Python58-XGBoost学习与实战之初识XGBoost（三）学习曲线.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost快速入门——与scikit-learn一起使用-学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 运行xgboost安装包中的实例程序\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# 加载LibSVM格式数据模块\n",
    "from sklearn.datasets import load_svmlight_file\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取\n",
    "scikit-learn支持多种格式的数据，包括LibSVM格式数据\n",
    "XGBoost可以加载libsvm格式的文本数据，libsvm的文件格式（稀疏特征）如下：\n",
    "\n",
    "1 101:1.2 102:0.03\n",
    "\n",
    "0 1:2.1 10001:300 10002:400\n",
    "\n",
    "...\n",
    "\n",
    "每一行表示一个样本，第一行的开头的“1”是样本的标签。“101”和“102”为特征索引，'1.2'和'0.03' 为特征的值。\n",
    "在两类分类中，用“1”表示正样本，用“0” 表示负样本。也支持[0,1]表示概率用来做标签，表示为正样本的概率。\n",
    "\n",
    "下面的示例数据需要我们通过一些蘑菇的若干属性判断这个品种是否有毒。\n",
    "\n",
    "UCI数据描述：http://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/ ，\n",
    "\n",
    "每个样本描述了蘑菇的22个属性，比如形状、气味等等(加工成libsvm格式后变成了126维特征)，\n",
    "然后给出了这个蘑菇是否可食用。其中6513个样本做训练，1611个样本做测试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6513, 126)\n",
      "(1611, 126)\n"
     ]
    }
   ],
   "source": [
    "# read in data，数据在xgboost安装的路径下的demo目录,\n",
    "\n",
    "my_workpath = 'files/data/python58-data/'\n",
    "X_train,y_train = load_svmlight_file(my_workpath + 'agaricus.txt.train')\n",
    "X_test,y_test = load_svmlight_file(my_workpath + 'agaricus.txt.test')\n",
    "\n",
    "print(X_train.shape)\n",
    "print (X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集-校验集分离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2150, 126)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split data into train and test sets, 1/3的训练数据作为校验数据\n",
    "seed=7\n",
    "test_size=0.33\n",
    "X_train_part,X_validate,y_train_part,y_validate=train_test_split(X_train,y_train,test_size=test_size,\n",
    "                                                                random_state=seed)\n",
    "X_train_part.shape\n",
    "X_validate.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练参数设置\n",
    "- max_depth： 树的最大深度。缺省值为6，取值范围为：[1,∞]\n",
    "- eta：为了防止过拟合，更新过程中用到的收缩步长。在每次提升计算之后，算法会直接获得新特征的权重。 \n",
    "- eta通过缩减特征的权重使提升计算过程更加保守。缺省值为0.3，取值范围为：[0,1]\n",
    "- silent：取0时表示打印出运行时信息，取1时表示以缄默方式运行，不打印运行时信息。缺省值为0\n",
    "- objective： 定义学习任务及相应的学习目标，“binary:logistic” 表示二分类的逻辑回归问题，输出为概率。\n",
    "\n",
    "- 其他参数取默认值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型\n",
    "有了参数列表和数据就可以训练模型了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, object='binary.logistic',\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置boosting迭代计算次数\n",
    "num_round=100\n",
    "\n",
    "# bst=XGBClassifier(**params)\n",
    "# bst=XGBClassifier()\n",
    "bst=XGBClassifier(max_depth=2,learning_rate=0.1,n_estimators=num_round,\n",
    "                  silent=True,object='binary.logistic')\n",
    "eval_set=[(X_train_part,y_train_part),(X_validate,y_validate)]\n",
    "bst.fit(X_train_part,y_train_part,eval_metric=['error','logloss'],eval_set=eval_set,verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型在每次校验集上的性能存在模型中，可用来进一步进行分析 model.evals result() 返回一个字典：评估数据集和分数\n",
    "\n",
    "显示学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aWlr4xje+wW233caaNWtmNv4CliyAJXPqkGCEgcAcGjo3AbBT53HsgiaObKtnb2wezdYMZYwpg5/85Cds2rSJdevWARCLxViyZAnnnHMOW7du5dprr+X888/n3e9+d1njsmTByIioff1tNCR/B8Ae5rKivYGVHY28tm0uR/dur2yQxhjvTKEG4BVV5fLLL+eLX/zimGO/+c1v+OEPf8itt97Kww8/zB133FG2uKzPwrWyvZHt6bkA9Prn0j6nmWjIz8r2Bn6fmIP27YJspsJRGmNq3dlnn82DDz7IgQMHAGfU1BtvvMH+/ftRVS666CJuvPFGnn32WQAaGxvp7+/3PC6rWbhWdjTwygtzOSsAu2jj6PZGAI7uaOBnOg/Jpp37MJoXVzhSY0wtO/744/nCF77A2WefTTabJRgMcvvtt+P3+7niiitQVUSEW265BYDLLruMj33sY0SjUZ5++ulRiyDNJEsWrpXtjfzC7eR+LTWHFR0NAKxob+T+3Eip3k5LFsaYGZc/RTnAJZdcwiWXXDLmvOeee25M2Qc/+EE++MEPehXaMGuGch3d0TA8fHZHtnW4ZrGstY690uacZMNnjTGzlNUsXIvn1LHP1w44I6FOc2sWAb+PUOsR0Af8+LPw5FfGPvktV8NJHxldtulO2HTXyP6yt8H5/+BR9MYY4y2rWbj8PiHTtorb0+/hR5lTWNHeMHxs6fx5fDtwESw5GVqPGv3o3Qm/K7Im7tYfOlOHtB4FqSF46XtlfDfGmFKoaqVDKJvpvlerWeRZMb+Fm3dfwpK5UepCI7+aozsaufGF9/PB958zqhyAf34npGNjXywVh47j4OL/Cz/6G3j2Ox5Hb4w5FJFIhK6uLlpbWxGRSofjKVWlq6uLSCQy5dewZJEnV5tY6fZX5Kxsb0AVXtk3yPGLm0c/KRiFVLFkMQR1c/POGQJVqPF/lMZUi8WLF9PZ2cn+/fsrHUpZRCIRFi+e+gAdSxZ5ju5wksTKjoZR5Svd8kdf3E3XYGLUseOTfpozvWN+kenkEAd983hp6z6W9mY5UjPO9OcBb4a1GWMOTTAYZPny5ZUOo2pYsshz3MImAj5hzeKWUeVHtNbRGA7wrSde4VtPvDLq2G3BGOuivRTO99jT28fP9w7xyW2buNy/n88HcZqrLFkYY6qQJYs8C1uiPPnps+hoCo8qD/p9/Ogvz2BvX3zMcw7c988EMmPLA5k4kWg9/3bl6Wx9ZBPsw2muijSPOdcYYw53liwKzG8u3gG0qCXKopbomPIfB+sIxscmi6DG8YXqWLt0DjsbmtxkYUuzGmOqk6dDZ0XkXBHZKiLbROT6IsfDIvJd9/hTIrKs4PhSERkQkUNfEaRMMv4IoWxiTHlYE2T8TuLxheqccxOWLIwx1cmzZCEifuCbwHnAKuCPRWRVwWlXAAdVdQXwVeCWguNfBX7oVYwzQf0RQiSdkU45mRQBMmjAqYn4Qs7PRHywEiEaY8y0eVmzOAXYpqqvqmoSeABYX3DOeuBed/sh4J3iDngWkfcBrwIveRjjtGkwip8sZPJWqMoNpQ06ScIfrgcgGbNkYYypTl4mi0VA/mRKnW5Z0XNUNQ30Aq0iUg98GrhxoguIyFUisllENldqrLQGnSamUf0RY5KFc44lC2NMtfIyWRS7+6zwfvPxzrkR+KqqDkx0AVW9Q1XXqeq6tra2KYY5TW5CIJXXyZ27o9tNJAG3ZpFKWLIwxlQnL0dDdQJL8vYXA7vGOadTRAJAM9ANvAW4UES+DLQAWRGJq+ptHsY7JT43WWhqaDjzpeODBAC/21cRitS55dbBbYypTl4mi03AShFZDuwENgCFE7RvBD4C/BK4EHhcndmu/iB3gojcAAwcjokCQEIjTUy5uzMS8QECjIyCCkacO8LTiQkrSsYYc9jyLFmoalpErgEeA/zA3ar6kojcBGxW1Y3AXcB9IrINp0axwat4vOLPJYv4wHCySMYGqQd8bl9FuM5phsomrWZhjKlOnt6Up6qPAo8WlH0+bzsOXDTJa9zgSXAzJJDrvM4bFptym5sCbiIJR51kkUkUmXDQGGOqgK1nMU3+yNiRTrnEEYg4SSIajpBWn9UsjDFVy5LFNAVzI53yahZpdzsYzSWLADHCNt2HMaZqWbKYpmAk18Q0kgjS7nbuWDToJ04I0mPnkDLGmGpgyWKaQm5CSOcli1xzU9g9Fgn6iWuo+CJJxhhTBSxZTFPIHemUyeuPyG1H3GYov0+ISxgptvyqMcZUAUsW0xR276HQZH7NIkZWhbDb+Q2QlDB+SxbGmCplyWKaotHcSKe8RJAaIk6IuvDIyOSUhPEVWSTJGGOqgSWLaaoLOiOd8msWJGPECBEN+UeKfGH8liyMMVXKksU0RUI+4oRG90ekh4gRJhIYSRZpX6To8qvGGFMNLFlMU8jvI8bokU6SjpMghM83Mqlu2h8hWGRFPWOMqQaWLKZJREgyuj/Cl46TlPCo8zL+CEG1ZGGMqU4TJgsR8YnI6eUKplolJIwvrxnKn4mNSRZZf4SQWjOUMaY6TZgsVDUL/GOZYqlaKd/omoU/kyDli4w6RwNRwlazMMZUqVKaoX4sIh/IrY1txirsvA5k4qR9o2sWGowQIg3ZTLnDM8aYaStlivJPAvVARkRiOEuhqqo2eRpZFUn5IgSy3cP7wWycdLCwZpFbqzsG4YZyhmeMMdM2abJQ1cZyBFLNMv4IweRIE1NQ42T80VHnSCi3VrclC2NM9Slp8SMRuQA4w919QlUf8S6k6pMpGBYb0iRZ/+iahQRHFkkKNbSVNT5jjJmuSfssRORm4Fpgi/u41i0zrmwgMqrzOqwJNDA6WeTW407kLZJkjDHVopSaxfnAGndkFCJyL/AccL2XgVWTbCBKGDdZZLOESZINjG6Gyl+r2xhjqk2pN+W15G03exFIVQtECZKGTHpkgaNg3ahTcsuvWs3CGFONSqlZ/D3wnIj8FGck1BnAZzyNqtoE3VpEOoamEwggBaOhAm7NIn/5VWOMqRYTJgv33oongVOBk3GSxadVdU8ZYqsebrLIJoZIpeKEAQmNrlkEomPX6jbGmGoxYbJQVRWR76vqScDGMsVUdYY7r+MDpBOJoskiFHZX1IsPFT7dGGMOe6X0WfxKRE72PJIqlksMiaFB4m4Htj9ckCyizr0V6aQlC2NM9Smlz+IdwMdF5HVgkJE7uN/saWRVJJcYEvEB0u7Nef7CmkXU2c9asjDGVKFSksV5nkdR5XKd18nYIJmUMxoqEBmdLCJ1Ts0im7Q+C2NM9Zmsg9sH/Ieqri5TPFUp4PZHpBJDZJJOsgi6ZTnRiLOvVrMwxlShUqYo/7WILC1TPFUp6NYi0vFBMokht6wgWYQDxDSEpmxNC2NM9SmlGWoB8JKIPI3TZwGAql7gWVRVJugOi00nBsmmnD6LUEGyCAd8HCSE5C2/aowx1aKUZHGj51FUuVDE6Y/IJIbQtJss6kbPLCsiJAgjaWuGMsZUn3GThYi8SVV/q6r/LSJh1ZGZ8kTk1PKEVx0idU4tIpuMDSeLSLR+zHkJCY9MB2KMMVVkoj6Lf8nb/mXBsX/yIJaqFY7kksXQ8NDYaN3YNSuSEsZvycIYU4UmaoaScbaL7c9q0bq8kU6ZJAkNEgkFx5yX8oXxZyxZGGOqz0Q1Cx1nu9h+USJyrohsFZFtIjJmSnMRCYvId93jT4nIMrf8FBF53n38WkTeX8r1KiUSCjCkYWekUypGjBDhwNhfrbP8qnVwG2Oqz0Q1i8UicitOLSK3jbu/aLIXFhE/8E3gXUAnsElENqrqlrzTrgAOquoKEdkA3AJcDLwIrFPVtIgswBm++wNVTR/qGyyHoN9HP0EkPYRkkiQI4czBOFraFyaQ6a9AhMYYMz0TJYu/ztveXHCscL+YU4BtqvoqgIg8AKzHWW0vZz1wg7v9EHCbiIiq5g8ZilBiTaaSEhJGUjF82aTTkV1Exh8lmE4UPWaMMYezcZOFqt47zddeBOzI2+8E3jLeOW4tohdoBQ6IyFuAu4EjgA8Vq1WIyFXAVQBLl1b2vsEEYXzpOL5skqREip6TCUQIxazPwhhTfUpdKW8qinWCF9YQxj1HVZ9S1eNw1tH4jMjYb2BVvUNV16nqura2tmkHPB1JXwRfJo4vEyfpK16zyPqjBDVZ5siMMWb6vEwWncCSvP3FwK7xzhGRAM6Srd35J6jqyzh3jh/W81OlJIw/GyeQjZMeJ1loIEIEa4YyxlQfL5PFJmCliCwXkRCwgbELKG0EPuJuXwg87i64tNxNHojIEcAxwHYPY502p/M6TjATJ+0r3gxFMEqUBOhh3wVjjDGjTDrdh4i0AVcCy/LPV9XLJ3qe2wdxDfAY4AfuVtWXROQmYLOqbgTuAu4TkW04NYoN7tPfBlwvIikgC3xCVQ8c6psrp7Q/QiDdR0BTpP3jJIuAs/yqpmJjVtIzxpjDWSlzQ/078HPgJ0DmUF5cVR8FHi0o+3zedhy4qMjz7gPuO5RrVVrGHyGUTBDQFNnxkkXISRaJ+BARSxbGmCpSSrKoU9VPex5Jlcv4o4Q0TlBTZN0aRCFf0EkQ8aEBIk3zyhmeMcZMSyl9Fo+IyPmeR1LlsoEIYU0QJkk2ULxm4cstvxobKGdoxhgzbaUki2txEkZcRPrdR5/XgVWdQB1hEoQ1AcHiTUx+d/W8RMyWVjXGVJdJm6FUtbEcgVQ7DUaIkHLuHBmnGSrg1ixScUsWxpjqUkqfBSJyAXCGu/uEqj7iXUjVSYLRotv5htfqtmRhjKkykzZDicjNOE1RW9zHtW6ZySN5TU8SLt4MlVuXO23JwhhTZUqpWZwPrFHVLICI3As8B4yZcnw2k9BIbcI/Ts0iGHGSSDphS6saY6pLqXdwt+RtN3sRSLXz59034QuPXVIVIBx11+pOWs3CGFNdSqlZ/D3wnIj8FKf79gzgM55GVYX8eQkiME4zVDi3VnfCFkAyxlSXUkZD3S8iT+DM/irAp1V1j9eBVRt/XoIIRCauWWRT1gxljKku4zZDicib3J9rgQU4M8TuABa6ZSZPrj+icDtfXZ2TLDRpNQtjTHWZqGbxSZyFhf6xyDEFzvIkoioVjDQMb4fGqVkEg0GSGoAy1iwuvfMp1ixp4VPnHFO2axpjas9EK+Vd5W6e5074N6zYQkSzXSg6kiBCeYmjUJwwki7fanm/3tFDXzxlycIYMy2ljIb6RYlls1p+bSJSN36ySEgISZenGSqRztCfSPPbPf2kM9myXNMYU5vGrVmIyHycNbKjInIiI0ugNgE2v3aBcH7NIlq8GQogIWF8ZUoW3YPOEq7JdJbXDgyyssNmbjHGTM1EfRbnAB/FWQ71K3nl/cDfeBhTVcqvTUQnqFmkJII/U55mqK6BkfW+t+zus2RhjJmyifos7gXuFZEPqOrDZYypKkXdYbEp9RMMFV+DGyDpC5ctWXQPJvm4/we8qgvYsvtI1q9ZVJbrGmNqTyn3WTwsIn8IHAdE8spv8jKwauMLBEhqgKQECU5wXm6t7nLoGkxwdeAHvMyRfGvXuWW5pjGmNpUykeDtwMXA/8Lpt7gIOMLjuKpSXMIkGL9WAc7yq4Fsoizx9PT2MUcGWBXo5OXd/WW5pjGmNpUyGup0Vf0wcFBVbwROA5Z4G1Z1ShAmIRMni7S7/Go5JHt2A9CS6SYzsJ99/eUbsmuMqS2lJIvc0J0hEVkIpIDl3oVUvRISJjlJslB/hGA2OeE5M0X7dg1vHy07rXZhjJmyUtfgbgH+AXgW2A484GVQ1SrlC5P2TZwssoEIYcrzF770j0zhdbRvB1t22Wq4xpipKaWD+4vu5sMi8ggQUdVeb8OqUoEo+Cbq3gYNRoloefosQjE3WQQinOTfxeO7LVkYY6amlA7uP3NrFqhqAvCJyCc8j6wKLX7PpzniPZ+e8BwN1hElQaYMd1TXxfc7zWILT+S4wC62WLIwxkxRKc1QV6pqT25HVQ8CV3oXUvUKHv9+gse9Z8JzJFiHX5R43PvJBBvT++kPtUH7Kpakt/Pq/n7iqYzn1zXG1J5SkoVPRHJTfSAifiDkXUi1TdwV9WKD3nY2J9IZWrPdxMPt0H4skcwA7drN1j1YX+ObAAAZAklEQVTWyW2MOXSlJIvHgAdF5J0ichZwP/Ajb8OqXRJ27vROxrz90j44mKKDgyTrO6DjOADe5NvBy9YUZYyZglKWVf008HHgT3FuyvsxcKeXQdUyX8iZZDAxNODpdboG4hwl3exuXABtbwLg+OAu/vWZTnb1lmc0VsgvXHrqEbTUja6Ibvz1LrbtK/7+372qg9WLRi/z/ts9ffTH05y8bK5nsRpjJlbKaKgs8C33YaYpt+RqMuZtsug9eICIpPA1LYS6udC4gLfJfr7x+kGeef2gp9fO19oQ5o9PWTq8n8kqf/nd58lktej5L+7s5e6Pnjyq7JYf/padPTF+/Jdv9zRWY8z4Jpqi/EFV/aCIvICzMt4oqvpmTyOrUX63GSoV97YZKtbVCUB4rjt5YPsq3jK0h+03/6Gn181JpDMc89kfcaB/9DDhg0NJMlnlxguO4yOnLxt17KrvbOb1rrEd/50HY/TEUl6Ga4yZxEQ1i79wf048vMcckmCdM014Ju5tzSLVsxOA+nmLnYL2Y2HTnZDNgM/v6bUBwgE/jeEAXYOj71bPrbExt37sGImFLVF++UrXqDJVZVdPjPQ4NRFjTHlM1MH9iPvzS6r6euGjHMHVopA7lXk6PujpdXJTfdS3utN4ta+CdBy6X/P0uvnmNoSGk0NObo2N1qLJIkJ/Ik1ffKQW0RdLM5jMkEhnbdivMRU0Uc0iJCIfAU4XkT8qPKiq/+ZdWLUrFHVrFglvk4VvYK/zs2mBU9Cxyvm5bwvMW+HptXNa60N0DY5uhsrttzaMnRZlYUsUgN09cZrmO3fC7+wZWVWwP54mEvS+VmSMGWuimsXVwKlAC/Degoc1TU1R2F1FT5PeJotQbC+90ghBdwmSeccAAvte9vS6+ebWh0et1geTN0MB7MpLEPnb+TUOY0x5TbRS3pPAkyKyWVXvmsqLi8i5wNcBP3Cnqt5ccDwMfAc4CegCLlbV7SLyLuBmnJv/ksBfq+rjU4nhcBOtd2oW6nHNoj6xn17/PIYHoYbqYO6R8N83w8//0dNr59ymfv5cPgOcMVzW1Z/g30OfZd7XLhtz/onAJwPnsbNn9XDZrt4Y1/ofZoF00Rc7vQxRG2OKmWg01FnuF/TBqTRDuXd6fxN4F9AJbBKRjaq6Je+0K3DWyVghIhuAW3AWWjoAvFdVd4nIapwbA2tiTdBIpIGsCnhcs2hK7ac/0ja68Pwvw2s/9/S6w7JpIr+8jeXp36Kq5CYBGOjv5gTfq3DEmbBgzejn/PYRzj7wHI/k1SZ29sR4n38TDcR4xUZEGVMxE/VZvB14HKfZqZACk/VZnAJsU9VXAUTkAWA9kJ8s1gM3uNsPAbeJiKjqc3nnvARERCTsTmRY1Xx+H4OEkLS3c0PNzXbRGXnT6MIVZzuPclAl9fRdtKW76Yulaa5z+iC0z1mQiRM/BMdfOOopohmO6rqdPQdHhhXvOTjAkbKLOCH64unyxG6MGWOiZqgvuD/HtheUZhGwI2+/E3jLeOeoalpEeoFWnJpFzgeA54olChG5CrgKYOnSpYWHD1txiSAp75JFMpmkVXt4rX6+Z9eYlAiJSDvzkwfpGkwMJwtfv5ssGovE1r6KMCkyXa8B7o15XdsIS5qgZugbqvq/FYypWqVMUX6tiDSJ404ReVZE3l3Ca0uRssLB8hOeIyLH4TRNfbzYBVT1DlVdp6rr2traip1yWIpLBH86NvmJU9S7fyd+0eJfyGWUrp9Ph3SPGj4bGnJGadG4YOwT2p0RWw29vxsuauz9PQA+UeIDPWOfY4wpi1ImErxcVfuAdwPtwGU4nc+T6WT0Wt2LgV3jnSMiAaAZ6Hb3FwPfAz6sqq+UcL2qkfA4WfTvdyp0geaFnl2jJE0L6eDgqBvzoon9zkaxZNF2DIrQHn+VTFZJZ7K0x18dPpwaKN80JcaY0UpJFrm//s8H7lHVX1O8RlBoE7BSRJaLSAjYAGwsOGcj8BF3+0LgcVVVd7Gl/wA+o6r/U8K1qkrSFyGQ8a4ZKt7tTPURmlvZMQGB5gV0SA9d7pQfmazSnN5P3N/ojM4qFIzSX7eUlexgX3+cvf0JjpGRlszMUHe5QjfGFCglWTwjIj/GSRaPiUgjMOkyb6qaBq7BGcn0MvCgqr4kIjeJyAXuaXcBrSKyDfgkcL1bfg2wAviciDzvPtoP6Z0dxlK+KIGsdzWLVI9TgWtoq2w/TqR1MWFJMdS7D4CeoSTt9BCLjP9RJuYezTGyg109MXb1xDhadpAMO7PNZmLWDGVMpZQyRfkVwBrgVVUdEpG5OE1Rk1LVR4FHC8o+n7cdBy4q8rwvAV8q5RrVKO2PUJ/ycF2Jvl2k1UfLvMo2Q+WawXLJq2swyXzpJlXfMe5zfB3HsWzHT/hhVy9kM5wk+xhaeC6h134IliyMqZhSahanAVtVtUdELgU+C/R6G1ZtS/ujhLLerSnhH9zLflporhs7pUZZNbrJyh0B1TWQpF0OQkOR/gpX3ZI34xcltmsLsd0v4xMldNRbAfAlbOEmYyqllGTxLWBIRE4ArgNex7nr2kxRxl9HWL1LFuHYXg5IKz5fKV1LHnJHYwUG9wDQPRCjnR58zeMni+gi5+5t3/6X8e1zbskJLXeSRTBpf6MYUymlNEOl3U7n9cDXVfUud4JBM0XZYJRIsWQxsA8yybHlh6gxsYe9gcXTfp3pB+Iki3DM6bMYOriHgGQJz50gtrlHkiJAXc/vEM2SJEho/pvJ4CfkZdOdMWZCpSSLfhH5DHApcIY7jUfQ27BqWzZQT6TwHsPf/gc8cMmMvP584Nn6dTPyWtMSCDPgb6Yh6QyXTR501tiITpQs/EF2B4+gdfAVfGTZEzqCpf4AiUAD4cTAqKlDjDHlU0qyuBi4BLhCVfeIyFLgH7wNq7ZpqI6gZNB0Agm4/Qrdzv0EsXd/GfxjZ2Q9FLf+1zYOdryD86cb6AwYDLfTMnAAVQV3jY1Ay8Qd7wcbjmLxwWfxk2X3nFNYCiSDTTQkBoinskRDNk25MeVWyhrce4Cv5O2/gfVZTIu49xgkhvqJNDnJ4vWdO1mswrEbF1HabSwTaeejLeP3C5RTItpB20AnffH08Bobwx3f44i1HMPCgz8G4LU5RwOQCTXRxCB98ZQlC2MqYNJkISKnAt8AjsWZMtwPDKhq84RPNOML1gMQH+wn0jQPgIHebvqo57pz30TIX8q4g/GJCOetruxUHzmZ+g7mH3iB7sEkodgesvjw1U8yNUvHseAu6Cfuok3ZcDPNso++WIqOpojHURtjCpXSDHUbzt3X/wqsAz4MrPQyqFrnDzvJIhkbmV3VF++hl3quPuOoyo9imkHStJBW+nijf4C6xH56/XOY45/4n13d4uOHt6OL3wyARppp5jV6bAEkYyqipD9hVXUb4FfVjKreA5zpaVQ1zucmi8TQwHCZP9nHoNTXVKIACLYsxCfKwIFdNKcOMBiafMLHeYtWMKAR+jRK+6IjAfBFW2iSIfpiNk25MZVQSs1iyJ3b6XkR+TKwG6j3NqzaFog4S6um4iPJIpjqo9fXWKmQPBNpdUY+xbo76ch2EY8un/Q57U0RNutyFOEUt8nJXz+XOgbpHZr+0GJjzKErpWbxIZx+imuAQZxZYj/gZVC1bjhZxEaSRSTd70ywV2Ma5jkTD/fv20GHHCRVN/5UHzkBv4+/j36Km+v+Cr9b0wrVzyEsaQbzamPGmPIpZTTU6+5mDLjR23Bmh1yySCfykkWmn2Sk9pJFeI5Ts0h3vcYcGWBPsanJi2hqX4Iv736KUIMzmWCy32aeNaYSJlqD+wXGLlY0TFXf7ElEs0Ao6iSLTF4zVH12gFSwqVIheaeulRQB5vW9BECgpbRp07928ej1uYP1LQCkBm1NC2MqYaKaxXvKFsUsE65zahDZ5KBTkIoTIkU2XIOjkX0+DvrmcEz6d+CD8JzSkkVrQ8EkiBEnWWSGLFkYUwkT9VkEgcWq+nr+A1hKaR3jZhyRXLJIuMki7ky9rZEaTBZAX7CNpT5nyo/6eUsmOXscUSdZqE1TbkxFTJQsvgb0FymPucfMFEXqnWYoks5qeelBpx1eonMqFZKnhsIjw2Wb2qe4IJNbsyBuM88aUwkTJYtlqvqbwkJV3Qws8yyiWSAaChLT0HCyGOpzkoW/rqWSYXkmGXVGQMUIEayfYkJ0k4U/YcnCmEqYKFlMNKdCdKYDmU2Cfh8xwkjaSRbxvi6nfKpfpIe5bIMz9Ui3zIWpzhgbcTr/gzZNuTEVMVGy2CQiVxYWisgVwDPehTQ7DEkESbnJYsCpWYQb51YyJO80ORMH9gTmTf01/EESvqitaWFMhUzUUf0XwPdE5E8YSQ7rcCYTfL/XgdW6hEQIuDWL1IAzwifa1FrJkDwTcqckHwxPPtXHRBKBRiIxW9PCmEoYN1mo6l7gdBF5B7DaLf4PVX28LJHVuIRE8LvJIjcctK6pNmsW0VZnBFQ8Mvnd2xNJBZtojA0ylMxQH7YBecaUUyl3cP8U+GkZYplVkr4o9dkYANlYD4Mapqm+rsJReaOx4wj6tI7B5qOn9TqZcDPNMkhvLGXJwpgym97CCWbKUr4IwYyzDre405M3RWtztdp5c+Zw2ZxvE143vWVjNdxMs7sAkjGmvOzPswpJ+6MEk07NQhJ99Gs9C2p0BbhQwMfDf3HO9F8o2kKjDLHLpik3puysZlEhaX+UcNapWQSSvQz6GqzTdhK+aItTs4hZzcKYcrNkUSHZQB1hdZJFKNXHUA1OTz7TAvVzaJQY/UOxSodizKxjyaJCsoEoERIARNIDJAKWLCaTm6Y83m+TCRpTbpYsKkSDdQRJQyZFNNtPMmjJYjIRN1kkBixZGFNuliwqREPuyrSJfup1iEwtrmUxw/zudChpm6bcmLKzZFEhEnTuqUj17gIgG67NSQRnlDuFe9aShTFlZ8miQiTs1CySBzud/WhtrmUxo9yZZ9WmKTem7CxZVIjPTRaJAzsAkBqdnnxGuQsg+SxZGFN2liwqxO/2WcS7nWQRqK/NeaFmlNsMFUhasjCm3DxNFiJyrohsFZFtInJ9keNhEfmue/wpEVnmlreKyE9FZEBEbvMyxkoJRJ3V8jI9OwEI1ehaFjMqWEeaAAGbptyYsvMsWYiIH/gmcB6wCvhjEVlVcNoVwEFVXQF8FbjFLY8DnwM+5VV8leYPO8lC+ncDNbyWxUwSIRFoIGzJwpiy87JmcQqwTVVfVdUk8ACwvuCc9cC97vZDwDtFRFR1UFWfxEkaNSkUde6rCA3tASBqyaIkyWAT0ewA8VSm0qEYM6t4mSwWATvy9jvdsqLnqGoa6AVKXgFIRK4Skc0isnn//v3TDLe8Qm4zVENiLyn109hoo6FKkQk5M892DyYrHYoxs4qXyaLYrHg6hXPGpap3qOo6VV3X1ja9VdjKLVTn1CzqsgPO9OR1oQpHVCUizTSJJQtjys3LZNEJLMnbXwzsGu8cEQkAzUC3hzEdNiJ1DcPbfVpHU6Q217KYaVI3hyaG6LJkYUxZeZksNgErRWS5iISADcDGgnM2Ah9xty8EHlfVkmsW1awuHCKuToLop55I0EYxlyLY0Eqr9NE1kKh0KMbMKp4tfqSqaRG5BngM8AN3q+pLInITsFlVNwJ3AfeJyDacGsWG3PNFZDvQBIRE5H3Au1V1i1fxlls05GeIMBFSDNlaFiULzVlERIbo7evFqawaY8rB05XyVPVR4NGCss/nbceBi8Z57jIvY6u0cMBHFxFggJhNT16y8BxnjESyZxdwXGWDMWYWsbaPChER4oQBSARsxtlSSdMCALR3d4UjMWZ2sWRRQXGJAJC2tSxK17gQAN+AJQtjysmSRQUlfW6yCNs9FiVzaxbB2L4KB2LM7GLJooKSvigAasmidOEmEhKhPm7JwphysmRRQSm3ZiFRm568ZCIMhNpoSlXXHfvGVDtLFhWU9js1C3+dzTh7KOKRNuZqN4m0zQ9lTLlYsqigdMBZWjVg05MfklTdfObTbVN+GFNGliwqKOsmi1CDzTh7KLRxPh3SQ1e/3cVtTLlYsqigjJssIo1WszgU/uZFhCVF70Hr5DamXCxZVNDrc07j/vQ7qGueV+lQqkp4rnMXd7y7s8KRGDN7WLKooO6W1XwmfSVN0XClQ6kq9a3OZMapgzsrHIkxs4cliwqKBv0ANEdtevJDUd/mTCCofXYXtzHlYsmigpbNq2NeQ8iSxSGSRucubv/AngpHYszs4emss2Zi71uziPe+eSEBv+XsQxII0yNNhGJ7Kx2JMbOGJYsKEhECflvHYip6A/OoT9hd3MaUi/1Ja6rSQKiNprQlC2PKxZKFqUrxSDtzM7NiuXZjDguWLExVytTPp5VeEol4pUMxZlawZGGqkjYuwCdK7/5dlQ7FmFnBkoWpSoFmZ8W8/v1vVDgSY2YHSxamKkVanRvzbMoPY8rDkoWpSvXznCk/0j3WDGVMOViyMFWpZd58kuqHfpvyw5hysGRhqlJTNMx+5tiUH8aUiSULU5V8PqHLN5ewTflhTFlYsjBVqzcwj/qk3cVtTDnY3FCmag2G2pk/+Eu4ZXmlQzGmso59D1zwDU8vYcnCVK3Nbe9nMJHiA6sXVjoUYyprwQmeX8KShala6bkrue53l3L71vpKh2JMRZ1JG//b42tYsjBV6/0nLmL/QAJVrXQoxlRUR1PE82tYsjBV64QlLXzzkrWVDsOYWcFGQxljjJmUJQtjjDGTsmRhjDFmUp4mCxE5V0S2isg2Ebm+yPGwiHzXPf6UiCzLO/YZt3yriJzjZZzGGGMm5lmyEBE/8E3gPGAV8McisqrgtCuAg6q6AvgqcIv73FXABuA44Fzgn9zXM8YYUwFe1ixOAbap6quqmgQeANYXnLMeuNfdfgh4p4iIW/6AqiZU9TVgm/t6xhhjKsDLZLEI2JG33+mWFT1HVdNAL9Ba4nMRkatEZLOIbN6/3+YIMsYYr3iZLKRIWeHdU+OdU8pzUdU7VHWdqq5ra2ubQojGGGNK4eVNeZ3Akrz9xUDhsma5czpFJAA0A90lPneUZ5555oCIvD6NeOcBB6bx/Go0G98zzM73be959jjU931EKSd5mSw2AStFZDmwE6fD+pKCczYCHwF+CVwIPK6qKiIbgX8Rka8AC4GVwNMTXUxVp1W1EJHNqrpuOq9RbWbje4bZ+b7tPc8eXr1vz5KFqqZF5BrgMcAP3K2qL4nITcBmVd0I3AXcJyLbcGoUG9znviQiDwJbgDTwZ6qa8SpWY4wxE/N0bihVfRR4tKDs83nbceCicZ77t8DfehmfMcaY0tgd3CPuqHQAFTAb3zPMzvdt73n28OR9i03vbIwxZjJWszDGGDMpSxbGGGMmNeuTxWSTHdYCEVkiIj8VkZdF5CURudYtnysi/ykiv3d/zql0rF4QEb+IPCcij7j7y92JK3/vTmQZqnSMM0lEWkTkIRH5rfuZnzYbPmsR+Uv33/eLInK/iERq8bMWkbtFZJ+IvJhXVvTzFcet7vfbb0RkyquFzepkUeJkh7UgDfyVqh4LnAr8mfs+rwf+S1VXAv/l7teia4GX8/ZvAb7qvu+DOBNa1pKvAz9S1TcBJ+C895r+rEVkEfDnwDpVXY0zXH8DtflZfxtngtV8432+5+Hcp7YSuAr41lQvOquTBaVNdlj1VHW3qj7rbvfjfHksYvREjvcC76tMhN4RkcXAHwJ3uvsCnIUzcSXU2PsWkSbgDJx7mFDVpKr2MAs+a5xbAaLubBB1wG5q8LNW1Z/h3JeWb7zPdz3wHXX8CmgRkQVTue5sTxYlTVhYS9w1Q04EngI6VHU3OAkFaK9cZJ75GnAdkHX3W4Eed+JKqL3P/EhgP3CP2/R2p4jUU+OftaruBP4P8AZOkugFnqG2P+t8432+M/YdN9uTRUkTFtYKEWkAHgb+QlX7Kh2P10TkPcA+VX0mv7jIqbX0mQeAtcC3VPVEYJAaa3Iqxm2jXw8sx5kiqB6nCaZQLX3WpZixf++zPVkc8oSF1UpEgjiJ4v+p6r+5xXtzVVL3575KxeeRtwIXiMh2nCbGs3BqGi1uUwXU3mfeCXSq6lPu/kM4yaPWP+uzgddUdb+qpoB/A06ntj/rfON9vjP2HTfbk8XwZIfuKIkNOJMb1hS3nf4u4GVV/UreodxEjrg//73csXlJVT+jqotVdRnOZ/u4qv4J8FOciSuhxt63qu4BdojIMW7RO3HmWKvpzxqn+elUEalz/73n3nfNftYFxvt8NwIfdkdFnQr05pqrDtWsv4NbRM7H+WszN9lhzc1HJSJvA34OvMBI2/3f4PRbPAgsxfnPdpGqFnac1QQRORP4lKq+R0SOxKlpzAWeAy5V1UQl45tJIrIGp0M/BLwKXIbzh2FNf9YiciNwMc7ov+eAj+G0z9fUZy0i9wNn4kxFvhf4AvB9iny+buK8DWf01BBwmapuntJ1Z3uyMMYYM7nZ3gxljDGmBJYsjDHGTMqShTHGmElZsjDGGDMpSxbGGGMmZcnCGGPMpCxZGGOMmdT/BxSAw8X3CUvPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retrieve performance metrics\n",
    "result=bst.evals_result()\n",
    "\n",
    "epochs=len(result['validation_0']['error'])\n",
    "x_axis=range(0,epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig,ax=plt.subplots()\n",
    "ax.plot(x_axis,result['validation_0']['logloss'],label='Train')\n",
    "ax.plot(x_axis,result['validation_1']['logloss'],label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title(\"XGBoost Log Loss\")\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig,ax=plt.subplots()\n",
    "ax.plot(x_axis,result['validation_0']['error'],label='Train')\n",
    "ax.plot(x_axis,result['validation_1']['error'],label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试\n",
    "模型训练好后，可以用训练好的模型对测试数据进行预测\n",
    "\n",
    "XGBoost预测的输出是概率，输出值是样本为第一类的概率。我们需要将概率值转换为0或1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 99.81%\n"
     ]
    }
   ],
   "source": [
    "# make prediction\n",
    "preds = bst.predict(X_test)\n",
    "predictions = [round(value) for value in preds]\n",
    "\n",
    "test_accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Test Accuracy: %.2f%%\" % (test_accuracy * 100.0))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
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