def predict(self, passages):
# ￌ￡ﾡￌￕ
for p in passages:
if not p.preprocessed: essayprepare.processPassage(p)
p.lf = self.extractor.extractLangFeather(p)
p.cf = self.extractor.extractContentFeather(p)
p.sf = self.extractor.extractStructureFeather(p)
# ￊ￤ﾳￌￕￖﾵ
f = open('fs_test.txt', 'w')
# ﾳￌￕￏ
endog = []
exog = []
labels = []
for p in passages:
score = int(p.score)
if score < 35: score = 35
endog.append(score)
x = self.__getFeatherList(p)
exog.append(x)
labels.append(p.label)
f.write(p.id + ' ')
f.write(p.score)
for v in x:
f.write(' ' + str(v))
f.write('\n')
f.close()
p_label, p_acc, p_val = svmutil.svm_predict(labels, exog, self.svm_model)
print p_label, p_acc, p_val
def rate_by_params(self, passage):
# 线性预测
extractor = FeatherExtractor(None)
if not passage.preprocessed: essayprepare.processPassage(passage)
passage.lf = extractor.extractLangFeather(passage)
passage.cf = extractor.extractContentFeather(passage)
passage.sf = extractor.extractStructureFeather(passage)
exog = []
x = self.__getFeatherList(passage)
score = dot(x, self.model_params)
passage.rateScore = score
passage.endogScore = score
# 调整分数
passage.filter_scores = []
filters = [self.tokenCountFilter, self.sentenceLengthAverageFilter,
self.wordLengthAverageFilter, self.aclWordCountFilter,
self.noneStopWordLengthAverageFilter, self.nounRatioFilter]
for filter in filters:
filter_score = filter(passage)
passage.rateScore += filter_score
passage.filter_scores.append(filter_score)
passage.rated = True
return [passage.rateScore]
def rate_by_params(self, passage):
# 线性预测
extractor = FeatherExtractor(None)
if not passage.preprocessed: essayprepare.processPassage(passage)
passage.lf = extractor.extractLangFeather(passage)
passage.cf = extractor.extractContentFeather(passage)
passage.sf = extractor.extractStructureFeather(passage)
exog = []
x = self.__getFeatherList(passage)
score = dot(x, self.model_params)
passage.rateScore = score
passage.endogScore = score
# 调整分数
passage.filter_scores = []
filters = [
self.tokenCountFilter, self.sentenceLengthAverageFilter,
self.wordLengthAverageFilter, self.aclWordCountFilter,
self.noneStopWordLengthAverageFilter, self.nounRatioFilter,
self.total_score_filter
]
for filter in filters:
filter_score = filter(passage)
passage.rateScore += filter_score
passage.filter_scores.append(filter_score)
passage.rated = True
return [passage.rateScore]
def rate(self, passage):
# 线性预测
if not passage.preprocessed: essayprepare.processPassage(passage)
passage.lf = self.extractor.extractLangFeather(passage)
passage.cf = self.extractor.extractContentFeather(passage)
passage.sf = self.extractor.extractStructureFeather(passage)
exog = []
x = self.__getFeatherList(passage)
exog.append(x)
exog = np.array(exog)
endog = self.gls_model.predict(exog)
passage.rateScore = endog[0]
passage.endogScore = endog[0]
passage.filters = []
# 调整分数
filter = self.tokenCountFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.sentenceLengthAverageFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.wordLengthAverageFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.aclWordCountFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.noneStopWordLengthAverageFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.nounRatioFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.verbRatioFilter(passage)
#passage.rateScore += filter
passage.filters.append(filter)
filter = self.adjRatioFilter(passage)
#passage.rateScore += filter
passage.filters.append(filter)
filter = self.posRatioFilter(passage)
#passage.rateScore += filter
passage.filters.append(filter)
passage.rated = True
endog[0] = passage.rateScore
return [passage.rateScore]
def rate(self, passage):
# 线性预测
if not passage.preprocessed: essayprepare.processPassage(passage)
passage.lf = self.extractor.extractLangFeather(passage)
passage.cf = self.extractor.extractContentFeather(passage)
passage.sf = self.extractor.extractStructureFeather(passage)
exog = []
x = self.__getFeatherList(passage)
exog.append(x)
exog = np.array(exog)
endog = self.gls_model.predict(exog)
passage.rateScore = endog[0]
passage.endogScore = endog[0]
passage.filters = []
# 调整分数
filter = self.tokenCountFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.sentenceLengthAverageFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.wordLengthAverageFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.aclWordCountFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.noneStopWordLengthAverageFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.nounRatioFilter(passage)
passage.rateScore += filter
passage.filters.append(filter)
filter = self.verbRatioFilter(passage)
#passage.rateScore += filter
passage.filters.append(filter)
filter = self.adjRatioFilter(passage)
#passage.rateScore += filter
passage.filters.append(filter)
filter = self.posRatioFilter(passage)
#passage.rateScore += filter
passage.filters.append(filter)
passage.rated = True
endog[0] = passage.rateScore
return [passage.rateScore]
def train(self, passages):
# pre-process passage
i = 1
for p in passages:
print "======================="
print "Passage", i, p.id
if not p.preprocessed: essayprepare.processPassage(p)
i += 1
self.extractor = FeatherExtractor(None)
for p in passages:
p.lf = self.extractor.extractLangFeather(p)
p.cf = self.extractor.extractContentFeather(p)
p.sf = self.extractor.extractStructureFeather(p)
# save feathers
f = open('fs_zhang_train.txt', 'w')
for p in passages:
x = self.__getFeatherList(p)
f.write(p.id + ' ')
f.write(str(p.score))
for v in x:
f.write(' ' + str(v))
f.write('\n')
f.close()
# generate feather vector
endog = []
exog = []
for p in passages:
score = int(p.score)
endog.append(score)
x = self.__getFeatherList(p)
exog.append(x)
# train model
endog = np.array(endog)
exog = np.array(exog)
self.gls_model = sm.GLS(endog, exog)
results = self.gls_model.fit()
#print results.summary()
print results.params
def train(self, passages):
# pre-process passage
i = 1
for p in passages:
print "======================="
print "Passage", i, p.id
if not p.preprocessed: essayprepare.processPassage(p)
i += 1
self.extractor = FeatherExtractor(None)
for p in passages:
p.lf = self.extractor.extractLangFeather(p)
p.cf = self.extractor.extractContentFeather(p)
p.sf = self.extractor.extractStructureFeather(p)
# save feathers
f = open('fs_zhang_train.txt', 'w')
for p in passages:
x = self.__getFeatherList(p)
f.write(p.id + ' ')
f.write(str(p.score))
for v in x:
f.write(' ' + str(v))
f.write('\n')
f.close()
# generate feather vector
endog = []
exog = []
for p in passages:
score = int(p.score)
endog.append(score)
x = self.__getFeatherList(p)
exog.append(x)
# train model
endog = np.array(endog)
exog = np.array(exog)
self.gls_model = sm.GLS(endog, exog)
results = self.gls_model.fit()
#print results.summary()
print results.params
def demo_one_sentence():
# 文章
passage = EssayPassage()
passage.passage = 'I am a students.'
passage.title = 'title'
passage.score = 5
passage.id = '1'
passage.reviewerId = 3
passage.content = 'I am a students.'
# 处理文章
essayprepare.processPassage(passage)
extractor = FeatherExtractor()
lf = extractor.extractLangFeather(passage)
passage.lf = lf
cf = extractor.extractContentFeather(passage)
sf = extractor.extractStructureFeather(passage)
print 'OK'
def demo_one_sentence():
# 文章
passage = EssayPassage()
passage.passage = 'I am a students.'
passage.title = 'title'
passage.score = 5
passage.id = '1'
passage.reviewerId = 3
passage.content = 'I am a students.'
# 处理文章
essayprepare.processPassage(passage)
extractor = FeatherExtractor()
lf = extractor.extractLangFeather(passage)
passage.lf = lf
cf = extractor.extractContentFeather(passage)
sf = extractor.extractStructureFeather(passage)
print 'OK'
def rate(self, passage):
# 线性预测
if not passage.preprocessed: essayprepare.processPassage(passage)
passage.lf = self.extractor.extractLangFeather(passage)
passage.cf = self.extractor.extractContentFeather(passage)
passage.sf = self.extractor.extractStructureFeather(passage)
passage.lsaScore = passage.cf.lsaScore
passage.lsaSimilarity = passage.cf.lsaSimilarity
passage.lsaSimilarityAll = passage.cf.lsaSimilarityAll
exog = []
x = self.__getFeatherList(passage)
exog.append(x)
# for i, xx in enumerate(x):
# x[i] -= self.m[i]
exog = np.array(exog)
# xxexog = dot(self.p, exog.transpose())
# endog = self.gls_model.predict(xxexog.transpose())
endog = self.gls_model.predict(exog)
passage.rateScore = endog[0]
passage.endogScore = endog[0]
# 调整分数
passage.filter_scores = []
filters = [self.tokenCountFilter, self.sentenceLengthAverageFilter,
self.wordLengthAverageFilter, self.aclWordCountFilter,
self.noneStopWordLengthAverageFilter, self.nounRatioFilter,
self.verbRatioFilter, self.adjRatioFilter,
self.posRatioFilter, self.lsaFilter]
for filter in filters:
filter_score = filter(passage)
passage.rateScore += filter_score
passage.filter_scores.append(filter_score)
self.generateRateResult(passage)
passage.rated = True
endog[0] = passage.rateScore
return [passage.rateScore]
def generatePassageFeathers(passages, outFilename):
f = open(outFilename, 'w')
e = FeatherExtractor()
i = 1
for p in passages:
print "Passage ", i
# 处理文章
essayprepare.processPassage(p)
# 提取语言特征
languageFeather = e.extractLangFeather(p)
p.lf = languageFeather
# 提取结构特征
structureFeather = e.extractStructureFeather(p)
p.sf = structureFeather
f.write(p.id + ' ')
f.write(str(p.score))
f.write(' ' + str(languageFeather))
f.write('\n')
i += 1
f.close()
def generatePassageFeathers(passages, outFilename):
f = open(outFilename, 'w')
e = FeatherExtractor()
i = 1
for p in passages:
print "Passage ", i
# 处理文章
essayprepare.processPassage(p)
# 提取语言特征
languageFeather = e.extractLangFeather(p)
p.lf = languageFeather
# 提取结构特征
structureFeather = e.extractStructureFeather(p)
p.sf = structureFeather
f.write(p.id + ' ')
f.write(str(p.score))
f.write(' ' + str(languageFeather))
f.write('\n')
i += 1
f.close()
for e in essays:
if e.id == "0092":
essay = e
break
# 文章
passage = EssayPassage()
passage.passage = essay.cleanContent()
passage.title = essay.title
passage.score = essay.score
passage.id = essay.id
passage.reviewerId = essay.reviewerId
passage.content = essay.content
# 处理文章
essayprepare.processPassage(passage)
# 输出来看看是啥样子
print "PASSAGE========================================="
print passage
print passage.id
print passage.title
print passage.score
print passage.passage
print len(passage.paragraphs)
print "PARAGRAPHS---------------------------------------"
for para in passage.paragraphs:
print para.paragraphNo
print para.paragraph
for sent in para.sentences:
print sent.sentenceNo
for e in essays:
if e.id == "0092":
essay = e
break
# 文章
passage = EssayPassage()
passage.passage = essay.cleanContent()
passage.title = essay.title
passage.score = essay.score
passage.id = essay.id
passage.reviewerId = essay.reviewerId
passage.content = essay.content
# 处理文章
essayprepare.processPassage(passage)
# 输出来看看是啥样子
print "PASSAGE========================================="
print passage
print passage.id
print passage.title
print passage.score
print passage.passage
print len(passage.paragraphs)
print "PARAGRAPHS---------------------------------------"
for para in passage.paragraphs:
print para.paragraphNo
print para.paragraph
for sent in para.sentences:
print sent.sentenceNo
def do_task(task):
newpassage = EssayPassage()
newpassage.passage = task['input']['content']
newpassage.orderId = task['id']
newpassage.score = 0
newpassage.processStatus = 0
try:
essayprepare.processPassage(newpassage, fn_prepare_progress)
fe = extractor.FeatherExtractor()
lf = fe.extractLangFeather(newpassage)
newpassage.lf = lf
cf = fe.extractContentFeather(newpassage)
newpassage.cf = cf
sf = fe.extractStructureFeather(newpassage)
newpassage.sf = sf
newpassage.score = rater.rate_by_params(newpassage)[0]
except:
task['progress'] = -2
task['status'] = 'TUTERR'
task['output'] = ""
task['simple_output'] = ""
task['detail_output'] = ""
commit_task(task)
return
# 生成最终结果
output = {}
passage = {}
passage['score'] = newpassage.score
passage['token_count'] = lf.tokenCount
passage['word_count'] = lf.wordCount
passage['word_type_count'] = lf.wordTypeCount
passage['word_lemma_count'] = lf.wordLemmaCount
passage['word_stem_count'] = lf.wordStemCount
passage['average_word_length'] = lf.wordLengthAverage
passage['average_sentence_length'] = lf.sentenceLengthAverage
passage['overly_use_word_count'] = lf.overlyUseWordCount
passage['paragraph_count'] = len(newpassage.paragraphs)
passage['sentence_count'] = newpassage.sentenceCount
passage['sentences'] = []
for para in newpassage.paragraphs:
for sent in para.sentences:
sentence = {}
sentence['no'] = sent.sentenceNo
sentence['para_no'] = para.paragraphNo
sentence['original'] = sent.sentence
sentence['score'] = 0
spell_errors = []
fs = []
for token in sent.tokens:
if token.isSpellError:
fs.append('<ESP>' + token.token + '</ESP>')
spell_error = {}
spell_error['token'] = token.token
spell_error['lemma'] = token.lemma
spell_error['suggest'] = token.candidates
spell_error['start_at'] = token.startAt
spell_error['end_at'] = token.endAt
spell_errors.append(spell_error)
else:
fs.append(token.token)
sentence['spell_errors'] = spell_errors
sentence['marked'] = ' '.join(fs)
sentence['lt_result'] = sent.ltCheckResults
sentence['lg_result'] = sent.lgCheckResults
sentence['links'] = []
passage['sentences'].append(sentence)
output['passage'] = passage
task['progress'] = 100
task['status'] = 'DONE'
task['output'] = json.dumps(output)
task['simple_output'] = json.dumps(output)
task['detail_output'] = json.dumps(generate_detail_output(newpassage))
commit_task(task)
def processEssay(self):
self.browser.clear()
id = unicode(self.lineedit.text())
essay = self.essayDict.get(id)
if not essay:
self.browser.append("<font color=red>%s is not found!</font>" % id)
return
self.browser.append(essay.content)
# 文章
passage = EssayPassage()
passage.passage = essay.cleanContent()
passage.title = essay.title
passage.score = essay.score
passage.id = essay.id
# 处理文章
essayprepare.processPassage(passage)
# 输出来看看是啥样子
self.browser.append("PASSAGE=========================================")
self.browser.append(passage.id)
#self.browser.append(passage.title)
self.browser.append(passage.score)
self.browser.append(passage.passage)
self.browser.append(str(len(passage.paragraphs)))
self.browser.append("PARAGRAPHS---------------------------------------")
for para in passage.paragraphs:
self.browser.append(str(para.paragraphNo))
self.browser.append(para.paragraph)
for sent in para.sentences:
self.browser.append(str(sent.sentenceNo))
self.browser.append(str(sent.paragraphSentenceNo))
self.browser.append(sent.sentence)
tokens = [token.token for token in sent.tokens]
tags = [token.pos for token in sent.tokens]
lemmas = [token.lemma for token in sent.tokens]
stems = [token.stem for token in sent.tokens]
levels = [token.level for token in sent.tokens]
nos = [token.tokenNo for token in sent.tokens]
sentNos = [token.sentenceTokenNo for token in sent.tokens]
paraNos = [token.paragraphTokenNo for token in sent.tokens]
errorTokens = [token.token for token in sent.tokens if token.isSpellError]
if not sent.canParsed:
self.browser.append("<font color=red>SENTENCE ERROR</font>")
self.browser.append("<font color=red>SPELLERROR %s</font>" % str(errorTokens))
self.browser.append(str(tokens))
self.browser.append(str(tags))
self.browser.append(str(lemmas))
self.browser.append(str(stems))
self.browser.append(str(levels))
self.browser.append(str(sentNos))
self.browser.append(str(paraNos))
self.browser.append(str(nos))
self.browser.append(str(sent.tokenCount))
self.browser.append(str(sent.wordCount))
self.browser.append(str(sent.realWordCount))
self.browser.append(u"三元词组" + ' ' + str(passage.trigrams))
e = FeatherExtractor()
# 提取语言特征
languageFeather = e.extractLangFeather(passage)
print u"词次总数", languageFeather.tokenCount
print u"单词总数", languageFeather.wordCount
print u"词形总数", languageFeather.wordTypeCount
print u"词元总数", languageFeather.wordLemmaCount
print u"介词个数", languageFeather.prepositionCount
print u"介词比例", languageFeather.prepositionRatio
print u"介词使用", languageFeather.prepositionUse
print u"定冠词个数", languageFeather.definiteArticleCount
print u"定冠词比例", languageFeather.definiteArticleRatio
print u"定冠词使用", languageFeather.definiteArticleUse
# 提取结构特征
#structureFeather = e.extractStructureFeather(passage)
#generateUSTCFeathers('USTC2011Jan.txt', 'USTCFeathers_503.txt')
print "...OVER"
def processEssay(self):
self.browser.clear()
id = unicode(self.lineedit.text())
essay = self.essayDict.get(id)
if not essay:
self.browser.append("<font color=red>%s is not found!</font>" % id)
return
self.browser.append(essay.content)
# 文章
passage = EssayPassage()
passage.passage = essay.cleanContent()
passage.title = essay.title
passage.score = essay.score
passage.id = essay.id
# 处理文章
essayprepare.processPassage(passage)
# 输出来看看是啥样子
self.browser.append("PASSAGE=========================================")
self.browser.append(passage.id)
#self.browser.append(passage.title)
self.browser.append(passage.score)
self.browser.append(passage.passage)
self.browser.append(str(len(passage.paragraphs)))
self.browser.append(
"PARAGRAPHS---------------------------------------")
for para in passage.paragraphs:
self.browser.append(str(para.paragraphNo))
self.browser.append(para.paragraph)
for sent in para.sentences:
self.browser.append(str(sent.sentenceNo))
self.browser.append(str(sent.paragraphSentenceNo))
self.browser.append(sent.sentence)
tokens = [token.token for token in sent.tokens]
tags = [token.pos for token in sent.tokens]
lemmas = [token.lemma for token in sent.tokens]
stems = [token.stem for token in sent.tokens]
levels = [token.level for token in sent.tokens]
nos = [token.tokenNo for token in sent.tokens]
sentNos = [token.sentenceTokenNo for token in sent.tokens]
paraNos = [token.paragraphTokenNo for token in sent.tokens]
errorTokens = [
token.token for token in sent.tokens if token.isSpellError
]
if not sent.canParsed:
self.browser.append(
"<font color=red>SENTENCE ERROR</font>")
self.browser.append("<font color=red>SPELLERROR %s</font>" %
str(errorTokens))
self.browser.append(str(tokens))
self.browser.append(str(tags))
self.browser.append(str(lemmas))
self.browser.append(str(stems))
self.browser.append(str(levels))
self.browser.append(str(sentNos))
self.browser.append(str(paraNos))
self.browser.append(str(nos))
self.browser.append(str(sent.tokenCount))
self.browser.append(str(sent.wordCount))
self.browser.append(str(sent.realWordCount))
self.browser.append(u"三元词组" + ' ' + str(passage.trigrams))
e = FeatherExtractor()
# 提取语言特征
languageFeather = e.extractLangFeather(passage)
print u"词次总数", languageFeather.tokenCount
print u"单词总数", languageFeather.wordCount
print u"词形总数", languageFeather.wordTypeCount
print u"词元总数", languageFeather.wordLemmaCount
print u"介词个数", languageFeather.prepositionCount
print u"介词比例", languageFeather.prepositionRatio
print u"介词使用", languageFeather.prepositionUse
print u"定冠词个数", languageFeather.definiteArticleCount
print u"定冠词比例", languageFeather.definiteArticleRatio
print u"定冠词使用", languageFeather.definiteArticleUse
# 提取结构特征
#structureFeather = e.extractStructureFeather(passage)
#generateUSTCFeathers('USTC2011Jan.txt', 'USTCFeathers_503.txt')
print "...OVER"
def do_task(task):
newpassage = EssayPassage()
newpassage.passage = task['input']['content']
newpassage.orderId = task['id']
newpassage.score = 0
newpassage.processStatus = 0
try:
essayprepare.processPassage(newpassage, fn_prepare_progress)
fe = extractor.FeatherExtractor()
lf = fe.extractLangFeather(newpassage)
newpassage.lf = lf
cf = fe.extractContentFeather(newpassage)
newpassage.cf = cf
sf = fe.extractStructureFeather(newpassage)
newpassage.sf = sf
newpassage.score = rater.rate_by_params(newpassage)[0]
except:
task['progress'] = -2
task['status'] = 'TUTERR'
task['output'] = ""
task['simple_output'] = ""
task['detail_output'] = ""
commit_task(task)
return
# 生成最终结果
output = {}
passage = {}
passage['score'] = newpassage.score
passage['token_count'] = lf.tokenCount
passage['word_count'] = lf.wordCount
passage['word_type_count'] = lf.wordTypeCount
passage['word_lemma_count'] = lf.wordLemmaCount
passage['word_stem_count'] = lf.wordStemCount
passage['average_word_length'] = lf.wordLengthAverage
passage['average_sentence_length'] = lf.sentenceLengthAverage
passage['overly_use_word_count'] = lf.overlyUseWordCount
passage['paragraph_count'] = len(newpassage.paragraphs)
passage['sentence_count'] = newpassage.sentenceCount
passage['sentences'] = []
for para in newpassage.paragraphs:
for sent in para.sentences:
sentence = {}
sentence['no'] = sent.sentenceNo
sentence['para_no'] = para.paragraphNo
sentence['original'] = sent.sentence
sentence['score'] = 0
spell_errors = []
fs = []
for token in sent.tokens:
if token.isSpellError:
fs.append('<ESP>' + token.token + '</ESP>')
spell_error = {}
spell_error['token'] = token.token
spell_error['lemma'] = token.lemma
spell_error['suggest'] = token.candidates
spell_error['start_at'] = token.startAt
spell_error['end_at'] = token.endAt
spell_errors.append(spell_error)
else:
fs.append(token.token)
sentence['spell_errors'] = spell_errors
sentence['marked'] = ' '.join(fs)
sentence['lt_result'] = sent.ltCheckResults
sentence['lg_result'] = sent.lgCheckResults
sentence['links'] = []
passage['sentences'].append(sentence)
output['passage'] = passage
task['progress'] = 100
task['status'] = 'DONE'
task['output'] = json.dumps(output)
task['simple_output'] = json.dumps(output)
task['detail_output'] = json.dumps(generate_detail_output(newpassage))
commit_task(task)
def train(self, passages):
# 预处理文章
i = 1
for p in passages:
#print "Passage ", i
# 处理文章
if not p.preprocessed: essayprepare.processPassage(p)
i += 1
# 训练模型
passages.sort(cmp=lambda x,y: cmp(x.score, y.score), reverse=True)
model = EssayModel()
model.train(passages)
self.models['1'] = model
#print model.triGramDicts
# 提取特征
self.extractor = FeatherExtractor(model)
for p in passages:
p.lf = self.extractor.extractLangFeather(p)
p.cf = self.extractor.extractContentFeather(p)
p.sf = self.extractor.extractStructureFeather(p)
# 输出特征值
f = open('fs_train.txt', 'w')
# 生成特征向量
endog = []
exog = []
labels = []
for p in passages:
score = int(p.score)
# if score > 90: score = 90
# if score < 35: score = 35
endog.append(score)
x = self.__getFeatherList(p)
exog.append(x)
labels.append(p.label)
f.write(p.id + ' ')
f.write(str(p.score))
for v in x:
f.write(' ' + str(v))
f.write('\n')
f.close()
# SVM分类器训练
#self.svm_model = svmutil.svm_train(labels, exog, '-c 3')
# 线性回归模型训练
endog = np.array(endog)
exog = np.array(exog)
# print endog
# print exog
# self.m = np.mean(exog,axis=0)
# print self.m
#
# T, P, e_var = PCA_svd(exog)
# print T
# print P
# print e_var
#
# r, c = P.shape
# print r, c
# for i in xrange(11, r):
# for j in xrange(0, c):
# P[i, j] = 0
# print P
# self.p = P
#
# xexog = dot(P, exog.transpose())
# print xexog
# print xexog.shape
#
# xxexog = xexog.transpose()
self.gls_model = sm.GLS(endog, exog)
self.gls_model.fit()
def rate(self, passage):
# ￏￔￔﾤﾲ￢
if not passage.preprocessed: essayprepare.processPassage(passage)
passage.lf = self.extractor.extractLangFeather(passage)
passage.cf = self.extractor.extractContentFeather(passage)
passage.sf = self.extractor.extractStructureFeather(passage)
exog = []
x = self.__getFeatherList(passage)
exog.append(x)
# for i, xx in enumerate(x):
# x[i] -= self.m[i]
exog = np.array(exog)
# xxexog = dot(self.p, exog.transpose())
# endog = self.gls_model.predict(xxexog.transpose())
endog = self.gls_model.predict(exog)
passage.rateScore = endog[0]
passage.endogScore = endog[0]
# ﾵￕﾷￖￊ�
# ﾸﾾￎￄￕￂￗￖￊ�ﾵￕ
if (passage.lf.tokenCount < 100):
passage.rateScore *= 0.8
elif passage.lf.tokenCount < 120:
passage.rateScore *= 0.9
# ﾸﾾￆﾽﾾﾾ￤ﾳﾤﾵￕ
filter = 0
slv = passage.lf.sentenceLengthAverage
if (slv < 10):
filter = (10 - slv) * 2
if filter > 6: filter = 6
elif slv > 23:
filter = (slv - 23) * 3
if filter > 9: filter = 9
passage.rateScore -= filter
# ﾸﾾￆﾽﾾﾴￊﾳﾤﾵￕ
filter = 0
wlv = passage.lf.wordLengthAverage
if wlv < 4:
filter = (4 - wlv) * 10
passage.rateScore -= filter
# ﾸﾾￊﾵﾴￊￆﾽﾾﾳﾤﾶﾵￕ
filter = 0
rwlv = passage.lf.noneStopWordLengthAverage
if rwlv < 5.5:
filter = (5.5 - rwlv) * 10
passage.rateScore -= filter
# ﾸﾾﾴￊￔﾱ�ﾵￕ
filter = 0
nr = passage.lf.nounRatio
if nr < 0.2:
filter = (0.2 - nr) * 100
elif nr > 0.35:
filter = (nr - 0.35) * 100
passage.rateScore -= filter
filter = 0
vr = passage.lf.verbRatio
if vr < 0.1:
filter = (0.1 - vr) * 200
elif vr > 0.2:
filter = (vr - 0.2) * 200
passage.rateScore -= filter
filter = 0
ar = passage.lf.adjRatio
if ar < 0.045:
filter = (0.045 - ar) * 500
passage.rateScore -= filter
filter = 0
badRatioCount = 0
offsetRatio = 0
if (nr < 0.2) or (nr > 0.3):
badRatioCount += 1
else:
offsetRatio += abs(nr - 0.25) / 0.1
if (vr < 0.1) or (vr > 0.2):
badRatioCount += 1
else:
offsetRatio += abs(vr - 0.15) / 0.1
if (ar < 0.06) or (ar > 0.13):
badRatioCount += 1
else:
offsetRatio += abs(ar - 0.095) / 0.14
if badRatioCount == 0:
if offsetRatio < 0.1:
filter = passage.rateScore * 0.05
elif badRatioCount == 1:
if offsetRatio > 0.6:
filter = - passage.rateScore * 0.05
elif badRatioCount > 1:
filter = - passage.rateScore * 0.02 * badRatioCount * badRatioCount
passage.rateScore += filter
passage.offsetRatio = offsetRatio
# ﾸﾾￄￚￏ￠ￋￆﾶﾵￕ
if (passage.cf.lsaScore > 75) and (passage.cf.lsaSimilarity > 89) and (passage.rateScore > 75):
passage.rateScore += 5
if ((passage.cf.lsaScore < 70) and (passage.rateScore < 70)) and (passage.cf.lsaSimilarity > 89):
passage.rateScore -=5
filter = 0
if ((passage.cf.lsaSimilarity <= 80) and (passage.cf.lsaSimilarity > 60)) or ((passage.cf.lsaSimilarityAll <= 56) and (passage.cf.lsaSimilarityAll > 32)):
filter = (15 - abs(passage.cf.lsaSimilarity - 70) / 3.0)
# if passage.rateScore < passage.cf.lsaScore:
# passage.rateScore = passage.cf.lsaScore
passage.rateScore += filter
self.generateRateResult(passage)
passage.rated = True
endog[0] = passage.rateScore
return [passage.rateScore]
def train(self, passages):
# ￔﾤﾴﾦ￭ￎￄￕￂ
i = 1
for p in passages:
#print "Passage ", i
# ﾴﾦ￭ￎￄￕￂ
if not p.preprocessed: essayprepare.processPassage(p)
i += 1
# ﾵﾷￄﾣￍ
passages.sort(cmp=lambda x,y: cmp(x.score, y.score), reverse=True)
model = EssayModel()
model.train(passages)
self.models['1'] = model
#print model.triGramDicts
# ￌ￡ﾡￌￕ
self.extractor = FeatherExtractor(model)
for p in passages:
p.lf = self.extractor.extractLangFeather(p)
p.cf = self.extractor.extractContentFeather(p)
p.sf = self.extractor.extractStructureFeather(p)
# ￊ￤ﾳￌￕￖﾵ
f = open('fs_train.txt', 'w')
# ﾳￌￕￏ
endog = []
exog = []
labels = []
for p in passages:
score = int(p.score)
#if score > 95: score = 95
if score < 40: score = 40
endog.append(score)
x = self.__getFeatherList(p)
exog.append(x)
labels.append(p.label)
f.write(p.id + ' ')
f.write(str(p.score))
for v in x:
f.write(' ' + str(v))
f.write('\n')
f.close()
# SVMﾷￖ￠ￆﾵﾷ
#self.svm_model = svmutil.svm_train(labels, exog, '-c 3')
# ￏￔﾻﾹ￩ￄﾣￍﾵﾷ
endog = np.array(endog)
exog = np.array(exog)
# print endog
# print exog
# self.m = np.mean(exog,axis=0)
# print self.m
#
# T, P, e_var = PCA_svd(exog)
# print T
# print P
# print e_var
#
# r, c = P.shape
# print r, c
# for i in xrange(11, r):
# for j in xrange(0, c):
# P[i, j] = 0
# print P
# self.p = P
#
# xexog = dot(P, exog.transpose())
# print xexog
# print xexog.shape
#
# xxexog = xexog.transpose()
self.gls_model = sm.GLS(endog, exog)
self.gls_model.fit()
