朴素贝叶斯模型

# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 13:12:52 2018

@author: fengjuan
"""

#朴素贝叶斯模型有着广泛的实际应用环境，特别是在文本分类处理中
#从sklearn.datasets导入新闻数据加载器
from sklearn.datasets import fetch_20newsgroups

#from sklearn.cross_validation import train_test_split
news=fetch_20newsgroups(subset="all")
print(len(news.data))
print(news.data[0])
'''结果：
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!!!'''
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(news.data,news.target,
                                               test_size=0.25,random_state=33)
print(y_train.shape)
print(y_test.shape)
'''结果：
(14134,)
(4712,)'''
#from sklearn_extration 导入 StandardScaler
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导入贝叶斯模型
from sklearn.naive_bayes import MultinomialNB
#默认配置初始化朴素贝叶斯模型
mnb=MultinomialNB()
mnb.fit(X_train,y_train)
y_predict=mnb.predict(X_test)
from sklearn.metrics import classification_report
print('Accuracy of Naive_Bayes is:',mnb.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=news.target_names))
#结果：
'''Accuracy of Naive_Bayes 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'''