def lda2topicMap(lda, corpus, ids, name):
print '>> generating topic map...'
evidenceTopicMap = {}
# dictionary = gensim.corpora.Dictionary.load('/tmp/' + name + '.dict')
i = 0
for c in corpus:
# b = dictionary.doc2bow(d)
evidenceTopicMap[ids[i]] = lda.get_document_topics(c, minimum_probability=0.01)
i += 1
print len(evidenceTopicMap)
return evidenceTopicMap
def get_document_topics(doc, name):
lda = gensim.models.ldamodel.LdaModel.load(name + '.lda')
englishStopWords = get_stopwords('english', name)
text = [singularize(word) for word in doc.lower().split() if singularize(word) not in englishStopWords and word.isalpha() and len(word) > 1]
dictionary = gensim.corpora.Dictionary.load(name + '.dict')
document_topics = lda.get_document_topics(dictionary.doc2bow(text), minimum_probability=0.05)
if len(document_topics) > 0:
primary_topic_tuple = max(document_topics, key=lambda x:x[1])
topic_terms = lda.show_topic(primary_topic_tuple[0])
print topic_terms
return document_topics, topic_terms
else:
return [], ''
def gensim_lda(pd_df_yelp, text_rev): #gensim lda
common_dict = Dictionary(text_rev)
common_corpus = [common_dict.doc2bow(text) for text in text_rev]
lda = LdaModel(common_corpus)
topics = [lda.get_document_topics(doc) for doc in common_corpus]
topicIDs = [topic[0][0] for topic in topics]
topic_prob_list = [lda.show_topic(topicID) for topicID in topicIDs]
topic_prob_list_split = [zip(*item) for item in topic_prob_list]
topic_prob_list_words = [list(map(lambda topID: dict(common_dict)[int(topID)],item[0]))\
for item in topic_prob_list_split]
topic_prob_list_prob = list(
map(lambda item: list(item[1]), topic_prob_list_split))
return (topic_prob_list_words, topic_prob_list_prob)
def topic_extraction(corpus, ntopics):
# gensim lda
common_dictionary = Dictionary(corpus)
common_corpus = [common_dictionary.doc2bow(text) for text in corpus]
lda = LdaModel(common_corpus,
num_topics=ntopics,
iterations=800,
random_state=1)
features = lda.get_document_topics(common_corpus, minimum_probability=0)
lda_list = []
for f in features:
lda_list.append([b[1] for b in f])
lda_df = pd.DataFrame(lda_list)
lda_df = lda_df.reset_index(drop=True)
return lda_df