def __init__(self, ntopic=15, nword=15, iteration=50):
"""
ntopic: number of topic wanted
nword: list words in each topic
iteration: passes the ldaprocessor run
"""
self.corpus_loader = CorpusProcessor()
self.ntopic = ntopic
self.nword = nword
self.iteration = iteration
self.lda_out_file_name = "lda_out"
class LdaProcessor:
def __init__(self, ntopic=15, nword=15, iteration=50):
"""
ntopic: number of topic wanted
nword: list words in each topic
iteration: passes the ldaprocessor run
"""
self.corpus_loader = CorpusProcessor()
self.ntopic = ntopic
self.nword = nword
self.iteration = iteration
self.lda_out_file_name = "lda_out"
def load_corpus(self):
self.corpus = self.corpus_loader.load_processed_corpus()
def save_lda_corpus_and_dic(self):
"""
Given a corpus as a list of docs contains list of tokens,
Store corpus to dict and mm file as ldaprocessor input data
:param original_corpus:
:return:
"""
logging.info("Saving LDA corpus files...")
# pprint(corpus)
# Save dictionary
dictionary = corpora.Dictionary(self.corpus)
dictionary.save("/tmp/appcat.dict")
# Save mmCorpus
corpus_vec = [dictionary.doc2bow(doc) for doc in self.corpus]
corpora.MmCorpus.serialize("/tmp/appcat.mm", corpus_vec)
logging.info("LDA corpus files have been saved.")
def load_corpus_from_file(self):
self.corpus = corpora.MmCorpus("/tmp/appcat.mm")
self.dictionary = corpora.Dictionary.load("/tmp/appcat.dict")
def load_corpus_from_db(self):
self.save_lda_corpus_and_dic()
self.load_corpus_from_file()
def perform(self, option="load"):
"""
Perform LDA analysis to generate topics
and topic distribution for each app
"""
logging.info("Start Lda analysis")
ldamodel = LdaMulticore(self.corpus, num_topics=self.ntopic, id2word=self.dictionary, passes=self.iteration)
logging.info("LDA multicore modeling done")
ldamodel.save(self.lda_out_file_name)
self.topics = {}
for i in range(0, self.ntopic, 1):
self.topics["topic{}".format(i)] = ldamodel.show_topic(i, topn=self.nword)
logging.info("Topic{}".format(i))
words = [w[1] for w in self.topics["topic{}".format(i)]]
logging.info(words)
# self.result = [ldamodel[doc_bow] for doc_bow in self.corpus]
# plogging.info(result)
# plogging.info(type(ldamodel[doc_bow]))
def load(self):
"""
Load previous saved ldaprocessor results
"""
try:
return LdaMulticore.load(self.lda_out_file_name)
except:
return None
