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朴素贝叶斯分类

#coding=utf-8'''Created on 2016年1月9日@author: admin'''from numpy import *# 加载数据集函数def loadDataSet():    # 定义邮件列表    postingList = [['my','dog','has','flea',\                    'problem','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']]    # 定义标签向量，1——abusive，0——not abusive    classVec = [0,1,0,1,0,1]    return postingList,classVec# 创建词汇列表def createVocabList(dataSet):    # 定义词汇集    vocabSet = set([])    # 遍历文档    for document in dataSet:        # 将每个document合并到vocabSet，|用来联合两个集合        vocabSet = vocabSet | set(document)    # 返回词汇集    return list(vocabSet)# 把单词转换成向量def setOfWords2Vec(vocabList,inputSet):    # 定义要返回的向量    returnVec = [0] * len(vocabList)    # 遍历输出集中的单词    for word in inputSet:        # 单词在词汇集中        if word in vocabList:            # 对应的位置设为1            returnVec[vocabList.index(word)] = 1        else:            print "the word: %s is not in my Vocabulary!" % word    # 返回向量    return returnVec# 把单词转换成向量,用词袋模型，计算词出现的次数def bagOfWords2VecMN(vocabList,inputSet):    # 定义要返回的向量    returnVec = [0] * len(vocabList)    # 遍历输出集中的单词    for word in inputSet:        # 单词在词汇集中        if word in vocabList:            # 对应的词出现次数 加1            returnVec[vocabList.index(word)] += 1    # 返回向量    return returnVec# 条件概率的计算def trainNB0(trainMatrix,trainCategory):    # 计算文档的数目    numTrainDocs = len(trainMatrix)    # 计算单词的数目    numWords = len(trainMatrix[0])    # 计算类别的概率，abusive为1，not abusive为0    pAbusive =sum(trainCategory) / float(numTrainDocs)    # 初始化计数器，1行numWords列，p0是not abusive    # p0Num =zeros(numWords)    p0Num = ones(numWords)    # 初始化计数器，p1是abusive    p1Num = ones(numWords)    # 初始化分母    p0Denom = 2.0    p1Denom = 2.0    # 遍历文档    for i in range(numTrainDocs):        # 计算abusive对应的词汇的数目，trainMatrix为0-1值形成的向量        if trainCategory[i] == 1:            # p1Num存储的是每个词出现的次数            p1Num += trainMatrix[i]            # p1Denom存储的是词的总数目            p1Denom += sum(trainMatrix[i])        # 计算not abusive词汇的数目        else:            # 每个词在not abusive下出现的次数            p0Num += trainMatrix[i]            # not abusive下的总词数            p0Denom += sum(trainMatrix[i])    # 计算abusive下每个词出现的概率    # p1Vect = p1Num / p1Denom    p1Vect = log(p1Num / p1Denom)    # 计算not abusive下每个词出现的概率    # p0Vect = p0Num / p0Denom    p0Vect = log(p0Num / p0Denom)    # 返回词出现的概率和文档为abusive的概率，not abusive的概率为1-pAbusive    return p0Vect,p1Vect,pAbusivedef classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):    # 计算abusive的概率    p1 = sum(vec2Classify * p1Vec) + log(pClass1)    # 计算not abusive的概率    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)    # 根据概率大小判断属于哪个类    if p1 > p0:        return 1    else:        return 0# 测试def testingNB():    # 加载数据集    listOPosts,listClass = loadDataSet()    # 创建词汇列表    myVocabList = createVocabList(listOPosts)    trainMat = []    for postinDoc in listOPosts:        trainMat.append(setOfWords2Vec(myVocabList,postinDoc))    p0V,p1V,pAb = trainNB0(array(trainMat),array(listClass))    # print p0V,p1V,pAb    # print trainMat    testEntry = ['love','my','dalmation']    thisDoc = array(setOfWords2Vec(myVocabList,testEntry))    print testEntry,'classified as:',classifyNB(thisDoc, p0V, p1V, pAb)    testEntry = ['stupid','garbage']    thisDoc = array(setOfWords2Vec(myVocabList,testEntry))    print testEntry,'classified as:',classifyNB(thisDoc, p0V, p1V, pAb)# 文本解析   # 输入是字符串，输出是单词列表def textParse(bigString):        # 导入正则表达式的包    import re    # 用正则表达式分割字符串    listOfTokens = re.split(r'\W*', bigString)    # 返回小写单词列表    return [tok.lower() for tok in listOfTokens if len(tok) > 2] # 垃圾邮件测试   def spamTest():    # 定义docList文档列表，classList类别列表，fullText所有文档词汇    docList=[]; classList = []; fullText =[]    # 遍历email/spam和email/ham下的txt文件    for i in range(1,26):        # 定义并读取垃圾邮件文件的词汇分割列表        wordList = textParse(open('email/spam/%d.txt' % i).read())        # 将词汇列表加到文档列表中        docList.append(wordList)        # 将所有词汇列表汇总到fullText中        fullText.extend(wordList)        # 文档类别为1，spam        classList.append(1)        # 读取非垃圾邮件的文档        wordList = textParse(open('email/ham/%d.txt' % i).read())        # 添加到文档列表中        docList.append(wordList)        # 添加到所有词汇列表中        fullText.extend(wordList)        # 类别为0，非垃圾邮件        classList.append(0)    # 创建词汇列表    vocabList = createVocabList(docList)    # 定义训练集的索引和测试集    trainingSet = range(50); testSet=[]    # 随机的选择10个作为测试集          for i in range(10):        # 随机索引        randIndex = int(random.uniform(0,len(trainingSet)))        # 将随机选择的文档加入到测试集中        testSet.append(trainingSet[randIndex])        # 从训练集中删除随机选择的文档        del(trainingSet[randIndex])    # 定义训练集的矩阵和类别      trainMat=[]; trainClasses = []    # 遍历训练集，求得先验概率和条件概率    for docIndex in trainingSet:        # 将词汇列表变为向量放到trainMat中        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))        # 训练集的类别标签        trainClasses.append(classList[docIndex])    # 计算先验概率，条件概率    p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))    # 定义错误计数    errorCount = 0    # 对测试集进行分类    for docIndex in testSet:         # 将测试集词汇向量化              wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])        # 对测试数据进行分类        if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:            # 分类不正确，错误计数加1            errorCount += 1            print "classification error",docList[docIndex]    # 输出错误率    print 'the error rate is: ',float(errorCount)/len(testSet)

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