def convert(self, id, model):
segmented = segmenter.morph_segments(model, id)
mathed_segments = [seg for seg in segmented if bool(re.match(self.match, seg))]
if len(mathed_segments) > self.max_num_segs:
mathed_segments = mathed_segments[:self.max_num_segs]
print "truncate %s" % id
mapped_id = [self.voca.get(seg, self.voca.get('*')) for seg in mathed_segments]
mapped_id.extend([self.voca.get('#')] * (self.max_num_segs - len(mathed_segments)))
return mapped_id, mathed_segments
def analyze(users, model, target='gender'): for user in users: segmented = segmenter.morph_segments(model, user.id) user.segmented = segmented print user analyzer = Analyzer(users, target) morph_prob_list = analyzer.morphs.values() morph_prob_list.sort(key=lambda x: x.mi, reverse=True) for morph_prob in morph_prob_list: print morph_prob
def get_word_probs(users, model, voca, target, labels):
for user in users:
segmented = segmenter.morph_segments(morfessor, user.id, match='[a-z]+')
user.segmented = segmented
analyzer = Analyzer(users, target)
probs = np.zeros((len(labels), len(voca)))
for word, morph_stat in analyzer.morphs.iteritems():
idx = voca.get(word)
probs[:, idx] = [morph_stat.p_given_att.get(label) for label in labels]
smoothed_to = min(morph_stat.p for morph_stat in analyzer.morphs.values())
smoothed_probs = np.where(probs <= smoothed_to, smoothed_to, probs)
return smoothed_probs, smoothed_to
def preprocess(self, ids, model, is_training):
segmented_ids = list()
word_freq = self.word_freq
for id in ids:
segmented = segmenter.morph_segments(model, id)
matched_segments = [seg for seg in segmented if bool(re.match(self.match, seg))]
if len(matched_segments) >= 0:
segmented_ids.append(matched_segments)
if is_training:
for seg in matched_segments:
word_freq[seg] += 1
if len(matched_segments) > self.max_num_segs:
if is_training:
self.max_num_segs = len(matched_segments)
else:
matched_segments = matched_segments[:self.max_num_segs]
print "truncate %s" % id
if is_training:
voca = dict(zip(word_freq.keys(), range(len(word_freq))))
voca['*'] = len(word_freq)
voca['#'] = len(word_freq) + 1
self.voca = voca
segmented_ids.append(['*'] * (self.max_num_segs - 1) + ['#'])
self.n_training = len(segmented_ids)
mapped_ids = list()
for segmented_id in segmented_ids:
mapped_id = [self.voca.get(seg, self.voca.get('*')) for seg in segmented_id]
mapped_id.extend([self.voca.get('#')] * (self.max_num_segs - len(segmented_id)))
mapped_ids.append(mapped_id)
data = np.array(mapped_ids, dtype='int32')
print data, data.shape
return data, segmented_ids