class IFindTextClassifier(BaseTextClassifier):
"""
"""
def __init__(self, topic, stopword_file=[], background_file=[]):
"""
"""
super(IFindTextClassifier, self).__init__(topic, stopword_file,
background_file)
self.make_topic_language_model()
def make_topic_language_model(self):
"""
"""
topic_text = self._topic.content + self._topic.title
document_extractor = SingleQueryGeneration(
minlen=3, stopwordfile=self._stopword_file)
document_extractor.extract_queries_from_text(topic_text)
document_term_counts = document_extractor.query_count
language_model = LanguageModel(term_dict=document_term_counts)
self.topic_language_model = SmoothedLanguageModel(
language_model, self.background_language_model, 100)
print "making topic", self.topic_language_model.docLM.total_occurrences
def is_relevant(self, document):
"""
"""
score = 0.0
count = 0.0
for term in document.title.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
for term in document.content.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
if (score / count) > self.threshold:
return True
return False
def __get_term_score(self, term):
"""
Returns a probability score for the given term when considering both the background and topic language models.
"""
topic_term_prob = self.topic_language_model.get_term_prob(term)
background_term_prob = self.background_language_model.get_term_prob(
term)
if background_term_prob == 0.0:
return 0.0
else:
return math.log(topic_term_prob / background_term_prob, 2)
class IFindTextClassifier(BaseTextClassifier):
"""
"""
def __init__(self, topic, stopword_file=[], background_file=[]):
"""
"""
super(IFindTextClassifier, self).__init__(topic, stopword_file, background_file)
self.make_topic_language_model()
def make_topic_language_model(self):
"""
"""
topic_text = self._topic.content + self._topic.title
document_extractor = SingleQueryGeneration(minlen=3, stopwordfile=self._stopword_file)
document_extractor.extract_queries_from_text(topic_text)
document_term_counts = document_extractor.query_count
language_model = LanguageModel(term_dict=document_term_counts)
self.topic_language_model = SmoothedLanguageModel(language_model, self.background_language_model, 100)
print "making topic", self.topic_language_model.docLM.total_occurrences
def is_relevant(self, document):
"""
"""
score = 0.0
count = 0.0
for term in document.title.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
for term in document.content.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
if (score / count) > self.threshold:
return True
return False
def __get_term_score(self, term):
"""
Returns a probability score for the given term when considering both the background and topic language models.
"""
topic_term_prob = self.topic_language_model.get_term_prob(term)
background_term_prob = self.background_language_model.get_term_prob(term)
if background_term_prob == 0.0:
return 0.0
else:
return math.log(topic_term_prob/background_term_prob, 2)
class iFindTextClassifier(TextClassifier):
def __init__(self, stopword_file=[], background_file=[]):
TextClassifier.__init__(self, stopword_file, background_file)
self.topicLM = None
self.threshold = -0.20
def set_topic(self, topic):
self.topic = topic
self.make_topic_lm()
def make_topic_lm(self):
topic_text = self.topic.content + self.topic.title
doc_extractor = SingleQueryGeneration(minlen=3, stopwordfile=self.stopword_file)
doc_extractor.extract_queries_from_text(topic_text)
doc_term_counts = doc_extractor.query_count
lm = LanguageModel(term_dict=doc_term_counts)
self.topicLM = SmoothedLanguageModel(lm,self.backgroundLM,100)
print "making topic", self.topicLM.docLM.total_occurrences
def is_relevant(self, document):
#print "computing relevance", document.docid
score = 0.0
count = 0.0
for t in document.title.split(' '):
score = score + self._get_term_score(t)
count += 1.0
for t in document.content.split(' '):
score = score + self._get_term_score(t)
count += 1.0
if (score/count) > self.threshold:
return True
else:
return False
def _get_term_score(self, term):
ptd = self.topicLM.get_term_prob(term)
pt = self.backgroundLM.get_term_prob(term)
if pt == 0.0:
return 0.0
else:
return math.log( ptd/pt, 2)
class iFindTextClassifier(TextClassifier):
def __init__(self, stopword_file=[], background_file=[]):
TextClassifier.__init__(self, stopword_file, background_file)
self.topicLM = None
self.threshold = -0.20
def set_topic(self, topic):
self.topic = topic
self.make_topic_lm()
def make_topic_lm(self):
topic_text = self.topic.content + self.topic.title
doc_extractor = SingleQueryGeneration(minlen=3,
stopwordfile=self.stopword_file)
doc_extractor.extract_queries_from_text(topic_text)
doc_term_counts = doc_extractor.query_count
lm = LanguageModel(term_dict=doc_term_counts)
self.topicLM = SmoothedLanguageModel(lm, self.backgroundLM, 100)
print "making topic", self.topicLM.docLM.total_occurrences
def is_relevant(self, document):
#print "computing relevance", document.docid
score = 0.0
count = 0.0
for t in document.title.split(' '):
score = score + self._get_term_score(t)
count += 1.0
for t in document.content.split(' '):
score = score + self._get_term_score(t)
count += 1.0
if (score / count) > self.threshold:
return True
else:
return False
def _get_term_score(self, term):
ptd = self.topicLM.get_term_prob(term)
pt = self.backgroundLM.get_term_prob(term)
if pt == 0.0:
return 0.0
else:
return math.log(ptd / pt, 2)
class IFindTextClassifier(BaseTextClassifier):
"""
"""
def __init__(self,
topic,
search_context,
stopword_file=[],
background_file=[]):
"""
"""
super(IFindTextClassifier,
self).__init__(topic, search_context, stopword_file,
background_file)
self.threshold = 0.0
self.mu = 100.0
self.make_topic_language_model()
def make_topic_language_model(self):
"""
"""
topic_text = '{title} {title} {title} {content}'.format(
**self._topic.__dict__)
document_extractor = SingleQueryGeneration(
minlen=3, stopwordfile=self._stopword_file)
document_extractor.extract_queries_from_text(topic_text)
document_term_counts = document_extractor.query_count
language_model = LanguageModel(term_dict=document_term_counts)
self.topic_language_model = SmoothedLanguageModel(
language_model, self.background_language_model, self.mu)
log.debug("Making topic {0}".format(
self.topic_language_model.docLM.total_occurrences))
def is_relevant(self, document):
"""
"""
score = 0.0
count = 0.0
for term in document.title.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
for term in document.content.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
self.doc_score = (score / count)
if self.doc_score > self.threshold:
return True
return False
def __get_term_score(self, term):
"""
Returns a probability score for the given term when considering both the background and topic language models.
"""
topic_term_prob = self.topic_language_model.get_term_prob(term)
background_term_prob = self.background_language_model.get_term_prob(
term)
if background_term_prob == 0.0:
return 0.0
else:
return math.log(topic_term_prob / background_term_prob, 2.0)
def update_model(self, search_context):
if self.updating:
## Once we develop more update methods, it is probably worth making this a strategy
## so that setting the update_method changes the list of documents to use.
if self.update_method == 1:
document_list = search_context.get_all_examined_documents()
else:
document_list = search_context.get_all_examined_snippets()
# iterate through document_list, pull out relevant snippets / text
rel_text_list = []
for doc in document_list:
if doc.judgment > 0:
rel_text_list.append('{0} {1}'.format(
doc.title, doc.content))
if rel_text_list:
self.__update_topic_language_model(rel_text_list)
return True
else:
return False
def __update_topic_language_model(self, text_list):
topic_text = '{title} {title} {title} {content}'.format(
**self._topic.__dict__)
n = len(text_list)
snippet_text = ' '.join(text_list)
term_extractor = SingleQueryGeneration(
minlen=3, stopwordfile=self._stopword_file)
term_extractor.extract_queries_from_text(topic_text)
topic_term_counts = term_extractor.query_count
term_extractor.extract_queries_from_text(snippet_text)
new_text_term_counts = term_extractor.query_count
for term in topic_term_counts:
if term in new_text_term_counts:
new_text_term_counts[term] += topic_term_counts[term]
else:
new_text_term_counts[term] = topic_term_counts[term]
new_language_model = LanguageModel(term_dict=new_text_term_counts)
self.topic_language_model = SmoothedLanguageModel(
new_language_model, self.background_language_model, self.mu)
log.debug("Updating topic {0}".format(self._topic.id))
class IFindTextClassifier(BaseTextClassifier):
"""
"""
def __init__(self, topic, stopword_file=[], background_file=[]):
"""
"""
super(IFindTextClassifier, self).__init__(topic, stopword_file, background_file)
self.threshold = 0.0
self.mu = 100.0
self.make_topic_language_model()
def make_topic_language_model(self):
"""
"""
topic_text = '{title} {title} {title} {content}'.format(**self._topic.__dict__)
document_extractor = SingleQueryGeneration(minlen=3, stopwordfile=self._stopword_file)
document_extractor.extract_queries_from_text(topic_text)
document_term_counts = document_extractor.query_count
language_model = LanguageModel(term_dict=document_term_counts)
self.topic_language_model = SmoothedLanguageModel(language_model, self.background_language_model, self.mu)
log.debug("Making topic {0}".format(self.topic_language_model.docLM.total_occurrences))
def is_relevant(self, document):
"""
"""
score = 0.0
count = 0.0
for term in document.title.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
for term in document.content.split(' '):
score = score + self.__get_term_score(term)
count = count + 1.0
self.doc_score = (score/count)
if self.doc_score > self.threshold:
return True
return False
def __get_term_score(self, term):
"""
Returns a probability score for the given term when considering both the background and topic language models.
"""
topic_term_prob = self.topic_language_model.get_term_prob(term)
background_term_prob = self.background_language_model.get_term_prob(term)
if background_term_prob == 0.0:
return 0.0
else:
return math.log(topic_term_prob/background_term_prob, 2.0)
def update_model(self, search_context):
if self.updating:
## Once we develop more update methods, it is probably worth making this a strategy
## so that setting the update_method changes the list of documents to use.
if self.update_method == 1:
document_list = search_context.get_all_examined_documents()
else:
document_list = search_context.get_all_examined_snippets()
# iterate through document_list, pull out relevant snippets / text
rel_text_list = []
for doc in document_list:
if doc.judgment > 0:
rel_text_list.append('{0} {1}'.format(doc.title, doc.content))
if rel_text_list:
self.__update_topic_language_model(rel_text_list)
return True
else:
return False
def __update_topic_language_model(self, text_list):
topic_text = '{title} {title} {title} {content}'.format(**self._topic.__dict__)
n = len(text_list)
snippet_text = ' '.join(text_list)
term_extractor = SingleQueryGeneration(minlen=3, stopwordfile=self._stopword_file)
term_extractor.extract_queries_from_text(topic_text)
topic_term_counts = term_extractor.query_count
term_extractor.extract_queries_from_text(snippet_text)
new_text_term_counts = term_extractor.query_count
for term in topic_term_counts:
if term in new_text_term_counts:
new_text_term_counts[term] += topic_term_counts[term]
else:
new_text_term_counts[term] = topic_term_counts[term]
new_language_model = LanguageModel(term_dict=new_text_term_counts)
self.topic_language_model = SmoothedLanguageModel(new_language_model, self.background_language_model, self.mu)
log.debug("Updating topic {0}".format(self._topic.id))