class TextFormInfer:
def __init__(self, messages):
self.clser = TextClassify(messages)
self.httptuning = HttpDataTuning()
self.ftptuning = FTPDataTuning()
self.redistuning = RedisDataTuning()
def ldaFormatInfer(self, wSize, TK, wLen, Kcls, infercls='H'):
clsDatas = self.clser.clsMessages(wSize, TK, wLen, Kcls)
clsFormats = []
formatInfer = Format()
clusters = [cluster for cluster in clsDatas.values()]
if infercls == 'H':
self.httptuning.getMsgsLen(clusters)
elif infercls == 'F':
self.ftptuning.getMsgsLen(clusters)
else:
self.redistuning.getMsgsLen(clusters)
for clsData in clsDatas.values():
tMessages = [RawMessage(message) for message in clsData]
tempFormat = Symbol(messages=tMessages)
formatInfer.splitAligned(tempFormat, doInternalSlick=True)
clsFormats.append(tempFormat)
return clsFormats
def ladDbscanFormatInfer(self, wSize, TK, wLen, mindis, minpt, infercls):
clsDatas = self.clser.clsByDbscan(wSize, TK, wLen, mindis, minpt)
clusters = [cluster for cluster in clsDatas.values()]
if infercls == 'H':
self.httptuning.getMsgsLen(clusters)
elif infercls == 'F':
self.ftptuning.getMsgsLen(clusters)
else:
self.redistuning.getMsgsLen(clusters)
clsFormats = []
formatInfer = Format()
for clsData in clsDatas.values():
tMessages = [RawMessage(message) for message in clsData]
tempFormat = Symbol(messages=tMessages)
formatInfer.splitAligned(tempFormat, doInternalSlick=True)
clsFormats.append(tempFormat)
return clsFormats
class TextClassifyLogic:
def __init__(self, messages, tRate, sRate, wRate, wHeight):
self.tRate = tRate
self.srate = sRate
self.wRate = self.srate
self.wHeight = wHeight
self.messages = messages
self.httpData = HttpDataTuning()
self.ftpData = FTPDataTuning()
self.redisData = RedisDataTuning()
def GetLocData(self, datas):
nowLocData = []
for data in datas:
nowLocData.append(data.now())
return nowLocData
def filterShort(self, freWords, h):
newFreWords = set()
for value in freWords:
if (len(value) >= h):
newFreWords.add(value)
return newFreWords
def GetFrequentWords(self, rate, h, datas):
nowLocDatas = self.GetLocData(datas)
Datas = [str(data) for data in nowLocDatas]
freWords = ApriorFreAnalyZer(Datas, rate).getApriorFre()
freWords = self.filterShort(freWords, h)
return freWords
def RankWord(self, word, datas):
nowLocDatas = self.GetLocData(datas)
Datas = [str(data) for data in nowLocDatas]
cnt = 0
loc = 0
for data in Datas:
tempLoc = data.find(word)
if tempLoc != -1:
cnt = cnt + 1
loc = loc + tempLoc
print(word, cnt, loc)
return (cnt, (loc + 1) / cnt)
def RankWords(self, freWords, datas):
words = []
for freWord in freWords:
nums = self.RankWord(freWord, datas)
words.append((freWord, nums[0], nums[1]))
words = BaseRankModel.sortList(words)
return words
def ConvertFreWords(self, data):
freSet = {}
for freWord in self.freWords:
lo = data.find(freWord)
if lo != -1:
freSet[freWord] = lo
frePattern = sorted(freSet.items(), key=lambda key: key[1])
finalPattern = ''.join([item[0] for item in frePattern])
return finalPattern
def GetWodsRank(self, datas):
freWords = self.GetFrequentWords(self.wRate, self.wHeight, datas)
rankWords = self.RankWords(freWords, datas)
return rankWords
def ClassifyMessages(self, messages):
msgSet = {}
for message in messages:
freWord = self.ConvertFreWords(str(message.message))
if freWord not in msgSet:
msgSet[freWord] = []
msgSet[freWord].append(message)
return msgSet
def ClassifyCircleLy(self, preWords, messages):
rankWords = self.GetWodsRank(messages)
funCode = None
for word in rankWords:
if word not in preWords and word[1] / len(
self.messages) > self.tRate and word[1] != len(messages):
funCode = word
break
fResult = []
print(funCode, len(messages))
#if funCode is not None and funCode[1] / len(messages) > self.trate:
if funCode is not None:
clsOne, clsTwo = self.ClassifyByCodes(funCode[0], messages)
print(len(clsOne), len(clsTwo))
if len(clsTwo) / len(self.messages) > self.tRate:
fResult.append(self.ClassifyCircleLy(preWords, clsTwo))
else:
if len(clsTwo) > 0:
fResult.append(clsTwo)
if len(clsOne) / len(self.messages) > self.tRate:
preWords.add(funCode)
fResult.append(self.ClassifyCircleLy(preWords, clsOne))
preWords.remove(funCode)
else:
fResult.append(clsOne)
else:
fResult = messages
return fResult
def ClassifyByCodes(self, codes, messages):
clsTwo = []
clsOne = []
for message in messages:
value = str(message.now())
if value.find(codes) != -1:
clsOne.append(message)
else:
clsTwo.append(message)
return (clsOne, clsTwo)
def FormatInfer(self, rate, h):
self.GetFrequentWords(rate, h)
messageClassify = self.ClassifyMessages(self.datas)
finalFormats = []
formatInfer = Format()
for key, value in messageClassify.items():
tMessages = []
for message in value:
singleMessage = RawMessage(message.message)
tMessages.append(singleMessage)
tempFormat = Symbol(messages=tMessages)
formatInfer.splitAligned(tempFormat, doInternalSlick=True)
finalFormats.append(tempFormat)
return finalFormats
def FormatInferCirclely(self, messages, Mtype):
preFre = set()
#result = textClassify.classifyMessages(preFre, messages)
result = self.classifyMessages(preFre, messages)
clsResult = []
for res in result:
clsr = []
for msg in res:
clsr.append(msg.message)
clsResult.append(clsr)
if Mtype == 'H':
self.httpData.getMsgsLen(clsResult)
elif Mtype == 'F':
self.ftpData.getMsgsLen(clsResult)
else:
self.redisData.getMsgsLen(clsResult)
#httpTuning = HttpDataTuning()
#print(httpTuning.getMsgsLen(clsResult))
#ftpTuning = FTPDataTuning()
#print(ftpTuning.getMsgsLen(clsResult))
#redisTuning = RedisDataTuning()
#redisTuning.getMsgsLen(clsResult)
finalFormats = []
formatInfer = Format()
for dataList in result:
tMessages = []
for data in dataList:
singMessage = RawMessage(data.message)
tMessages.append(singMessage)
tempFormat = Symbol(messages=tMessages)
formatInfer.splitAligned(tempFormat, doInternalSlick=True)
finalFormats.append(tempFormat)
return finalFormats
def formatInfer(self, messages):
preFre = set()
result = self.classifyMessages(preFre, messages)
finalFormats = []
formatInfer = Format()
for dataList in result:
tMessages = []
for data in dataList:
singMessage = RawMessage(data.message)
tMessages.append(singMessage)
tempFormat = Symbol(messages=tMessages)
formatInfer.splitAligned(tempFormat, doInternalSlick=True)
finalFormats.append(tempFormat)
return finalFormats
def filterSets(self, result, fResult):
cverter = Converter()
cverter.ConvertMultiListPure(result, fResult)
def classifyMessages(self, preSet, messages):
datas = self.ClassifyCircleLy(preSet, messages)
result = []
self.filterSets(datas, result)
return result
