def evaluate(args):
# file paths
system_pred_file = args['output_file']
gold_file = args['gold_file']
model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \
else '{}/{}_tagger.pt'.format(args['save_dir'], args['shorthand'])
pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'],
args['save_name'])
# load pretrain
pretrain = Pretrain(pretrain_file)
# load model
print("Loading model from: {}".format(model_file))
use_cuda = args['cuda'] and not args['cpu']
trainer = Trainer(args=args,
pretrain=pretrain,
model_file=model_file,
use_cuda=use_cuda)
loaded_args, vocab = trainer.args, trainer.vocab
# load config
for k in args:
if k.endswith('_dir') or k.endswith('_file') or k in ['shorthand'
] or k == 'mode':
loaded_args[k] = args[k]
# load data
print("Loading data with batch size {}...".format(args['batch_size']))
batch = DataLoader(args['eval_file'],
args['batch_size'],
loaded_args,
pretrain,
vocab=vocab,
evaluation=True)
if len(batch) > 0:
print("Start evaluation...")
preds = []
for i, b in enumerate(batch):
preds += trainer.predict(b)
else:
# skip eval if dev data does not exist
preds = []
# write to file and score
batch.conll.set(['upos', 'xpos', 'feats'], [y for x in preds for y in x])
batch.conll.write_conll(system_pred_file)
if gold_file is not None:
_, _, score = scorer.score(system_pred_file, gold_file)
print("Tagger score:")
print("{} {:.2f}".format(args['shorthand'], score * 100))
def evaluate(args):
# file paths
system_pred_file = args['output_file']
gold_file = args['gold_file']
model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \
else '{}/{}_tagger.pt'.format(args['save_dir'], args['shorthand'])
pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'], args['shorthand']) if args['pretrain_file'] is None \
else args['pretrain_file']
pretrain = Pretrain(pretrain_file)
# load model
print("Loading model from: {}".format(model_file))
use_cuda = args['cuda'] and not args['cpu']
trainer = Trainer(args=args, pretrain=pretrain, model_file=model_file, use_cuda=use_cuda)
loaded_args, vocab = trainer.args, trainer.vocab
# load config
for k in args:
if k.endswith('_dir') or k.endswith('_file') or k in ['shorthand'] or k == 'mode':
loaded_args[k] = args[k]
# load data
print("Loading data with batch size {}...".format(args['batch_size']))
doc, metasentences = CoNLL.conll2dict(input_file=args['eval_file'])
doc = Document(doc, metasentences=metasentences)
batch = DataLoader(doc, args['batch_size'], loaded_args, pretrain, vocab=vocab, evaluation=True, sort_during_eval=True)
if len(batch) > 0:
print("Start evaluation...")
preds = []
for i, b in enumerate(batch):
preds += trainer.predict(b)
else:
# skip eval if dev data does not exist
preds = []
preds = utils.unsort(preds, batch.data_orig_idx)
# write to file and score
batch.doc.set([UPOS, XPOS, FEATS], [y for x in preds for y in x])
CoNLL.dict2conll(batch.doc.to_dict(), system_pred_file)
if gold_file is not None:
_, _, score = scorer.score(system_pred_file, gold_file)
print("Tagger score:")
print("{} {:.2f}".format(args['shorthand'], score*100))
def train(args):
utils.ensure_dir(args['save_dir'])
model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \
else '{}/{}_tagger.pt'.format(args['save_dir'], args['shorthand'])
# load pretrained vectors
vec_file = args['wordvec_file']
pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'], args['shorthand']) if args['pretrain_file'] is None \
else args['pretrain_file']
pretrain = Pretrain(pretrain_file, vec_file, args['pretrain_max_vocab'])
# load data
print("Loading data with batch size {}...".format(args['batch_size']))
doc, metasentences = CoNLL.conll2dict(input_file=args['train_file'])
train_doc = Document(doc, metasentences=metasentences)
train_batch = DataLoader(train_doc, args['batch_size'], args, pretrain, evaluation=False)
vocab = train_batch.vocab
doc, metasentences = CoNLL.conll2dict(input_file=args['eval_file'])
dev_doc = Document(doc, metasentences=metasentences)
dev_batch = DataLoader(dev_doc, args['batch_size'], args, pretrain, vocab=vocab, evaluation=True, sort_during_eval=True)
# 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:
print("Skip training because no data available...")
sys.exit(0)
print("Training tagger...")
trainer = Trainer(args=args, vocab=vocab, pretrain=pretrain, use_cuda=args['cuda'])
global_step = 0
max_steps = args['max_steps']
dev_score_history = []
best_dev_preds = []
current_lr = args['lr']
global_start_time = time.time()
format_str = '{}: step {}/{}, loss = {:.6f} ({:.3f} sec/batch), lr: {:.6f}'
if args['adapt_eval_interval']:
args['eval_interval'] = utils.get_adaptive_eval_interval(dev_batch.num_examples, 2000, args['eval_interval'])
print("Evaluating the model every {} steps...".format(args['eval_interval']))
using_amsgrad = False
last_best_step = 0
# start training
train_loss = 0
while True:
do_break = False
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
print(format_str.format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), global_step,\
max_steps, loss, duration, current_lr))
if global_step % args['eval_interval'] == 0:
# eval on dev
print("Evaluating on dev set...")
dev_preds = []
for batch in dev_batch:
preds = trainer.predict(batch)
dev_preds += preds
dev_preds = utils.unsort(dev_preds, dev_batch.data_orig_idx)
dev_batch.doc.set([UPOS, XPOS, FEATS], [y for x in dev_preds for y in x])
CoNLL.dict2conll(dev_batch.doc.to_dict(), system_pred_file)
_, _, dev_score = scorer.score(system_pred_file, gold_file)
train_loss = train_loss / args['eval_interval'] # avg loss per batch
print("step {}: train_loss = {:.6f}, dev_score = {:.4f}".format(global_step, train_loss, dev_score))
train_loss = 0
# save best model
if len(dev_score_history) == 0 or dev_score > max(dev_score_history):
last_best_step = global_step
trainer.save(model_file)
print("new best model saved.")
best_dev_preds = dev_preds
dev_score_history += [dev_score]
print("")
if global_step - last_best_step >= args['max_steps_before_stop']:
if not using_amsgrad:
print("Switching to AMSGrad")
last_best_step = global_step
using_amsgrad = True
trainer.optimizer = optim.Adam(trainer.model.parameters(), amsgrad=True, lr=args['lr'], betas=(.9, args['beta2']), eps=1e-6)
else:
do_break = True
break
if global_step >= args['max_steps']:
do_break = True
break
if do_break: break
train_batch.reshuffle()
print("Training ended with {} steps.".format(global_step))
best_f, best_eval = max(dev_score_history)*100, np.argmax(dev_score_history)+1
print("Best dev F1 = {:.2f}, at iteration = {}".format(best_f, best_eval * args['eval_interval']))