机器学习三
1. 朴素贝叶斯
朴素贝叶斯的两个假设：
- 特征之间相互独立，即一个单词出现的可能性与它和其他单词相邻没有关系
- 每个特征同等重要
2. 文本分类
2.1 准备数据
从文本中构建词向量, 将文本看成单词向量或词条向量(也就是说将句子转换为向量)
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 is abusive, 0 not
return postingList,classVec
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 returnVecloadDataSet()创建了一些实验样本
createVocabList()会创建一个包含在所有文档中出现的不重复词的列表
获取词汇表后，使用setOfWords2Vec()函数，该函数的输入参数为词汇表及某个文档，输出的是文档向量，向量的每一元素为1或0，分别表示在词汇表中的单词在输入文档中是否出现
2.2 训练算法
从词向量计算概率
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上述训练算法原本应该为：
p0Num = zeros(numWords); p1Num = zeros(numWords)
p0Denom = 0; p1Denom = 0
......
p1Vect = p1Num/p1Denom
p0Vect = p0Num/p0Denom原来初始化为0，使用zeros()，然而若其中一个概率值为0，则最终也为0，故改用ones()，并且同时修改p0(1)Denom为2
原来也没有对数，为了防止乘积数字太小下溢出为0，改用对数
代码函数中的输入参数为文档矩阵trainMatrix，以及由每篇文档类别标签所构成的向量trainCategory。首先计算文档属于侮辱性文档(class=1)的概率，即P(1)，因为这是一个二分类问题较为简单，多于两类的分类问题要对代码稍作修改；之后计算条件概率；最后对每个元素除以该类别中的总词数
2.3 测试算法
根据现实情况修改分类器
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
p1 = sum(vec2Classify * p1Vec) + log(pClass1) #element-wise mult
p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
if p1 > p0:
return 1
else:
return 0
def testingNB():
listOPosts,listClasses = loadDataSet()
myVocabList = createVocabList(listOPosts)
trainMat=[]
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
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)使用bayes.testingNB()测试算法。
2.4 准备数据
文档词袋模型: 我们将每个词的出现与否作为一个特征，这可以被描述为词集模型(set-of-words model)，如果一个词在文档中出现不止一次，这可能意味着包含该词是否出现在文档中所不能表达的某种信息，这种方法被称为词袋模型(bag-of-words model)。在词袋中，每个单词可出现多次，而词集中每个词只能出现一次，为适应词袋模型，将setOfWords2Vec()函数稍作修改为bagOfWords2VecMN()
def bagOfWords2VecMN(vocabList, inputSet):
returnVec = [0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)] += 1
return returnVec3. 过滤垃圾邮件
3.1 准备数据
切分文本: 利用正则表达式切分文本
emailText = open('email/ham/6.txt').read()
import re
regExpress = re.compile('\\w*')
listOfTokens = regExpress.split(emailText)
[tok.lower() for tok in listOfTokens if len(tok) > 0]3.2 测试算法
使用朴素贝叶斯进行交叉验证
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 spamTest():
docList=[]; classList = []; fullText =[]
for i in range(1,26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
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)#create vocabulary
trainingSet = range(50); testSet=[] #create test set
for i in range(10):
randIndex = int(random.uniform(0,len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
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)
#return vocabList,fullTextspamTest()对贝叶斯垃圾邮件分类器进行自动化处理，导入文件夹spam和ham下的文本文件，并将它们解析为词列表，接下来构建一个测试集和训练集，两个集合中的元素是被随机选出的。随机选取数据的一部分为训练集，而剩余部分作为测试集的过程称为*留存交叉验证(hold-out cross validation)*。重复多次求平均值可以更好的估计错误率
4. 示例
使用朴素贝叶斯分类器从个人广告中获取区域倾向
4.1 收集数据
导入RSS源, 安装feedparser库pip install feedparser, 使用方法如下:
import feedparser
ny = feedparser.parse('http://newyork.craigslist.org/stp/index.rss')
print ny['entries']RSS源分类器及高频词去除函数
def calcMostFreq(vocabList,fullText):
import operator
freqDict = {}
for token in vocabList:
freqDict[token]=fullText.count(token)
sortedFreq = sorted(freqDict.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedFreq[:30]
def localWords(feed1,feed0):
import feedparser
docList=[]; classList = []; fullText =[]
minLen = min(len(feed1['entries']),len(feed0['entries']))
for i in range(minLen):
wordList = textParse(feed1['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(1) #NY is class 1
wordList = textParse(feed0['entries'][i]['summary'])
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)#create vocabulary
top30Words = calcMostFreq(vocabList,fullText) #remove top 30 words
for pairW in top30Words:
if pairW[0] in vocabList: vocabList.remove(pairW[0])
trainingSet = range(2*minLen); testSet=[] #create test set
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:#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 'the error rate is: ',float(errorCount)/len(testSet)
return vocabList,p0V,p1V移除高频词是由于词汇表中的一小部分单词却占据了所有文本用词的一大部分，产生这种现象的原因是语言中大部分都是冗余和结构辅助性内容。另一个常用方法是不仅移除高频词，同时从某个预定词表中移除结构上的辅助词，该词表称为停用词表(stop word list)，可以在网上找到(例如 http://www.ranks.nl/resources/stopwords.html)
通过以下命令测试上述代码：
ny = feedparser.parse('http://newyork.craigslist.org/stp/index.rss')
sf = feedparser.parse('http://sfbay.craigslist.org/stp/index.rss')
vocabList, pSF, pNY = bayes.localWords(ny,sf)
vocabList, pSF, pNY = bayes.localWords(ny,sf)为了得到错误率的精确估计，应该多次进行上述实验取平均值
4.2 分析数据
显示地域相关的用词, 可以先对向量pSF和pNY进行排序, 然后按照顺序将词打印出来。
def getTopWords(ny,sf):
import operator
vocabList,p0V,p1V=localWords(ny,sf)
topNY=[]; topSF=[]
for i in range(len(p0V)):
if p0V[i] > -6.0 : topSF.append((vocabList[i],p0V[i]))
if p1V[i] > -6.0 : topNY.append((vocabList[i],p1V[i]))
sortedSF = 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**"
for item in sortedSF:
print item[0]
sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)
print "NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**"
for item in sortedNY:
print item[0]getTopWords()使用两个RSS源作为输入，然后训练并测试朴素贝叶斯分类器，返回使用的概率值。bayes.getTopWords(ny,sf)可查看运行结果
All articles in this blog are licensed under CC BY-NC-SA 4.0 unless stating additionally.
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