def train(self, sentences, min_eojeol_count=1):
if self.verbose:
print('[Noun Extractor] counting eojeols')
eojeol_counter = EojeolCounter(sentences, min_eojeol_count,
max_length=self.l_max_length + self.r_max_length,
filtering_checkpoint=self.eojeol_counter_filtering_checkpoint,
verbose=self.verbose)
self._num_of_eojeols = eojeol_counter._count_sum
self._num_of_covered_eojeols = 0
if self.verbose:
print('[Noun Extractor] complete eojeol counter -> lr graph')
self.lrgraph = eojeol_counter.to_lrgraph(
self.l_max_length, self.r_max_length)
if self.verbose:
print('[Noun Extractor] has been trained. mem={} Gb'.format(
'%.3f'%get_process_memory()))
def train(self, sentences):
if self.verbose:
print('[Noun Extractor] counting eojeols')
eojeol_counter = EojeolCounter(
sentences,
self.min_eojeol_frequency,
max_length=self.max_left_length + self.max_right_length,
filtering_checkpoint=self.eojeol_counter_filtering_checkpoint,
verbose=self.verbose)
self._num_of_eojeols = eojeol_counter._count_sum
self._num_of_covered_eojeols = 0
if self.verbose:
print('[Noun Extractor] complete eojeol counter -> lr graph')
self.lrgraph = eojeol_counter.to_lrgraph(self.max_left_length,
self.max_right_length)
if self.verbose:
print('[Noun Extractor] has been trained. mem={} Gb'.format(
'%.3f' % get_process_memory()))
def _prepare_predicator_lrgraph(self):
def contains_noun(eojeol):
n = len(eojeol)
for e in range(2, n + 1):
if eojeol[:e] in self._nouns:
return True
return False
eojeols = {k: v for k, v in self.eojeol_counter._counter.items()}
eojeols = {
eojeol: count
for eojeol, count in eojeols.items()
if len(eojeol) > 1 and not contains_noun(eojeol)
}
lrgraph = EojeolCounter()._to_lrgraph(eojeols)
return lrgraph
def _train_with_sentences(self, sentences, min_eojeol_frequency=1):
if self.verbose:
print('[Noun Extractor] counting eojeols')
if self.ensure_normalized:
preprocess = lambda x:x
else:
preprocess = normalize_sent_for_lrgraph
eojeol_counter = EojeolCounter(
sentences,
min_count = min_eojeol_frequency,
max_length = self.max_left_length + self.max_right_length,
filtering_checkpoint = self.eojeol_counter_filtering_checkpoint,
verbose = self.verbose,
preprocess = preprocess
)
self._train_with_eojeol_counter(eojeol_counter)
def _train_with_sentences(self,
sentences,
min_eojeol_frequency=2,
filtering_checkpoint=100000):
check = filtering_checkpoint > 0
if self.verbose:
message = 'counting eojeols ... '
self._print(message, replace=False, newline=False)
if self.ensure_normalized:
preprocess = lambda x: x
else:
preprocess = normalize_sent_for_lrgraph
# Eojeol counting
eojeol_counter = EojeolCounter(sentences,
min_count=min_eojeol_frequency,
verbose=self.verbose,
preprocess=preprocess)
self._train_with_eojeol_counter(eojeol_counter)
def _train_with_sentences(self,
sentences,
min_eojeol_count=2,
filtering_checkpoint=100000):
check = filtering_checkpoint > 0
if self.verbose:
message = 'counting eojeols'
self._print(message, replace=False, newline=False)
# Eojeol counting
counter = {}
def contains_noun(eojeol, n):
for e in range(2, n + 1):
if eojeol[:e] in self.nouns:
return True
return False
for i_sent, sent in enumerate(sentences):
if check and i_sent > 0 and i_sent % filtering_checkpoint == 0:
counter = {
eojeol: count
for eojeol, count in counter.items()
if count >= min_eojeol_count
}
if self.verbose and i_sent % 100000 == 99999:
message = 'n eojeol = {} from {} sents. mem={} Gb{}'.format(
len(counter), i_sent + 1, '%.3f' % get_process_memory(),
' ' * 20)
self._print(message, replace=True, newline=False)
for eojeol in sent.split():
n = len(eojeol)
if n <= 1 or contains_noun(eojeol, n):
continue
counter[eojeol] = counter.get(eojeol, 0) + 1
if self.verbose:
message = 'counting eojeols was done. {} eojeols, mem={} Gb{}'.format(
len(counter), '%.3f' % get_process_memory(), ' ' * 20)
self._print(message, replace=True, newline=True)
counter = {
eojeol: count
for eojeol, count in counter.items() if count >= min_eojeol_count
}
self._num_of_eojeols = sum(counter.values())
self._num_of_covered_eojeols = 0
if self.verbose:
message = 'complete eojeol counter -> lr graph'
self._print(message, replace=False, newline=True)
self.lrgraph = EojeolCounter()._to_lrgraph(counter,
l_max_length=10,
r_max_length=9)
if self.verbose:
message = 'has been trained. mem={} Gb'.format(
'%.3f' % get_process_memory())
self._print(message, replace=False, newline=True)
class EomiExtractor:
def __init__(self,
nouns,
noun_pos_features=None,
stems=None,
eomis=None,
verbose=True):
if not noun_pos_features:
noun_pos_features = self._load_default_noun_pos_features()
if not stems:
stems = self._load_default_stems()
if not eomis:
eomis = self._load_default_eomis()
self._nouns = nouns
self._noun_pos_features = noun_pos_features
self._stems = stems
self._pos_l = {l for stem in stems for l in _conjugate_stem(stem)}
self._eomis = eomis
self.verbose = verbose
self.lrgraph = None
def _load_default_noun_pos_features(self):
path = '%s/trained_models/noun_predictor_ver2_pos' % installpath
with open(path, encoding='utf-8') as f:
pos_features = {word.split()[0] for word in f}
return pos_features
def _load_default_stems(self, min_count=100):
dirs = '%s/lemmatizer/dictionary/default/stem' % installpath
paths = ['%s/Adjective.txt', '%s/Verb.txt']
paths = [p % dirs for p in paths]
stems = set()
for path in paths:
with open(path, encoding='utf-8') as f:
for line in f:
word, count = line.split()
if int(count) < min_count:
continue
stems.add(word)
return stems
def _load_default_eomis(self, min_count=20):
path = '%s/lemmatizer/dictionary/default/Eomi/Eomi.txt' % installpath
eomis = set()
with open(path, encoding='utf-8') as f:
for line in f:
word, count = line.split()
if int(count) < min_count:
continue
eomis.add(word)
return eomis
@property
def is_trained(self):
return self.lrgraph
def train(self,
sentences,
min_eojeol_count=2,
filtering_checkpoint=100000):
check = filtering_checkpoint > 0
if self.verbose:
print('[Eomi Extractor] counting eojeols', end='')
# Eojeol counting
counter = {}
def contains_noun(eojeol, n):
for e in range(2, n + 1):
if eojeol[:e] in self.nouns:
return True
return False
for i_sent, sent in enumerate(sentences):
if check and i_sent > 0 and i_sent % filtering_checkpoint == 0:
counter = {
eojeol: count
for eojeol, count in counter.items()
if count >= min_eojeol_count
}
if self.verbose and i_sent % 100000 == 99999:
message = '\r[Eomi Extractor] n eojeol = {} from {} sents. mem={} Gb{}'.format(
len(counter), i_sent + 1, '%.3f' % get_process_memory(),
' ' * 20)
print(message, flush=True, end='')
for eojeol in sent.split():
n = len(eojeol)
if n <= 1 or contains_noun(eojeol, n):
continue
counter[eojeol] = counter.get(eojeol, 0) + 1
if self.verbose:
message = '\r[Eomi Extractor] counting eojeols was done. {} eojeols, mem={} Gb{}'.format(
len(counter), '%.3f' % get_process_memory(), ' ' * 20)
print(message)
counter = {
eojeol: count
for eojeol, count in counter.items() if count >= min_eojeol_count
}
self._num_of_eojeols = sum(counter.values())
self._num_of_covered_eojeols = 0
if self.verbose:
print('[Eomi Extractor] complete eojeol counter -> lr graph')
self.lrgraph = EojeolCounter()._to_lrgraph(counter,
l_max_length=10,
r_max_length=9)
if self.verbose:
print('[Eomi Extractor] has been trained. mem={} Gb'.format(
'%.3f' % get_process_memory()))
def extract(self,
minimum_eomi_score=0.3,
min_count=10,
reset_lrgraph=True):
# reset covered eojeol count
self._num_of_covered_eojeols = 0
# base prediction
eomi_candidates = self._eomi_candidates_from_stems()
def _eomi_candidates_from_stems(self, condition=None):
def satisfy(word, e):
return word[-e:] == condition
# noun candidates from positive featuers such as Josa
R_from_L = {}
for l in self._pos_l:
for r, c in self.lrgraph.get_r(l, -1):
# candidates filtering for debugging
# condition is last chars in R
if not condition:
R_from_L[r] = R_from_L.get(r, 0) + c
continue
# for debugging
if satisfy(r, len(condition)):
R_from_L[r] = R_from_L.get(r, 0) + c
# sort by length of word
R_from_L = sorted(R_from_L.items(), key=lambda x: -len(x[0]))
return R_from_L
def predict_r(self,
r,
minimum_r_score=0.3,
min_num_of_features=5,
debug=False):
features = self.lrgraph.get_l(r, -1)
pos, neg, unk = self._predict_r(features, r)
base = pos + neg
score = 0 if base == 0 else (pos - neg) / base
support = pos + unk if score >= minimum_r_score else neg + unk
features_ = self._refine_features(features, r)
n_features_ = len(features_)
if debug:
print('pos={}, neg={}, unk={}, n_features_={}'.format(
pos, neg, unk, n_features_))
if n_features_ >= min_num_of_features:
return score, support
else:
# TODO
return (0, 0)
def _predict_r(self, features, r):
pos, neg, unk = 0, 0, 0
for l, freq in features:
if self._exist_longer_pos(l, r): # ignore
continue
if l in self._pos_l:
pos += freq
elif self._has_stem_at_last(l):
unk += freq
else:
neg += freq
return pos, neg, unk
def _exist_longer_pos(self, l, r):
for i in range(1, len(r) + 1):
if (l + r[:i]) in self._pos_l:
return True
return False
def _has_stem_at_last(self, l):
for i in range(1, len(l)):
if l[-i:] in self._pos_l:
return True
return False
def _refine_features(self, features, r):
return [(l, count) for l, count in features
if ((l in self._pos_l) and (not self._exist_longer_pos(l, r)))]
def _batch_prediction_order_by_word_length(self,
eomi_candidates,
minimum_eomi_score=0.3,
min_num_of_features=5):
prediction_scores = {}
n = len(eomi_candidates)
for i, (r, _) in enumerate(eomi_candidates):
if self.verbose and i % 1000 == 999:
percentage = '%.3f' % (100 * (i + 1) / n)
print('\r -- batch prediction {} % of {} words'.format(
percentage, n),
flush=True,
end='')
# base prediction
score, support = self.predict_r(r, minimum_eomi_score,
min_num_of_features)
prediction_scores[r] = (score, support)
# if their score is higher than minimum_eomi_score,
# remove eojeol pattern from lrgraph
if score >= minimum_eomi_score:
for l, count in self.lrgraph.get_l(r, -1):
if l in self._pos_l:
self.lrgraph.remove_eojeol(l + r, count)
if self.verbose:
print(
'\r[Eomi Extractor] batch prediction was completed for {} words'
.format(n),
flush=True)
return prediction_scores
