def batchify_training_data(self, xy_ids_and_targets, batch_size, is_dev_data):
logger.debug('Batchifying data')
# sort according to the src lengths -> xy pairs
sorted_data = sorted(zip(*xy_ids_and_targets), key=lambda p: len(p[0]), reverse=True)
data_size = len(sorted_data)
num_batches = data_size // batch_size
data_indices = self.index_data(data_size, mode='no_shuffling')
batch_pairs = []
batch_forms_all = []
for bi in range(num_batches+1): # including the last (smaller) batch
batch_data_tuple, batch_forms = self.make_one_batch(sorted_data, data_indices, bi, batch_size)
batch_pairs.append(batch_data_tuple)
batch_forms_all.extend(batch_forms)
if is_dev_data:
self.dev_references = batch_forms_all
logger.info('Saving dev (training) references to --> %s', self.fnames.dev_ref_fn)
save_txt(self.dev_references, self.fnames.dev_ref_fn)
assert data_size == len(batch_forms_all)
return batch_pairs
def save_predictions(self, predictions, fname):
assert type(predictions[0]) == str, \
'Predictions are not strings -- consider re-implementing the method!'
logger.debug('Saving Morph predictions to --> %s', fname)
save_txt(predictions, fname)
def batchify_training_data(self, xy_ids, batch_size, is_dev_data):
logger.debug('Batchifying data')
x_data_ids, y_data_ids, forms = xy_ids
data_size = len(x_data_ids)
num_batches = data_size // batch_size
data_indices = self.index_data(data_size, mode='no_shuffling')
batch_pairs = []
for bi in range(num_batches + 1):
batch_x = []
batch_y = []
curr_batch_indices = data_indices[bi * batch_size:(bi + 1) *
batch_size]
for idx in curr_batch_indices:
x_ids = x_data_ids[idx]
y_ids = y_data_ids[idx]
x_enc_ids_copy = copy.deepcopy(x_ids)
batch_x.append(x_enc_ids_copy)
y_ids_copy = copy.deepcopy(y_ids)
batch_y.append(y_ids_copy)
batch_enc_x_var = cuda_if_gpu(Variable(torch.LongTensor(batch_x)))
batch_dec_y_var = cuda_if_gpu(Variable(torch.LongTensor(batch_y)))
batch_pairs.append((batch_enc_x_var, batch_dec_y_var))
if is_dev_data:
self.dev_references = forms
logger.info('Saving dev (training) references to --> %s',
self.fnames.dev_ref_fn)
save_txt(self.dev_references, self.fnames.dev_ref_fn)
assert data_size == len(forms)
return batch_pairs
def save_predictions(self, depgraphs, fname):
predicted_snts = []
random_dg = depgraphs[0]
if 'PRED_FORM' not in random_dg.node['1']:
logger.debug('*** USING ORACLE FORMS *** ')
for dg in depgraphs:
snt_tokens = [
SynAlgo.get_node_gold_form(dg, node_id)
for node_id in dg.graph['node_order']
]
predicted_snts.append(' '.join(snt_tokens))
else:
logger.debug('*** USING PREDICTED FORMS *** ')
for dg in depgraphs:
snt_tokens = [
SynAlgo.get_node_pred_form(dg, node_id)
for node_id in dg.graph['node_order']
]
predicted_snts.append(' '.join(snt_tokens))
logger.debug('Saving Syn predictions to --> %s', fname)
save_txt(predicted_snts, fname)
def training_start(self, model, data, evaluator, nlgen):
logger.debug("Preparing training data")
dev_data_fname = data.fnames.dev_fn
assert os.path.exists(dev_data_fname), logger.error(
'File %s does not exist', dev_data_fname)
# dev data for evaluation
dev_data_ref_fname = data.fnames.dev_ref_fn
dev_data_raw = read_conll_data_file(data.fnames.dev_fn)
logger.info('Saving Syn reference --> %s', data.fnames.dev_ref_fn)
save_txt(itemlist=conll2snt(dev_data_raw), fname=dev_data_ref_fname)
train_batches = data.batchify_vectorized_data(
data.train, self.batch_size) # [(np_x, np_y_1hot), ...]
dev_batches = data.batchify_vectorized_data(data.dev, self.batch_size)
# need to move the model before setting the optimizer
# see: http://pytorch.org/docs/stable/optim.html
if self.use_cuda:
model.cuda()
self.set_optimizer(model, self.config['optimizer'])
self.set_train_criterion(len(data.vocab.id2tok), PAD_ID)
training_start_time = time.time()
logger.info("Start training")
best_score = 0
best_model_fn = None
best_weights = None
for epoch_idx in range(1, self.n_epochs + 1):
epoch_start = time.time()
logger.info('Epoch %d/%d', epoch_idx, self.n_epochs)
# compute loss on train and dev data
train_loss = self.train_epoch(epoch_idx, model, train_batches)
dev_loss = self.compute_val_loss(model, dev_batches)
evaluator.record_loss(train_loss, dev_loss)
# run on dev data in prediction mode (no oracle decoding)
predictions_fname = self.get_predictions_fname(epoch_idx)
depgraphs = nlgen.predict_from_raw_data(model, dev_data_raw,
data.vocab)
nlgen.save_predictions(depgraphs, predictions_fname)
# evaluate using metrics
scores = evaluator.external_metric_eval(ref_fn=dev_data_ref_fname,
pred_fn=predictions_fname)
avg_score = (scores.bleu + scores.edist) / 2
model_fn = os.path.join(
self.model_dir, 'weights.epoch%d_%0.3f_%0.3f' %
(epoch_idx, scores.bleu, scores.edist))
if avg_score > best_score:
best_score = avg_score
best_model_fn = model_fn
best_weights = model.state_dict()
logger.debug('Time = %s', asMinutes(time.time() - epoch_start))
logger.info('Total training time=%s' %
(asMinutes(time.time() - training_start_time)))
self.best_model_fn = best_model_fn
logger.debug('Saving model to --> %s', best_model_fn)
torch.save(best_weights, best_model_fn)
score_fname = os.path.join(self.model_dir, 'scores.csv')
scores = evaluator.get_scores_to_save()
evaluator.save_scores(scores, self.score_file_header, score_fname)
evaluator.plot_lcurve(fname=os.path.join(self.model_dir, "lcurve.pdf"),
title=self.model_type)
def save_dev_references(self, fname):
logger.debug('Saving Morph references to --> %s', fname)
save_txt(self.targets, fname)
