import numpy as np

import  matplotlib.pyplot as plt

from sklearn import datasets,naive_bayes

from sklearn.model_selection import train_test_split

# 加载 scikit-learn 自带的 digits 数据集

def load_data():

    '''

    加载用于分类问题的数据集。这里使用 scikit-learn 自带的 digits 数据集

    '''

    digits=datasets.load_digits()

    return train_test_split(digits.data,digits.target,test_size=0.25,random_state=0,stratify=digits.target)

#伯努利贝叶斯BernoulliNB模型

def test_BernoulliNB(*data):

    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))

# 产生用于分类问题的数据集

X_train,X_test,y_train,y_test=load_data()

# 调用 test_BernoulliNB

test_BernoulliNB(X_train,X_test,y_train,y_test)

def test_BernoulliNB_alpha(*data):

    '''

    测试 BernoulliNB 的预测性能随 alpha 参数的影响

    '''

    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))

    ## 绘图

    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()

# 调用 test_BernoulliNB_alpha

test_BernoulliNB_alpha(X_train,X_test,y_train,y_test)

def test_BernoulliNB_binarize(*data):

    '''

    测试 BernoulliNB 的预测性能随 binarize 参数的影响

    '''

    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))

    ## 绘图

    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()

# 调用 test_BernoulliNB_binarize

test_BernoulliNB_binarize(X_train,X_test,y_train,y_test)