def train(self):
train_data = StreamingSparseDataset(self.train_features, self.train_labels)
train_dataloader = DataLoader(train_data, shuffle=True, batch_size=self.args.batch_size)
print("Number of examples: ", len(self.train_labels))
print("Batch size:", self.args.batch_size)
for epoch in trange(int(self.args.epochs), desc="Epoch"):
self.train_epoch(train_dataloader)
if self.args.evaluate_dev:
dev_evaluator = BagOfWordsEvaluator(self.model, self.vectorizer, self.processor, self.args, split='dev')
dev_precision, dev_recall, dev_f1, dev_acc, dev_loss = dev_evaluator.get_scores()[0][:5]
# Print validation results
tqdm.write(self.log_header)
tqdm.write(self.log_template.format(epoch + 1, self.nb_train_steps, epoch + 1, self.args.epochs,
dev_acc, dev_precision, dev_recall, dev_f1, dev_loss))
# Update validation results
if dev_f1 > self.best_dev_f1:
self.unimproved_iters = 0
self.best_dev_f1 = dev_f1
torch.save(self.model, self.snapshot_path)
else:
self.unimproved_iters += 1
if self.unimproved_iters >= self.args.patience:
self.early_stop = True
tqdm.write("Early Stopping. Epoch: {}, Best Dev F1: {}".format(epoch, self.best_dev_f1))
break
# save model at end of training
# when evaluating on test
if self.args.evaluate_test:
torch.save(self.model, self.snapshot_path)
def evaluate_split(model, vectorizer, processor, args, split='dev'):
evaluator = BagOfWordsEvaluator(model, vectorizer, processor, args, split)
accuracy, precision, recall, f1, avg_loss = evaluator.get_scores(
silent=True)[0]
print('\n' + LOG_HEADER)
print(
LOG_TEMPLATE.format(split.upper(), accuracy, precision, recall, f1,
avg_loss))
def evaluate_split(model, vectorizer, processor, args, save_file, split='dev'):
evaluator = BagOfWordsEvaluator(model, vectorizer, processor, args, split)
scores, score_names = evaluator.get_scores(silent=True)
p_micro, r_micro, f1_micro, accuracy, avg_loss = scores[:5]
print('\n' + LOG_HEADER)
print(
LOG_TEMPLATE.format(split.upper(), accuracy, p_micro, r_micro,
f1_micro, avg_loss))
scores_dict = dict(zip(score_names, scores))
with open(save_file, 'w') as f:
f.write(json.dumps(scores_dict))