def evaluate(args):
# file paths
system_pred_file = args['output_file']
gold_file = args['gold_file']
save_name = args['save_name'] if args[
'save_name'] else '{}_mwt_expander.pt'.format(args['shorthand'])
model_file = os.path.join(args['save_dir'], save_name)
# load model
use_cuda = args['cuda'] and not args['cpu']
trainer = Trainer(model_file=model_file, use_cuda=use_cuda)
loaded_args, vocab = trainer.args, trainer.vocab
for k in args:
if k.endswith('_dir') or k.endswith('_file') or k in ['shorthand']:
loaded_args[k] = args[k]
logger.debug('max_dec_len: %d' % loaded_args['max_dec_len'])
# load data
logger.debug("Loading data with batch size {}...".format(
args['batch_size']))
doc = CoNLL.conll2doc(input_file=args['eval_file'])
batch = DataLoader(doc,
args['batch_size'],
loaded_args,
vocab=vocab,
evaluation=True)
if len(batch) > 0:
dict_preds = trainer.predict_dict(
batch.doc.get_mwt_expansions(evaluation=True))
# decide trainer type and run eval
if loaded_args['dict_only']:
preds = dict_preds
else:
logger.info("Running the seq2seq model...")
preds = []
for i, b in enumerate(batch):
preds += trainer.predict(b)
if loaded_args.get('ensemble_dict', False):
preds = trainer.ensemble(
batch.doc.get_mwt_expansions(evaluation=True), preds)
else:
# skip eval if dev data does not exist
preds = []
# write to file and score
doc = copy.deepcopy(batch.doc)
doc.set_mwt_expansions(preds)
CoNLL.write_doc2conll(doc, system_pred_file)
if gold_file is not None:
_, _, score = scorer.score(system_pred_file, gold_file)
logger.info("MWT expansion score: {} {:.2f}".format(
args['shorthand'], score * 100))
def train(args):
# load data
logger.debug('max_dec_len: %d' % args['max_dec_len'])
logger.debug("Loading data with batch size {}...".format(
args['batch_size']))
train_doc = CoNLL.conll2doc(input_file=args['train_file'])
train_batch = DataLoader(train_doc,
args['batch_size'],
args,
evaluation=False)
vocab = train_batch.vocab
args['vocab_size'] = vocab.size
dev_doc = CoNLL.conll2doc(input_file=args['eval_file'])
dev_batch = DataLoader(dev_doc,
args['batch_size'],
args,
vocab=vocab,
evaluation=True)
utils.ensure_dir(args['save_dir'])
save_name = args['save_name'] if args[
'save_name'] else '{}_mwt_expander.pt'.format(args['shorthand'])
model_file = os.path.join(args['save_dir'], save_name)
# pred and gold path
system_pred_file = args['output_file']
gold_file = args['gold_file']
# skip training if the language does not have training or dev data
if len(train_batch) == 0 or len(dev_batch) == 0:
logger.warning("Skip training because no data available...")
return
# train a dictionary-based MWT expander
trainer = Trainer(args=args, vocab=vocab, use_cuda=args['cuda'])
logger.info("Training dictionary-based MWT expander...")
trainer.train_dict(train_batch.doc.get_mwt_expansions(evaluation=False))
logger.info("Evaluating on dev set...")
dev_preds = trainer.predict_dict(
dev_batch.doc.get_mwt_expansions(evaluation=True))
doc = copy.deepcopy(dev_batch.doc)
doc.set_mwt_expansions(dev_preds)
CoNLL.write_doc2conll(doc, system_pred_file)
_, _, dev_f = scorer.score(system_pred_file, gold_file)
logger.info("Dev F1 = {:.2f}".format(dev_f * 100))
if args.get('dict_only', False):
# save dictionaries
trainer.save(model_file)
else:
# train a seq2seq model
logger.info("Training seq2seq-based MWT expander...")
global_step = 0
max_steps = len(train_batch) * args['num_epoch']
dev_score_history = []
best_dev_preds = []
current_lr = args['lr']
global_start_time = time.time()
format_str = '{}: step {}/{} (epoch {}/{}), loss = {:.6f} ({:.3f} sec/batch), lr: {:.6f}'
# start training
for epoch in range(1, args['num_epoch'] + 1):
train_loss = 0
for i, batch in enumerate(train_batch):
start_time = time.time()
global_step += 1
loss = trainer.update(batch, eval=False) # update step
train_loss += loss
if global_step % args['log_step'] == 0:
duration = time.time() - start_time
logger.info(format_str.format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), global_step,\
max_steps, epoch, args['num_epoch'], loss, duration, current_lr))
# eval on dev
logger.info("Evaluating on dev set...")
dev_preds = []
for i, batch in enumerate(dev_batch):
preds = trainer.predict(batch)
dev_preds += preds
if args.get('ensemble_dict', False) and args.get(
'ensemble_early_stop', False):
logger.info("[Ensembling dict with seq2seq model...]")
dev_preds = trainer.ensemble(
dev_batch.doc.get_mwt_expansions(evaluation=True),
dev_preds)
doc = copy.deepcopy(dev_batch.doc)
doc.set_mwt_expansions(dev_preds)
CoNLL.write_doc2conll(doc, system_pred_file)
_, _, dev_score = scorer.score(system_pred_file, gold_file)
train_loss = train_loss / train_batch.num_examples * args[
'batch_size'] # avg loss per batch
logger.info(
"epoch {}: train_loss = {:.6f}, dev_score = {:.4f}".format(
epoch, train_loss, dev_score))
# save best model
if epoch == 1 or dev_score > max(dev_score_history):
trainer.save(model_file)
logger.info("new best model saved.")
best_dev_preds = dev_preds
# lr schedule
if epoch > args['decay_epoch'] and dev_score <= dev_score_history[
-1]:
current_lr *= args['lr_decay']
trainer.change_lr(current_lr)
dev_score_history += [dev_score]
logger.info("Training ended with {} epochs.".format(epoch))
best_f, best_epoch = max(dev_score_history) * 100, np.argmax(
dev_score_history) + 1
logger.info("Best dev F1 = {:.2f}, at epoch = {}".format(
best_f, best_epoch))
# try ensembling with dict if necessary
if args.get('ensemble_dict', False):
logger.info("[Ensembling dict with seq2seq model...]")
dev_preds = trainer.ensemble(
dev_batch.doc.get_mwt_expansions(evaluation=True),
best_dev_preds)
doc = copy.deepcopy(dev_batch.doc)
doc.set_mwt_expansions(dev_preds)
CoNLL.write_doc2conll(doc, system_pred_file)
_, _, dev_score = scorer.score(system_pred_file, gold_file)
logger.info("Ensemble dev F1 = {:.2f}".format(dev_score * 100))
best_f = max(best_f, dev_score)
def _set_up_model(self, config, use_gpu):
self._trainer = Trainer(model_file=config['model_path'],
use_cuda=use_gpu)