breathing-gates",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=20, n_informative=8,\n",
    "                         n_redundant=3, n_repeated=2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "pacific-ensemble",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "focal-fetish",
   "metadata": {},
   "outputs": [],
   "source": [
    "default_params = {\n",
    "    'objective': 'binary:logistic',\n",
    "    'max_depth': 1,\n",
    "    'learning': 0.3,\n",
    "    'silent': 1.0,\n",
    "    'seed': 42\n",
    "    \n",
    "}\n",
    "\n",
    "n_estimators_range = np.linspace(1, 200, 10).astype('int')\n",
    "\n",
    "train_scores, test_scores = validation_curve(\n",
    "    XGBClassifier(**default_params),\n",
    "    X, y,\n",
    "    param_name = 'n_estimators',\n",
    "    param_range = n_estimators_range,\n",
    "    cv = cv.split(X, y),\n",
    "    scoring = 'accuracy',\n",
    "    n_jobs = -1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "protective-tragedy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation result (0.75) obtained for 45 trees\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6), dpi=100)\n",
    "\n",
    "plt.title('Validation Curve with XGBoost (eta=0.3)')\n",
    "plt.xlabel('number of trees')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0.5, 1.01)\n",
    "\n",
    "plt.plot(n_estimators_range,\n",
    "            train_scores_mean,\n",
    "            label='Training score',\n",
    "            color='r')\n",
    "\n",
    "plt.plot(n_estimators_range,\n",
    "            test_scores_mean,\n",
    "            label='Cross-validation score',\n",
    "            color='g')\n",
    "\n",
    "plt.fill_between(n_estimators_range,\n",
    "                 train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std,\n",
    "                 alpha=0.2, color='r'\n",
    "                 )\n",
    "\n",
    "plt.fill_between(n_estimators_range,\n",
    "                 test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std,\n",
    "                 alpha=0.2, color='g'\n",
    "                 )\n",
    "\n",
    "plt.axhline(y=1, color='k', ls='dashed')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show\n",
    "i = np.argmax(test_scores_mean)\n",
    "print(\"Best cross-validation result ({0:.2f}) obtained for {1} trees\".format(test_scores_mean[i], n_estimators_range[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interstate-essence",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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