def simpleTest():
# 加载训练好的模型信息
vocabularyList, pWordsSpamicity, pWordsHealthy, pSpam = \
naiveBayes.getTrainedModelInfo()
# 加载测试数据
filename = '../emails/test/test.txt'
smsWords, classLables = naiveBayes.loadSMSData(filename)
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, smsWords[0])
print(smsType)
def simpleTest():
# load saved model from training
vocabularyList, pWordsSpamicity, pWordsHealthy, pSpam = \
naiveBayes.getTrainedModelInfo()
# load test data
filename = '../emails/test/test.txt'
smsWords, classLables = naiveBayes.loadSMSData(filename)
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, smsWords[0])
print smsType
def simpleTest():
# 加载训练好的模型信息
vocabularyList, pWordsSpamicity, pWordsHealthy, pSpam = \
naiveBayes.getTrainedModelInfo()
# 加载测试数据
filename = '../emails/test/test.txt'
smsWords, classLables = naiveBayes.loadSMSData(filename)
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, smsWords[0])
print smsType
def testClassifyErrorRate():
# 数据集预处理与存储
filename = '../emails/training/SMSCollection.txt'
smsWords, classLables = naiveBayes.loadSMSData(filename)
# 从训练集中随机选取测试集并从训练集中删除
testWords = []
testWordsType = []
testCount = 1000
for i in range(testCount):
randomIndex = int(random.uniform(0, len(smsWords)))
testWordsType.append(classLables[randomIndex])
testWords.append(smsWords[randomIndex])
# 从训练集中删除要测试的数据
del (smsWords[randomIndex])
del (classLables[randomIndex])
# 创建词库
vocabularyList = naiveBayes.createVocabularyList(smsWords)
print("生成语料库！")
# 构建词向量
trainMarkedWords = naiveBayes.setOfWordsListToVecTor(
vocabularyList, smsWords)
print("数据标记完成！")
trainMarkedWords = np.array(trainMarkedWords)
print("数据转成矩阵！")
# 通过词库和词向量计算P(S)、P(Wi|S) 、P(Wi|H)
pWordsSpamicity, pWordsHealthy, pSpam = naiveBayes.trainingNaiveBayes(
trainMarkedWords, classLables)
# 计算联合概率进行分类
errorCount = 0.0
for i in range(testCount):
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, testWords[i])
print('预测类别：', smsType, '实际类别：', testWordsType[i])
if smsType != testWordsType[i]:
errorCount += 1
print('错误个数：', errorCount, '错误率：', errorCount / testCount)
def testClassifyErrorRate():
"""
error rate test
:return:
"""
filename = '../emails/training/SMSCollection.txt'
smsWords, classLables = naiveBayes.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 = naiveBayes.createVocabularyList(smsWords)
print "generate one hot vector based on the word set！"
trainMarkedWords = naiveBayes.setOfWordsListToVecTor(
vocabularyList, smsWords)
print "mark data！"
# convert to nd array
trainMarkedWords = np.array(trainMarkedWords)
print "data -> matrix！"
pWordsSpamicity, pWordsHealthy, pSpam = naiveBayes.trainingNaiveBayes(
trainMarkedWords, classLables)
errorCount = 0.0
for i in range(testCount):
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, testWords[i])
print 'predict type：', smsType, 'actual type：', testWordsType[i]
if smsType != testWordsType[i]:
errorCount += 1
print 'error count：', errorCount, 'error rate：', errorCount / testCount
def testClassifyErrorRate():
"""
测试分类的错误率
:return:
"""
filename = '../emails/training/SMSCollection.txt'
smsWords, classLables = naiveBayes.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])
vocabularyList = naiveBayes.createVocabularyList(smsWords)
print "生成语料库！"
trainMarkedWords = naiveBayes.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 = naiveBayes.trainingNaiveBayes(
trainMarkedWords, classLables)
errorCount = 0.0
for i in range(testCount):
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, testWords[i])
print '预测类别：', smsType, '实际类别：', testWordsType[i]
if smsType != testWordsType[i]:
errorCount += 1
print '错误个数：', errorCount, '错误率：', errorCount / testCount
def testClassifyErrorRate():
"""
测试分类的错误率
:return:
"""
filename = '../emails/training/SMSCollection.txt'
smsWords, classLables = naiveBayes.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])
vocabularyList = naiveBayes.createVocabularyList(smsWords)
print "生成语料库！"
trainMarkedWords = naiveBayes.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 = naiveBayes.trainingNaiveBayes(trainMarkedWords, classLables)
errorCount = 0.0
for i in range(testCount):
smsType = naiveBayes.classify(vocabularyList, pWordsSpamicity,
pWordsHealthy, pSpam, testWords[i])
print '预测类别：', smsType, '实际类别：', testWordsType[i]
if smsType != testWordsType[i]:
errorCount += 1
print '错误个数：', errorCount, '错误率：', errorCount / testCount
