class predict_w(predict):
"""prediction class for word level model (LSTM-CRF)
args:
if_cuda: if use cuda to speed up
f_map: dictionary for words
l_map: dictionary for labels
pad_word: word padding
pad_label: label padding
start_label: start label
batch_size: size of batch in decoding
caseless: caseless or not
"""
def __init__(self,
if_cuda,
f_map,
l_map,
pad_word,
pad_label,
start_label,
batch_size=1):
predict.__init__(self, if_cuda, l_map, batch_size)
self.decoder = CRFDecode_vb(len(l_map), start_label, pad_label)
self.pad_word = pad_word
self.f_map = f_map
self.l_map = l_map
def apply_model(self, ner_model, features):
"""
apply_model function for LSTM-CRF
args:
ner_model: sequence labeling model
feature (list): list of words list
"""
features = encode_safe(features, self.f_map, self.f_map['<unk>'])
f_len = max(map(lambda t: len(t) + 1, features))
masks = torch.ByteTensor(
list(
map(lambda t: [1] * (len(t) + 1) + [0] * (f_len - len(t) - 1),
features)))
word_features = torch.LongTensor(
list(
map(lambda t: t + [self.pad_word] * (f_len - len(t)),
features)))
if self.if_cuda:
fea_v = autograd.Variable(word_features.transpose(0, 1)).cuda()
mask_v = masks.transpose(0, 1).cuda()
else:
fea_v = autograd.Variable(word_features.transpose(0, 1))
mask_v = masks.transpose(0, 1).contiguous()
scores, _ = ner_model(fea_v)
decoded = self.decoder.decode(scores.data, mask_v)
return decoded
class eval_wc(eval_batch):
"""evaluation class for LM-LSTM-CRF
args:
packer: provide method to convert target into original space [TODO: need to improve]
l_map: dictionary for labels
score_type: use f1score with using 'f'
"""
def __init__(self, packer, l_map, score_type):
eval_batch.__init__(self, packer, l_map)
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'], l_map['<pad>'])
if 'f' in score_type:
self.eval_b = self.calc_f1_batch
self.calc_s = self.f1_score
else:
self.eval_b = self.calc_acc_batch
self.calc_s = self.acc_score
def calc_score(self, ner_model, dataset_loader, elmo_embeddings):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v, origin_w_f, index in itertools.chain.from_iterable(dataset_loader):
f_f, f_p, b_f, b_p, w_f, _, mask_v = self.packer.repack_vb(f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v)
index = index.data.tolist()
elmo_emb = []
max_len = 0
padded_elmo = []
for i in index:
elmo_emb.append(elmo_embeddings[i])
if len(elmo_embeddings[i]) > max_len:
max_len = len(elmo_embeddings[i])
max_len += 1
for w in elmo_emb:
if len(w) < max_len:
w = np.concatenate((w, [[0.]*1024]*(max_len - len(w))), axis=0)
padded_elmo.append(w)
elmo_emb = torch.FloatTensor(padded_elmo).data.permute(1, 0, 2)
scores = ner_model(f_f, f_p, b_f, b_p, w_f, elmo_emb.cuda())
decoded = self.decoder.decode(scores.data, mask_v.data)
self.eval_b(decoded, tg)
return self.calc_s()
class predict_w(predict):
"""prediction class for word level model (LSTM-CRF)
args:
if_cuda: if use cuda to speed up
f_map: dictionary for words
l_map: dictionary for labels
pad_word: word padding
pad_label: label padding
start_label: start label
label_seq: type of decode function, set `True` to couple label with text, or set 'False' to insert label into test
batch_size: size of batch in decoding
caseless: caseless or not
"""
def __init__(self, if_cuda, f_map, l_map, pad_word, pad_label, start_label, label_seq = True, batch_size = 50, caseless=True):
predict.__init__(self, if_cuda, l_map, label_seq, batch_size)
self.decoder = CRFDecode_vb(len(l_map), start_label, pad_label)
self.pad_word = pad_word
self.f_map = f_map
self.l_map = l_map
self.caseless = caseless
def apply_model(self, ner_model, features):
"""
apply_model function for LSTM-CRF
args:
ner_model: sequence labeling model
feature (list): list of words list
"""
if self.caseless:
features = list(map(lambda t: list(map(lambda x: x.lower(), t)), features))
features = encode_safe(features, self.f_map, self.f_map['<unk>'])
f_len = max(map(lambda t: len(t) + 1, features))
masks = torch.ByteTensor(list(map(lambda t: [1] * (len(t) + 1) + [0] * (f_len - len(t) - 1), features)))
word_features = torch.LongTensor(list(map(lambda t: t + [self.pad_word] * (f_len - len(t)), features)))
if self.if_cuda:
fea_v = autograd.Variable(word_features.transpose(0, 1)).cuda()
mask_v = masks.transpose(0, 1).cuda()
else:
fea_v = autograd.Variable(word_features.transpose(0, 1))
mask_v = masks.transpose(0, 1).contiguous()
scores, _ = ner_model(fea_v)
decoded = self.decoder.decode(scores.data, mask_v)
return decoded
class predict_w(predict):
"""prediction class for word level model (LSTM-CRF)
args:
if_cuda: if use cuda to speed up
f_map: dictionary for words
l_map: dictionary for labels
pad_word: word padding
pad_label: label padding
start_label: start label
label_seq: type of decode function, set `True` to couple label with text, or set 'False' to insert label into test
batch_size: size of batch in decoding
caseless: caseless or not
"""
def __init__(self, if_cuda, f_map, l_map, pad_word, pad_label, start_label, label_seq = True, batch_size = 50, caseless=True, keep_iobes=False):
predict.__init__(self, if_cuda, l_map, label_seq, batch_size, keep_iobes=keep_iobes)
self.decoder = CRFDecode_vb(len(l_map), start_label, pad_label)
self.pad_word = pad_word
self.f_map = f_map
self.l_map = l_map
self.caseless = caseless
def apply_model(self, ner_model, features):
"""
apply_model function for LSTM-CRF
args:
ner_model: sequence labeling model
feature (list): list of words list
"""
if self.caseless:
features = list(map(lambda t: list(map(lambda x: x.lower(), t)), features))
features = encode_safe(features, self.f_map, self.f_map['<unk>'])
f_len = max(map(lambda t: len(t) + 1, features))
masks = torch.ByteTensor(list(map(lambda t: [1] * (len(t) + 1) + [0] * (f_len - len(t) - 1), features)))
word_features = torch.LongTensor(list(map(lambda t: t + [self.pad_word] * (f_len - len(t)), features)))
if self.if_cuda:
fea_v = autograd.Variable(word_features.transpose(0, 1)).cuda()
mask_v = masks.transpose(0, 1).cuda()
else:
fea_v = autograd.Variable(word_features.transpose(0, 1))
mask_v = masks.transpose(0, 1).contiguous()
scores, _ = ner_model(fea_v)
decoded = self.decoder.decode(scores.data, mask_v)
return decoded
class eval_wc(eval_batch):
"""evaluation class for LM-LSTM-CRF
args:
packer: provide method to convert target into original space [TODO: need to improve]
l_map: dictionary for labels
score_type: use f1score with using 'f'
"""
def __init__(self, packer, l_map, score_type):
eval_batch.__init__(self, packer, l_map)
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'],
l_map['<pad>'])
if 'f' in score_type:
self.eval_b = self.calc_f1_batch
self.calc_s = self.f1_score
else:
self.eval_b = self.calc_acc_batch
self.calc_s = self.acc_score
def calc_score(self, ner_model, dataset_loader):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v in itertools.chain.from_iterable(
dataset_loader):
f_f, f_p, b_f, b_p, w_f, _, mask_v = self.packer.repack_vb(
f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v)
scores = ner_model(f_f, f_p, b_f, b_p, w_f)
score = scores[-1]
#print(score.data.size(), mask_v.data.size())
decoded = self.decoder.decode(score.data, mask_v.data)
self.eval_b(decoded, tg)
return self.calc_s()
class eval_wc(eval_batch):
"""evaluation class for LM-LSTM-CRF
args:
packer: provide method to convert target into original space [TODO: need to improve]
l_map: dictionary for labels
score_type: use f1score with using 'f'
"""
def __init__(self, packer, l_map, score_type):
eval_batch.__init__(self, packer, l_map)
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'], l_map['<pad>'])
if 'f' in score_type:
self.eval_b = self.calc_f1_batch
self.calc_s = self.f1_score
else:
self.eval_b = self.calc_acc_batch
self.calc_s = self.acc_score
def calc_score(self, ner_model, dataset_loader):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v in itertools.chain.from_iterable(dataset_loader):
f_f, f_p, b_f, b_p, w_f, _, mask_v = self.packer.repack_vb(f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v)
scores = ner_model(f_f, f_p, b_f, b_p, w_f)
decoded = self.decoder.decode(scores.data, mask_v.data)
self.eval_b(decoded, tg)
return self.calc_s()
class predict_wc(predict):
"""prediction class for LM-LSTM-CRF
args:
if_cuda: if use cuda to speed up
f_map: dictionary for words
c_map: dictionary for chars
l_map: dictionary for labels
pad_word: word padding
pad_char: word padding
pad_label: label padding
start_label: start label
label_seq: type of decode function, set `True` to couple label with text, or set 'False' to insert label into test
batch_size: size of batch in decoding
caseless: caseless or not
"""
def __init__(self, if_cuda, f_map, c_map, l_map, pad_word, pad_char, pad_label, start_label, label_seq = True, batch_size = 50, caseless=True):
predict.__init__(self, if_cuda, l_map, label_seq, batch_size)
self.decoder = CRFDecode_vb(len(l_map), start_label, pad_label)
self.pad_word = pad_word
self.pad_char = pad_char
self.f_map = f_map
self.c_map = c_map
self.l_map = l_map
self.caseless = caseless
def apply_model(self, ner_model, features):
"""
apply_model function for LM-LSTM-CRF
args:
ner_model: sequence labeling model
feature (list): list of words list
"""
char_features = encode2char_safe(features, self.c_map)
if self.caseless:
word_features = encode_safe(list(map(lambda t: list(map(lambda x: x.lower(), t)), features)), self.f_map, self.f_map['<unk>'])
else:
word_features = encode_safe(features, self.f_map, self.f_map['<unk>'])
fea_len = [list( map( lambda t: len(t) + 1, f) ) for f in char_features]
forw_features = concatChar(char_features, self.c_map)
word_len = max(map(lambda t: len(t) + 1, word_features))
char_len = max(map(lambda t: len(t[0]) + word_len - len(t[1]), zip(forw_features, word_features)))
forw_t = list( map( lambda t: t + [self.pad_char] * ( char_len - len(t) ), forw_features ) )
back_t = torch.LongTensor( list( map( lambda t: t[::-1], forw_t ) ) )
forw_t = torch.LongTensor( forw_t )
forw_p = torch.LongTensor( list( map( lambda t: list(itertools.accumulate( t + [1] * (word_len - len(t) ) ) ), fea_len) ) )
back_p = torch.LongTensor( list( map( lambda t: [char_len - 1] + [ char_len - 1 - tup for tup in t[:-1] ], forw_p) ) )
masks = torch.ByteTensor(list(map(lambda t: [1] * (len(t) + 1) + [0] * (word_len - len(t) - 1), word_features)))
word_t = torch.LongTensor(list(map(lambda t: t + [self.pad_word] * (word_len - len(t)), word_features)))
if self.if_cuda:
f_f = autograd.Variable(forw_t.transpose(0, 1)).cuda()
f_p = autograd.Variable(forw_p.transpose(0, 1)).cuda()
b_f = autograd.Variable(back_t.transpose(0, 1)).cuda()
b_p = autograd.Variable(back_p.transpose(0, 1)).cuda()
w_f = autograd.Variable(word_t.transpose(0, 1)).cuda()
mask_v = masks.transpose(0, 1).cuda()
else:
f_f = autograd.Variable(forw_t.transpose(0, 1))
f_p = autograd.Variable(forw_p.transpose(0, 1))
b_f = autograd.Variable(back_t.transpose(0, 1))
b_p = autograd.Variable(back_p.transpose(0, 1))
w_f = autograd.Variable(word_t.transpose(0, 1))
mask_v = masks.transpose(0, 1)
scores = ner_model(f_f, f_p, b_f, b_p, w_f)
decoded = self.decoder.decode(scores.data, mask_v)
return decoded
class eval_wc(eval_batch):
"""evaluation class for LM-LSTM-CRF
args:
packer: provide method to convert target into original space [TODO: need to improve]
l_map: dictionary for labels
score_type: use f1score with using 'f'
"""
def __init__(self, packer, c_map, l_map, removed_label=[]):
eval_batch.__init__(self, packer, c_map, l_map, removed_label)
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'],
l_map['<pad>'])
self.eval_b = self.calc_f1_batch
self.calc_s = self.f1_score
def calc_score(self,
ner_model,
dataset_loader,
out,
f_map,
emb,
word_to_id,
gpu,
r_c_map,
l_map,
knowledge_dict,
no_dict=False):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v in itertools.chain.from_iterable(
dataset_loader):
mask_v = mask_v.bool()
f_f, f_p, b_f, b_p, w_f, _, mask_v = self.packer.repack_vb(
f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v)
w_f_word = utils.reconstruct_word_input(w_f, f_map, emb,
word_to_id, gpu)
prior_prob = utils.generate_prior_prob(r_c_map, l_map, f_f,
knowledge_dict)
scores = ner_model(f_f, f_p, b_f, b_p, w_f, w_f_word, prior_prob)
decoded = self.decoder.decode(scores.data, mask_v.data, prior_prob,
no_dict)
self.eval_b(decoded, tg)
return self.calc_s()
def check_output(self,
ner_model,
dataset_loader,
out,
f_map,
emb,
word_to_id,
gpu,
knowledge_dict,
no_dict=False):
ner_model.eval()
self.reset()
f = open('model_output.txt', 'w')
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v in itertools.chain.from_iterable(
dataset_loader):
mask_v = mask_v.bool()
f_f, f_p, b_f, b_p, w_f, _, mask_v = self.packer.repack_vb(
f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v)
w_f_word = utils.reconstruct_word_input(w_f, f_map, emb,
word_to_id, gpu)
prior_prob = utils.generate_prior_prob(self.r_c_map, self.l_map,
f_f, knowledge_dict)
scores = ner_model(f_f, f_p, b_f, b_p, w_f, w_f_word, prior_prob)
decoded = self.decoder.decode(scores.data, mask_v.data, prior_prob,
no_dict)
self.eval_b(decoded, tg)
batch_decoded = torch.unbind(decoded, 1)
batch_targets = torch.unbind(tg, 0)
batch_f_f = torch.unbind(f_f, 1)
for decoded, target, character in zip(batch_decoded, batch_targets,
batch_f_f):
gold = self.packer.convert_for_eval(target)
# remove padding
length = utils.find_length_from_labels(gold, self.l_map)
gold = gold[:length].numpy()
best_path = decoded[:length].numpy()
character_filted = []
for c in character.cpu().numpy():
if c != 42:
character_filted.append(c)
char = character_filted[:length]
for i in range(len(gold)):
f.write(self.r_c_map[char[i]] + ' ' +
self.r_l_map[gold[i]] + ' ' +
self.r_l_map[best_path[i]])
f.write('\n')
f.write('\n')
f.close()
return self.calc_s()
class eval_w(eval_batch):
"""evaluation class for word level model (LSTM-CRF)
args:
packer: provide method to convert target into original space [TODO: need to improve]
l_map: dictionary for labels
score_type: use f1score with using 'f'
"""
def __init__(self, packer, l_map, score_type):
eval_batch.__init__(self, packer, l_map)
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'],
l_map['<pad>'])
if 'f' in score_type:
self.eval_b = self.calc_f1_batch
self.calc_s = self.f1_score
else:
self.eval_b = self.calc_acc_batch
self.calc_s = self.acc_score
def calc_score(self, ner_model, dataset_loader, illegal_idx, is_bichar):
"""
calculate score for pre-selected metrics
args:
ner_model: LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
for i in range(len(dataset_loader[0])):
fea_v, tg_v, mask_v, bi_fea_v = self.packer.repack_vb(
np.asarray(dataset_loader[0][i]),
np.asarray(dataset_loader[1][i]),
np.asarray(dataset_loader[2][i]),
np.asarray(dataset_loader[4][i]))
ner_model.zero_grad()
scores, hidden = ner_model(fea_v, bi_fea_v, dataset_loader[3][i],
illegal_idx, self.l_map, is_bichar)
decoded = self.decoder.decode(scores.data, mask_v.data)
self.eval_b(
decoded,
torch.LongTensor(np.asarray(
dataset_loader[1][i])).unsqueeze(0))
return self.calc_s()
def calc_predict(self, ner_model, dataset_loader, test_features, file_out,
file_out_2, f_map):
"""
calculate score for pre-selected metrics
args:
ner_model: LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
idx2label = {v: k for k, v in self.l_map.items()}
idx2word = {v: k for k, v in f_map.items()}
for i in range(len(dataset_loader[0])):
fea_v, tg_v, mask_v = self.packer.repack_vb(
np.asarray(dataset_loader[0][i]),
np.asarray(dataset_loader[1][i]),
np.asarray(dataset_loader[2][i]))
ner_model.zero_grad()
scores, hidden = ner_model(fea_v, dataset_loader[3][i])
decoded = self.decoder.decode(scores.data, mask_v.data)
gold = [d % len(self.l_map) for d in dataset_loader[1][i]]
# words = [idx2word[w] for w in dataset_loader[0][i]]
length = utils.find_length_from_labels(gold, self.l_map)
gold = gold[:length]
words = test_features[i][:length]
best_path = decoded.squeeze(1).tolist()[:length]
gold = [idx2label[g] for g in gold]
best_path = [idx2label[g] for g in best_path]
for i in range(length):
file_out.write("%s %s\n" % (words[i], best_path[i]))
file_out.write("\n")
sent = ''
pos = None
word = ''
for i in range(length):
if best_path[i].startswith('B'):
if pos != None:
sent += word + '_' + pos + ' '
word = ''
pos = None
word += words[i]
pos = best_path[i].split('-')[1]
else:
assert pos != None
word += words[i]
if len(word) > 0:
sent += word + '_' + pos + ' '
file_out_2.write("%s\n" % (sent))
class predict_wc(predict):
"""prediction class for LM-LSTM-CRF
args:
if_cuda: if use cuda to speed up
f_map: dictionary for words
c_map: dictionary for chars
l_map: dictionary for labels
pad_word: word padding
pad_char: word padding
pad_label: label padding
start_label: start label
label_seq: type of decode function, set `True` to couple label with text, or set 'False' to insert label into test
batch_size: size of batch in decoding
caseless: caseless or not
"""
def __init__(self, if_cuda, f_map, c_map, l_map, pad_word, pad_char, pad_label, start_label, label_seq = True, batch_size = 50, caseless=True):
predict.__init__(self, if_cuda, l_map, label_seq, batch_size)
self.decoder = CRFDecode_vb(len(l_map), start_label, pad_label)
self.pad_word = pad_word
self.pad_char = pad_char
self.f_map = f_map
self.c_map = c_map
self.l_map = l_map
self.caseless = caseless
def apply_model(self, ner_model, features):
"""
apply_model function for LM-LSTM-CRF
args:
ner_model: sequence labeling model
feature (list): list of words list
"""
char_features = encode2char_safe(features, self.c_map)
if self.caseless:
word_features = encode_safe(list(map(lambda t: list(map(lambda x: x.lower(), t)), features)), self.f_map, self.f_map['<unk>'])
else:
word_features = encode_safe(features, self.f_map, self.f_map['<unk>'])
fea_len = [list( map( lambda t: len(t) + 1, f) ) for f in char_features]
forw_features = concatChar(char_features, self.c_map)
word_len = max(map(lambda t: len(t) + 1, word_features))
char_len = max(map(lambda t: len(t[0]) + word_len - len(t[1]), zip(forw_features, word_features)))
forw_t = list( map( lambda t: t + [self.pad_char] * ( char_len - len(t) ), forw_features ) )
back_t = torch.LongTensor( list( map( lambda t: t[::-1], forw_t ) ) )
forw_t = torch.LongTensor( forw_t )
forw_p = torch.LongTensor( list( map( lambda t: list(itertools.accumulate( t + [1] * (word_len - len(t) ) ) ), fea_len) ) )
back_p = torch.LongTensor( list( map( lambda t: [char_len - 1] + [ char_len - 1 - tup for tup in t[:-1] ], forw_p) ) )
masks = torch.ByteTensor(list(map(lambda t: [1] * (len(t) + 1) + [0] * (word_len - len(t) - 1), word_features)))
word_t = torch.LongTensor(list(map(lambda t: t + [self.pad_word] * (word_len - len(t)), word_features)))
if self.if_cuda:
f_f = autograd.Variable(forw_t.transpose(0, 1)).cuda()
f_p = autograd.Variable(forw_p.transpose(0, 1)).cuda()
b_f = autograd.Variable(back_t.transpose(0, 1)).cuda()
b_p = autograd.Variable(back_p.transpose(0, 1)).cuda()
w_f = autograd.Variable(word_t.transpose(0, 1)).cuda()
mask_v = masks.transpose(0, 1).cuda()
else:
f_f = autograd.Variable(forw_t.transpose(0, 1))
f_p = autograd.Variable(forw_p.transpose(0, 1))
b_f = autograd.Variable(back_t.transpose(0, 1))
b_p = autograd.Variable(back_p.transpose(0, 1))
w_f = autograd.Variable(word_t.transpose(0, 1))
mask_v = masks.transpose(0, 1)
scores = ner_model(f_f, f_p, b_f, b_p, w_f)
decoded = self.decoder.decode(scores.data, mask_v)
return decoded
class eval_wc(eval_batch):
"""evaluation class for LM-LSTM-CRF
args:
packer: provide method to convert target into original space [TODO: need to improve]
l_map: dictionary for labels
score_type: use f1score with using 'f'
"""
def __init__(self, packer, l_map, score_type):
eval_batch.__init__(self, packer, l_map)
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'],
l_map['<pad>'])
if 'f' in score_type:
self.eval_b = self.calc_f1_batch
self.calc_s = self.f1_score
else:
self.eval_b = self.calc_acc_batch
self.calc_s = self.acc_score
def calc_score(self,
ner_model,
dataset_loader,
crf_no,
crit_ner,
verbose=0):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
self.reset()
epoch_loss = 0
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v, corpus_mask_v, reorder in itertools.chain.from_iterable(
dataset_loader):
f_f, f_p, b_f, b_p, w_f, _, mask_v, corpus_mask_v = self.packer.repack_vb(
f_f,
f_p,
b_f,
b_p,
w_f,
tg,
mask_v,
len_v,
corpus_mask_v,
volatile=True)
scores = ner_model(f_f, f_p, b_f, b_p, w_f, crf_no, corpus_mask_v)
loss = crit_ner(scores, _, mask_v, corpus_mask_v)
#print(loss)
epoch_loss += utils.to_scalar(loss)
decoded = self.decoder.decode(scores.data, mask_v.data)
self.eval_b(decoded, tg)
###################
# tests code
print("validation loss: {}, {}".format(
epoch_loss,
epoch_loss / sum(map(lambda t: len(t), dataset_loader))))
if verbose > 0:
print("pred", self.pred_cnter)
print("gold", self.gold_cnter)
############
return self.calc_s()
class Predictor:
"""Base class for prediction, provide method to calculate f1 score and accuracy
args:
if_cuda: if use cuda to speed up
l_map: dictionary for labels
label_seq: type of decode function, set `True` to couple label with text, or set 'False' to insert label into test
batch_size: size of batch in decoding
"""
def __init__(self, l_map, packer, label_seq = True, batch_size = 50):
self.l_map = l_map
self.decoder = CRFDecode_vb(len(l_map), l_map['<start>'], l_map['<pad>'])
self.packer = packer
self.r_l_map = revlut(l_map)
self.batch_size = batch_size
if label_seq:
self.decode_str = self.decode_l
else:
self.decode_str = self.decode_s
def decode_l(self, feature, label):
"""
decode a sentence coupled with label
args:
feature (list): words list
label (list): label list
"""
return '\n'.join(map(lambda t: t[0] + ' '+ self.r_l_map[t[1]], zip(feature, label)))
def decode_s(self, feature, label):
"""
decode a sentence in the format of <>
args:
feature (list): words list
label (list): label list
"""
chunks = ""
current = None
for f, y in zip(feature, label):
label = self.r_l_map[y]
if label.startswith('B-'):
if current is not None:
chunks += "</"+current+"> "
current = label[2:]
chunks += "<"+current+"> " + f + " "
elif label.startswith('S-'):
if current is not None:
chunks += " </"+current+"> "
current = label[2:]
chunks += "<"+current+"> " + f + " </"+current+"> "
current = None
elif label.startswith('I-'):
if current is not None:
base = label[2:]
if base == current:
chunks += f+" "
else:
chunks += "</"+current+"> <"+base+"> " + f + " "
current = base
else:
current = label[2:]
chunks += "<"+current+"> " + f + " "
elif label.startswith('E-'):
if current is not None:
base = label[2:]
if base == current:
chunks += f + " </"+base+"> "
current = None
else:
chunks += "</"+current+"> <"+base+"> " + f + " </"+base+"> "
current = None
else:
current = label[2:]
chunks += "<"+current+"> " + f + " </"+current+"> "
current = None
else:
if current is not None:
chunks += "</"+current+"> "
chunks += f+" "
current = None
if current is not None:
chunks += "</"+current+"> "
return chunks
def output_batch(self, ner_model, documents, fout, crf_no):
"""
decode the whole corpus in the specific format by calling apply_model to fit specific models
args:
ner_model: sequence labeling model
feature (list): list of words list
fout: output file
"""
ner_model.eval()
d_len = len(documents)
for d_ind in tqdm( range(0, d_len), mininterval=1,
desc=' - Process', leave=False, file=sys.stdout):
fout.write('-DOCSTART- -DOCSTART- -DOCSTART-\n\n')
features = documents[d_ind]
f_len = len(features)
for ind in range(0, f_len, self.batch_size):
eind = min(f_len, ind + self.batch_size)
labels = self.apply_model(ner_model, features[ind: eind], file_no)
labels = torch.unbind(labels, 1)
for ind2 in range(ind, eind):
f = features[ind2]
l = labels[ind2 - ind][0: len(f) ]
fout.write(self.decode_str(features[ind2], l) + '\n\n')
def predict(self, ner_model, dataset_loader, crf_no, pred_method, merge_batch=False, totag=False):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
ner_model.eval()
corpus_labels = []
num_sample = sum(map(lambda t: len(t), dataset_loader))
for f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v, corpus_mask_v, reorder in tqdm(
itertools.chain.from_iterable(dataset_loader), mininterval=2,
desc=' - Total it %d' % (num_sample), leave=False, file=sys.stdout):
f_f, f_p, b_f, b_p, w_f, _, mask_v, corpus_mask_v = self.packer.repack_vb(f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v, corpus_mask_v, volatile=True)
labels, scores = self.predict_batch(ner_model, crf_no, f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v, corpus_mask_v, pred_method)
labels = torch.unbind(labels, 1)
_, length = torch.unbind(len_v, 1)
length = length.tolist()
unpad_labels = []
for pad_labels, sent_len, rerdr in zip(labels, length, reorder):
# subtract the <start> tag
unpad_labels.append((rerdr, pad_labels[:sent_len - 1]))
if totag:
unpad_labels = [(rerdr, [self.r_l_map[idx_label] for idx_label in sent_idx_label]) for rerdr, sent_idx_label in unpad_labels]
corpus_labels.append(unpad_labels)
if merge_batch:
corpus_labels = functools.reduce(lambda x, y: x + y, corpus_labels)
return corpus_labels
def predict_batch(self, ner_model, crf_no, f_f, f_p, b_f, b_p, w_f, tg, mask_v, len_v, corpus_mask_v, pred_method):
"""
calculate score for pre-selected metrics
args:
ner_model: LM-LSTM-CRF model
dataset_loader: loader class for test set
"""
if ner_model.training:
ner_model.eval()
scores = ner_model(f_f, f_p, b_f, b_p, w_f, crf_no, corpus_mask_v)
assert pred_method in ["M", "U"]
if pred_method == "M":
# no matter take sigmoid or not, setting undesired scores to -inf
neg_inf_scores = autograd.Variable(torch.FloatTensor(np.full(scores.shape, -1e9))).cuda()
selected_scores = utils.switch(neg_inf_scores.contiguous(), scores.contiguous(), corpus_mask_v).view(scores.shape)
decoded = self.decoder.decode(selected_scores.data, mask_v.data)
return decoded, scores
if pred_method == "U":
decoded = self.decoder.decode(scores.data, mask_v.data)
for i in range(decoded.shape[0]):
for j in range(decoded.shape[1]):
idx_annotated = np.where(corpus_mask_v[i,j,0].cpu().data)[0]
if not decoded[i,j] in idx_annotated:
decoded[i,j] = self.l_map['O']
return decoded, scores