def trainingAdaboostGetDS(iterateNum=40):
"""
测试分类的错误率
:param iterateNum:
:return:
"""
filename = '../emails/training/SMSCollection.txt'
smsWords, classLables = boostNaiveBayes.loadSMSData(filename)
# cross validation
testWords = []
testWordsType = []
testCount = 1000
for i in range(testCount):
randomIndex = int(random.uniform(0, len(smsWords)))
testWordsType.append(classLables[randomIndex])
testWords.append(smsWords[randomIndex])
del (smsWords[randomIndex])
del (classLables[randomIndex])
vocabularyList = boostNaiveBayes.createVocabularyList(smsWords)
print "Generating vector"
trainMarkedWords = boostNaiveBayes.setOfWordsListToVecTor(
vocabularyList, smsWords)
print "Data marked"
# 转成array向量
trainMarkedWords = np.array(trainMarkedWords)
print "To matrix"
pWordsSpamicity, pWordsHealthy, pSpam = \
boostNaiveBayes.trainingNaiveBayes(trainMarkedWords, classLables)
DS = np.ones(len(vocabularyList))
ds_errorRate = {}
minErrorRate = np.inf
for i in range(iterateNum):
errorCount = 0.0
for j in range(testCount):
testWordsCount = boostNaiveBayes.setOfWordsToVecTor(
vocabularyList, testWords[j])
ps, ph, smsType = boostNaiveBayes.classify(pWordsSpamicity,
pWordsHealthy, DS,
pSpam, testWordsCount)
if smsType != testWordsType[j]:
errorCount += 1
# alpha = (ph - ps) / ps
alpha = ps - ph
if alpha > 0: # actual: ham，predict: spam !!!!serious problem, to make D smaller, so it's more likely to predict ham
DS[testWordsCount != 0] = np.abs(
(DS[testWordsCount != 0] - np.exp(alpha)) /
DS[testWordsCount != 0])
else: # actual: spam，predict: ham, although a problem, need to make D bigger, so it's more likely to predict spam
DS[testWordsCount != 0] = (
DS[testWordsCount != 0] +
np.exp(alpha)) / DS[testWordsCount != 0]
print 'DS:', DS
errorRate = errorCount / testCount
if errorRate < minErrorRate:
minErrorRate = errorRate
ds_errorRate['minErrorRate'] = minErrorRate
ds_errorRate['DS'] = DS
print 'Iteration %d times，number of error prediction %d ，error rate: %f' % (
i, errorCount, errorRate)
if errorRate == 0.0:
break
ds_errorRate['vocabularyList'] = vocabularyList
ds_errorRate['pWordsSpamicity'] = pWordsSpamicity
ds_errorRate['pWordsHealthy'] = pWordsHealthy
ds_errorRate['pSpam'] = pSpam
return ds_errorRate
def trainingAdaboostGetDS(iterateNum=40):
"""
测试分类的错误率
:param iterateNum:
:return:
"""
filename = '../emails/training/SMSCollection.txt'
smsWords, classLables = boostNaiveBayes.loadSMSData(filename)
# 交叉验证
testWords = []
testWordsType = []
testCount = 1000
for i in range(testCount):
randomIndex = int(random.uniform(0, len(smsWords)))
testWordsType.append(classLables[randomIndex])
testWords.append(smsWords[randomIndex])
del (smsWords[randomIndex])
del (classLables[randomIndex])
"""
训练阶段，可将选择的vocabularyList也放到整个循环中，以选出
错误率最低的情况，获取最低错误率的vocabularyList
"""
vocabularyList = boostNaiveBayes.createVocabularyList(smsWords)
print "生成语料库！"
trainMarkedWords = boostNaiveBayes.setOfWordsListToVecTor(vocabularyList, smsWords)
print "数据标记完成！"
# 转成array向量
trainMarkedWords = np.array(trainMarkedWords)
print "数据转成矩阵！"
pWordsSpamicity, pWordsHealthy, pSpam = \
boostNaiveBayes.trainingNaiveBayes(trainMarkedWords, classLables)
DS = np.ones(len(vocabularyList))
ds_errorRate = {}
minErrorRate = np.inf
for i in range(iterateNum):
errorCount = 0.0
for j in range(testCount):
testWordsCount = boostNaiveBayes.setOfWordsToVecTor(vocabularyList, testWords[j])
ps, ph, smsType = boostNaiveBayes.classify(pWordsSpamicity, pWordsHealthy,
DS, pSpam, testWordsCount)
if smsType != testWordsType[j]:
errorCount += 1
# alpha = (ph - ps) / ps
alpha = ps - ph
if alpha < 0: # 原先为spam，预测成ham
DS[testWordsCount != 0] = np.abs(
(DS[testWordsCount != 0] - np.exp(alpha)) / DS[testWordsCount != 0])
else: # 原先为ham，预测成spam
DS[testWordsCount != 0] = (DS[testWordsCount != 0] + np.exp(alpha)) / DS[testWordsCount != 0]
print 'DS:', DS
errorRate = errorCount / testCount
if errorRate < minErrorRate:
minErrorRate = errorRate
ds_errorRate['minErrorRate'] = minErrorRate
ds_errorRate['DS'] = DS
print '第 %d 轮迭代，错误个数 %d ，错误率 %f' % (i, errorCount, errorRate)
if errorRate == 0.0:
break
ds_errorRate['vocabularyList'] = vocabularyList
ds_errorRate['pWordsSpamicity'] = pWordsSpamicity
ds_errorRate['pWordsHealthy'] = pWordsHealthy
ds_errorRate['pSpam'] = pSpam
return ds_errorRate