def __init__(self, k=10, lda_model=None):
# build model
if lda_model:
self.lda = lda_model
else:
self.lda = LDAmodel(n_topics=500,
n_passes=50,
vocabulary='combined')
# parameters
self.k = k
class LDAExpansion(QueryExpansion):
def __init__(self, k=10, lda_model=None):
# build model
if lda_model:
self.lda = lda_model
else:
self.lda = LDAmodel(n_topics=500,
n_passes=50,
vocabulary='combined')
# parameters
self.k = k
def expand(self, query):
tokens = tokenize(query.lower())
latent = self.lda.tokens2latent(tokens)
extra_terms = []
for topic in latent:
topn = self.lda.model.show_topic(topicid=topic[0],
topn=round(self.k * topic[1]))
extra_terms += [e[1] for e in topn]
extra_terms = list(set(extra_terms))
new_query = query + " " + " ".join(extra_terms)
return new_query
def __str__(self):
return self.__class__.__name__ + str("(k=%s)" % self.k)
def __init__(self, k=10, lda_model=None):
# build model
if lda_model:
self.lda = lda_model
else:
self.lda = LDAmodel(n_topics=500, n_passes=50, vocabulary='combined')
# parameters
self.k = k
class LDAExpansion(QueryExpansion):
def __init__(self, k=10, lda_model=None):
# build model
if lda_model:
self.lda = lda_model
else:
self.lda = LDAmodel(n_topics=500, n_passes=50, vocabulary='combined')
# parameters
self.k = k
def expand(self, query):
tokens = tokenize(query.lower())
latent = self.lda.tokens2latent(tokens)
extra_terms = []
for topic in latent:
topn = self.lda.model.show_topic(topicid=topic[0], topn=round(self.k*topic[1]))
extra_terms += [e[1] for e in topn]
extra_terms = list(set(extra_terms))
new_query = query + " " + " ".join(extra_terms)
return new_query
def __str__(self):
return self.__class__.__name__ + str("(k=%s)" % self.k )
