数据挖掘经典算法复现:朴素贝叶斯

2023年2月6日 201次阅读 来源: 常用算法

原理：　P(Ci|W)
　　= P(w1w2…wn|C)P(Ci) / P(W)
Ci为类别

创建测试数据集

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 代表侮辱性文字, 0 代表正常言论
    return postingList,classVec

创建词典，后续步骤转为词向量时作为位置对照

def createVocabList(dataSet):
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document)
    return list(vocabSet)

对照着字典将数据集转为词向量

词袋模型 或者 词集模型 或者其他

def setOfWord2vec(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 vocabuList"%word
    return returnVec

计算P(Ci)及 P(W|Ci)

P(W|Ci) = P(W1|Ci)P(W2|Ci)…..P(Wn|Ci)

如果某个P（Wk|Ci）为0 则P（W|Ci）为0 为了消除这种影响 基准个数设为1

def trainNB0(trainMatrix,trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbuse = sum(trainCategory)/float(numTrainDocs)
    P0Num = np.ones(numWords)
    p1Num = np.Ones(numwords)
    P0Denom = 1.0
    P1Denom = 1.0
    for i in range(numTrainDocs)
        if trainCategory[i] == 1:
            P1Denom += sum(trainMatrix[i])
            p1Num += trainMatrix[i]
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect = np.log(p1Num/p1Denom) #取对数解决数太小相乘四舍五入为0的下溢问题
    p0Vect = np.log(p0Num/p0Denom)
    return p0Vect,p1Vect,pAbusive
def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
    p1 = sum(vec2Classify * p1Vec) + math.log(pClass1)
    p0 = sum(vec2Classify * p0Vec) + math.log(1-pClass1)
    if p1>p0:
        return 1
    else:
        return 0

封装下函数进行测试

def testingNB():
    listOfPosts,listClasses = loadDataSet()
    myVocabList = createVocablist(listOfPosts)
    trainMat = []
    for postinDoc in listOfPosts:
        trainMat.append(setOfWord2Vec(myVocabList,postinDoc))
    p0V,p1V,pAb = trainNB0(trainMat,listClasses)
    testEntry = np.array(['love','my','dalmation'])
    thisDoc = setOfWOrd2Vec(myVocabList,testEntry)
    print testEntry ,'classified as :',classifyNB(thisDoc,p0V,p1V,pAb)
    原文作者：常用算法
    原文地址: https://blog.csdn.net/py_mxy/article/details/62065777
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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