Bayes算法分类案例

#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])

18846
From: Mamatha Devineni Ratnam <mr47+@andrew.cmu.edu>
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 val idation导人train test split。
from sklearn.cross_validation 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)

C:\ProgramData\Anaconda3\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.
  "This module will be removed in 0.20.", DeprecationWarning)

#从sklearn. feature_ extraction. text里导人用于文本特征向量转化模块。
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里导人朴素贝叶斯模型。
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里导人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))

The accuracy of Naive Bayes Classifier is 0.8397707979626485
                          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