FireAI_011_ClassifierEvaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "np.random.seed(37) # 使得每次运行得到的随机数都一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 1 0 0]\n",
      " [0 2 0 0]\n",
      " [0 0 1 0]\n",
      " [0 1 0 2]]\n"
     ]
    }
   ],
   "source": [
    "# 假如有一个模型在测试集上得到的预测结果为：\n",
    "y_true = [1, 0, 0, 2, 1, 0, 3, 3, 3] # 实际的类别\n",
    "y_pred = [1, 1, 0, 2, 1, 0, 1, 3, 3] # 模型预测的类别\n",
    "\n",
    "# 使用sklearn 模块计算混淆矩阵\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "print(confusion_mat) #看看混淆矩阵长啥样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(confusion_mat):\n",
    "    '''将混淆矩阵画图并显示出来'''\n",
    "    plt.imshow(confusion_mat, interpolation='nearest', cmap=plt.cm.gray)\n",
    "    plt.title('Confusion matrix')\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(confusion_mat.shape[0])\n",
    "    plt.xticks(tick_marks, tick_marks)\n",
    "    plt.yticks(tick_marks, tick_marks)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(confusion_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "    Class-0       1.00      0.67      0.80         3\n",
      "    Class-1       0.50      1.00      0.67         2\n",
      "    Class-2       1.00      1.00      1.00         1\n",
      "    Class-3       1.00      0.67      0.80         3\n",
      "\n",
      "avg / total       0.89      0.78      0.79         9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 打印该模型的性能报告\n",
    "# 直接使用sklearn打印精度，召回率和F1值\n",
    "from sklearn.metrics import classification_report\n",
    "target_names = ['Class-0', 'Class-1', 'Class-2', 'Class-3']\n",
    "print(classification_report(y_true, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率：99.00%\n",
      "精确度：99.17%\n",
      "召回率：99.00%\n",
      "F1  值：98.97%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dingr\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 评估朴素贝叶斯多分类模型\n",
    "# 1， 准备数据集\n",
    "data_path='D:\\PyProjects\\DataSet/NaiveBayers/data_multivar.txt'\n",
    "df=pd.read_csv(data_path,header=None)\n",
    "dataset_X,dataset_y=df.iloc[:,:-1],df.iloc[:,-1]\n",
    "dataset_X=dataset_X.values\n",
    "dataset_y=dataset_y.values\n",
    "# 将整个数据集划分为train set和test set\n",
    "from sklearn.cross_validation import train_test_split\n",
    "train_X, test_X, train_y, test_y=train_test_split(dataset_X,dataset_y,test_size=0.25,random_state=42)\n",
    "\n",
    "# 构建朴素贝叶斯分类模型\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "gaussianNB_new=GaussianNB()\n",
    "gaussianNB_new.fit(train_X,train_y)\n",
    "\n",
    "# 2 用交叉验证来检验模型的准确性，只是在test set上验证准确性\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "num_validations=5\n",
    "accuracy=cross_val_score(gaussianNB_new,test_X,test_y,\n",
    "                         scoring='accuracy',cv=num_validations)\n",
    "print('准确率：{:.2f}%'.format(accuracy.mean()*100))\n",
    "precision=cross_val_score(gaussianNB_new,test_X,test_y,\n",
    "                         scoring='precision_weighted',cv=num_validations)\n",
    "print('精确度：{:.2f}%'.format(precision.mean()*100))\n",
    "recall=cross_val_score(gaussianNB_new,test_X,test_y,\n",
    "                         scoring='recall_weighted',cv=num_validations)\n",
    "print('召回率：{:.2f}%'.format(recall.mean()*100))\n",
    "f1=cross_val_score(gaussianNB_new,test_X,test_y,\n",
    "                         scoring='f1_weighted',cv=num_validations)\n",
    "print('F1  值：{:.2f}%'.format(f1.mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27  0  0  0]\n",
      " [ 0 18  0  0]\n",
      " [ 0  0 33  0]\n",
      " [ 0  0  0 22]]\n",
      "**************************************************\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "    Class-0       1.00      1.00      1.00        27\n",
      "    Class-1       1.00      1.00      1.00        18\n",
      "    Class-2       1.00      1.00      1.00        33\n",
      "    Class-3       1.00      1.00      1.00        22\n",
      "\n",
      "avg / total       1.00      1.00      1.00       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用sklearn 模块计算混淆矩阵\n",
    "y_pred=gaussianNB_new.predict(test_X)\n",
    "confusion_mat = confusion_matrix(test_y, y_pred)\n",
    "print(confusion_mat) #看看混淆矩阵长啥样\n",
    "\n",
    "print('*'*50)\n",
    "# 打印该模型的性能报告\n",
    "# 直接使用sklearn打印精度，召回率和F1值\n",
    "target_names = ['Class-0', 'Class-1', 'Class-2', 'Class-3']\n",
    "print(classification_report(test_y, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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