吴裕雄--天生自然python机器学习：使用朴素贝叶斯过滤垃圾邮件

使用朴素贝叶斯解决一些现实生活中
的问题时，需要先从文本内容得到字符串列表，然后生成词向量。

准备数据：切分文本

测试算法：使用朴素贝叶斯进行交叉验证

文件解析及完整的垃圾邮件测试函数

def createVocabList(dataSet):
    vocabSet = set([])  #create empty set
    for document in dataSet:
        vocabSet = vocabSet | set(document) #union of the two sets
    return list(vocabSet)

def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    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)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec

def textParse(bigString):    #input is big string, #output is word list
    import re
    listOfTokens = re.split(r'W*', bigString)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2] 

def trainNB0(trainMatrix,trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs)
    p0Num = ones(numWords)
    p1Num = ones(numWords)      #change to ones() 
    p0Denom = 2.0
    p1Denom = 2.0                        #change to 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 = log(p1Num/p1Denom)          #change to log()
    p0Vect = log(p0Num/p0Denom)          #change to log()
    return p0Vect,p1Vect,pAbusive

def aloneIndex(datasetLen):
    a = []
    while(True):
        randIndex = int(random.uniform(0,len(trainingSet)))
        a.append(randIndex)
        if(len(set(a))==10):
            break
    return a

def spamTest():
    docList=[]
    classList = []
    fullText =[]
    for i in range(1,26):
        wordList = textParse(open('F:\machinelearninginaction\Ch04\email\spam\%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('F:\machinelearninginaction\Ch04\email\ham\%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)#create vocabulary
    trainingSet = range(50)
    testSet = aloneIndex(trainingSet) #create test set
    trainingSetT = []
    for i in range(len(trainingSet)):
        for j in range(len(testSet)):
            if(testSet[j] != trainingSet[i]):
                trainingSetT.append(trainingSet[i])
    trainingSet = trainingSetT
    trainMat=[]
    trainClasses = []
    for docIndex in trainingSet:#train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
    errorCount = 0
    for docIndex in testSet:        #classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if(classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]):
            errorCount += 1
            print("classification error",docList[docIndex])
    print('the error rate is: ',float(errorCount)/len(testSet))
    
spamTest()