基于朴素贝叶斯分类算法构建文本分类器的Python实现

Author: Thinkgamer

Bayes/bayes.py

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+#encoding:utf-8
+'''
+Created on 2016年5月12日
+
+@author: Gamer Think
+'''
+
+from numpy import *
+
+#词表到向量的转换函数
+def loadDataSet():
+    postingList = [['my','dog','has','flea','problems','help','please'],
+                   ['maybe','not','take','him','to','dog','park','stupid'],
+                   ['my','dalmation','is','so','cute','I','love','him'],
+                   ['stop','posting','stupid','worthless','garbage'],
+                   ['mr','licks','ate','my','steak','how','to','stop','him'],
+                   ['quit','buying','worthless','dog','food','stupid']]
+    classVec = [0,1,0,1,0,1]      #1,侮辱  0,正常
+    return postingList,classVec
+
+def createVocabList(dataSet):
+    vocabSet = set([])  #调用set方法,创建一个空集
+    for document in dataSet:
+        vocabSet = vocabSet | set(document)     #创建两个集合的并集
+    return list(vocabSet)
+
+def setOfWords2Vec(vocabList,inputSet):
+    returnVec = [0]*len(vocabList)   #创建一个所含元素都为0的向量
+    for word in inputSet:
+        if word in vocabList:
+            returnVec[vocabList.index(word)] = 1
+        else:
+            print "the word:%s is not in my Vocabulary" % word
+    return returnVec
+
+
+def bagOfWords2VecMN(vocabList,inputSet):
+    returnVec = [0]*len(vocabList)   #创建一个所含元素都为0的向量
+    for word in inputSet:
+        if word in vocabList:
+            returnVec[vocabList.index(word)] += 1
+    return returnVec
+
+
+#朴素贝叶斯分类器训练集
+def trainNB0(trainMatrix,trainCategory):  #传入参数为文档矩阵，每篇文档类别标签所构成的向量
+    numTrainDocs = len(trainMatrix)      #文档矩阵的长度
+    numWords = len(trainMatrix[0])       #第一个文档的单词个数
+    pAbusive = sum(trainCategory)/float(numTrainDocs)  #任意文档属于侮辱性文档概率
+    #p0Num = zeros(numWords);p1Num = zeros(numWords)        #初始化两个矩阵，长度为numWords，内容值为0
+    p0Num = ones(numWords);p1Num = ones(numWords)        #初始化两个矩阵，长度为numWords，内容值为1
+    #p0Denom = 0.0;p1Denom = 0.0                         #初始化概率
+    p0Denom = 2.0;p1Denom = 2.0 
+    for i in range(numTrainDocs):
+        if trainCategory[i]==1:
+            p1Num +=trainMatrix[i]
+            p1Denom += sum(trainMatrix[i])
+        else:
+            p0Num +=trainMatrix[i]
+            p0Denom += sum(trainMatrix[i])
+    #p1Vect = p1Num/p1Denom #对每个元素做除法
+    #p0Vect = p0Num/p0Denom
+    p1Vect = log(p1Num/p1Denom)
+    p0Vect = log(p0Num/p0Denom)
+    return p0Vect,p1Vect,pAbusive
+
+#朴素贝叶斯分类函数
+def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
+    p1 = sum(vec2Classify * p1Vec) + log(pClass1)   #元素相乘
+    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
+    if p1>p0:
+        return 1
+    else:
+        return 0
+
+def testingNB():
+    listOPosts,listClasses = loadDataSet()   #产生文档矩阵和对应的标签
+    myVocabList = createVocabList(listOPosts) #创建并集
+    trainMat = []   #创建一个空的列表
+    for postinDoc in listOPosts:
+        trainMat.append(setOfWords2Vec(myVocabList,postinDoc))  #使用词向量来填充trainMat列表
+    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))  #训练函数
+    testEntry = ['love','my','dalmation']   #测试文档列表
+    thisDoc = array(setOfWords2Vec(myVocabList,testEntry)) #声明矩阵
+    print testEntry,'classified as:',classifyNB(thisDoc,p0V,p1V,pAb)
+    testEntry = ['stupid','garbage']
+    thisDoc = array(setOfWords2Vec(myVocabList,testEntry))    #声明矩阵
+    print testEntry,'classified as:',classifyNB(thisDoc,p0V,p1V,pAb)
+    
+if __name__=="__main__":
+    testingNB()