機器學習之伯努利貝葉斯分類器bernoulliNB
阿新 • • 發佈:2018-11-25
- 機器學習之伯努利貝葉斯分類器bernoulliNB
# -*- coding: utf-8 -*- """ Created on Sun Nov 25 11:45:17 2018 @author: muli """ from sklearn import naive_bayes,datasets,cross_validation import numpy as np import matplotlib.pyplot as plt def load_data(): ''' 載入用於分類問題的資料集。這裡使用 scikit-learn 自帶的 digits 資料集 :return: 一個元組,用於分類問題。元組元素依次為:訓練樣本集、測試樣本集、訓練樣本集對應的標記、測試樣本集對應的標記 ''' # 載入 scikit-learn 自帶的 digits 資料集 digits=datasets.load_digits() #分層取樣拆分成訓練集和測試集,測試集大小為原始資料集大小的 1/4 return cross_validation.train_test_split(digits.data,digits.target, test_size=0.25,random_state=0,stratify=digits.target) def test_BernoulliNB(*data): ''' 測試 BernoulliNB 的用法 :param data: 可變引數。它是一個元組,這裡要求其元素依次為:訓練樣本集、測試樣本集、訓練樣本的標記、測試樣本的標記 :return: None ''' X_train,X_test,y_train,y_test=data cls=naive_bayes.BernoulliNB() cls.fit(X_train,y_train) print('Training Score: %.2f' % cls.score(X_train,y_train)) print('Testing Score: %.2f' % cls.score(X_test, y_test)) def test_BernoulliNB_alpha(*data): ''' 測試 BernoulliNB 的預測效能隨 alpha 引數的影響 :param data: 可變引數。它是一個元組,這裡要求其元素依次為:訓練樣本集、測試樣本集、訓練樣本的標記、測試樣本的標記 :return: None ''' X_train,X_test,y_train,y_test=data alphas=np.logspace(-2,5,num=200) train_scores=[] test_scores=[] for alpha in alphas: cls=naive_bayes.BernoulliNB(alpha=alpha) cls.fit(X_train,y_train) train_scores.append(cls.score(X_train,y_train)) test_scores.append(cls.score(X_test, y_test)) ## 繪圖 fig=plt.figure() ax=fig.add_subplot(1,1,1) ax.plot(alphas,train_scores,label="Training Score") ax.plot(alphas,test_scores,label="Testing Score") ax.set_xlabel(r"$\alpha$") ax.set_ylabel("score") ax.set_ylim(0,1.0) ax.set_title("BernoulliNB") ax.set_xscale("log") ax.legend(loc="best") plt.show() def test_BernoulliNB_binarize(*data): ''' 測試 BernoulliNB 的預測效能隨 binarize 引數的影響 :param data: 可變引數。它是一個元組,這裡要求其元素依次為:訓練樣本集、測試樣本集、訓練樣本的標記、測試樣本的標記 :return: None ''' X_train,X_test,y_train,y_test=data min_x=min(np.min(X_train.ravel()),np.min(X_test.ravel()))-0.1 max_x=max(np.max(X_train.ravel()),np.max(X_test.ravel()))+0.1 binarizes=np.linspace(min_x,max_x,endpoint=True,num=100) train_scores=[] test_scores=[] for binarize in binarizes: cls=naive_bayes.BernoulliNB(binarize=binarize) cls.fit(X_train,y_train) train_scores.append(cls.score(X_train,y_train)) test_scores.append(cls.score(X_test, y_test)) ## 繪圖 fig=plt.figure() ax=fig.add_subplot(1,1,1) ax.plot(binarizes,train_scores,label="Training Score") ax.plot(binarizes,test_scores,label="Testing Score") ax.set_xlabel("binarize") ax.set_ylabel("score") ax.set_ylim(0,1.0) ax.set_xlim(min_x-1,max_x+1) ax.set_title("BernoulliNB") ax.legend(loc="best") plt.show() if __name__=='__main__': # 產生用於分類問題的資料集 X_train,X_test,y_train,y_test=load_data() # 呼叫 test_BernoulliNB # test_BernoulliNB(X_train,X_test,y_train,y_test) # 呼叫 test_BernoulliNB_alpha # test_BernoulliNB_alpha(X_train,X_test,y_train,y_test) # 呼叫 test_BernoulliNB_binarize test_BernoulliNB_binarize(X_train,X_test,y_train,y_test)