def spamTest():
"""
使用朴素贝叶斯算法对垃圾邮件自动分类
"""
docList = [] #文档列表
classList = [] #文档类别列表
fullText = []
#1.解析并导入文本文件
for i in range(1, 26):
#先处理标记为spam（垃圾邮件）的文件
spamfilename = 'email/spam/%d.txt' % i
spamfile = open(spamfilename, 'r')
text = spamfile.read()
wordList = textParse(text)
spamfile.close()
docList.append(wordList) #将词汇加入文档列表中
fullText.extend(wordList) #将词汇列表扩充进fullText列表中
classList.append(1) #加入类标为1
#处理正常邮件
hamfilename = 'email/ham/%d.txt' % i
hamtext = open(hamfilename, 'r').read()
wordList = textParse(hamtext)
docList.append(wordList)
fullText.extend(wordList)
classList.append(0) #加入类标为0
vocabList = naiveBayes.createVocabList(docList) #创建无重复的词汇表
#2. 随机构建训练集和测试集
trainingSet = list(range(50)) #已知一共有50个邮件样本
testSet = [] #创建测试集
for i in range(10):
#随机选取10个作为测试集样本，并将其从训练集中剔除
randIndex = int(random.uniform(0, len(trainingSet)))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
#3. 构建向量词矩阵
trainMat = [] #01元素构成的训练集矩阵
trainClasses = [] #文档类别列表
for docIndex in trainingSet:
#遍历训练集，构建向量词矩阵
#将词包加入训练集矩阵
trainMat.append(
naiveBayes.bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex]) #将相应的类别加入类别列表
#4. 计算分类的向量，以及分类为垃圾邮件的概率
p0V, p1V, pSpam = naiveBayes.trainNB0(np.array(trainMat),
np.array(trainClasses))
errorCount = 0 #初始化错误分类
for docIndex in testSet:
#遍历测试集，对测试集进行分类
wordVector = naiveBayes.bagOfWords2VecMN(vocabList, docList[docIndex])
if naiveBayes.classifyNB(np.array(wordVector), p0V, p1V,
pSpam) != classList[docIndex]:
errorCount += 1
print("当前分类错误，第{:}个邮件分类错误".format(docIndex))
print('错误率为: ', errorCount / len(testSet))
def runClassification(trainingData, trainingClassVec):
# split training and test data
TESTINGDATASIZE = 10
testingData = []
actualTestingVec = []
for index in range(0,TESTINGDATASIZE):
import random
i = int(random.uniform(0,len(trainingData)))
testingData.append(trainingData[i])
actualTestingVec.append(trainingClassVec[i])
del(trainingData[i])
del(trainingClassVec[i])
trainingVocabList = naiveBayes.createVocabList(trainingData)
(pC0,pWGivenC0), (pC1,pWGivenC1) = naiveBayes.trainData(trainingVocabList, trainingData, trainingClassVec)
predictedTestingVec = []
for testData in testingData:
testDataVector = np.array(naiveBayes.bagOfWordsToVector(trainingVocabList, testData))
pC0GivenData = testDataVector * pWGivenC0 * pC0 + 1
pC1GivenData = testDataVector * pWGivenC1 * pC1 + 1
if sum(np.log(pC0GivenData)) > sum(np.log(pC1GivenData)):
predictedTestingVec.append(0)
else:
predictedTestingVec.append(1)
i = 0
error = 0
misClassified = []
for predicted in predictedTestingVec:
if (actualTestingVec[i] != predicted):
error += 1
misClassified.append(testingData[i])
i += 1
if(DEBUG):
print predictedTestingVec
print actualTestingVec
print 'num errors: %d' % error
print 'misclassified:'
print misClassified
return float(error)/TESTINGDATASIZE
def test(bag=False, prt=True):
docList = []
classList = []
fullText = []
# 导入文件夹spam和ham下的文本文件并将它们解析为词列表
for i in range(1, 26):
wordList = textParse(open('./data/email/spam/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('./data/email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
# 产生词汇表
vocabList = createVocabList(docList)
# 随机选择10个样本作为测试集, 剩下40个作为训练集
trainingSet = range(50)
testSet = []
for i in range(10):
randIndex = random.randrange(len(trainingSet))
testSet.append(trainingSet[randIndex])
del (trainingSet[randIndex])
if not bag:
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(setOfwords2Vec(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
BNBModel.fit(trainMat, trainClasses)
errorCount = 0
errorClassList = []
for docIndex in testSet:
wordVector = np.array(setOfwords2Vec(vocabList,
docList[docIndex])).reshape(
1, -1)
predict = BNBModel.predict(wordVector)
if predict != classList[docIndex]:
errorClassList.append(classList[docIndex])
errorCount += 1
errorRate = float(errorCount) / len(testSet)
if prt:
print('the error rate is {}.'.format(errorRate))
return errorRate, errorClassList
else:
trainMat = []
trainClasses = []
for docIndex in trainingSet:
trainMat.append(bagOfwords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
MModel.fit(trainMat, trainClasses)
errorCount = 0
errorClassList = []
for docIndex in testSet:
wordVector = np.array(
bagOfwords2VecMN(vocabList, docList[docIndex])).reshape(1, -1)
predict = MModel.predict(wordVector)
if predict != classList[docIndex]:
errorClassList.append(classList[docIndex])
errorCount += 1
errorRate = float(errorCount) / len(testSet)
if prt:
print('the error rate is {}.'.format(errorRate))
return errorRate, errorClassList
minlen = min(len(ny),len(sf))
fullData = []
fullClassVec = []
allWords = []
for index in range(0,minlen):
words = parse(ny['entries'][index]['summary'])
fullData.append(words)
allWords.extend(words)
fullClassVec.append(1) # 1 is ny
words = parse(sf['entries'][index]['summary'])
fullData.append(words)
allWords.extend(words)
fullClassVec.append(0) # 0 is sf
# remove the most frequent words (combined in both cities).
trainingVocabList = naiveBayes.createVocabList(fullData)
trainingVocabList = removeNMostFrequentWords(trainingVocabList, allWords, 30)
NUMRUNS = 2
topPC0 = []
topPC1 = []
for index in range(0,NUMRUNS):
tPC0, tPC1 = runClassification(trainingVocabList, list(fullData), list(fullClassVec))
topPC0 += tPC0
topPC1 += tPC1
topPC0 = getTopNFromList(topPC0, 30)
topPC1 = getTopNFromList(topPC1, 30)
print "Most common words for New York:"
print '\n'.join([x for (x,y) in topPC0])
print "\nMost common words for SF:"
# -*- coding: utf-8 -*- """ Created on Sun Jul 22 20:27:04 2018 @author: zhe E-mail: [email protected] """ import naiveBayes listOPosts, listClasses = naiveBayes.loadDataSet() myVocabList = naiveBayes.createVocabList(listOPosts) print("Vocubulary：", myVocabList) #vocabulary wordVec = naiveBayes.setOfWords2Vec(myVocabList, listOPosts[0]) print("test word vector：", wordVec) trainMat = [] for postinDoc in listOPosts: trainMat.append(naiveBayes.setOfWords2Vec(myVocabList, postinDoc)) p0V, p1V, PAb = naiveBayes.trainNB0(trainMat, listClasses) print("Probability vector for 0 classification:", p0V) print("Probability vector for 1 classification:", p1V) print("Probability of being 0 classification:", 1 - PAb) print("Probability of being 1 classification", PAb)