机器学习之旅---朴素贝叶斯分类器
[python]
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def loadDataSet():
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postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
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['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
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['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
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['stop', 'posting', 'stupid', 'worthless', 'garbage'],
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['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
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['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
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classVec = [0,1,0,1,0,1]
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return postingList,classVec
[python]
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def createVocabList(dataSet):
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vocabSet = set([])
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for document in dataSet:
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vocabSet = vocabSet | set(document)
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return list(vocabSet)
[python]
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def setOfWords2Vec(vocabList, inputSet):
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returnVec = [0]*len(vocabList)
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for word in inputSet:
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if word in vocabList:
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returnVec[vocabList.index(word)] = 1
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else: print"the word: %s is not in my Vocabulary!" % word
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return returnVec
[python]
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def trainNB0(trainMatrix,trainCategory):
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numTrainDocs = len(trainMatrix)
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numWords = len(trainMatrix[0])
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pAbusive = sum(trainCategory)/float(numTrainDocs)
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p0Num = ones(numWords); p1Num = ones(numWords)
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p0Denom = 2.0; p1Denom = 2.0
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for i in range(numTrainDocs):
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if trainCategory[i] == 1:
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p1Num += trainMatrix[i]
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p1Denom += sum(trainMatrix[i])
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else:
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p0Num += trainMatrix[i]
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p0Denom += sum(trainMatrix[i])
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p1Vect = log(p1Num/p1Denom)
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p0Vect = log(p0Num/p0Denom)
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return p0Vect,p1Vect,pAbusive
[python]
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def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
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p1 = sum(vec2Classify * p1Vec) + log(pClass1)
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p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
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if p1 > p0:
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return1
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else:
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return0
d. 完整的测试流程
[python]
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def testingNB():
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listOPosts,listClasses = loadDataSet()
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myVocabList = createVocabList(listOPosts)
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trainMat=[]
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for postinDoc in listOPosts:
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trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
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p0V,p1V,pAb = trainNB0(array(trainMat),array(listClasses))
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testEntry = ['love', 'my', 'dalmation']
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thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
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print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
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testEntry = ['stupid', 'garbage']
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thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
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print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
执行结果:
[python]
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def textParse(bigString):
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import re
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listOfTokens = re.split(r'\W*', bigString)
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return [tok.lower() for tok in listOfTokens if len(tok) > 2]
[python]
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def bagOfWords2VecMN(vocabList, inputSet):
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returnVec = [0]*len(vocabList)
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for word in inputSet:
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if word in vocabList:
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returnVec[vocabList.index(word)] += 1
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return returnVec
下面给出垃圾邮件预测的完整代码:
[python]
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def spamTest():
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docList=[]; classList = []; fullText =[]
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for i in range(1,26):
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wordList = textParse(open('email/spam/%d.txt' % i).read())
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docList.append(wordList)
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fullText.extend(wordList)
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classList.append(1)
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wordList = textParse(open('email/ham/%d.txt' % i).read())
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docList.append(wordList)
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fullText.extend(wordList)
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classList.append(0)
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vocabList = createVocabList(docList)
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trainingSet = range(50); testSet=[]
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for i in range(10):
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randIndex = int(random.uniform(0,len(trainingSet)))
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testSet.append(trainingSet[randIndex])
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del(trainingSet[randIndex])
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trainMat=[]; trainClasses = []
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for docIndex in trainingSet:
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trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
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trainClasses.append(classList[docIndex])
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p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
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errorCount = 0
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for docIndex in testSet:
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wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
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if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
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errorCount += 1
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print"classification error",docList[docIndex]
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print'the error rate is: ',float(errorCount)/len(testSet)
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return vocabList,fullText
执行结果:
[python]
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def calcMostFreq(vocabList,fullText):
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import operator
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freqDict = {}
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for token in vocabList:
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freqDict[token]=fullText.count(token)
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sortedFreq = sorted(freqDict.iteritems(), key=operator.itemgetter(1), reverse=True)
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return sortedFreq[:30]
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def localWords(feed1,feed0):
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import feedparser
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docList=[]; classList = []; fullText =[]
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minLen = min(len(feed1['entries']),len(feed0['entries']))
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for i in range(minLen):
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wordList = textParse(feed1['entries'][i]['summary'])
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docList.append(wordList)
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fullText.extend(wordList)
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classList.append(1)
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wordList = textParse(feed0['entries'][i]['summary'])
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docList.append(wordList)
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fullText.extend(wordList)
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classList.append(0)
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vocabList = createVocabList(docList)
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top30Words = calcMostFreq(vocabList,fullText)
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for pairW in top30Words:
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if pairW[0] in vocabList: vocabList.remove(pairW[0])
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trainingSet = range(2*minLen); testSet=[]
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for i in range(20):
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randIndex = int(random.uniform(0,len(trainingSet)))
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testSet.append(trainingSet[randIndex])
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del(trainingSet[randIndex])
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trainMat=[]; trainClasses = []
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for docIndex in trainingSet:
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trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
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trainClasses.append(classList[docIndex])
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p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
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errorCount = 0
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for docIndex in testSet:
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wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
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if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
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errorCount += 1
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print'the error rate is: ',float(errorCount)/len(testSet)
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return vocabList,p0V,p1V
执行结果:
[python]
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def getTopWords(ny,sf):
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import operator
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vocabList,p0V,p1V=localWords(ny,sf)
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topNY=[]; topSF=[]
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for i in range(len(p0V)):
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if p0V[i] > -6.0 : topSF.append((vocabList[i],p0V[i]))
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if p1V[i] > -6.0 : topNY.append((vocabList[i],p1V[i]))
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sortedSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)
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print"SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**"
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for item in range(15):
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print sortedSF[item][0]
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sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)
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print"NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**"
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for item in range(15):
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print sortedNY[item][0]
执行结果:
好久不更新了,华为上班好累,都是回家抽空看的。
初次接触,很蛋疼。不能有中文注释,运行环境掌握的也不是很好。
大家了解下算法实现原理及应用即可
上文来自:http://blog.csdn.net/jinshengtao/article/details/39532043
来源: http://lib.csdn.net/article/machinelearning/44330