class Predictor:
"""Wrapper class so model can sit in memory for multiple predictions"""
def __init__(self, args=None, feature_config=None):
if args is None:
args = DEFAULT_PARSER_ARGS.copy()
if feature_config is None:
self.feature_config = FEATURE_CONFIG.copy()
model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \
else '{}/{}_parser.pt'.format(args['save_dir'], args['shorthand'])
# load pretrain; note that we allow the pretrain_file to be non-existent
pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'],
args['shorthand'])
self.pretrain = Pretrain(pretrain_file)
# load model
print("Loading model from: {}".format(model_file))
use_cuda = args['cuda'] and not args['cpu']
self.trainer = Trainer(pretrain=self.pretrain,
model_file=model_file,
use_cuda=use_cuda)
self.loaded_args, self.vocab = self.trainer.args, self.trainer.vocab
self.batch_size = args['batch_size']
# load config
for k in args:
if k.endswith('_dir') or k.endswith('_file') or k in [
'shorthand'
] or k == 'mode':
self.loaded_args[k] = args[k]
def predict(self, eval_file_or_string):
eval_file = _read_conllu_arg(eval_file_or_string,
self.feature_config,
predict=True)
doc = Document(CoNLL.conll2dict(input_file=eval_file))
batch = DataLoader(doc,
self.batch_size,
self.loaded_args,
self.pretrain,
vocab=self.vocab,
evaluation=True,
sort_during_eval=True)
preds = []
if len(batch) > 0:
for i, b in enumerate(batch):
preds += self.trainer.predict(b)
preds = utils.unsort(preds, batch.data_orig_idx)
batch.doc.set([HEAD, DEPREL], [y for x in preds for y in x])
doc_conll = CoNLL.convert_dict(batch.doc.to_dict())
conll_string = CoNLL.conll_as_string(doc_conll)
return conll_string
def evaluate(args):
# file paths
system_pred_file = args['output_file']
gold_file = args['gold_file']
model_file = model_file_name(args)
# load pretrained vectors if needed
pretrain = load_pretrain(args)
# load model
logger.info("Loading model from: {}".format(model_file))
use_cuda = args['cuda'] and not args['cpu']
trainer = Trainer(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
logger.info("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,
pretrain,
vocab=vocab,
evaluation=True,
sort_during_eval=True)
if len(batch) > 0:
logger.info("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([HEAD, DEPREL], [y for x in preds for y in x])
CoNLL.write_doc2conll(batch.doc, system_pred_file)
if gold_file is not None:
_, _, score = scorer.score(system_pred_file, gold_file)
logger.info("Parser score:")
logger.info("{} {:.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 '{}/{}_parser.pt'.format(args['save_dir'], args['shorthand'])
# load pretrain; note that we allow the pretrain_file to be non-existent
pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'], args['shorthand'])
pretrain = Pretrain(pretrain_file)
# load model
print("Loading model from: {}".format(model_file))
use_cuda = args['cuda'] and not args['cpu']
trainer = Trainer(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 = Document(CoNLL.conll2dict(input_file=args['eval_file']))
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([HEAD, DEPREL], [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("Parser score:")
print("{} {:.2f}".format(args['shorthand'], score*100))
def train(args):
model_file = model_file_name(args)
utils.ensure_dir(os.path.split(model_file)[0])
# load pretrained vectors if needed
pretrain = load_pretrain(args)
# load data
logger.info("Loading data with batch size {}...".format(
args['batch_size']))
train_data, _ = CoNLL.conll2dict(input_file=args['train_file'])
# possibly augment the training data with some amount of fake data
# based on the options chosen
logger.info("Original data size: {}".format(len(train_data)))
train_data.extend(
augment_punct(train_data,
args['augment_nopunct'],
keep_original_sentences=False))
logger.info("Augmented data size: {}".format(len(train_data)))
train_doc = Document(train_data)
train_batch = DataLoader(train_doc,
args['batch_size'],
args,
pretrain,
evaluation=False)
vocab = train_batch.vocab
dev_doc = CoNLL.conll2doc(input_file=args['eval_file'])
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:
logger.info("Skip training because no data available...")
sys.exit(0)
logger.info("Training parser...")
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 = 'Finished STEP {}/{}, loss = {:.6f} ({:.3f} sec/batch), lr: {:.6f}'
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
logger.info(
format_str.format(global_step, max_steps, loss, duration,
current_lr))
if global_step % args['eval_interval'] == 0:
# eval on dev
logger.info("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([HEAD, DEPREL],
[y for x in dev_preds for y in x])
CoNLL.write_doc2conll(dev_batch.doc, system_pred_file)
_, _, dev_score = scorer.score(system_pred_file, gold_file)
train_loss = train_loss / args[
'eval_interval'] # avg loss per batch
logger.info(
"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)
logger.info("new best model saved.")
best_dev_preds = dev_preds
dev_score_history += [dev_score]
if global_step - last_best_step >= args['max_steps_before_stop']:
if not using_amsgrad:
logger.info("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()
logger.info("Training ended with {} steps.".format(global_step))
best_f, best_eval = max(dev_score_history) * 100, np.argmax(
dev_score_history) + 1
logger.info("Best dev F1 = {:.2f}, at iteration = {}".format(
best_f, best_eval * args['eval_interval']))
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 '{}/{}_parser.pt'.format(args['save_dir'], args['shorthand'])
# load pretrained vectors if needed
pretrain = None
if args['pretrain']:
vec_file = args['wordvec_file'] if args['wordvec_file'] else utils.get_wordvec_file(args['wordvec_dir'], args['shorthand'])
pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'], args['shorthand'])
pretrain = Pretrain(pretrain_file, vec_file, args['pretrain_max_vocab'])
# load data
print("Loading data with batch size {}...".format(args['batch_size']))
train_doc = Document(CoNLL.conll2dict(input_file=args['train_file']))
train_batch = DataLoader(train_doc, args['batch_size'], args, pretrain, evaluation=False)
vocab = train_batch.vocab
dev_doc = Document(CoNLL.conll2dict(input_file=args['eval_file']))
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 parser...")
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}'
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([HEAD, DEPREL], [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']))