1200字范文 > 机器学习实战 - 基于概率论的分类方法：朴素贝叶斯

机器学习实战 - 基于概率论的分类方法：朴素贝叶斯

时间：2023-08-04 08:55:39

之前关于k-近邻和决策树的讲解没有一步一步具体的说明每个函数的实现功能和测试样例，让不是学习本书的人看起来一片代码一头雾水，这里开始，会仔细的讲解每个函数实现的功能和其对应的测试样例，之前的也会抽空修改。

跟概率的作用一样，如果A事件发生的概率大于B时间发生的概率那么我们的“决策机构”就选择A事件，反则选B。我们首先得有铺垫的知识点——条件概率：就是事件A在另外一个事件B已经发生条件下的发生概率。条件概率表示为P(A|B)，读作“在B条件下A的概率”，其计算公式为：P(A|B) = P(AB)/P(B)，证明及学习可自行搜索。

这里没听懂？没关系，通过下面的例子就很容易理解上面所说的什么P什么c1、c2、x之类的了。

例：假设我们有一个留言板，要其动判断某个用户的某个留言是否是属于侮辱性的，那么如何做呢？我们要训练一下机器，让它自己判断，不过这种判断是有误差的，而且在我们这个简单的例子中误差还是挺大的。我们根据单词来分类，比如“stupid”是属于侮辱性的，而“like”则不是，在这里我们考虑每个单词都是相互独立的，也就是说某个单词出现于否与其它单词无关联，这也就是我们这个朴素贝叶斯的“朴素”的意思，只考虑最简单的条件。实际上，某些单词与其它单词关联挺大的，举个例子，to这个单词跟want或者其它的be等词关联很大，往往出现在它们的后面，或者它也常常出现在动词前面。同时，书本上说朴素贝叶斯的另一个特征是每个次都同等重要，这明显是不符合实际的，但目前我们就将其视为同等重要来考虑，学习其基本概念。

这里我使用python3.6运行，后面打开文件的时候有地方编码方式没搞明白python3.6笔者无法解决，届时会改为2.7运行。注意2.7使用中文要在代码最前面加#encoding=utf-8

我们先创建一个bayes.py，存入下面代码：

我们新建一个test.py

接下来就是训练算法：从词向量计算概率

计算每个类别中的文档数目对每个类别：如果词条(单词)出现在文档中->增加该词条(单词)的计数值增加所有词条(单词)的计数值对每个类别：对每个词条(单词)：将该词条(单词)的数目除以总词(单词)条数得到条件概率返回每个类别的条件概率

很难理解上面的伪代码？我们来看看下面的函数（该函数在我们创建的bayes.py中）

def trainNB0(trainMatrix, trainCategory):"""获取总的文档数目numTrainDocs"""numTrainDocs = len(trainMatrix)"""获取每个文档的词条(单词)的数目"""numWords = len(trainMatrix[0])"""由于trainCategory中的分类要不就是1要不就是0, 所以sum(trainCategory)就是所有类别为1的数目要转换为浮点数, 否则计算会有误差出错"""pAbusive = float(sum(trainCategory)) / numTrainDocs"""创建两个数量跟每个文档单词数量相同的向量, 全部初始化为1, 为什么初始化为1下面解释其实应该理解为p0num = zeros(numWords)p1num = zeros(numWords)一开始应该初始化为0, 但我们初始化为1"""p0num = ones(numWords)p1num = ones(numWords)"""总数也初始化为2其实这里也应该是p0Denom = 0.0p1Denom = 0.0"""p0Denom = 2.0p1Denom = 2.0"""下面的if和else就是：对每个类别：如果词条(单词)出现在文档中->增加该词条(单词)的计数值增加所有词条(单词)的计数值"""for i in range(numTrainDocs):if trainCategory[i] == 1:p1num += trainMatrix[i]p1Denom += sum(trainMatrix[i])else:p0num += trainMatrix[i]p0Denom += sum(trainMatrix[i])"""下面return就是：对每个类别：对每个词条(单词)：将该词条(单词)的数目除以总词(单词)条数得到条件概率返回每个类别的条件概率"""return log(p0num / p0Denom), log(p1num / p1Denom), pAbusive

最后 p0num/p0Denom 和 p1num/p1Denom 应该是整个文档出现的单词中每个单词属于0（非侮辱性）和1（侮辱性）的概率，但是我们利用贝叶斯分类器对文档进行分类时，要计算多个概率的乘积以获得文档属于某个类别的概率，根据概率的大小判断其属于哪一类别，如计算属于1（侮辱性）的概率亦即计算 P(x0|1)P(x1|1)P(x2|1)...P(xn|1) ，如果我们刚才起始个数初始化为0，那假设我们输入的文档中出现了我们训练文档中未出现过的单词，它的概率就变成了0，0乘任何数都得0，那么会使得整个表达式的答案为0，为降低0带来的影响，所以我们初始化为1，虽然初始化为1确实也带来了误差方面的问题，但好过0带来的问题。

除此之外，我们还需要考虑的问题是计算机计算时的下溢问题，也就是说很多小数相乘可能使最终结果变得非常小而使误差非常大，所以我们引入log对数来解决此问题。

从上图我们看到，当f(x)处于0-1时，其变化趋势和ln(f(x))非常相似，而 f(x1)∗f(x2) 可以变为 ln(f(x1))+ln(f(x1)) ，乘法变成加法，解决了我们下溢问题。所以我们返回的时不再返回概率的向量，而是返回其对数的向量，后面我们处理的时候相加即可，不用相乘。也就是返回 log(p0num/p0Denom),log(p1num/p1Denom) 。

在test.py中测试一下：

trainMat = []for i in range(len(listOfPosts)):trainMat.append(bayes.setOfWords2Vec(myVocabList, listOfPosts[i]))p0, p1, pAbusive = bayes.trainNB0(trainMat, listClasses)print(p0)print(p1)print(pAbusive)"""答案如下：[-2.56494936 -2.56494936 -2.56494936 -2.56494936 -2.56494936 -2.56494936-3.25809654 -2.56494936 -3.25809654 -2.56494936 -1.87180218 -2.56494936 -2.56494936 -3.25809654 -3.25809654 -2.56494936 -3.25809654 -2.56494936 -3.25809654 -3.25809654 -2.56494936 -2.56494936 -2.56494936 -3.25809654 -3.25809654 -2.56494936 -2.56494936 -3.25809654 -3.25809654 -2.56494936 -2.56494936 -2.15948425][-3.04452244 -3.04452244 -3.04452244 -3.04452244 -3.04452244 -3.04452244-2.35137526 -3.04452244 -1.65822808 -3.04452244 -3.04452244 -2.35137526 -3.04452244 -2.35137526 -2.35137526 -3.04452244 -1.94591015 -2.35137526 -2.35137526 -2.35137526 -3.04452244 -3.04452244 -3.04452244 -2.35137526 -2.35137526 -3.04452244 -3.04452244 -2.35137526 -2.35137526 -3.04452244 -1.94591015 -2.35137526]0.5"""

接着就是测试一下我们的写的程序，在bayes.py中加入：

"""第一个参数是输入单词转化后的向量, 如[1, 2, 3, 1, 1， ……], 其它几个如字面意思我们计算p1的log值和p0的log值, 由我们前面的分析, log函数在0-1内跟概率变化情况是一样的我们利用log的大小（也就是p1和p0的大小近似预估为其概率的大小, 谁大返回谁）"""def classifyNB(vec2classify, p0Vec, p1Vec, pClass1):p1 = sum(vec2classify * p1Vec) + log(pClass1)p0 = sum(vec2classify * p0Vec) + log(1.0 - pClass1)# print( "p0 is: ", p0, ", p1 is: ", p1)if p1 > p0:return 1else:return 0def testingNB():"""首先得到单词向量和分类向量"""listOPosts, listclasses = loadDataSet()"""根据得到的单词向量得到不含重复词汇的set集"""myVocabList = createVocabList(listOPosts)trainMat = []"""将每一行的单词都放到myVocabList这个向量里面举个栗子, 假设的单词向量listOPosts是[['a', 'b', 'c']['a', 'c', 'd']['e', 'f']]那么我们的myVocabList就是['a', 'b', 'c', 'd', 'e', 'f'], 顺序不一定是abcdef跟你的单词向量有关setOfWords2Vec这个函数是将每一行都转换相应的数量如['a', 'b', 'c']对应于['a', 'b', 'c', 'd', 'e', 'f']就是[1, 1, 1, 0, 0, 0]如['e', 'f']对应于['a', 'b', 'c', 'd', 'e', 'f']就是[0, 0, 0, 0, 1, 1]得到的每一行都放入到trainMat中"""for postinDoc in listOPosts:trainMat.append(setOfWords2Vec(myVocabList, postinDoc))"""调用函数得到相应的log值和为1的概率"""p0Vec, p1Vec, pAbusive = trainNB0(trainMat, listclasses)"""下面是一些测试"""testEntry = ['love', 'my', 'dalmation']thisDoc = array(setOfWords2Vec(myVocabList, testEntry))print(testEntry, " classified as: ", classifyNB(thisDoc, p0Vec, p1Vec, pAbusive))testEntry = ['stupid', 'garbage']thisDoc = array(setOfWords2Vec(myVocabList, testEntry))print(testEntry, " classified as: ", classifyNB(thisDoc, p0Vec, p1Vec, pAbusive))testEntry = ['stupid', 'garbage', 'food']thisDoc = array(setOfWords2Vec(myVocabList, testEntry))print(testEntry, " classified as: ", classifyNB(thisDoc, p0Vec, p1Vec, pAbusive))

接着我们调用我们刚才写的，在test.py中加入：

bayes.testingNB()"""答案如下：['love', 'my', 'dalmation'] classified as: 0['stupid', 'garbage'] classified as: 1['stupid', 'garbage', 'food'] classified as: 1"""

好了，我们使用我们刚才的思路和已经编写好的代码来写一个简单的例子，使用朴素贝叶斯过滤垃圾邮件。

到目前为止我们都是使用pyhton3.6，下面的程序我们改用pyhton2.7：

"""解析我们的文件, 解析为字符串列表, 然后去掉我们长度小于3的单词，因为诸如is, of之类的单词实际上不用于分类"""def textParse(bigString):import relistOfTikens = re.split(r'\W*', bigString)return [tok.lower() for tok in listOfTikens if len(tok) > 2]def spamTest():docList = []classes = []fullText = []"""打开我们的文件, 放到我们的列表中"""for i in range(1, 26):docList.append(textParse(open('spam/%d.txt' % i).read()))fullText.extend(textParse(open('spam/%d.txt' % i).read()))classes.append(1)"""下面两句在python2.7中可以运行, 但是在python3.6中提示编码方式什么的问题,目前还没找到解决方法, 应该是编码方式没对应上但上面两句解析文档又没有错误, 按理同他们的编码方式都是相同的, 暂不得其解"""docList.append(textParse(open('ham/%d.txt' % i).read()))fullText.extend(textParse(open('ham/%d.txt' % i).read()))classes.append(0)"""这下面的思路基本上与刚才的相同"""vocabList = createVocabList(docList)trainingSet = list(range(25))testSet = []"""这里我们随机选取出10篇文章, 剩下的用于训练, 选出的用于测试"""for i in range(10):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del (trainingSet[randIndex])errorCount = 0trainMat = []trainClasses = []for docIndex in trainingSet:trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))trainClasses.append(classes[docIndex])p0, p1, pAbusive = trainNB0(array(trainMat), array(trainClasses))for docIndex in testSet:if classifyNB(array(setOfWords2Vec(vocabList, docList[docIndex])), p0, p1, pAbusive) != classes[docIndex]:errorCount += 1print("'the error rate is : ", float(errorCount) / len(testSet))

在test.py中添加：

bayes.spamTest()"""答案如下：("'the error rate is : ", 0.1)"""

课本的下个例子使用朴素贝叶斯分类器从个人广告中获取区域倾向。我们将分别从美国的两个城市中选取一些人，通过分析这些人发布的征婚广告信息，比较这两个城市人们在广告用词上是否不同，如果结论确实不同，那么他们各自常用的词都有哪些？其实简单来说就是获取数据的数据源不一样了，一开始是从文件中获取现在变成RSS源获取，获取到的内容还是一样分析，不过我没弄明白feed1[‘entries’][i][‘summary’]中的’entries’和’summary’是怎么弄出来的，也就是键值是怎么知道的，反正课本是这么写的，毕竟我也是新手。

这里我们再次使用python3.6

"""该函数将词汇按出现的频率排序返回前30个最常出现的, 也就是出现次数最多的30个"""def calMostFreq(vocabList, fullText):import operatorfreqDict = {}for word in vocabList:freqDict[word] = fullText.count(word)sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True)return sortedFreq[:30]"""该函数类似于spamTest(), 但是它访问的是RSS源而不是文件"""def localWords(feed1, feed0):classes = []fullText = []docList = []"""根据短的那一个来选取"""minLength = min(len(feed1['entries']), len(feed0['entries']))for i in range(minLength):docList.append(textParse(feed1['entries'][i]['summary']))classes.append(1)fullText.extend(textParse(feed1['entries'][i]['summary']))docList.append(textParse(feed0['entries'][i]['summary']))classes.append(0)fullText.extend(textParse(feed0['entries'][i]['summary']))# print(docList)vocabList = createVocabList(docList)freqDict = calMostFreq(vocabList, fullText)for word in freqDict:if word in vocabList:vocabList.remove(word[0])trainingSet = list(range(2*minLength))testSet = []for i in range(20):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del(trainingSet[randIndex])trainMat = []trainClasses = []for docIndex in trainingSet:trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))trainClasses.append(classes[docIndex])p0Vec, p1Vec, pAbuive = trainNB0(array(trainMat), array(trainClasses))errorCount = 0.0for docIndex in testSet:wordVecotr = setOfWords2Vec(vocabList, docList[docIndex])if classifyNB(array(wordVecotr), p0Vec, p1Vec, pAbuive) != classes[docIndex]:errorCount += 1.0print("the error rate is: ", float(errorCount)/len(testSet))return vocabList, p0Vec, p1Vec"""将出现较多的单词打印出来"""def getTopWord(ny, sf):import operatorvocabList, p0Vec, p1Vec = localWords(ny, sf)topNY = []topSF = []for i in range(len(p0Vec)):if p0Vec[i] > -6.0:"""此处需要将vocabList转换为list才能用数字索引, 否则会提示'set' ... index...之类的错误"""topSF.append((list(vocabList)[i],p0Vec[i]))if p1Vec[i] > -6.0:topNY.append((list(vocabList)[i],p1Vec[i]))sortedTopNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)sortedTopSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)print("SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF")for word in sortedTopSF:print(word[0])print("NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY")for word in sortedTopNY:print(word[0])

测试一下test.py加入以下代码：

import feedparserny = feedparser.parse('/stp/index.rss')sf = feedparser.parse('/stp/index.rss')bayes.localWords(ny, sf)bayes.getTopWord(ny, sf)"""答案如下：the error rate is: 0.35the error rate is: 0.2SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SFandfortheyoulookingfitwithhellolovelikesingletimefriendsseecanlatinowilltryfullstudenthavenotexperiencewouldparejaactivehostfirstfromoutoldsomemissfunmovieparahealthyplayasfannudistasthatmassagebayfemaleverysayolderwhofrequentwhatresortslugaresyeahbooksincerecornyinquirycomicmujerspookingthanricandatesalsoburlingamebuscajumphangtimescoolbeachesslooporiginallyforehazelpreferablydaddycraigslistcheesysailseeksneedfacedramafulfillingparejashitlongmessageuponnicepurposesdoesntowelsentertainmentstraightclearreleasesmyselffavorsshareetcdaysailercomorangebasissexualprovidingaroundownerwidebbcbodylatinoscomecouplemaybecomedyneedsbutterfliesattachedenbuscamessingtakeavailableemployedtallwhitephone200lbbigpassionateguardiansserioslyasistirthisrandomely420eastprettynudistmaydarkmanlighterherlunchweirdonlygalaxyacknowledgepluseyeslittleanossemirealdickmaturetheaterdoespersonwaitdetailshugetalkbusinessmustmidnighttummyreadingthanksfurtherhispanickisseswomansimplymoviesjustdiseasesearchingexchangesoilsbetweeninterestfreefamilynumberlatestslimemailcollegethinkadmitpuertotableshorttrainingcompaniesyeartruetermfriendshipnothingselfsomeonetonerearlymostopentillphotobitsuckedlolexistbutanywayssometimetoopossesspeoplecomespartnerattractivelivingwaterfriendamigosattenddudeshoweuropenudehonestmakingcompanionactiongoingmarriedcompanionshipyour6ftwhenrolecustomernewpleaseweekendassisttheirdrinkhate5thmattersallowjusguyssoontrèsojafavoriteunlessloyalsleeplesskobergenchat1980vallejomindedfriendlyweekendsraisedamproadaskingdinnerstatedudeschilledlifegoespicturepresentableracefingershangingbuddyfellladyhardhasbbwtouchedintosupportknowwenttoldspeciesrelaxing20slivesobviouscutelastlivedmindcockeachlupefindconnectionrequestedsidewayinterestingreceivinghypnoticotherdominantwhateverheypicsmeettravelconfirmationwonderfulexplorenamesemphasizingcircleonetalkingcureyearsconversationsportsdirectorcouldneverwouldnattemptscoldwantgreatestpercareflastartedadventurehalfreadywelcomthruupscalewannadutchssenioruseuppergivingkindexcellentsleeppicfathercenturycompanyresourcefulsoundsdoganimechicdigpreferencepreviouscaresshy41yrseasternlatepossibilitysoccercreedworkboredsmokeafternooncrossdresserforwardamusejoininclude155offlbsalcoholicsweetbackroundyourselftriviawonderfullyanyonedrinksbronumbershopefullyunderneathbluntswonderedtypeteamtheyaboutretiredbiggerwondersafeproudkiddescribednecessarily1973namerowcontactactuallygentlementhoughbringpersionconsideredlovesofferplatonicmeanintelligentpreferinterestedanyqualitysexhearingslitherybobalooconcordalouddiseasesgoopersonalitytwentyawaygeneralevenmanywherepossiblehereworldfeellightgoddessdefinitelyskinnedsitblondskinnylookappreciategenerousthoroughlysmokingpervertedfoundmiddlebeingpackingrelaxcityprofessionalsomethingpassionspeakchillinvolvedliquorbuddriftingshipageboatwaistwerenightsbackexpectyouthfulgenuinesightgamingdressingblack40sareaaverageturndonassistancetracenailssizelatinfunnymaidsletlovedgentlyasiangivefewsupplypaperscompassionategrasscalsmalldateamongsenseyoungersuppliedgreenmentiondecgettinghandsoothingtruthfullyfuckinggoodswgmdriveshapesummerstillisotrulysubmissiveeveryorkvillemusicianhadkidsperhapsproblemdivorcedmuscleshavennightartistlegsqueenslikesoverwellthemtonightsetcanadathenmightcuddlingmassageshomesensualdescentreallysincdiscretepersonalitiesdesirefridayclueraremore56yobecauseinfo30safterpartybetterwhileoddmomentnerdspanishpointersrighttripsstuffplaynormaldontwasthereguyagesladiesfailedtruckreadsecondarydankjetsonssamesareeourareeasylatelymuttbeentravelingnerdywhyshoppingcrushingdiningrememberracialtripseenhipdressspoilmeetingsoulmatewriteplacerobteethintoxicatingsendeducatedethnicongoinggirllongtermbuildgetthoroughbredputallitalianenjoyhowsinceleastshouldgotdefathletefashionablemakeimportantfactmaledriverkarenenclosedserve41tripinchcatchNY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NYyouandforthelookingwiththatcouldthereyourhangoldanywhitecanbutwayheynicefromoutmanjustsomeonearewhowhatwhenchatthanchilledladybbwcoolmeetyearssoccersmokeforwardthoughinterestedtimegooherelookgenerousrelaxprofessionalsomethinginvolvedagefriendsdonhispanicgoodwomandriveshapetrulyperhapstrywellemailthenthinkyearmorefriendshipafterguyreadlovehaveattractivenotdudesendeducatedgetallgotgoingmalecatchrolepleaseassistdrinkhate5thmattersallowguystrèssincereunlessloyalsleeplesskobergen1980mindedweekendsampaskingstatedudeslifepresentableracefingershangingbuddyfelltouchedintosupportspeciesrelaxingobviouslivedeachconnectionrequestedsideoriginallyinterestingpreferablyhypnoticotherdominantpicsconfirmationneedexploreemphasizinglonghostdirectoruponfirstattemptsdoesncareadventurethruupscalewannasenioruseupperfavorskindexcellentsleeppiccenturysomecompanyresourcefulsoundschiccaresshy41yrslatesexualcreedworkboredafternoonamusefunalcoholicsweetbackroundyourselfbodydrinksbrohopefullyunderneathtypetheyaboutwondersafeprouddescribedcouplemaybe1973rowgentlemenlovesmeanqualitysexhearingslitheryaloudtwentyavailableawaypossibletallphonelightskinnedsitblondappreciateeastfoundmaybeingdarkspeakchillliquordriftingshipboatwerebacklittleyouthfulrealblackfitturnassistancetracesizemaidsletlovedgentlyasiantalkgivebusinessmustcompassionategrasssenseyoungersuppliedgreenmentiongettinghandsoothingfurthertruthfullyfuckingsimplyswgmsummerisosubmissiveyorkvillemusicianwilldivorcedmusclesartistlegsslimovertonightcollegecanadafemalesensualdescentreallysincdiscretestudentdesirecluerare56yonothingpartybetterwhileoddverymomentspanishrightstuffplayopenbitladiesfailedtrucksecondarydankjetsonsoldersameeasymuttbeenwhyfriendrememberracialseenhipplaceethnicongoinglongtermbuildthoroughbredputcompanionitalianhowleastshoulddefathletefashionmakeimportantdriverkarenserve41trip"""

附上整个文件bayes.py

# encoding=utf-8from numpy import *"""创建两个列表, 一个是包含多句话的列表, 另一个则对应每句话的标签, 1是侮辱性的我们可以理解为把一篇文章分总了6句话"""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]return postingList, classVec"""这个集合返回的是刚才6句话中所有的单词, 重复多次出现的只统计一次, 也就是统计所有出现的单词"""def createVocabList(dataSet):vacabSet = set([])for document in dataSet:vacabSet = vacabSet | set(document)return vacabSet"""首先创建一个元素个数和刚才createVocabList函数得到的集合一样的列表, 全部初始化为0也就是没有出现过对于输入inputSet中每个单词, 如果其在刚才的单词集合中则+1"""def setOfWords2Vec(vacabList, inputSet):returnVec = [0] * len(vacabList)for word in inputSet:if word in vacabList:"""此处要转换为list, set没有index这个属性, pyhton3不转换list提示错误"""returnVec[list(vacabList).index(word)] += 1else:print("the word %s is not in my Vocabulary!" % word)return returnVecdef trainNB0(trainMatrix, trainCategory):"""获取总的文档数目numTrainDocs"""numTrainDocs = len(trainMatrix)"""获取每个文档的词条(单词)的数目"""numWords = len(trainMatrix[0])"""由于trainCategory中的分类要不就是1要不就是0, 所以sum(trainCategory)就是所有类别为1的数目要转换为浮点数, 否则计算会有误差出错"""pAbusive = float(sum(trainCategory)) / numTrainDocs"""创建两个数量跟每个文档单词数量相同的向量, 全部初始化为1, 为什么初始化为1下面解释其实应该理解为p0num = zeros(numWords)p1num = zeros(numWords)一开始应该初始化为0, 但我们初始化为1"""p0num = ones(numWords)p1num = ones(numWords)"""总数也初始化为2其实这里也应该是p0Denom = 0.0p1Denom = 0.0"""p0Denom = 2.0p1Denom = 2.0"""下面的if和else就是：对每个类别：如果词条(单词)出现在文档中->增加该词条(单词)的计数值增加所有词条(单词)的计数值"""for i in range(numTrainDocs):if trainCategory[i] == 1:p1num += trainMatrix[i]p1Denom += sum(trainMatrix[i])else:p0num += trainMatrix[i]p0Denom += sum(trainMatrix[i])"""下面return就是：对每个类别：对每个词条(单词)：将该词条(单词)的数目除以总词(单词)条数得到条件概率返回每个类别的条件概率"""return log(p0num / p0Denom), log(p1num / p1Denom), pAbusive"""第一个参数是输入单词转化后的向量, 如[1, 2, 3, 1, 1， ……], 其它几个如字面意思我们计算p1的log值和p0的log值, 由我们前面的分析, log函数在0-1内跟概率变化情况是一样的我们利用log的大小（也就是p1和p0的大小近似预估为其概率的大小, 谁大返回谁）"""def classifyNB(vec2classify, p0Vec, p1Vec, pClass1):p1 = sum(vec2classify * p1Vec) + log(pClass1)p0 = sum(vec2classify * p0Vec) + log(1.0 - pClass1)# print( "p0 is: ", p0, ", p1 is: ", p1)if p1 > p0:return 1else:return 0def testingNB():"""首先得到单词向量和分类向量"""listOPosts, listclasses = loadDataSet()"""根据得到的单词向量得到不含重复词汇的set集"""myVocabList = createVocabList(listOPosts)trainMat = []"""将每一行的单词都放到myVocabList这个向量里面举个栗子, 假设的单词向量listOPosts是[['a', 'b', 'c']['a', 'c', 'd']['e', 'f']]那么我们的myVocabList就是['a', 'b', 'c', 'd', 'e', 'f'], 顺序不一定是abcdef跟你的单词向量有关setOfWords2Vec这个函数是将每一行都转换相应的数量如['a', 'b', 'c']对应于['a', 'b', 'c', 'd', 'e', 'f']就是[1, 1, 1, 0, 0, 0]如['e', 'f']对应于['a', 'b', 'c', 'd', 'e', 'f']就是[0, 0, 0, 0, 1, 1]得到的每一行都放入到trainMat中"""for postinDoc in listOPosts:trainMat.append(setOfWords2Vec(myVocabList, postinDoc))"""调用函数得到相应的log值和为1的概率"""p0Vec, p1Vec, pAbusive = trainNB0(trainMat, listclasses)"""下面是一些测试"""testEntry = ['love', 'my', 'dalmation']thisDoc = array(setOfWords2Vec(myVocabList, testEntry))print(testEntry, " classified as: ", classifyNB(thisDoc, p0Vec, p1Vec, pAbusive))testEntry = ['stupid', 'garbage']thisDoc = array(setOfWords2Vec(myVocabList, testEntry))print(testEntry, " classified as: ", classifyNB(thisDoc, p0Vec, p1Vec, pAbusive))testEntry = ['stupid', 'garbage', 'food']thisDoc = array(setOfWords2Vec(myVocabList, testEntry))print(testEntry, " classified as: ", classifyNB(thisDoc, p0Vec, p1Vec, pAbusive))"""解析我们的文件, 解析为字符串列表, 然后去掉我们长度小于3的单词，因为诸如is, of之类的单词实际上不用于分类"""def textParse(bigString):import relistOfTikens = re.split(r'\W*', bigString)return [tok.lower() for tok in listOfTikens if len(tok) > 2]def spamTest():docList = []classes = []fullText = []"""打开我们的文件, 放到我们的列表中"""for i in range(1, 26):docList.append(textParse(open('spam/%d.txt' % i).read()))fullText.extend(textParse(open('spam/%d.txt' % i).read()))classes.append(1)"""下面两句在python2.7中可以运行, 但是在python3.6中提示编码方式什么的问题,目前还没找到解决方法, 应该是编码方式没对应上但上面两句解析文档又没有错误, 按理同他们的编码方式都是相同的, 暂不得其解"""docList.append(textParse(open('ham/%d.txt' % i).read()))fullText.extend(textParse(open('ham/%d.txt' % i).read()))classes.append(0)"""这下面的思路基本上与刚才的相同"""vocabList = createVocabList(docList)trainingSet = list(range(25))testSet = []"""这里我们随机选取出10篇文章, 剩下的用于训练, 选出的用于测试"""for i in range(10):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del (trainingSet[randIndex])errorCount = 0trainMat = []trainClasses = []for docIndex in trainingSet:trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))trainClasses.append(classes[docIndex])p0, p1, pAbusive = trainNB0(array(trainMat), array(trainClasses))for docIndex in testSet:if classifyNB(array(setOfWords2Vec(vocabList, docList[docIndex])), p0, p1, pAbusive) != classes[docIndex]:errorCount += 1print("'the error rate is : ", float(errorCount) / len(testSet))"""该函数将词汇按出现的频率排序返回前30个最常出现的, 也就是出现次数最多的30个"""def calMostFreq(vocabList, fullText):import operatorfreqDict = {}for word in vocabList:freqDict[word] = fullText.count(word)sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True)return sortedFreq[:30]"""该函数类似于spamTest(), 但是它访问的是RSS源而不是文件"""def localWords(feed1, feed0):classes = []fullText = []docList = []"""根据短的哪一个来选取"""minLength = min(len(feed1['entries']), len(feed0['entries']))for i in range(minLength):docList.append(textParse(feed1['entries'][i]['summary']))classes.append(1)fullText.extend(textParse(feed1['entries'][i]['summary']))docList.append(textParse(feed0['entries'][i]['summary']))classes.append(0)fullText.extend(textParse(feed0['entries'][i]['summary']))# print(docList)vocabList = createVocabList(docList)freqDict = calMostFreq(vocabList, fullText)for word in freqDict:if word in vocabList:vocabList.remove(word[0])trainingSet = list(range(2*minLength))testSet = []for i in range(20):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del(trainingSet[randIndex])trainMat = []trainClasses = []for docIndex in trainingSet:trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))trainClasses.append(classes[docIndex])p0Vec, p1Vec, pAbuive = trainNB0(array(trainMat), array(trainClasses))errorCount = 0.0for docIndex in testSet:wordVecotr = setOfWords2Vec(vocabList, docList[docIndex])if classifyNB(array(wordVecotr), p0Vec, p1Vec, pAbuive) != classes[docIndex]:errorCount += 1.0print("the error rate is: ", float(errorCount)/len(testSet))return vocabList, p0Vec, p1Vec"""将出现较多的单词打印出来"""def getTopWord(ny, sf):import operatorvocabList, p0Vec, p1Vec = localWords(ny, sf)topNY = []topSF = []for i in range(len(p0Vec)):if p0Vec[i] > -6.0:"""此处需要将vocabList转换为list才能用数字索引, 否则会提示'set' ... index...之类的错误"""topSF.append((list(vocabList)[i],p0Vec[i]))if p1Vec[i] > -6.0:topNY.append((list(vocabList)[i],p1Vec[i]))sortedTopNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)sortedTopSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)print("SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF")for word in sortedTopSF:print(word[0])print("NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY")for word in sortedTopNY:print(word[0])

test.py

#encoding=utf-8import bayeslistOfPosts, listClasses = bayes.loadDataSet()print(listOfPosts)print(listClasses)"""答案如下：[['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']][0, 1, 0, 1, 0, 1]"""myVocabList = bayes.createVocabList(listOfPosts)print(myVocabList)"""答案如下：set(['cute', 'love', 'help', 'garbage', 'quit', 'I', 'problems', 'is', 'park', 'stop', 'flea', 'dalmation', 'licks', 'food', 'not', 'him', 'buying', 'posting', 'has', 'worthless', 'ate', 'to', 'maybe', 'please', 'dog', 'how', 'stupid', 'so', 'take', 'mr', 'steak', 'my'])"""returnVec = bayes.setOfWords2Vec(myVocabList, listOfPosts[0])print(returnVec)"""答案如下：[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1]""""""我们先将前面得到的每篇文章转换为单词向量"""trainMat = []for i in range(len(listOfPosts)):trainMat.append(bayes.setOfWords2Vec(myVocabList, listOfPosts[i]))p0, p1, pAbusive = bayes.trainNB0(trainMat, listClasses)print(p0)print(p1)print(pAbusive)"""答案如下：[-2.56494936 -2.56494936 -2.56494936 -2.56494936 -2.56494936 -2.56494936-3.25809654 -2.56494936 -3.25809654 -2.56494936 -1.87180218 -2.56494936-2.56494936 -3.25809654 -3.25809654 -2.56494936 -3.25809654 -2.56494936-3.25809654 -3.25809654 -2.56494936 -2.56494936 -2.56494936 -3.25809654-3.25809654 -2.56494936 -2.56494936 -3.25809654 -3.25809654 -2.56494936-2.56494936 -2.15948425][-3.04452244 -3.04452244 -3.04452244 -3.04452244 -3.04452244 -3.04452244-2.35137526 -3.04452244 -1.65822808 -3.04452244 -3.04452244 -2.35137526-3.04452244 -2.35137526 -2.35137526 -3.04452244 -1.94591015 -2.35137526-2.35137526 -2.35137526 -3.04452244 -3.04452244 -3.04452244 -2.35137526-2.35137526 -3.04452244 -3.04452244 -2.35137526 -2.35137526 -3.04452244-1.94591015 -2.35137526]0.5"""bayes.testingNB()"""答案如下：['love', 'my', 'dalmation'] classified as: 0['stupid', 'garbage'] classified as: 1['stupid', 'garbage', 'food'] classified as: 1"""bayes.spamTest()"""答案如下：("'the error rate is : ", 0.1)"""import feedparserny = feedparser.parse('/stp/index.rss')sf = feedparser.parse('/stp/index.rss')bayes.localWords(ny, sf)bayes.getTopWord(ny, sf)

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