数学之路(机器学习实践指南)-文本挖掘与NLP(6)

def wordfeatures(word):
    return {"cnword":word}

.....
classifier=nltk.NaiveBayesClassifier.train(samplewords)
#大学所属的类别 http://blog.csdn.net/myhaspl
print u"----大学所属的类别-----"
print classifier.classify({"cnword":u"大学"})
#大脑所属的类别http://blog.csdn.net/myhaspl
print u"----大脑所属的类别-----"
print classifier.classify({"cnword":u"大脑"})
#测试数据分类准确率http://blog.csdn.net/myhaspl
print nltk.classify.accuracy(classifier,testwords)

#特征0分类最有效的10个词http://blog.csdn.net/myhaspl
for wf,mostword in classifier.most_informative_features(10):
    print mostword,
print


#为显示utf-8，将show_most_informative_features代码进行修改http://blog.csdn.net/myhaspl
#classifier.show_most_informative_features(10)  也可直接调用这句，但是UTF8显示有问题  http://blog.csdn.net/myhaspl 
cpdist = classifier._feature_probdist
print('Most Informative Features')

for (fname, fval) in classifier.most_informative_features(10):
    def labelprob(l):
        return cpdist[l, fname].prob(fval)

    labels = sorted([l for l in classifier._labels
                     if fval in cpdist[l, fname].samples()],
                    key=labelprob)
    if len(labels) == 1:
        continue
    l0 = labels[0]
    l1 = labels[-1]
    if cpdist[l0, fname].prob(fval) == 0:
        ratio = 'INF'
    else:
        ratio = '%8.1f' % (cpdist[l1, fname].prob(fval) /
                           cpdist[l0, fname].prob(fval))
    print fname+"="+fval, 
print(('%6s : %-6s = %s : 1.0' % (("%s" % l1)[:6], ("%s" % l0)[:6], ratio)))    
运行结果:
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =>|
----大学所属的类别-----
教育
----大脑所属的类别-----
科技
0.977346278317
世界 公司 事先 游戏 之后 领域 采用 学科 里面 技术
Most Informative Features
cnword=世界     科技 : 教育     =     20.6 : 1.0
cnword=公司     科技 : 教育     =     12.4 : 1.0
cnword=事先     科技 : 教育     =      5.8 : 1.0
cnword=游戏     科技 : 教育     =      5.8 : 1.0
cnword=之后     科技 : 教育     =      4.5 : 1.0
cnword=领域     科技 : 教育     =      4.5 : 1.0
cnword=采用     科技 : 教育     =      4.5 : 1.0
cnword=学科     科技 : 教育     =      4.1 : 1.0
cnword=里面     科技 : 教育     =      4.1 : 1.0
cnword=技术     科技 : 教育     =      4.1 : 1.0

本博客所有内容是原创，如果转载请注明来源

http://blog.csdn.net/myhaspl/

朴素贝叶斯分类，对词条分类如上