python3.6环境下代码
‘‘‘ Created on Oct 19, 2010 @author: Peter ‘‘‘ from numpy import * 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 returnVec 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 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 bagOfWords2VecMN(vocabList, inputSet): returnVec = [0]*len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] += 1 return returnVec 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)) 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,fullText 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 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])