机器学习实战之使用朴素贝叶斯过滤垃圾邮件（python3）

1、代码

from numpy import *

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 trainNB0(trainMatrix,trainCategory): #朴素贝叶斯分类器训练函数
    numtraindocs = len(trainMatrix)
    numwords = len(trainMatrix[0])
    pbsive  = sum(trainCategory) / float(numtraindocs)
    p1 = 2.0; p2 = 2.0
    p1nums = ones(numwords); p2nums = ones(numwords)
    for i in range(numtraindocs):
        if trainCategory[i] == 1:
            p1nums += trainMatrix[i]
            p1 += sum(trainMatrix[i])
        else:
            p2nums += trainMatrix[i]
            p2  += sum(trainMatrix[i])
    p1vects = log(p1nums/p1)
    p2vects = log(p2nums/p2)
    return p1vects, p2vects, pbsive

def classifyNB(testvect,p1vect,p2vect,pbsive):
    p1 = sum(testvect*p1vect) + log(pbsive) #此处应是矩阵点乘，即矩阵的对应元素想乘
    p2 = sum(testvect*p2vect) + log(1-pbsive)
    if p1 > p2:
        return 1
    else:
        return 0

def textparse(bigstring):
    import re
    listwords = re.split(r'\W*',bigstring)
    return [tok.lower() for tok in listwords if len(tok) > 2]

def spamtest():
    doclist = []; classlist = [] ;fulltext = []
    for i in range(1,26):
        wordlist = textparse(open('email/spam/%d.txt'% i).read())
        print (wordlist)
        doclist.append(wordlist)
        fulltext.extend(wordlist)
        classlist.append(1)
        wordlist = textparse(open('email/ham/%d.txt'% i).read())
        doclist.append(wordlist)
        fulltext.extend(wordlist)
        classlist.append(0)
    vocablist = createVocabList(doclist)
    trainingset = list(range(50))
    testset = []
    for i in range(10):
        randeindex = int(random.uniform(0,len(trainingset)))
        testset.append(trainingset[randeindex])
        del(trainingset[randeindex])
    trainmat = []; trainclasses = []
    for i in trainingset:
        trainmat.append(setOfWords2Vec(vocablist,doclist[i]))
        trainclasses.append(classlist[i])
    p0v, p1v, pbsive = trainNB0(array(trainmat),array(trainclasses))
    errorcount = 0
    for docindex in testset:
        wordlist = setOfWords2Vec(vocablist,doclist[docindex])
        item = classifyNB(array(wordlist),p0v,p1v,pbsive)
        if item != classlist[docindex]:
            errorcount += 1
    print ('the error is:',float(errorcount)/len(testset))

注意事项：

python3报错解决办法：UnicodeDecodeError: 'gbk' codec can't decode byte 0xae in position 199: illegal multib

解决办法：打开email\ham\23.txt，找到SciFinance?，把?替换成空格即可。

'range' object doesn't support item deletion

python3.x , 出现错误 'range' object doesn't support item deletion

原因：python3.x   range返回的是range对象，不返回数组对象

解决方法：

把 trainingSet = range(50) 改为 trainingset = list(range(50))

注意array对象相乘是点乘，对应元素的相乘。matrix相乘是矩阵的乘法