8000 Preserve best metrics by DeNeutoy · Pull Request #1504 · allenai/allennlp · GitHub
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Preserve best metrics #1504

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merged 6 commits into from
Jul 20, 2018
Merged

Preserve best metrics #1504

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47b365c
save the best validation metrics for all metrics
Jul 17, 2018
815ce88
lint
Jul 17, 2018
bc7aa45
remove print
Jul 17, 2018
898686e
Merge branch 'master' into preserve-best-metrics
DeNeutoy Jul 17, 2018
9bd1e26
Merge branch 'master' into preserve-best-metrics
DeNeutoy Jul 20, 2018
1b2766c
Merge branch 'master' into preserve-best-metrics
DeNeutoy Jul 20, 2018
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8 changes: 8 additions & 0 deletions allennlp/tests/training/trainer_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,10 @@ def test_trainer_can_run(self):
metrics = trainer.train()
assert 'best_validation_loss' in metrics
assert isinstance(metrics['best_validation_loss'], float)
assert 'best_validation_accuracy' in metrics
assert isinstance(metrics['best_validation_accuracy'], float)
assert 'best_validation_accuracy3' in metrics
assert isinstance(metrics['best_validation_accuracy3'], float)
assert 'best_epoch' in metrics
assert isinstance(metrics['best_epoch'], int)

Expand All @@ -67,6 +71,10 @@ def test_trainer_can_run(self):
metrics = trainer.train()
assert 'best_validation_loss' in metrics
assert isinstance(metrics['best_validation_loss'], float)
assert 'best_validation_accuracy' in metrics
assert isinstance(metrics['best_validation_accuracy'], float)
assert 'best_validation_accuracy3' in metrics
assert isinstance(metrics['best_validation_accuracy3'], float)
assert 'best_epoch' in metrics
assert isinstance(metrics['best_epoch'], int)

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7 changes: 5 additions & 2 deletions allennlp/training/trainer.py
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Expand Up @@ -706,6 +706,7 @@ def train(self) -> Dict[str, Any]:

train_metrics: Dict[str, float] = {}
val_metrics: Dict[str, float] = {}
best_epoch_val_metrics: Dict[str, float] = {}
epochs_trained = 0
training_start_time = time.time()
for epoch in range(epoch_counter, self._num_epochs):
Expand All @@ -723,7 +724,8 @@ def train(self) -> Dict[str, Any]:

# Check validation metric to see if it's the best so far
is_best_so_far = self._is_best_so_far(this_epoch_val_metric, validation_metric_per_epoch)

if is_best_so_far:
best_epoch_val_metrics = val_metrics.copy()
validation_metric_per_epoch.append(this_epoch_val_metric)
if self._should_stop_early(validation_metric_per_epoch):
logger.info("Ran out of patience. Stopping training.")
Expand All @@ -733,6 +735,7 @@ def train(self) -> Dict[str, Any]:
# No validation set, so just assume it's the best so far.
is_best_so_far = True
val_metrics = {}
best_epoch_val_metrics = {}
this_epoch_val_metric = None

self._save_checkpoint(epoch, validation_metric_per_epoch, is_best=is_best_so_far)
Expand Down Expand Up @@ -773,7 +776,7 @@ def train(self) -> Dict[str, Any]:
best_validation_metric = min(validation_metric_per_epoch)
else:
best_validation_metric = max(validation_metric_per_epoch)
metrics[f"best_validation_{self._validation_metric}"] = best_validation_metric
metrics.update({f"best_validation_{k}": v for k, v in best_epoch_val_metrics.items()})
metrics['best_epoch'] = [i for i, value in enumerate(validation_metric_per_epoch)
if value == best_validation_metric][-1]
return metrics
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0