def __init__(self, train, test, embeddings_filename, batch_size=1):
self.train_path = train
self.test_path = test
self.mode = 'LSTM'
self.dropout = 'std'
self.num_epochs = 1
self.batch_size = batch_size
self.hidden_size = 256
self.num_filters = 30
self.learning_rate = 0.01
self.momentum = 0.9
self.decay_rate = 0.05
self.gamma = 0.0
self.schedule = 1
self.p_rnn = tuple([0.33, 0.5])
self.p_in = 0.33
self.p_out = 0.5
self.unk_replace = 0.0
self.bigram = True
self.embedding = 'glove'
self.logger = get_logger("NERCRF")
self.char_dim = 30
self.window = 3
self.num_layers = 1
self.tag_space = 128
self.initializer = nn.init.xavier_uniform
self.use_gpu = torch.cuda.is_available()
self.embedd_dict, self.embedd_dim = utils.load_embedding_dict(
self.embedding, embeddings_filename)
self.word_alphabet, self.char_alphabet, self.pos_alphabet, \
self.chunk_alphabet, self.ner_alphabet = conll03_data.create_alphabets("data/alphabets/ner_crf/", self.train_path, data_paths=[self.test_path],
embedd_dict=self.embedd_dict, max_vocabulary_size=50000)
self.word_table = self.construct_word_embedding_table()
self.logger.info("Word Alphabet Size: %d" % self.word_alphabet.size())
self.logger.info("Character Alphabet Size: %d" %
self.char_alphabet.size())
self.logger.info("POS Alphabet Size: %d" % self.pos_alphabet.size())
self.logger.info("Chunk Alphabet Size: %d" %
self.chunk_alphabet.size())
self.logger.info("NER Alphabet Size: %d" % self.ner_alphabet.size())
self.num_labels = self.ner_alphabet.size()
self.data_test = conll03_data.read_data_to_variable(
self.test_path,
self.word_alphabet,
self.char_alphabet,
self.pos_alphabet,
self.chunk_alphabet,
self.ner_alphabet,
use_gpu=self.use_gpu,
volatile=True)
self.writer = CoNLL03Writer(self.word_alphabet, self.char_alphabet,
self.pos_alphabet, self.chunk_alphabet,
self.ner_alphabet)
def extract_features(data_name, feat_name):
with open('temp/' + data_name, 'rb') as f:
train_data = pickle.load(f)
print(len(train_data))
network = torch.load('temp/ner_tuned.pt')
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("ner_alphabet/", None)
feats = []
for sent, mention, ont_types, weight in train_data:
with open('tmp', 'w') as f:
for i, word in enumerate(sent.words):
f.write('{0} {1} -- -- O\n'.format(i + 1, word.word))
sent_data = conll03_data.read_data_to_variable('tmp',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=True)
os.system('rm tmp')
word, char, pos, chunk, labels, masks, lengths = conll03_data.iterate_batch_variable(
sent_data, 1).next()
feat = network.feature(word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
print(feat.size())
feat_vec = feat[0, mention['head_index'], :]
feats.append((feat_vec.data.numpy(), ont_types, weight))
print(np.shape(feats[-1][0]))
with open('temp/' + feat_name, 'wb') as f:
pickle.dump(feats, f)
def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN-CRF')
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
required=True)
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--num_filters',
type=int,
default=30,
help='Number of filters in CNN')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--learning_rate',
type=float,
default=0.015,
help='Learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.1,
help='Decay rate of learning rate')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
parser.add_argument('--dropout',
choices=['std', 'variational'],
help='type of dropout',
required=True)
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--embedding',
choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("NERCRF")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("data/alphabets/ner_crf/", train_path, data_paths=[dev_path, test_path],
embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conll03_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu,
volatile=True)
data_test = conll03_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu,
volatile=True)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
else:
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
if use_gpu:
network.cuda()
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d, crf=%s"
% (mode, num_layers, hidden_size, num_filters, tag_space,
'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data / batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
tmp_filename = 'tmp/%s_dev%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_variable(data_dev, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
preds, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' %
(acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_test%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_variable(
data_test, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
preds, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename)
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
def main():
parser = argparse.ArgumentParser(
description='NER with bi-directional RNN-CNN')
parser.add_argument('--config',
type=str,
help='config file',
required=True)
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--loss_type',
choices=['sentence', 'token'],
default='sentence',
help='loss type (default: sentence)')
parser.add_argument('--optim',
choices=['sgd', 'adam'],
help='type of optimizer',
required=True)
parser.add_argument('--learning_rate',
type=float,
default=0.1,
help='Learning rate')
parser.add_argument('--lr_decay',
type=float,
default=0.999995,
help='Decay rate of learning rate')
parser.add_argument('--amsgrad', action='store_true', help='AMS Grad')
parser.add_argument('--grad_clip',
type=float,
default=0,
help='max norm for gradient clip (default 0: no clip')
parser.add_argument('--warmup_steps',
type=int,
default=0,
metavar='N',
help='number of steps to warm up (default: 0)')
parser.add_argument('--weight_decay',
type=float,
default=0.0,
help='weight for l2 norm decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--embedding',
choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument('--train',
help='path for training file.',
required=True)
parser.add_argument('--dev', help='path for dev file.', required=True)
parser.add_argument('--test', help='path for test file.', required=True)
parser.add_argument('--model_path',
help='path for saving model file.',
required=True)
args = parser.parse_args()
logger = get_logger("NER")
args.cuda = torch.cuda.is_available()
device = torch.device('cuda', 0) if args.cuda else torch.device('cpu')
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
optim = args.optim
learning_rate = args.learning_rate
lr_decay = args.lr_decay
amsgrad = args.amsgrad
warmup_steps = args.warmup_steps
weight_decay = args.weight_decay
grad_clip = args.grad_clip
loss_ty_token = args.loss_type == 'token'
unk_replace = args.unk_replace
model_path = args.model_path
model_name = os.path.join(model_path, 'model.pt')
embedding = args.embedding
embedding_path = args.embedding_dict
print(args)
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets')
word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet = conll03_data.create_alphabets(
alphabet_path,
train_path,
data_paths=[dev_path, test_path],
embedd_dict=embedd_dict,
max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
data_train = conll03_data.read_bucketed_data(train_path, word_alphabet,
char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data(dev_path, word_alphabet, char_alphabet,
pos_alphabet, chunk_alphabet,
ner_alphabet)
data_test = conll03_data.read_data(test_path, word_alphabet, char_alphabet,
pos_alphabet, chunk_alphabet,
ner_alphabet)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
hyps = json.load(open(args.config, 'r'))
json.dump(hyps,
open(os.path.join(model_path, 'config.json'), 'w'),
indent=2)
dropout = hyps['dropout']
crf = hyps['crf']
bigram = hyps['bigram']
assert embedd_dim == hyps['embedd_dim']
char_dim = hyps['char_dim']
mode = hyps['rnn_mode']
hidden_size = hyps['hidden_size']
out_features = hyps['out_features']
num_layers = hyps['num_layers']
p_in = hyps['p_in']
p_out = hyps['p_out']
p_rnn = hyps['p_rnn']
activation = hyps['activation']
if dropout == 'std':
if crf:
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
mode,
hidden_size,
out_features,
num_layers,
num_labels,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
activation=activation)
else:
network = BiRecurrentConv(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
mode,
hidden_size,
out_features,
num_layers,
num_labels,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
activation=activation)
elif dropout == 'variational':
if crf:
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
mode,
hidden_size,
out_features,
num_layers,
num_labels,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
activation=activation)
else:
network = BiVarRecurrentConv(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
mode,
hidden_size,
out_features,
num_layers,
num_labels,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
activation=activation)
else:
raise ValueError('Unkown dropout type: {}'.format(dropout))
network = network.to(device)
optimizer, scheduler = get_optimizer(network.parameters(), optim,
learning_rate, lr_decay, amsgrad,
weight_decay, warmup_steps)
model = "{}-CNN{}".format(mode, "-CRF" if crf else "")
logger.info("Network: %s, num_layer=%d, hidden=%d, act=%s" %
(model, num_layers, hidden_size, activation))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (weight_decay, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): %s(%.2f, %.2f, %s)" %
(dropout, p_in, p_out, p_rnn))
print('# of Parameters: %d' %
(sum([param.numel() for param in network.parameters()])))
best_f1 = 0.0
best_acc = 0.0
best_precision = 0.0
best_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
patient = 0
num_batches = num_data // batch_size + 1
result_path = os.path.join(model_path, 'tmp')
if not os.path.exists(result_path):
os.makedirs(result_path)
for epoch in range(1, num_epochs + 1):
start_time = time.time()
train_loss = 0.
num_insts = 0
num_words = 0
num_back = 0
network.train()
lr = scheduler.get_lr()[0]
print('Epoch %d (%s, lr=%.6f, lr decay=%.6f, amsgrad=%s, l2=%.1e): ' %
(epoch, optim, lr, lr_decay, amsgrad, weight_decay))
if args.cuda:
torch.cuda.empty_cache()
gc.collect()
for step, data in enumerate(
iterate_data(data_train,
batch_size,
bucketed=True,
unk_replace=unk_replace,
shuffle=True)):
optimizer.zero_grad()
words = data['WORD'].to(device)
chars = data['CHAR'].to(device)
labels = data['NER'].to(device)
masks = data['MASK'].to(device)
nbatch = words.size(0)
nwords = masks.sum().item()
loss_total = network.loss(words, chars, labels, mask=masks).sum()
if loss_ty_token:
loss = loss_total.div(nwords)
else:
loss = loss_total.div(nbatch)
loss.backward()
if grad_clip > 0:
clip_grad_norm_(network.parameters(), grad_clip)
optimizer.step()
scheduler.step()
with torch.no_grad():
num_insts += nbatch
num_words += nwords
train_loss += loss_total.item()
# update log
if step % 100 == 0:
torch.cuda.empty_cache()
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
curr_lr = scheduler.get_lr()[0]
log_info = '[%d/%d (%.0f%%) lr=%.6f] loss: %.4f (%.4f)' % (
step, num_batches, 100. * step / num_batches, curr_lr,
train_loss / num_insts, train_loss / num_words)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('total: %d (%d), loss: %.4f (%.4f), time: %.2fs' %
(num_insts, num_words, train_loss / num_insts,
train_loss / num_words, time.time() - start_time))
print('-' * 100)
# evaluate performance on dev data
with torch.no_grad():
outfile = os.path.join(result_path, 'pred_dev%d' % epoch)
scorefile = os.path.join(result_path, "score_dev%d" % epoch)
acc, precision, recall, f1 = eval(data_dev, network, writer,
outfile, scorefile, device)
print(
'Dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (acc, precision, recall, f1))
if best_f1 < f1:
torch.save(network.state_dict(), model_name)
best_f1 = f1
best_acc = acc
best_precision = precision
best_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
outfile = os.path.join(result_path, 'pred_test%d' % epoch)
scorefile = os.path.join(result_path, "score_test%d" % epoch)
test_acc, test_precision, test_recall, test_f1 = eval(
data_test, network, writer, outfile, scorefile, device)
print(
'test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (test_acc, test_precision, test_recall, test_f1))
patient = 0
else:
patient += 1
print('-' * 100)
print(
"Best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d (%d))"
% (best_acc, best_precision, best_recall, best_f1, best_epoch,
patient))
print(
"Best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d (%d))"
% (test_acc, test_precision, test_recall, test_f1, best_epoch,
patient))
print('=' * 100)
if patient > 4:
logger.info('reset optimizer momentums')
scheduler.reset_state()
patient = 0
def regen_train_data(nlp, fpath):
ontology = OntologyType()
decisions = ontology.load_decision_tree()
network = torch.load('temp/ner_tuned.pt')
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("ner_alphabet/", None)
train_set = set()
for fname in os.listdir('../../data/txt/'):
if fname.endswith('.dump'):
train_set.add(fname[:-5])
print(train_set)
train_data = []
train_feat = []
for root, dirs, files in os.walk('../../data/ltf/'):
for file in files:
if file in train_set:
print(file)
sents, doc = read_ltf_offset(os.path.join(root, file))
for sent in sents:
named_ents, ners, feats = extract_ner(sent)
for mention, feat in zip(named_ents, feats):
prdt_type = infer_type(feat, decisions)
coherence = type_coherence(mention['type'], prdt_type,
ontology)
if coherence > 0:
train_data.append(
(sent, mention,
[prdt_type] + ontology.lookup_all(prdt_type),
coherence))
train_feat.append(
(feat,
[prdt_type] + ontology.lookup_all(prdt_type),
coherence))
print(sent.get_text())
nominals = extract_nominals(sent, nlp, ners)
for mention in nominals:
ont_types = ontology.lookup_all(mention['headword'])
if ont_types:
train_data.append((sent, mention, ont_types, 1.0))
with open('tmp', 'w') as f:
for i, word in enumerate(sent.words):
f.write('{0} {1} -- -- O\n'.format(
i + 1, word.word))
sent_data = conll03_data.read_data_to_variable(
'tmp',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=True)
os.system('rm tmp')
word, char, pos, chunk, labels, masks, lengths = conll03_data.iterate_batch_variable(
sent_data, 1).next()
feat = network.feature(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS
)
feat_vec = feat[0, mention['head_index'], :]
train_feat.append(
(feat_vec.data.numpy(), ont_types, 1.0))
print(sent.get_text())
with open('temp/' + fpath + 'data.dump', 'wb') as f:
pickle.dump(train_data, f)
with open('temp/' + fpath + 'feat.dump', 'wb') as f:
pickle.dump(train_feat, f)
def main():
# Arguments parser
parser = argparse.ArgumentParser(description='Tuning with DNN Model for NER')
# Model Hyperparameters
parser.add_argument('--mode', choices=['RNN', 'LSTM', 'GRU'], help='architecture of rnn', default='LSTM')
parser.add_argument('--encoder_mode', choices=['cnn', 'lstm'], help='Encoder type for sentence encoding',
default='lstm')
parser.add_argument('--char_method', choices=['cnn', 'lstm'], help='Method to create character-level embeddings',
required=True)
parser.add_argument('--hidden_size', type=int, default=128, help='Number of hidden units in RNN for sentence level')
parser.add_argument('--char_hidden_size', type=int, default=30, help='Output character-level embeddings size')
parser.add_argument('--char_dim', type=int, default=30, help='Dimension of Character embeddings')
parser.add_argument('--tag_space', type=int, default=0, help='Dimension of tag space')
parser.add_argument('--num_layers', type=int, default=1, help='Number of layers of RNN')
parser.add_argument('--dropout', choices=['std', 'gcn'], help='Dropout method',
default='gcn')
parser.add_argument('--p_em', type=float, default=0.33, help='dropout rate for input embeddings')
parser.add_argument('--p_in', type=float, default=0.33, help='dropout rate for input of RNN model')
parser.add_argument('--p_rnn', nargs=3, type=float, required=True, help='dropout rate for RNN')
parser.add_argument('--p_tag', type=float, default=0.33, help='dropout rate for output layer')
parser.add_argument('--bigram', action='store_true', help='bi-gram parameter for CRF')
parser.add_argument('--adj_attn', choices=['cossim', 'flex_cossim', 'flex_cossim2', 'concat', '', 'multihead'],
default='')
# Data loading and storing params
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument('--dataset_name', type=str, default='alexa', help='Which dataset to use')
parser.add_argument('--train', type=str, required=True, help='Path of train set')
parser.add_argument('--dev', type=str, required=True, help='Path of dev set')
parser.add_argument('--test', type=str, required=True, help='Path of test set')
parser.add_argument('--results_folder', type=str, default='results', help='The folder to store results')
parser.add_argument('--alphabets_folder', type=str, default='data/alphabets',
help='The folder to store alphabets files')
# Training parameters
parser.add_argument('--cuda', action='store_true', help='whether using GPU')
parser.add_argument('--num_epochs', type=int, default=100, help='Number of training epochs')
parser.add_argument('--batch_size', type=int, default=16, help='Number of sentences in each batch')
parser.add_argument('--learning_rate', type=float, default=0.001, help='Base learning rate')
parser.add_argument('--decay_rate', type=float, default=0.95, help='Decay rate of learning rate')
parser.add_argument('--schedule', type=int, default=3, help='schedule for learning rate decay')
parser.add_argument('--gamma', type=float, default=0.0, help='weight for l2 regularization')
parser.add_argument('--max_norm', type=float, default=1., help='Max norm for gradients')
parser.add_argument('--gpu_id', type=int, nargs='+', required=True, help='which gpu to use for training')
parser.add_argument('--learning_rate_gcn', type=float, default=5e-4, help='Base learning rate')
parser.add_argument('--gcn_warmup', type=int, default=200, help='Base learning rate')
parser.add_argument('--pretrain_lstm', type=float, default=10, help='Base learning rate')
parser.add_argument('--adj_loss_lambda', type=float, default=0.)
parser.add_argument('--lambda1', type=float, default=1.)
parser.add_argument('--lambda2', type=float, default=0.)
parser.add_argument('--seed', type=int, default=None)
# Misc
parser.add_argument('--embedding', choices=['glove', 'senna', 'alexa'], help='Embedding for words', required=True)
parser.add_argument('--restore', action='store_true', help='whether restore from stored parameters')
parser.add_argument('--save_checkpoint', type=str, default='', help='the path to save the model')
parser.add_argument('--o_tag', type=str, default='O', help='The default tag for outside tag')
parser.add_argument('--unk_replace', type=float, default=0., help='The rate to replace a singleton word with UNK')
parser.add_argument('--evaluate_raw_format', action='store_true', help='The tagging format for evaluation')
parser.add_argument('--eval_type', type=str, default="micro_f1",choices=['micro_f1', 'acc'])
parser.add_argument('--show_network', action='store_true', help='whether to display the network structure')
parser.add_argument('--smooth', action='store_true', help='whether to skip all pdb break points')
parser.add_argument('--uid', type=str, default='temp')
parser.add_argument('--misc', type=str, default='')
args = parser.parse_args()
show_var(['args'])
uid = args.uid
results_folder = args.results_folder
dataset_name = args.dataset_name
use_tensorboard = True
save_dset_dir = '{}../dset/{}/graph'.format(results_folder, dataset_name)
result_file_path = '{}/{dataset}_{uid}_result'.format(results_folder, dataset=dataset_name, uid=uid)
save_loss_path = '{}/{dataset}_{uid}_loss'.format(results_folder, dataset=dataset_name, uid=uid)
save_lr_path = '{}/{dataset}_{uid}_lr'.format(results_folder, dataset=dataset_name, uid='temp')
save_tb_path = '{}/tensorboard/'.format(results_folder)
logger = get_logger("NERCRF")
loss_recorder = LossRecorder(uid=uid)
record = TensorboardLossRecord(use_tensorboard, save_tb_path, uid=uid)
# rename the parameters
mode = args.mode
encoder_mode = args.encoder_mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
char_hidden_size = args.char_hidden_size
char_method = args.char_method
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
max_norm = args.max_norm
schedule = args.schedule
dropout = args.dropout
p_em = args.p_em
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_tag = args.p_tag
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
evaluate_raw_format = args.evaluate_raw_format
o_tag = args.o_tag
restore = args.restore
save_checkpoint = args.save_checkpoint
alphabets_folder = args.alphabets_folder
use_elmo = False
p_em_vec = 0.
graph_model = 'gnn'
coref_edge_filt = ''
learning_rate_gcn = args.learning_rate_gcn
gcn_warmup = args.gcn_warmup
pretrain_lstm = args.pretrain_lstm
adj_loss_lambda = args.adj_loss_lambda
lambda1 = args.lambda1
lambda2 = args.lambda2
if args.smooth:
import pdb
pdb.set_trace = lambda: None
misc = "{}".format(str(args.misc))
score_file = "{}/{dataset}_{uid}_score".format(results_folder, dataset=dataset_name, uid=uid)
for folder in [results_folder, alphabets_folder, save_dset_dir]:
if not os.path.exists(folder):
os.makedirs(folder)
def set_seed(seed):
if not seed:
seed = int(show_time())
print("[Info] seed set to: {}".format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
set_seed(args.seed)
embedd_dict, embedd_dim = utils.load_embedding_dict(embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, ner_alphabet = conll03_data.create_alphabets(
"{}/{}/".format(alphabets_folder, dataset_name), train_path, data_paths=[dev_path, test_path],
embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
device = torch.device('cuda') if args.cuda else torch.device('cpu')
print(device)
data_train = conll03_data.read_data(train_path, word_alphabet, char_alphabet,
ner_alphabet,
graph_model, batch_size, ori_order=False,
total_batch="{}x".format(num_epochs + 1),
unk_replace=unk_replace, device=device,
save_path=save_dset_dir + '/train', coref_edge_filt=coref_edge_filt
)
# , shuffle=True,
num_data = data_train.data_len
num_labels = ner_alphabet.size()
graph_types = data_train.meta_info['graph_types']
data_dev = conll03_data.read_data(dev_path, word_alphabet, char_alphabet,
ner_alphabet,
graph_model, batch_size, ori_order=True, unk_replace=unk_replace, device=device,
save_path=save_dset_dir + '/dev',
coref_edge_filt=coref_edge_filt)
data_test = conll03_data.read_data(test_path, word_alphabet, char_alphabet,
ner_alphabet,
graph_model, batch_size, ori_order=True, unk_replace=unk_replace, device=device,
save_path=save_dset_dir + '/test',
coref_edge_filt=coref_edge_filt)
writer = CoNLL03Writer(word_alphabet, char_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform_
p_gcn = [0.5, 0.5]
d_graph = 256
d_out = 256
d_inner_hid = 128
d_k = 32
d_v = 32
n_head = 4
n_gcn_layer = 1
p_rnn2 = [0.0, 0.5, 0.5]
adj_attn = args.adj_attn
mask_singles = True
post_lstm = 1
position_enc_mode = 'none'
adj_memory = False
if dropout == 'gcn':
network = BiRecurrentConvGraphCRF(embedd_dim, word_alphabet.size(), char_dim, char_alphabet.size(),
char_hidden_size, window, mode, encoder_mode, hidden_size, num_layers,
num_labels,
graph_model, n_head, d_graph, d_inner_hid, d_k, d_v, p_gcn, n_gcn_layer,
d_out, post_lstm=post_lstm, mask_singles=mask_singles,
position_enc_mode=position_enc_mode, adj_attn=adj_attn,
adj_loss_lambda=adj_loss_lambda,
tag_space=tag_space, embedd_word=word_table,
use_elmo=use_elmo, p_em_vec=p_em_vec, p_em=p_em, p_in=p_in, p_tag=p_tag,
p_rnn=p_rnn, p_rnn2=p_rnn2,
bigram=bigram, initializer=initializer)
elif dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim, word_alphabet.size(), char_dim, char_alphabet.size(), char_hidden_size,
window, mode, encoder_mode, hidden_size, num_layers, num_labels,
tag_space=tag_space, embedd_word=word_table, use_elmo=use_elmo, p_em_vec=p_em_vec,
p_em=p_em, p_in=p_in, p_tag=p_tag, p_rnn=p_rnn, bigram=bigram,
initializer=initializer)
# whether restore from trained model
if restore:
network.load_state_dict(torch.load(save_checkpoint + '_best.pth')) # load trained model
logger.info("cuda()ing network...")
network = network.to(device)
if dataset_name == 'conll03' and data_dev.data_len > 26:
sample = data_dev.pad_batch(data_dev.dataset[25:26])
else:
sample = data_dev.pad_batch(data_dev.dataset[:1])
plot_att_change(sample, network, record, save_tb_path + 'att/', uid='temp', epoch=0, device=device,
word_alphabet=word_alphabet, show_net=args.show_network,
graph_types=data_train.meta_info['graph_types'])
logger.info("finished cuda()ing network...")
lr = learning_rate
lr_gcn = learning_rate_gcn
optim = Optimizer('sgd', 'adam', network, dropout, lr=learning_rate,
lr_gcn=learning_rate_gcn,
wd=0., wd_gcn=0., momentum=momentum, lr_decay=decay_rate, schedule=schedule,
gcn_warmup=gcn_warmup,
pretrain_lstm=pretrain_lstm)
nn.utils.clip_grad_norm_(network.parameters(), max_norm)
logger.info(
"Network: %s, encoder_mode=%s, num_layer=%d, hidden=%d, char_hidden_size=%d, char_method=%s, tag_space=%d, crf=%s" % \
(mode, encoder_mode, num_layers, hidden_size, char_hidden_size, char_method, tag_space,
'bigram' if bigram else 'unigram'))
logger.info("training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)" % (
gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" % (p_in, p_tag, p_rnn))
num_batches = num_data // batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
best_test_f1 = 0.0
best_test_acc = 0.0
best_test_precision = 0.0
best_test_recall = 0.0
best_test_epoch = 0.0
loss_recorder.start(save_loss_path, mode='w', misc=misc)
fwrite('', save_lr_path)
fwrite(json.dumps(vars(args)) + '\n', result_file_path)
for epoch in range(1, num_epochs + 1):
show_var(['misc'])
lr_state = 'Epoch %d (uid=%s, lr=%.2E, lr_gcn=%.2E, decay rate=%.4f): ' % (
epoch, uid, Decimal(optim.curr_lr), Decimal(optim.curr_lr_gcn), decay_rate)
print(lr_state)
fwrite(lr_state[:-2] + '\n', save_lr_path, mode='a')
train_err = 0.
train_err2 = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch_i in range(1, num_batches + 1):
batch_doc = data_train.next()
char, word, posi, labels, feats, adjs, words_en = [batch_doc[i] for i in [
"chars", "word_ids", "posi", "ner_ids", "feat_ids", "adjs", "words_en"]]
sent_word, sent_char, sent_labels, sent_mask, sent_length, _ = network._doc2sent(
word, char, labels)
optim.zero_grad()
adjs_into_model = adjs if adj_memory else adjs.clone()
loss, (ner_loss, adj_loss) = network.loss(None, word, char, adjs_into_model, labels,
graph_types=graph_types, lambda1=lambda1, lambda2=lambda2)
# loss = network.loss(_, sent_word, sent_char, sent_labels, mask=sent_mask)
loss.backward()
optim.step()
with torch.no_grad():
num_inst = sent_mask.size(0)
train_err += ner_loss * num_inst
train_err2 += adj_loss * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch_i
time_left = (num_batches - batch_i) * time_ave
# update log
if batch_i % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss1: %.4f, loss2: %.4f, time left (estimated): %.2fs' % (
batch_i, num_batches, train_err / train_total, train_err2 / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
optim.update(epoch, batch_i, num_batches, network)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, loss2: %.4f, time: %.2fs' % (
num_batches, train_err / train_total, train_err2 / train_total, time.time() - start_time))
# evaluate performance on dev data
with torch.no_grad():
network.eval()
tmp_filename = "{}/{dataset}_{uid}_output_dev".format(results_folder, dataset=dataset_name, uid=uid)
writer.start(tmp_filename)
for batch in data_dev:
char, word, posi, labels, feats, adjs, words_en = [batch[i] for i in [
"chars", "word_ids", "posi", "ner_ids", "feat_ids", "adjs", "words_en"]]
sent_word, sent_char, sent_labels, sent_mask, sent_length, _ = network._doc2sent(
word, char, labels)
preds, _ = network.decode(
None, word, char, adjs.clone(), target=labels, leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS,
graph_types=graph_types)
# preds, _ = network.decode(_, sent_word, sent_char, target=sent_labels, mask=sent_mask,
# leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(sent_word.cpu().numpy(), preds.cpu().numpy(), sent_labels.cpu().numpy(),
sent_length.cpu().numpy())
writer.close()
if args.eval_type == "acc":
acc, precision, recall, f1 =evaluate_tokenacc(tmp_filename)
f1 = acc
else:
acc, precision, recall, f1 = evaluate(tmp_filename, score_file, evaluate_raw_format, o_tag)
print('dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' % (acc, precision, recall, f1))
# plot loss and attention
record.plot_loss(epoch, train_err / train_total, f1)
plot_att_change(sample, network, record, save_tb_path + 'att/', uid="{}_{:03d}".format(uid, epoch),
epoch=epoch, device=device,
word_alphabet=word_alphabet, show_net=False, graph_types=graph_types)
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = "{}/{dataset}_{uid}_output_test".format(results_folder, dataset=dataset_name,
uid=uid)
writer.start(tmp_filename)
for batch in data_test:
char, word, posi, labels, feats, adjs, words_en = [batch[i] for i in [
"chars", "word_ids", "posi", "ner_ids", "feat_ids", "adjs", "words_en"]]
sent_word, sent_char, sent_labels, sent_mask, sent_length, _ = network._doc2sent(
word, char, labels)
preds, _ = network.decode(
None, word, char, adjs.clone(), target=labels, leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS,
graph_types=graph_types)
# preds, _ = network.decode(_, sent_word, sent_char, target=sent_labels, mask=sent_mask,
# leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(sent_word.cpu().numpy(), preds.cpu().numpy(), sent_labels.cpu().numpy(),
sent_length.cpu().numpy())
writer.close()
if args.eval_type == "acc":
test_acc, test_precision, test_recall, test_f1 = evaluate_tokenacc(tmp_filename)
test_f1 = test_acc
else:
test_acc, test_precision, test_recall, test_f1 = evaluate(tmp_filename, score_file, evaluate_raw_format, o_tag)
if best_test_f1 < test_f1:
best_test_acc, best_test_precision, best_test_recall, best_test_f1 = test_acc, test_precision, test_recall, test_f1
best_test_epoch = epoch
# save the model parameters
if save_checkpoint:
torch.save(network.state_dict(), save_checkpoint + '_best.pth')
print("best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)" % (
dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print("best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)" % (
test_acc, test_precision, test_recall, test_f1, best_epoch))
print("overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)" % (
best_test_acc, best_test_precision, best_test_recall, best_test_f1, best_test_epoch))
# optim.update(epoch, 1, num_batches, network)
loss_recorder.write(epoch, train_err / train_total, train_err2 / train_total,
Decimal(optim.curr_lr), Decimal(optim.curr_lr_gcn), f1, best_test_f1, test_f1)
with open(result_file_path, 'a') as ofile:
ofile.write("best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n" % (
dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
ofile.write("best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n" % (
test_acc, test_precision, test_recall, test_f1, best_epoch))
ofile.write("overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n\n" % (
best_test_acc, best_test_precision, best_test_recall, best_test_f1, best_test_epoch))
record.close()
print('Training finished!')
def main():
embedding = 'glove'
embedding_path = '/media/xianyang/OS/workspace/ner/glove.6B/glove.6B.100d.txt'
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("/media/xianyang/OS/workspace/ner/NeuroNLP2/data/alphabets/ner_crf/", None)
char_dim = 30
num_filters = 30
window = 3
mode = 'LSTM'
hidden_size = 256
num_layers = 1
num_labels = ner_alphabet.size()
tag_space = 128
p = 0.5
bigram = True
embedd_dim = 100
use_gpu = False
print(len(word_alphabet.get_content()['instances']))
print(ner_alphabet.get_content())
# writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet)
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=None,
p_rnn=p,
bigram=bigram)
network.load_state_dict(torch.load('temp/23df51_model45'))
ner_alphabet.add('B-VEH')
ner_alphabet.add('I-VEH')
ner_alphabet.add('B-WEA')
ner_alphabet.add('I-WEA')
num_new_word = 0
with open('temp/target.train.conll', 'r') as f:
sents = []
sent_buffer = []
for line in f:
if len(line) <= 1:
sents.append(sent_buffer)
sent_buffer = []
else:
id, word, _, _, ner = line.strip().split()
if word_alphabet.get_index(word) == 0:
word_alphabet.add(word)
num_new_word += 1
sent_buffer.append((word_alphabet.get_index(word),
ner_alphabet.get_index(ner)))
print(len(word_alphabet.get_content()['instances']))
print(ner_alphabet.get_content())
init_embed = network.word_embedd.weight.data
init_embed = np.concatenate(
(init_embed, np.zeros((num_new_word, embedd_dim))), axis=0)
network.word_embedd = Embedding(word_alphabet.size(), embedd_dim,
torch.from_numpy(init_embed))
old_crf = network.crf
new_crf = ChainCRF(tag_space, ner_alphabet.size(), bigram=bigram)
trans_matrix = np.zeros((new_crf.num_labels, old_crf.num_labels))
for i in range(old_crf.num_labels):
trans_matrix[i, i] = 1
new_crf.state_nn.weight.data = torch.FloatTensor(
np.dot(trans_matrix, old_crf.state_nn.weight.data))
network.crf = new_crf
target_train_data = conll03_data.read_data_to_variable(
'temp/target.train.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
target_dev_data = conll03_data.read_data_to_variable(
'temp/target.dev.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
target_test_data = conll03_data.read_data_to_variable(
'temp/target.test.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
num_epoch = 50
batch_size = 32
num_data = sum(target_train_data[1])
num_batches = num_data / batch_size + 1
unk_replace = 0.0
# optim = SGD(network.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0, nesterov=True)
optim = Adam(network.parameters(), lr=1e-3)
for epoch in range(1, num_epoch + 1):
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
target_train_data, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
if batch % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d loss: %.4f, time: %.2fs' % (
num_batches, train_err / train_total,
time.time() - start_time)
print(log_info)
num_back = len(log_info)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm temp/output.txt')
writer.start('temp/output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(
target_dev_data, batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('temp/output.txt')
log_info = 'dev: %f %f %f %f' % (acc, precision, recall, f1)
print(log_info)
if epoch % 10 == 0:
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm temp/output.txt')
writer.start('temp/output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(
target_test_data, batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('temp/output.txt')
log_info = 'test: %f %f %f %f' % (acc, precision, recall, f1)
print(log_info)
torch.save(network, 'temp/tuned_0905.pt')
alphabet_directory = '0905_alphabet/'
word_alphabet.save(alphabet_directory)
char_alphabet.save(alphabet_directory)
pos_alphabet.save(alphabet_directory)
chunk_alphabet.save(alphabet_directory)
ner_alphabet.save(alphabet_directory)
def main():
# Arguments parser
parser = argparse.ArgumentParser(
description='Tuning with DNN Model for NER')
# Model Hyperparameters
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
default='LSTM')
parser.add_argument('--encoder_mode',
choices=['cnn', 'lstm'],
help='Encoder type for sentence encoding',
default='lstm')
parser.add_argument('--char_method',
choices=['cnn', 'lstm'],
help='Method to create character-level embeddings',
required=True)
parser.add_argument(
'--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN for sentence level')
parser.add_argument('--char_hidden_size',
type=int,
default=30,
help='Output character-level embeddings size')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--dropout',
choices=['std', 'weight_drop'],
help='Dropout method',
default='weight_drop')
parser.add_argument('--p_em',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input of RNN model')
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
# Data loading and storing params
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument('--dataset_name',
type=str,
default='alexa',
help='Which dataset to use')
parser.add_argument('--train',
type=str,
required=True,
help='Path of train set')
parser.add_argument('--dev',
type=str,
required=True,
help='Path of dev set')
parser.add_argument('--test',
type=str,
required=True,
help='Path of test set')
parser.add_argument('--results_folder',
type=str,
default='results',
help='The folder to store results')
parser.add_argument('--tmp_folder',
type=str,
default='tmp',
help='The folder to store tmp files')
parser.add_argument('--alphabets_folder',
type=str,
default='data/alphabets',
help='The folder to store alphabets files')
parser.add_argument('--result_file_name',
type=str,
default='hyperparameters_tuning',
help='File name to store some results')
parser.add_argument('--result_file_path',
type=str,
default='results/hyperparameters_tuning',
help='File name to store some results')
# Training parameters
parser.add_argument('--cuda',
action='store_true',
help='whether using GPU')
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Base learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.95,
help='Decay rate of learning rate')
parser.add_argument('--schedule',
type=int,
default=3,
help='schedule for learning rate decay')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for l2 regularization')
parser.add_argument('--max_norm',
type=float,
default=1.,
help='Max norm for gradients')
parser.add_argument('--gpu_id',
type=int,
nargs='+',
required=True,
help='which gpu to use for training')
# Misc
parser.add_argument('--embedding',
choices=['glove', 'senna', 'alexa'],
help='Embedding for words',
required=True)
parser.add_argument('--restore',
action='store_true',
help='whether restore from stored parameters')
parser.add_argument('--save_checkpoint',
type=str,
default='',
help='the path to save the model')
parser.add_argument('--o_tag',
type=str,
default='O',
help='The default tag for outside tag')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--evaluate_raw_format',
action='store_true',
help='The tagging format for evaluation')
args = parser.parse_args()
logger = get_logger("NERCRF")
# rename the parameters
mode = args.mode
encoder_mode = args.encoder_mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
char_hidden_size = args.char_hidden_size
char_method = args.char_method
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
max_norm = args.max_norm
schedule = args.schedule
dropout = args.dropout
p_em = args.p_em
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
dataset_name = args.dataset_name
result_file_name = args.result_file_name
evaluate_raw_format = args.evaluate_raw_format
o_tag = args.o_tag
restore = args.restore
save_checkpoint = args.save_checkpoint
gpu_id = args.gpu_id
results_folder = args.results_folder
tmp_folder = args.tmp_folder
alphabets_folder = args.alphabets_folder
use_elmo = False
p_em_vec = 0.
result_file_path = args.result_file_path
score_file = "%s/score_gpu_%s" % (tmp_folder, '-'.join(map(str, gpu_id)))
if not os.path.exists(results_folder):
os.makedirs(results_folder)
if not os.path.exists(tmp_folder):
os.makedirs(tmp_folder)
if not os.path.exists(alphabets_folder):
os.makedirs(alphabets_folder)
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, ner_alphabet = conll03_data.create_alphabets(
"{}/{}/".format(alphabets_folder, dataset_name),
train_path,
data_paths=[dev_path, test_path],
embedd_dict=embedd_dict,
max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
device = torch.device('cuda') if args.cuda else torch.device('cpu')
print(device)
data_train = conll03_data.read_data_to_tensor(train_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_tensor(dev_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
data_test = conll03_data.read_data_to_tensor(test_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
writer = CoNLL03Writer(word_alphabet, char_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform_
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
use_elmo=use_elmo,
p_em_vec=p_em_vec,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
elif args.dropout == 'var':
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
use_elmo=use_elmo,
p_em_vec=p_em_vec,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
else:
network = BiWeightDropRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
network = network.to(device)
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
# optim = Adam(network.parameters(), lr=lr, weight_decay=gamma, amsgrad=True)
nn.utils.clip_grad_norm_(network.parameters(), max_norm)
logger.info("Network: %s, encoder_mode=%s, num_layer=%d, hidden=%d, char_hidden_size=%d, char_method=%s, tag_space=%d, crf=%s" % \
(mode, encoder_mode, num_layers, hidden_size, char_hidden_size, char_method, tag_space, 'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data // batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
best_test_f1 = 0.0
best_test_acc = 0.0
best_test_precision = 0.0
best_test_recall = 0.0
best_test_epoch = 0.0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
_, word, char, labels, masks, lengths = conll03_data.get_batch_tensor(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(_, word, char, labels, mask=masks)
loss.backward()
optim.step()
with torch.no_grad():
num_inst = word.size(0)
train_err += loss * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
with torch.no_grad():
network.eval()
tmp_filename = '%s/gpu_%s_dev' % (tmp_folder, '-'.join(
map(str, gpu_id)))
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_dev, batch_size):
_, word, char, labels, masks, lengths = batch
preds, _ = network.decode(
_,
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename, score_file,
evaluate_raw_format, o_tag)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = '%s/gpu_%s_test' % (tmp_folder, '-'.join(
map(str, gpu_id)))
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_test, batch_size):
_, word, char, labels, masks, lengths = batch
preds, _ = network.decode(
_,
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename, score_file, evaluate_raw_format, o_tag)
if best_test_f1 < test_f1:
best_test_acc, best_test_precision, best_test_recall, best_test_f1 = test_acc, test_precision, test_recall, test_f1
best_test_epoch = epoch
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
print(
"overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (best_test_acc, best_test_precision, best_test_recall,
best_test_f1, best_test_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
with open(result_file_path, 'a') as ofile:
ofile.write(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
ofile.write(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
ofile.write(
"overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n\n"
% (best_test_acc, best_test_precision, best_test_recall,
best_test_f1, best_test_epoch))
print('Training finished!')
def sample():
network = torch.load('temp/ner_active.pt')
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("active_alphabet/", None)
unannotated_data = conll03_data.read_data_to_variable(
'temp/unannotated.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=True)
annotated = set()
with open('temp/annotated.conll', 'r') as f:
sent_buffer = []
for line in f:
if len(line) > 1:
_, word, _, _, _ = line.strip().split()
sent_buffer.append(word)
else:
annotated.add(' '.join(sent_buffer))
sent_buffer = []
print('total annotated data: {}'.format(len(annotated)))
uncertain = []
max_sents = 100
max_words = 500
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
writer.start('temp/output.txt')
network.eval()
tiebreaker = count()
for batch in conll03_data.iterate_batch_variable(unannotated_data, 32):
word, char, pos, chunk, labels, masks, lengths, raws = batch
preds, _, confidence = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
for _ in range(confidence.size()[0]):
heapq.heappush(uncertain,
(confidence[_].numpy()[0] / lengths[_],
tiebreaker.next(), word[_].data.numpy(), raws[_]))
writer.close()
cost_sents = 0
cost_words = 0
with open('temp/query.conll', 'w') as q:
while cost_sents < max_sents and cost_words < max_words and uncertain:
sample = heapq.heappop(uncertain)
if len(sample[3]) <= 5:
continue
# print(sample[0])
# print([word_alphabet.get_instance(wid) for wid in sample[2]])
print(sample[3])
to_write = []
for word in sample[3]:
if is_url(word):
word = '<_URL>'
to_write.append(word.encode('ascii', 'ignore'))
if ' '.join(to_write) in annotated:
continue
for wn, word in enumerate(to_write):
q.write('{0} {1} -- -- O\n'.format(wn + 1, word))
q.write('\n')
cost_sents += 1
cost_words += len(sample[3])
def retrain(train_path, dev_path):
network = torch.load('temp/ner_tuned.pt')
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("ner_alphabet/", None)
num_new_word = 0
with open(train_path, 'r') as f:
sents = []
sent_buffer = []
for line in f:
if len(line) <= 1:
sents.append(sent_buffer)
sent_buffer = []
else:
id, word, _, _, ner = line.strip().split()
if word_alphabet.get_index(word) == 0:
word_alphabet.add(word)
num_new_word += 1
sent_buffer.append((word_alphabet.get_index(word),
ner_alphabet.get_index(ner)))
print('{} new words.'.format(num_new_word))
init_embed = network.word_embedd.weight.data
embedd_dim = init_embed.shape[1]
init_embed = np.concatenate(
(init_embed, np.zeros((num_new_word, embedd_dim))), axis=0)
network.word_embedd = Embedding(word_alphabet.size(), embedd_dim,
torch.from_numpy(init_embed))
target_train_data = conll03_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
num_epoch = 50
batch_size = 20
num_data = sum(target_train_data[1])
num_batches = num_data / batch_size + 1
unk_replace = 0.0
optim = SGD(network.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=0.0,
nesterov=True)
for epoch in range(num_epoch):
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
target_train_data, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total,
time.time() - start_time))
torch.save(network, 'temp/ner_active.pt')
alphabet_directory = 'active_alphabet/'
word_alphabet.save(alphabet_directory)
char_alphabet.save(alphabet_directory)
pos_alphabet.save(alphabet_directory)
chunk_alphabet.save(alphabet_directory)
ner_alphabet.save(alphabet_directory)
target_dev_data = conll03_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm output.txt')
writer.start('output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(target_dev_data,
batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('output.txt')
print(acc, precision, recall, f1)
return acc, precision, recall, f1
