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moving intermediate_results logging from model.py to huggingface/trainer.py (#403)

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* replacing val_loss with automl_metric
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Xueqing Liu 2022-01-14 20:26:10 -05:00 committed by GitHub
parent 569908fbe6
commit dda4ac90a1
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3 changed files with 8 additions and 6 deletions
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9
flaml/model.py
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@ -591,6 +591,8 @@ class TransformersEstimator(BaseEstimator):
num_labels=self._num_labels, num_labels=self._num_labels,
per_model_config=self._per_model_config, per_model_config=self._per_model_config,
) )
if hasattr(self._trainer, "intermediate_results"):
self._intermediate_results = self._trainer.intermediate_results
self._trainer = None self._trainer = None
def _delete_one_ckpt(self, ckpt_location): def _delete_one_ckpt(self, ckpt_location):
@ -656,7 +658,7 @@ class TransformersEstimator(BaseEstimator):
else np.argmax(predictions, axis=1) else np.argmax(predictions, axis=1)
) )
metric_dict = { metric_dict = {
"val_loss": metric_loss_score( "automl_metric": metric_loss_score(
metric_name=self._metric, y_predict=predictions, y_true=labels metric_name=self._metric, y_predict=predictions, y_true=labels
) )
} }
@ -669,10 +671,7 @@ class TransformersEstimator(BaseEstimator):
X_train=self._X_train, X_train=self._X_train,
y_train=self._y_train, y_train=self._y_train,
) )
metric_dict["val_loss"] = loss metric_dict["automl_metric"] = loss
if not hasattr(self, "intermediate_results"):
self.intermediate_results = []
self.intermediate_results.append(metric_dict)
return metric_dict return metric_dict
def _init_model_for_predict(self, X_test): def _init_model_for_predict(self, X_test):

3
flaml/nlp/huggingface/trainer.py
View File

@ -74,6 +74,9 @@ class TrainerForAuto(Seq2SeqTrainer):
ignore_keys, ignore_keys,
metric_key_prefix, metric_key_prefix,
) )
if not hasattr(self, "intermediate_results"):
self.intermediate_results = []
self.intermediate_results.append(metrics)
# if metrics: # if metrics:
# for key in list(metrics.keys()): # for key in list(metrics.keys()):
# if key.startswith("eval_"): # if key.startswith("eval_"):

2
test/nlp/test_autohf_custom_metric.py
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@ -36,7 +36,7 @@ def custom_metric(
metrics = trainer.evaluate(eval_dataset) metrics = trainer.evaluate(eval_dataset)
estimator._metric = estimator_metric_backup estimator._metric = estimator_metric_backup
return metrics.pop("eval_val_loss"), metrics return metrics.pop("eval_automl_metric"), metrics
@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os") @pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")