def gen_sentiment_vectors(reviews, word_lists, popular_words):
sentiment_vectors = dict()
for i in reviews:
doc_tag = reviews[i]['title']
prev_sentiment = None
for line in reviews[i]['reviews']:
if line == ("<p>", ) or line == ("</p>", ):
continue
sentiment = line[0]
sentence = line[1]
features = f.extract_features(sentence, word_lists, popular_words, doc_tag, prev_sentiment)
if sentiment in sentiment_vectors:
sentiment_vectors[sentiment] = f.merge_features(sentiment_vectors[sentiment], features)
else:
sentiment_vectors[sentiment] = features
prev_sentiment = sentiment
# if i % 20 == 0:
# print "Done with " + str(i)
for sentiment in sentiment_vectors:
sentiment_vectors[sentiment] = f.smooth_features(sentiment_vectors[sentiment])
return sentiment_vectors
def enrichment_array_combined(sp_base, sp_dict_elutfs, cxs, func=np.average, nsp=1, scores=["poisson"], exs=None):
"""
sp_dict_elutfs: {'Ce': [Ce_elution_1, Ce_elution_2, ...] , ...}
"""
exs = exs or correlation_enrichment([(i, set(c)) for i, c in enumerate(cxs)])
elutfs = ut.flatten([elutfs for sp, elutfs in sp_dict_elutfs.items()])
ppio = ppi.feature_array(sp_base, elutfs, exs, nsp, scores=scores, extdata=[], do_filter=False)
newarr = ppio.arrfeats
for sp in sp_dict_elutfs.keys():
newarr = fe.merge_features(newarr, "%s.*" % sp, func, False)
return newarr
