def main():
sentences = ReadFile.readTXTFile(config.SimilarlySentencePath + "AllQueriesWithID.txt")
test_sentences_doc = ReadFile.readTXTFile(config.SimilarlySentencePath + "corpus_0829.txt")
test_sentences = []
for sen in test_sentences_doc:
sen_iterms = sen.strip().split("\t")
if len(sen_iterms) >= 2:
test_sentences.append(sen_iterms[1])
# train_sentences = [
# '0无偿居间介绍买卖毒品的行为应如何定性',
# '1吸毒男动态持有大量毒品的行为该如何认定',
# '2如何区分是非法种植毒品原植物罪还是非法制造毒品罪',
# '3为毒贩贩卖毒品提供帮助构成贩卖毒品罪',
# '4将自己吸食的毒品原价转让给朋友吸食的行为该如何认定',
# '5为获报酬帮人购买毒品的行为该如何认定',
# '6毒贩出狱后再次够买毒品途中被抓的行为认定',
# '7虚夸毒品功效劝人吸食毒品的行为该如何认定',
# '8妻子下落不明丈夫又与他人登记结婚是否为无效婚姻',
# '9一方未签字办理的结婚登记是否有效',
# '10夫妻双方1990年按农村习俗举办婚礼没有结婚证 一方可否起诉离婚',
# '11结婚前对方父母出资购买的住房写我们二人的名字有效吗',
# '12身份证被别人冒用无法登记结婚怎么办？',
# '13同居后又与他人登记结婚是否构成重婚罪',
# '14未办登记只举办结婚仪式可起诉离婚吗',
# '15同居多年未办理结婚登记，是否可以向法院起诉要求离婚'
# ]
# print type(train_sentences[0])
# print len(sentences)
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ",""))
print type(train_sentences[0])
#
# print "build simi_model"
SSO = SentenceSimilarlyObj()
# corpus = SSO.getCorpus(train_sentences)
# SSO.setSimilar(corpus=corpus)
# print "save simi model"
# SSO.save()
# SSO.save("simi_model_little","word_dic_little")
# print "build success"
print "load model"
SSO.load()
# print SSO.similar
print "test"
# indexs = SSO.calSentenceSimilarly(sentence=u"说说后天是礼拜几")
# for index in indexs:
# print index[0],train_sentences[index[0]],index[1]
result = SSO.calSentencesSimilarly(train_sentences,train_sentences)
Wr = WriteResult()
can_not_deal = Wr.WriteSimilarlySentence(result,"docSim_simi.txt")
def main():
sentences = ReadFile.readTXTFile(config.SimilarlySentencePath +
"AllQueriesWithID.txt")
test_sentences_doc = ReadFile.readTXTFile(config.SimilarlySentencePath +
"corpus_0829.txt")
test_sentences = []
for sen in test_sentences_doc:
sen_iterms = sen.strip().split("\t")
if len(sen_iterms) >= 2:
test_sentences.append(sen_iterms[1])
# train_sentences = [
# '0无偿居间介绍买卖毒品的行为应如何定性',
# '1吸毒男动态持有大量毒品的行为该如何认定',
# '2如何区分是非法种植毒品原植物罪还是非法制造毒品罪',
# '3为毒贩贩卖毒品提供帮助构成贩卖毒品罪',
# '4将自己吸食的毒品原价转让给朋友吸食的行为该如何认定',
# '5为获报酬帮人购买毒品的行为该如何认定',
# '6毒贩出狱后再次够买毒品途中被抓的行为认定',
# '7虚夸毒品功效劝人吸食毒品的行为该如何认定',
# '8妻子下落不明丈夫又与他人登记结婚是否为无效婚姻',
# '9一方未签字办理的结婚登记是否有效',
# '10夫妻双方1990年按农村习俗举办婚礼没有结婚证 一方可否起诉离婚',
# '11结婚前对方父母出资购买的住房写我们二人的名字有效吗',
# '12身份证被别人冒用无法登记结婚怎么办？',
# '13同居后又与他人登记结婚是否构成重婚罪',
# '14未办登记只举办结婚仪式可起诉离婚吗',
# '15同居多年未办理结婚登记，是否可以向法院起诉要求离婚'
# ]
# print type(train_sentences[0])
# print len(sentences)
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ", ""))
print type(train_sentences[0])
#
# print "build simi_model"
SSO = SentenceSimilarlyObj()
corpus = SSO.getCorpus(train_sentences)
SSO.setSimilar(corpus=corpus)
print "save simi model"
# SSO.save()
# SSO.save("simi_model_little","word_dic_little")
# print "build success"
print "load model"
SSO.load()
# print SSO.similar
print "test"
def getNewWords(self):
file = config.WordDicPath + "birds.txt"
lines = ReadFile.readTXTFile(file)
words = []
for line in lines:
words.extend(line.strip().split(" "))
return words
def MyTest():
print "1"
filename = config.SimilarlySentencePath + "AllQueriesWithID.txt"
sentences = ReadFile.readTXTFile(filename)
# sentences = ReadFile.readTXTFile(config.SimilarlySentencePath + "AllQueriesWithID.txt")
# test_sentences_doc = ReadFile.readTXTFile(config.SimilarlySentencePath + "corpus_0829.txt")
test_sentences = []
# for sen in test_sentences_doc:
# sen_iterms = sen.strip().split("\t")
# if len(sen_iterms) >= 2:
# test_sentences.append(sen_iterms[1])
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ", ""))
print type(train_sentences[0])
test_sentences = train_sentences
tsf = Ranker()
tsf.load(config.SimilarlySentencePath+"AllQueriesWithID.txt")
result= tsf.getSimilarSentences(test_sentences[:100])
wr = WriteResult()
wr.WriteSimilarSentence(result,file=config.SimilarlySentencePath+"rank_simi.txt")
def getNewWords(self):
file = config.WordDicPath + "birds.txt"
lines = ReadFile.readTXTFile(file)
words = []
for line in lines:
words.extend(line.strip().split(" "))
return words
def MyTest():
print "1"
filename = config.SimilarlySentencePath + "AllQueriesWithID.txt"
sentences = ReadFile.readTXTFile(filename)
# sentences = ReadFile.readTXTFile(config.SimilarlySentencePath + "AllQueriesWithID.txt")
# test_sentences_doc = ReadFile.readTXTFile(config.SimilarlySentencePath + "corpus_0829.txt")
test_sentences = []
# for sen in test_sentences_doc:
# sen_iterms = sen.strip().split("\t")
# if len(sen_iterms) >= 2:
# test_sentences.append(sen_iterms[1])
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ", ""))
print type(train_sentences[0])
test_sentences = train_sentences
tsf = Ranker()
tsf.load(config.SimilarlySentencePath + "AllQueriesWithID.txt")
result = tsf.getSimilarSentences(test_sentences)
wr = WriteResult()
wr.WriteSimilarSentence(result,
file=config.SimilarlySentencePath +
"rank_simi.txt")
def getFileContext_Participle(dirPath=MyCode.config.CorpusFilePath):
# files = ReadFile.getAllFilesInDir(dirPath)
# sentences = ReadFile.getAllFilesContext(files,dirPath)
#for test
sentences = ReadFile.readTXTFile(dirPath + 'corpus.txt')
par_sentences = Participle.Participle(sentences[:10])
par_filter_sentences = filterStopWords.filterStopWords(par_sentences)
# return wordTostr(par_filter_sentences)
return par_filter_sentences
def getFileContext_Participle(dirPath=MyCode.config.CorpusFilePath):
# files = ReadFile.getAllFilesInDir(dirPath)
# sentences = ReadFile.getAllFilesContext(files,dirPath)
#for test
sentences = ReadFile.readTXTFile(dirPath + 'corpus.txt')
par_sentences = Participle.Participle(sentences[:10])
par_filter_sentences = filterStopWords.filterStopWords(par_sentences)
# return wordTostr(par_filter_sentences)
return par_filter_sentences
def main(): sentences = ReadFile.readTXTFile(config.TopicFilePath+"QRpair.txt") qaQueryPairTopic = QAQueryPairTopic() result = qaQueryPairTopic.getgetQAQueriesTopicId(sentences) wr = WriteResult() wr.WriteTopicRegular(result[0]) wr.WriteTopic(result[1]) result = qaQueryPairTopic.getResponsesTopic(sentences) wr.WriteResponseWithTopicId(result)
def insertDicItem():
file = MyCode.config.CaiCaiPath + 'AllQueriesWithID_mid2.txt'
fileEnd = MyCode.config.CaiCaiPath + 'AllQueriesWithIDfinished.txt'
sentences = ReadFile.readTXTFile(file)
with open(fileEnd,'a+') as fp:
for sen in sentences:
lines = sen.split("\t")
lines[2] = lines[2][:-3]+', "client_id": "c_00000007"}'
# print lines[2]
fp.write(lines[0] +"\t"+lines[1]+'\t'+lines[2]+'\n')
def Topic2Vec_v2():
"""
分析句子在，将句子转换为topic 向量
:return:
"""
lda = LDA()
sentences = ReadFile.readTXTFile(config.BTMData +
"topic_data_processed.txt")
docs = []
lab = []
for index, line in enumerate(sentences):
term = line.strip().split("\t")
if len(term) != 3:
continue
docs.append(term[1])
lab.append(term[2])
documents = line_Cut_Word(docs)
documents = [" ".join(doc) for doc in documents]
lda.load_word_dic()
lda.load_LdaModel()
# lda.build_word_dic(lines(documents))
# print len(lda.word_dic.keys())
# lda.buildModel(lines(documents))
result_lab = []
topic2vec = []
x_index, y_index = [], []
count = 0
print len(lab)
for index, doc_lab in enumerate(list(zip(docs, lab))):
if index % 1000 == 0 and index != 0:
print doc_lab[0], doc_lab[1]
# break
doc = doc_lab[0]
la = doc_lab[1]
topics = lda.getQuerySimilarly(doc)
if topics:
# print doc, "\t", la
for topic in topics:
x_index.append(count)
y_index.append(topic[0])
topic2vec.append(topic[1])
count += 1
result_lab.append(la)
print len(x_index), len(y_index), len(topic2vec), len(result_lab), count
result = [x_index, y_index, topic2vec, result_lab]
with open(config.BTMData + "topic2vec_2.txt", 'wb') as fp:
cPickle.dump(result, fp)
def Topic2Vec_v2():
"""
分析句子在，将句子转换为topic 向量
:return:
"""
lda = LDA()
sentences = ReadFile.readTXTFile(config.BTMData + "topic_data_processed.txt")
docs = []
lab = []
for index, line in enumerate(sentences):
term = line.strip().split("\t")
if len(term) != 3:
continue
docs.append(term[1])
lab.append(term[2])
documents = line_Cut_Word(docs)
documents = [" ".join(doc) for doc in documents]
lda.load_word_dic()
lda.load_LdaModel()
# lda.build_word_dic(lines(documents))
# print len(lda.word_dic.keys())
# lda.buildModel(lines(documents))
result_lab = []
topic2vec = []
x_index, y_index = [], []
count = 0
print len(lab)
for index, doc_lab in enumerate(list(zip(docs, lab))):
if index % 1000 == 0 and index != 0:
print doc_lab[0], doc_lab[1]
# break
doc = doc_lab[0]
la = doc_lab[1]
topics = lda.getQuerySimilarly(doc)
if topics:
# print doc, "\t", la
for topic in topics:
x_index.append(count)
y_index.append(topic[0])
topic2vec.append(topic[1])
count += 1
result_lab.append(la)
print len(x_index), len(y_index), len(topic2vec), len(result_lab), count
result = [x_index, y_index, topic2vec, result_lab]
with open(config.BTMData + "topic2vec_2.txt", 'wb') as fp:
cPickle.dump(result, fp)
def TestTopic(self):
Ta = TopicAnalysis()
sentences = ReadFile.readTXTFile(config.TopicFilePath+"test_topic.txt")
for text in sentences:
# text = raw_input('query:\n')
print "问 ："+text
sentence = Sentence()
sentence.text = text.strip()
response = Ta.getResponse(sentence)
if response:
print "答 ："+response
else:
print "没有合适转移话题！"
def buildModel(Name = "wordRank_filter"):
file = MyCode.config.ModelPath + Name + '.model'
model = None
try:
model = word2vec.Word2Vec.load(file)
except :
# word_sentences = getFileContext_Participle()
# sentences = wordTostr(word_sentences)
# sentences = TextIter()TextIter
sentences = ReadFile.readTXTFile(MyCode.config.CorpusFilePath + 'souhu_fenci.txt')
model = word2vec.Word2Vec(sentences[:10],min_count=1,workers=8)
model.save(file)
return model
def getWords(self):
file = config.Semantic_dicPath + "semantic_wordgroup_new.txt"
words = {}
sentences = ReadFile.readTXTFile(file)
for sen in sentences:
items = sen.strip().split(' ')
if len(items) < 2:
continue
if words.has_key(items[1]):
words[items[1]].append(items[0])
else:
words[items[1]] = [items[0]]
return words
def getWords(self):
file = config.Semantic_dicPath+"semantic_wordgroup_new.txt"
words = {}
sentences = ReadFile.readTXTFile(file)
for sen in sentences:
items = sen.strip().split(' ')
if len(items) < 2:
continue
if words.has_key(items[1]):
words[items[1]].append(items[0])
else:
words[items[1]] = [items[0]]
return words
def filter(file=config.WordDicPath + "semantic_wordgroup_new.txt"):
word_sentences = ReadFile.readTXTFile(file)
word_dic = {}
for word in word_sentences:
iterms = word.strip().split(" ")
if len(iterms) != 2:
continue
if iterms[0] in word_dic:
if iterms[1] not in word_dic[iterms[0]]:
word_dic[iterms[0]].append(iterms[1])
else:
word_dic[iterms[0]] = [iterms[1]]
return word_dic
def TestTopic(self):
Ta = TopicAnalysis()
sentences = ReadFile.readTXTFile(config.TopicFilePath +
"test_topic.txt")
for text in sentences:
# text = raw_input('query:\n')
print "问 ：" + text
sentence = Sentence()
sentence.text = text.strip()
response = Ta.getResponse(sentence)
if response:
print "答 ：" + response
else:
print "没有合适转移话题！"
def buildModel(Name="wordRank_filter"):
file = MyCode.config.ModelPath + Name + '.model'
model = None
try:
model = word2vec.Word2Vec.load(file)
except:
# word_sentences = getFileContext_Participle()
# sentences = wordTostr(word_sentences)
# sentences = TextIter()TextIter
sentences = ReadFile.readTXTFile(MyCode.config.CorpusFilePath +
'souhu_fenci.txt')
model = word2vec.Word2Vec(sentences[:10], min_count=1, workers=8)
model.save(file)
return model
def buildWordFromTxt():
file = "../Result/sentence1.txt"
sentences = ReadFile.readTXTFile(file)
par_Sentences = []
par_Sentences = Participle.Participle(sentences[10000:11000])
w_Sentence = []
with open("../Result/fenci.txt", 'w') as fp:
for s in par_Sentences:
p_sentence = ''
for word in s:
p_sentence += word + ' '
w_Sentence.append(p_sentence)
# print 'Start writing ... ...'
# fp.writelines(w_Sentence)
# print 'Finished writing !'
return w_Sentence
def main():
model = Doc2VecObj()
model.load()
sc = SentencesClusters(20,model)
filename = config.SimilarlySentencePath + "AllQueriesWithID.txt"
sentences = ReadFile.readTXTFile(filename)
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ", ""))
sc.getCluster(train_sentences[:100])
def buildWordFromTxt():
file = "../Result/sentence1.txt"
sentences = ReadFile.readTXTFile(file)
par_Sentences = []
par_Sentences = Participle.Participle(sentences[10000:11000])
w_Sentence = []
with open("../Result/fenci.txt",'w') as fp:
for s in par_Sentences:
p_sentence = ''
for word in s:
p_sentence += word + ' '
w_Sentence.append(p_sentence)
# print 'Start writing ... ...'
# fp.writelines(w_Sentence)
# print 'Finished writing !'
return w_Sentence
def main():
# 老版本
# Lda_model = LdaTopic(modelName='LDAsimiWord_5')
# # print Lda_model[0]
# lda_model = Lda_model[0]
# words = Lda_model[1]
# n_top_words = 1
# for i ,topic_dist in enumerate(lda_model.topic_word_):
# topic_words = numpy.array(words)[numpy.argsort(topic_dist)][:n_top_words:-1]
# print('Topic {}: {}'.format(i, ' '.join(topic_words)))
#
# Lda_model2 = LdaTopic(modelName='LDAsimiWord_6')
# # print Lda_model[0]
# lda_model2 = Lda_model2[0]
# words2 = Lda_model2[1]
# n_top_words2 = 1
# for i, topic_dist in enumerate(lda_model2.topic_word_):
# topic_words = numpy.array(words2)[numpy.argsort(topic_dist)][:n_top_words2:-1]
# print('Topic {}: {}'.format(i, ' '.join(topic_words)))
# documents = getFileSentences()
# sentences = [u"你知道周杰伦是谁么？",u"周杰伦是谁？",u"你知道周杰伦吗？",u"你认识周杰伦吗？",u"你认识周杰伦么？",u"你知道周杰伦么？",\
# u"周杰伦知道么？",u"周杰伦知道是谁么？",u"周杰伦知道吗",u"周杰伦知道是谁吗？",u"周杰伦是谁？",u"周杰伦你认识么？",u"周杰伦你知道是谁么？",\
# u"周杰伦你认识吗？",u"周杰伦你知道是谁吗？",u"你认识周杰伦吗？",u"你知道周杰伦吗？",u"你知道周杰伦么？",u"你认识周杰伦么？",u"你认识周杰伦吗？",\
# u"你认识周杰伦是谁么？",u"你认识周杰伦是谁吗？",u"你知道周杰伦吗？",u"你知道周杰伦是谁吗？",u"你知道周杰伦是谁么？"]
sentences = ReadFile.readTXTFile("./BitermTopicModel/document_corpus.txt")
queries = getQueries(sentences[:100])
for query in queries:
for q in query:
print q,
print
docs_topic = getQueriySimilarly(queries)
# for topic in docs_topic:
# for re in topic:
# print re,1
# print
results = groupByTopic(docs_topic,sentences)
sh = Show()
sh.showDocTopicResult(results)
Wr = WriteResult()
Wr.WriteTopicResult(results)
def main():
# 老版本
# Lda_model = LdaTopic(modelName='LDAsimiWord_5')
# # print Lda_model[0]
# lda_model = Lda_model[0]
# words = Lda_model[1]
# n_top_words = 1
# for i ,topic_dist in enumerate(lda_model.topic_word_):
# topic_words = numpy.array(words)[numpy.argsort(topic_dist)][:n_top_words:-1]
# print('Topic {}: {}'.format(i, ' '.join(topic_words)))
#
# Lda_model2 = LdaTopic(modelName='LDAsimiWord_6')
# # print Lda_model[0]
# lda_model2 = Lda_model2[0]
# words2 = Lda_model2[1]
# n_top_words2 = 1
# for i, topic_dist in enumerate(lda_model2.topic_word_):
# topic_words = numpy.array(words2)[numpy.argsort(topic_dist)][:n_top_words2:-1]
# print('Topic {}: {}'.format(i, ' '.join(topic_words)))
# documents = getFileSentences()
# sentences = [u"你知道周杰伦是谁么？",u"周杰伦是谁？",u"你知道周杰伦吗？",u"你认识周杰伦吗？",u"你认识周杰伦么？",u"你知道周杰伦么？",\
# u"周杰伦知道么？",u"周杰伦知道是谁么？",u"周杰伦知道吗",u"周杰伦知道是谁吗？",u"周杰伦是谁？",u"周杰伦你认识么？",u"周杰伦你知道是谁么？",\
# u"周杰伦你认识吗？",u"周杰伦你知道是谁吗？",u"你认识周杰伦吗？",u"你知道周杰伦吗？",u"你知道周杰伦么？",u"你认识周杰伦么？",u"你认识周杰伦吗？",\
# u"你认识周杰伦是谁么？",u"你认识周杰伦是谁吗？",u"你知道周杰伦吗？",u"你知道周杰伦是谁吗？",u"你知道周杰伦是谁么？"]
sentences = ReadFile.readTXTFile("./BitermTopicModel/document_corpus.txt")
queries = getQueries(sentences[:100])
for query in queries:
for q in query:
print q,
print
docs_topic = getQueriySimilarly(queries)
# for topic in docs_topic:
# for re in topic:
# print re,1
# print
results = groupByTopic(docs_topic, sentences)
sh = Show()
sh.showDocTopicResult(results)
Wr = WriteResult()
Wr.WriteTopicResult(results)
def main():
# train_sentences = [
# '0无偿居间介绍买卖毒品的行为应如何定性',
# '1吸毒男动态持有大量毒品的行为该如何认定',
# '2如何区分是非法种植毒品原植物罪还是非法制造毒品罪',
# '3为毒贩贩卖毒品提供帮助构成贩卖毒品罪',
# '4将自己吸食的毒品原价转让给朋友吸食的行为该如何认定',
# '5为获报酬帮人购买毒品的行为该如何认定',
# '6毒贩出狱后再次够买毒品途中被抓的行为认定',
# '7虚夸毒品功效劝人吸食毒品的行为该如何认定',
# '8妻子下落不明丈夫又与他人登记结婚是否为无效婚姻',
# '9一方未签字办理的结婚登记是否有效',
# '10夫妻双方1990年按农村习俗举办婚礼没有结婚证 一方可否起诉离婚',
# '11结婚前对方父母出资购买的住房写我们二人的名字有效吗',
# '12身份证被别人冒用无法登记结婚怎么办？',
# '13同居后又与他人登记结婚是否构成重婚罪',
# '14未办登记只举办结婚仪式可起诉离婚吗',
# '15同居多年未办理结婚登记，是否可以向法院起诉要求离婚'
# ]
filename = config.SimilarlySentencePath+"AllQueriesWithID.txt"
sentences = ReadFile.readTXTFile(filename)
# sentences = ReadFile.readTXTFile(config.SimilarlySentencePath + "AllQueriesWithID.txt")
test_sentences_doc = ReadFile.readTXTFile(config.SimilarlySentencePath + "corpus_0829.txt")
test_sentences = []
for sen in test_sentences_doc:
sen_iterms = sen.strip().split("\t")
if len(sen_iterms) >= 2:
test_sentences.append(sen_iterms[1])
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ", ""))
print type(train_sentences[0])
docs = LabelSentences(filename=None,sentences=train_sentences)
# docs = LabelSentences.LabelSentences(sentences=train_sentences)
# sentences = ReadFile.readTXTFile(config.SimilarlySentencePath+"corpus_0829.txt")
# train_sentences = ReadFile.getFileSentence(config.SimilarlySentencePath + "")
# print len(sentences)
# train_sentences = []
# for sen in sentences:
# sen_iterms = sen.split("\t")
# if len(sen_iterms) == 2:
# print sen_iterms[1]
# train_sentences.append(sen_iterms[1])
# test_sentences = ReadFile.readTXTFile(config.SimilarlySentencePath+"corpus_0829_t.txt")
# test_sentences = ['周涛知道是谁吗']
test_sentences = train_sentences[:100]
SSO = Doc2VecObj()
# corpus = SSO.getCorpus(docs)
# SSO.buildModel(docs)
# SSO.save()
# load model
SSO.load()
result = SSO.calSentencesSimilarly(test_sentences,train_sentences)
Wr = WriteResult()
can_not_deal = Wr.WriteSimilarlySentence(result,"Doc2Vec_simi.txt")
def train_lad():
lda = LDA()
sentences = ReadFile.readTXTFile(config.BTMData + "btm_text_corpus.txt")
# line = LineSetence(sentences=sentences)
lda.buildModel(lines(sentences), num_topics=21)
def getQuestionFile(Q_R_sentences):
QIdfile = MyCode.config.CaiCaiDataPath + "AllQueriesWithID.txt"
QnIdfile = MyCode.config.CaiCaiPath + "AllQueriesWithID.txt"
mapQRfile = MyCode.config.CaiCaiDataPath + "AllQueryResponseIdMap.txt"
mapnQRfile = MyCode.config.CaiCaiPath + "AllQueryResponseIdMap.txt"
RIdfile = MyCode.config.CaiCaiDataPath + "AllResponsesWithID.txt"
RnIdfile = MyCode.config.CaiCaiPath + "AllResponsesWithID.txt"
Q_sentences = ReadFile.readTXTFile(QIdfile)
QR_map = []
OldMapQid = {}
MapRId = {}
MapQid = {}
r_id = 1
q_id = 1
OldRIdSentences = ReadFile.readTXTFile(RIdfile)
R_sens = []
for line in OldRIdSentences:
R_sens.append(line.strip().split("\t")[1])
R_sens = list(set(R_sens))
for qr_sentence in Q_R_sentences:
csentence = []
exist = False
for sentence in Q_sentences:
csentence = sentence.strip().split('\t')
line = csentence[1].replace(' ','')
if line == qr_sentence[0]:
exist = True
break
if exist:
sq_id = csentence[0]
OldMapQid.setdefault(sq_id,'')
else:
sq_id = "CAICAI_Q_"+str(time.strftime("%Y%m%d%H%M", time.localtime()))+"%05d"%q_id
q_id += 1
MapQid.setdefault(sq_id,qr_sentence[1].replace(' ',''))
for i in xrange(2, 5):
if qr_sentence[i] in R_sens:
continue
if qr_sentence[i] != '' and len(qr_sentence[i]) > 2:
print qr_sentence[i]
sr_id = 'CAICAI_R_'+str(time.strftime("%Y%m%d%H%M", time.localtime()))+'%05d' % r_id
QR_map.append((sq_id, sr_id))
MapRId.setdefault(sr_id,qr_sentence[i])
r_id += 1
fileEnd = MyCode.config.CaiCaiPath + 'AllQueriesWithIDfinished.txt'
# 重写Questions 文件
with open(fileEnd,'w') as fp:
# print len(OldMapQid.keys())
for sen in Q_sentences:
lines = sen.split('\t')[0]
if OldMapQid.has_key(lines):
fp.write(sen[:-2]+',"client_id": "c_00000007"}\n')
else:
fp.write(sen)
# 结果写入文件
with open(QnIdfile,'w') as fp:
MapQid = sorted(MapQid.iteritems(),key=lambda asd:asd[0],reverse=False)
for id in MapQid:
fp.write(id[0]+'\t'+id[1]+"\n")
with open(mapnQRfile,'w') as fp:
sen = ReadFile.readTXTFile(mapQRfile)
for s in sen:
lines = s.split('\t')
print lines
QR_map.append((lines[0],lines[1][:-1]))
QR_map = list(set(QR_map))
for qr in sorted(QR_map,key=lambda asd:asd[0],reverse=False):
fp.write(qr[0]+'\t'+qr[1]+'\n')
with open(RnIdfile,'w') as fp:
MapRId = sorted(MapRId.iteritems(),key=lambda asd:asd[0],reverse=False)
for id in MapRId:
fp.write(id[0]+'\t'+id[1].strip()+'\t{"client_id": "c_00000007"}\n')
def main():
# train_sentences = [
# '0无偿居间介绍买卖毒品的行为应如何定性',
# '1吸毒男动态持有大量毒品的行为该如何认定',
# '2如何区分是非法种植毒品原植物罪还是非法制造毒品罪',
# '3为毒贩贩卖毒品提供帮助构成贩卖毒品罪',
# '4将自己吸食的毒品原价转让给朋友吸食的行为该如何认定',
# '5为获报酬帮人购买毒品的行为该如何认定',
# '6毒贩出狱后再次够买毒品途中被抓的行为认定',
# '7虚夸毒品功效劝人吸食毒品的行为该如何认定',
# '8妻子下落不明丈夫又与他人登记结婚是否为无效婚姻',
# '9一方未签字办理的结婚登记是否有效',
# '10夫妻双方1990年按农村习俗举办婚礼没有结婚证 一方可否起诉离婚',
# '11结婚前对方父母出资购买的住房写我们二人的名字有效吗',
# '12身份证被别人冒用无法登记结婚怎么办？',
# '13同居后又与他人登记结婚是否构成重婚罪',
# '14未办登记只举办结婚仪式可起诉离婚吗',
# '15同居多年未办理结婚登记，是否可以向法院起诉要求离婚'
# ]
filename = config.SimilarlySentencePath + "AllQueriesWithID.txt"
sentences = ReadFile.readTXTFile(filename)
# sentences = ReadFile.readTXTFile(config.SimilarlySentencePath + "AllQueriesWithID.txt")
test_sentences_doc = ReadFile.readTXTFile(config.SimilarlySentencePath +
"corpus_0829.txt")
test_sentences = []
for sen in test_sentences_doc:
sen_iterms = sen.strip().split("\t")
if len(sen_iterms) >= 2:
test_sentences.append(sen_iterms[1])
train_sentences = []
for sen in sentences:
sen_iterms = sen.split("\t")
# print sen_iterms[1]
if len(sen_iterms) >= 2:
# print sen_iterms[1].strip().replace(" ","")
train_sentences.append(sen_iterms[1].strip().replace(" ", ""))
print type(train_sentences[0])
docs = LabelSentences(filename=None, sentences=train_sentences)
# docs = LabelSentences.LabelSentences(sentences=train_sentences)
# sentences = ReadFile.readTXTFile(config.SimilarlySentencePath+"corpus_0829.txt")
# train_sentences = ReadFile.getFileSentence(config.SimilarlySentencePath + "")
# print len(sentences)
# train_sentences = []
# for sen in sentences:
# sen_iterms = sen.split("\t")
# if len(sen_iterms) == 2:
# print sen_iterms[1]
# train_sentences.append(sen_iterms[1])
# test_sentences = ReadFile.readTXTFile(config.SimilarlySentencePath+"corpus_0829_t.txt")
# test_sentences = ['周涛知道是谁吗']
test_sentences = train_sentences[:100]
SSO = Doc2VecObj()
# corpus = SSO.getCorpus(docs)
# SSO.buildModel(docs)
# SSO.save()
print " load model"
SSO.load()
value = SSO.similarly(u"早起吃的油条，很好吃。", u"今天吃什么")
# result = SSO.most_similarSentence(test_sentences[9],test_sentences[:200],topn=10)
# print test_sentences[9]
# for re in result:
# print re[0],re[1]
print "similarly : ", value
def train_lad(): lda = LDA() sentences = ReadFile.readTXTFile(config.BTMData + "btm_text_corpus.txt") # line = LineSetence(sentences=sentences) lda.buildModel(lines(sentences), num_topics=21)