《python机器学习及实践-从零开始通往kaggle竞赛之路（代码Python 3.6 版）》chapter2.1.1.3

# 从sklearn.datasets里导入新闻数据抓取器fetch_20newsgroups。
from sklearn.datasets import fetch_20newsgroups
# 与之前预存的数据不同，fetch_20newsgroups需要即时从互联网下载数据。
news = fetch_20newsgroups(subset='all')
# 查验数据规模和细节。
print (len(news.data))
print (news.data[0])

结果输出为：

18846
From: Mamatha Devineni Ratnam 
Subject: Pens fans reactions
Organization: Post Office, Carnegie Mellon, Pittsburgh, PA
Lines: 12
NNTP-Posting-Host: po4.andrew.cmu.edu



I am sure some bashers of Pens fans are pretty confused about the lack
of any kind of posts about the recent Pens massacre of the Devils. Actually,
I am  bit puzzled too and a bit relieved. However, I am going to put an end
to non-PIttsburghers' relief with a bit of praise for the Pens. Man, they
are killing those Devils worse than I thought. Jagr just showed you why
he is much better than his regular season stats. He is also a lot
fo fun to watch in the playoffs. Bowman should let JAgr have a lot of
fun in the next couple of games since the Pens are going to beat the pulp out of Jersey anyway. I was very disappointed not to see the Islanders lose the final
regular season game.          PENS RULE!!!

# 从sklearn.cross_validation 导入 train_test_split。
from sklearn.model_selection import train_test_split
# 随机采样25%的数据样本作为测试集。
X_train, X_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.25, random_state=33)

# 从sklearn.feature_extraction.text里导入用于文本特征向量转化模块。详细介绍请读者参考3.1.1.1 特征抽取一节。
from sklearn.feature_extraction.text import CountVectorizer

vec = CountVectorizer()
X_train = vec.fit_transform(X_train)
X_test = vec.transform(X_test)

# 从sklearn.naive_bayes里导入朴素贝叶斯模型。
from sklearn.naive_bayes import MultinomialNB

# 从使用默认配置初始化朴素贝叶斯模型。
mnb = MultinomialNB()
# 利用训练数据对模型参数进行估计。
mnb.fit(X_train, y_train)
# 对测试样本进行类别预测，结果存储在变量y_predict中。
y_predict = mnb.predict(X_test)

# 从sklearn.metrics里导入classification_report用于详细的分类性能报告。
from sklearn.metrics import classification_report
print ('The accuracy of Naive Bayes Classifier is', mnb.score(X_test, y_test))
print (classification_report(y_test, y_predict, target_names = news.target_names))

结果为：

The accuracy of Naive Bayes Classifier is 0.839770797963
                          precision    recall  f1-score   support

             alt.atheism       0.86      0.86      0.86       201
           comp.graphics       0.59      0.86      0.70       250
 comp.os.ms-windows.misc       0.89      0.10      0.17       248
comp.sys.ibm.pc.hardware       0.60      0.88      0.72       240
   comp.sys.mac.hardware       0.93      0.78      0.85       242
          comp.windows.x       0.82      0.84      0.83       263
            misc.forsale       0.91      0.70      0.79       257
               rec.autos       0.89      0.89      0.89       238
         rec.motorcycles       0.98      0.92      0.95       276
      rec.sport.baseball       0.98      0.91      0.95       251
        rec.sport.hockey       0.93      0.99      0.96       233
               sci.crypt       0.86      0.98      0.91       238
         sci.electronics       0.85      0.88      0.86       249
                 sci.med       0.92      0.94      0.93       245
               sci.space       0.89      0.96      0.92       221
  soc.religion.christian       0.78      0.96      0.86       232
      talk.politics.guns       0.88      0.96      0.92       251
   talk.politics.mideast       0.90      0.98      0.94       231
      talk.politics.misc       0.79      0.89      0.84       188
      talk.religion.misc       0.93      0.44      0.60       158

             avg / total       0.86      0.84      0.82      4712