多项式朴素贝叶斯实现文本分类

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#coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.naive_bayes import GaussianNB,MultinomialNB
from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import GridSearchCV

if __name__=='__main__':
    #加载文本分类数据
    #只选择4个类别进行分类
    #若分类所有类别，请注意内存是否够用
    categories = 'alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space'
    # remove = ('headers', 'footers', 'quotes')
    remove = ()
    data_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=0, remove=remove)
    data_test = fetch_20newsgroups(subset='test', categories=categories, shuffle=True, random_state=0, remove=remove)
    y_train = data_train.target
    y_test = data_test.target
    #print u'数据类型：', type(data_train)
    print u'训练集包含的文本数目：', len(data_train.data)
    print u'测试集包含的文本数目：', len(data_test.data)
    #打印5个文本的详细信息
    '''
    for i in np.arange(5):
        print u'文本%d(属于类别 - %s)：' % (i+1, categories[y_train[i]])
        print data_train.data[i]
        print '\n\n'
    '''
    
    #文本特征tf-idf向量化
    vectorizer = TfidfVectorizer(input='content', stop_words='english', max_df=0.5, sublinear_tf=True)
    
    x_train = vectorizer.fit_transform(data_train.data)  # x_train是稀疏的，scipy.sparse.csr.csr_matrix
    x_test = vectorizer.transform(data_test.data)  #注意此处一定要用 transform 保证两个列表纵向维度一致 
    #print x_train.shape
    #print x_test.shape
    
    feature_names = np.asarray(vectorizer.get_feature_names())
    #print feature_names
    
    #开始预测
    model = MultinomialNB()
    #需要交叉验证获取平滑系数#
    alpha = GridSearchCV(model,param_grid={"alpha":[0.001,0.01,0.1,1,10,100,1000]},cv=10)
    alpha.fit(x_train, y_train)
    #最佳参数为0.1
    #print alpha.best_params_
    
    #用测试数据，测试效果
    pre_y = alpha.predict(x_test)
    m=0
    for i in range(len(pre_y)):
        if pre_y[i]==y_test[i]:
            m=m+1
    print "准确率:",m*1.0/len(pre_y)