def train(self, train_set, dev_set):
self.iterations, self.nb_tr_steps, self.tr_loss = 0, 0, 0
self.best_valid_metric, self.unimproved_iters = 0, 0
self.early_stop = False
if self.args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(self.args.gradient_accumulation_steps))
self.args.batch_size = (self.args.batch_size //
self.args.gradient_accumulation_steps)
self.init_optimizer()
train_dataset = convert_df_to_ids(train_set, self.word2id,
self.args.max_seq_length)
dev_dataset = convert_df_to_ids(dev_set, self.word2id,
self.args.max_seq_length)
train_dataloader = DataLoader(
train_dataset,
sampler=RandomSampler(train_dataset),
batch_size=self.args.batch_size,
)
dev_dataloader = DataLoader(
dev_dataset,
sampler=SequentialSampler(dev_dataset),
batch_size=self.args.batch_size,
)
for epoch in tqdm(range(int(self.args.epochs))):
self.tr_loss = self.train_an_epoch(train_dataloader)[0]
tqdm.write(f"[Epoch {epoch}] loss: {self.tr_loss}".format(
epoch, self.best_valid_metric))
self.tr_loss = 0
eval_result = self.eval(dev_dataloader)
# Update validation results
if eval_result[self.args.valid_metric] > self.best_valid_metric:
self.unimproved_iters = 0
self.best_valid_metric = eval_result[self.args.valid_metric]
print_dict_as_table(
eval_result,
tag=f"[Epoch {epoch}]performance on validation set",
columns=["metrics", "values"],
)
ensureDir(self.args.model_save_dir)
self.save_pretrained(self.args.model_save_dir)
else:
self.unimproved_iters += 1
if self.unimproved_iters >= self.args.patience:
self.early_stop = True
tqdm.write(
"Early Stopping. Epoch: {}, best_valid_metric ({}): {}"
.format(epoch, self.args.valid_metric,
self.best_valid_metric))
break
def predict(self, test_set):
"""
Args:
test_set: list of :obj:InputExample
Returns:
ndarray: An array of predicted label scores.
"""
test_dataset = convert_df_to_ids(test_set, self.word2id,
self.args.max_seq_length)
test_dataloader = DataLoader(
test_dataset,
sampler=SequentialSampler(test_dataset),
batch_size=self.args.batch_size,
)
return self.scores(test_dataloader)[0]
def test(self, test_set):
"""Get a evaluation result for a test set.
Args:
test_set:
Returns:
"""
test_dataset = convert_df_to_ids(test_set, self.word2id,
self.args.max_seq_length)
test_dataloader = DataLoader(
test_dataset,
sampler=SequentialSampler(test_dataset),
batch_size=self.args.batch_size,
)
return self.eval(test_dataloader)