机器学习之路： python 朴素贝叶斯分类器 MultinomialNB 预测新闻类别

使用python3 学习朴素贝叶斯分类api

设计到字符串提取特征向量

欢迎来到我的git下载源代码: https://github.com/linyi0604/MachineLearning

 from sklearn.datasets import fetch_20newsgroups
 from sklearn.cross_validation import train_test_split
 # 导入文本特征向量转化模块
 from sklearn.feature_extraction.text import CountVectorizer
 # 导入朴素贝叶斯模型
 from sklearn.naive_bayes import MultinomialNB
 # 模型评估模块
 from sklearn.metrics import classification_report

 '''
 朴素贝叶斯模型广泛用于海量互联网文本分类任务。
 由于假设特征条件相互独立，预测需要估计的参数规模从幂指数量级下降接近线性量级，节约内存和计算时间
 但是 该模型无法将特征之间的联系考虑，数据关联较强的分类任务表现不好。
 '''

 '''
 1 读取数据部分
 '''
 # 该api会即使联网下载数据
 news = fetch_20newsgroups(subset="all")
 # 检查数据规模和细节
 # 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!!!
 '''

 '''
 2 分割数据部分
 '''
 x_train, x_test, y_train, y_test = train_test_split(news.data,
                                                     news.target,
                                                     test_size=0.25,
                                                     random_state=33)

 '''
 3 贝叶斯分类器对新闻进行预测
 '''
 # 进行文本转化为特征
 vec = CountVectorizer()
 x_train = vec.fit_transform(x_train)
 x_test = vec.transform(x_test)
 # 初始化朴素贝叶斯模型
 mnb = MultinomialNB()
 # 训练集合上进行训练， 估计参数
 mnb.fit(x_train, y_train)
 # 对测试集合进行预测 保存预测结果
 y_predict = mnb.predict(x_test)

 '''
 4 模型评估
 '''
 print("准确率:", mnb.score(x_test, y_test))
 print("其他指标：\n",classification_report(y_test, y_predict, target_names=news.target_names))
 '''
 准确率: 0.8397707979626485
 其他指标：
                            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
 '''