forked from goxdve/BiLSTM-CRF
-
Notifications
You must be signed in to change notification settings - Fork 0
/
run.py
257 lines (229 loc) · 10.6 KB
/
run.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
"""
Usage:
run.py train TRAIN SENT_VOCAB TAG_VOCAB [options]
run.py test TEST SENT_VOCAB TAG_VOCAB MODEL [options]
Options:
--dropout-rate=<float> dropout rate [default: 0.5]
--embed-size=<int> size of word embedding [default: 256]
--hidden-size=<int> size of hidden state [default: 256]
--batch-size=<int> batch-size [default: 32]
--max-epoch=<int> max epoch [default: 10]
--clip_max_norm=<float> clip max norm [default: 5.0]
--lr=<float> learning rate [default: 0.001]
--log-every=<int> log every [default: 10]
--validation-every=<int> validation every [default: 250]
--patience-threshold=<float> patience threshold [default: 0.98]
--max-patience=<int> time of continuous worse performance to decay lr [default: 4]
--max-decay=<int> time of lr decay to early stop [default: 4]
--lr-decay=<float> decay rate of lr [default: 0.5]
--model-save-path=<file> model save path [default: ./model/model.pth]
--optimizer-save-path=<file> optimizer save path [default: ./model/optimizer.pth]
--cuda use GPU
"""
from docopt import docopt
from vocab import Vocab
import time
import torch
import torch.nn as nn
import bilstm_crf
import utils
import random
def train(args):
""" Training BiLSTMCRF model
Args:
args: dict that contains options in command
"""
sent_vocab = Vocab.load(args['SENT_VOCAB'])
tag_vocab = Vocab.load(args['TAG_VOCAB'])
train_data, dev_data = utils.generate_train_dev_dataset(args['TRAIN'], sent_vocab, tag_vocab)
print('num of training examples: %d' % (len(train_data)))
print('num of development examples: %d' % (len(dev_data)))
max_epoch = int(args['--max-epoch'])
log_every = int(args['--log-every'])
validation_every = int(args['--validation-every'])
model_save_path = args['--model-save-path']
optimizer_save_path = args['--optimizer-save-path']
min_dev_loss = float('inf')
device = torch.device('cuda' if args['--cuda'] else 'cpu')
patience, decay_num = 0, 0
model = bilstm_crf.BiLSTMCRF(sent_vocab, tag_vocab, float(args['--dropout-rate']), int(args['--embed-size']),
int(args['--hidden-size'])).to(device)
for name, param in model.named_parameters():
if 'weight' in name:
nn.init.normal_(param.data, 0, 0.01)
else:
nn.init.constant_(param.data, 0)
optimizer = torch.optim.Adam(model.parameters(), lr=float(args['--lr']))
train_iter = 0 # train iter num
record_loss_sum, record_tgt_word_sum, record_batch_size = 0, 0, 0 # sum in one training log
cum_loss_sum, cum_tgt_word_sum, cum_batch_size = 0, 0, 0 # sum in one validation log
record_start, cum_start = time.time(), time.time()
print('start training...')
for epoch in range(max_epoch):
for sentences, tags in utils.batch_iter(train_data, batch_size=int(args['--batch-size'])):
train_iter += 1
current_batch_size = len(sentences)
sentences, sent_lengths = utils.pad(sentences, sent_vocab[sent_vocab.PAD], device)
tags, _ = utils.pad(tags, tag_vocab[tag_vocab.PAD], device)
# back propagation
optimizer.zero_grad()
batch_loss = model(sentences, tags, sent_lengths) # shape: (b,)
loss = batch_loss.mean()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=float(args['--clip_max_norm']))
optimizer.step()
record_loss_sum += batch_loss.sum().item()
record_batch_size += current_batch_size
record_tgt_word_sum += sum(sent_lengths)
cum_loss_sum += batch_loss.sum().item()
cum_batch_size += current_batch_size
cum_tgt_word_sum += sum(sent_lengths)
if train_iter % log_every == 0:
print('log: epoch %d, iter %d, %.1f words/sec, avg_loss %f, time %.1f sec' %
(epoch + 1, train_iter, record_tgt_word_sum / (time.time() - record_start),
record_loss_sum / record_batch_size, time.time() - record_start))
record_loss_sum, record_batch_size, record_tgt_word_sum = 0, 0, 0
record_start = time.time()
if train_iter % validation_every == 0:
print('dev: epoch %d, iter %d, %.1f words/sec, avg_loss %f, time %.1f sec' %
(epoch + 1, train_iter, cum_tgt_word_sum / (time.time() - cum_start),
cum_loss_sum / cum_batch_size, time.time() - cum_start))
cum_loss_sum, cum_batch_size, cum_tgt_word_sum = 0, 0, 0
dev_loss = cal_dev_loss(model, dev_data, 64, sent_vocab, tag_vocab, device)
if dev_loss < min_dev_loss * float(args['--patience-threshold']):
min_dev_loss = dev_loss
model.save(model_save_path)
torch.save(optimizer.state_dict(), optimizer_save_path)
patience = 0
else:
patience += 1
if patience == int(args['--max-patience']):
decay_num += 1
if decay_num == int(args['--max-decay']):
print('Early stop. Save result model to %s' % model_save_path)
return
lr = optimizer.param_groups[0]['lr'] * float(args['--lr-decay'])
model = bilstm_crf.BiLSTMCRF.load(model_save_path, device)
optimizer.load_state_dict(torch.load(optimizer_save_path))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
patience = 0
print('dev: epoch %d, iter %d, dev_loss %f, patience %d, decay_num %d' %
(epoch + 1, train_iter, dev_loss, patience, decay_num))
cum_start = time.time()
if train_iter % log_every == 0:
record_start = time.time()
print('Reached %d epochs, Save result model to %s' % (max_epoch, model_save_path))
def test(args):
""" Testing the model
Args:
args: dict that contains options in command
"""
sent_vocab = Vocab.load(args['SENT_VOCAB'])
tag_vocab = Vocab.load(args['TAG_VOCAB'])
sentences, tags = utils.read_corpus(args['TEST'])
sentences = utils.words2indices(sentences, sent_vocab)
tags = utils.words2indices(tags, tag_vocab)
test_data = list(zip(sentences, tags))
print('num of test samples: %d' % (len(test_data)))
device = torch.device('cuda' if args['--cuda'] else 'cpu')
model = bilstm_crf.BiLSTMCRF.load(args['MODEL'], device)
print('start testing...')
print('using device', device)
start = time.time()
n_iter, num_words = 0, 0
tp, fp, fn = 0, 0, 0
model.eval()
with torch.no_grad():
for sentences, tags in utils.batch_iter(test_data, batch_size=int(args['--batch-size']), shuffle=False):
sentences, sent_lengths = utils.pad(sentences, sent_vocab[sent_vocab.PAD], device)
predicted_tags = model.predict(sentences, sent_lengths)
n_iter += 1
num_words += sum(sent_lengths)
for tag, predicted_tag in zip(tags, predicted_tags):
current_tp, current_fp, current_fn = cal_statistics(tag, predicted_tag, tag_vocab)
tp += current_tp
fp += current_fp
fn += current_fn
if n_iter % int(args['--log-every']) == 0:
print('log: iter %d, %.1f words/sec, precision %f, recall %f, f1_score %f, time %.1f sec' %
(n_iter, num_words / (time.time() - start), tp / (tp + fp), tp / (tp + fn),
(2 * tp) / (2 * tp + fp + fn), time.time() - start))
num_words = 0
start = time.time()
print('tp = %d, fp = %d, fn = %d' % (tp, fp, fn))
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1_score = (2 * tp) / (2 * tp + fp + fn)
print('Precision: %f, Recall: %f, F1 score: %f' % (precision, recall, f1_score))
def cal_dev_loss(model, dev_data, batch_size, sent_vocab, tag_vocab, device):
""" Calculate loss on the development data
Args:
model: the model being trained
dev_data: development data
batch_size: batch size
sent_vocab: sentence vocab
tag_vocab: tag vocab
device: torch.device on which the model is trained
Returns:
the average loss on the dev data
"""
is_training = model.training
model.eval()
loss, n_sentences = 0, 0
with torch.no_grad():
for sentences, tags in utils.batch_iter(dev_data, batch_size, shuffle=False):
sentences, sent_lengths = utils.pad(sentences, sent_vocab[sent_vocab.PAD], device)
tags, _ = utils.pad(tags, tag_vocab[sent_vocab.PAD], device)
batch_loss = model(sentences, tags, sent_lengths) # shape: (b,)
loss += batch_loss.sum().item()
n_sentences += len(sentences)
model.train(is_training)
return loss / n_sentences
def cal_statistics(tag, predicted_tag, tag_vocab):
""" Calculate TN, FN, FP for the given true tag and predicted tag.
Args:
tag (list[int]): true tag
predicted_tag (list[int]): predicted tag
tag_vocab: tag vocab
Returns:
tp: true positive
fp: false positive
fn: false negative
"""
tp, fp, fn = 0, 0, 0
def func(tag1, tag2):
a, b, i = 0, 0, 0
while i < len(tag1):
if tag1[i] == tag_vocab['O']:
i += 1
continue
begin, end = i, i
while end + 1 < len(tag1) and tag1[end + 1] != tag_vocab['O']:
end += 1
equal = True
for j in range(max(0, begin - 1), min(len(tag1), end + 2)):
if tag1[j] != tag2[j]:
equal = False
break
a, b = a + equal, b + 1 - equal
i = end + 1
return a, b
t, f = func(tag, predicted_tag)
tp += t
fn += f
t, f = func(predicted_tag, tag)
fp += f
return tp, fp, fn
def main():
args = docopt(__doc__)
random.seed(0)
torch.manual_seed(0)
if args['--cuda']:
torch.cuda.manual_seed(0)
if args['train']:
train(args)
elif args['test']:
test(args)
if __name__ == '__main__':
main()