def _execute_trial(self, trial_number: int, repo_trial: Trial,
context: ExecutionContext,
validation_splits: List[Tuple[DataContainer,
DataContainer]]):
for training_data_container, validation_data_container in validation_splits:
p = copy.deepcopy(self.pipeline)
p.update_hyperparams(repo_trial.hyperparams)
repo_trial.set_hyperparams(p.get_hyperparams())
with repo_trial.new_validation_split(p) as repo_trial_split:
trial_split_description = self._get_trial_split_description(
repo_trial=repo_trial,
repo_trial_split=repo_trial_split,
validation_splits=validation_splits,
trial_number=trial_number)
self.print_func(
'fitting trial {}'.format(trial_split_description))
repo_trial_split = self.trainer.fit_trial_split(
trial_split=repo_trial_split,
train_data_container=training_data_container,
validation_data_container=validation_data_container,
context=context)
repo_trial_split.set_success()
self.print_func('success trial {} score: {}'.format(
trial_split_description,
repo_trial_split.get_validation_score()))
return repo_trial_split
def execute_trial(self,
pipeline: BaseStep,
trial_number: int,
repo_trial: Trial,
context: ExecutionContext,
validation_splits: List[Tuple[DataContainer,
DataContainer]],
n_trial: int,
delete_pipeline_on_completion: bool = True):
"""
Train pipeline using the validation splitter.
Track training, and validation metrics for each epoch.
:param pipeline: pipeline to train on
:param trial_number: trial number
:param repo_trial: repo trial
:param validation_splits: validation splits
:param context: execution context
:param n_trial: total number of trials that will be executed
:param delete_pipeline_on_completion: bool to delete pipeline on completion or not
:return: executed trial split
"""
for training_data_container, validation_data_container in validation_splits:
p = copy.deepcopy(pipeline)
p.update_hyperparams(repo_trial.hyperparams)
repo_trial.set_hyperparams(p.get_hyperparams())
repo_trial_split: TrialSplit = repo_trial.new_validation_split(
pipeline=p,
delete_pipeline_on_completion=delete_pipeline_on_completion)
with repo_trial_split:
trial_split_description = _get_trial_split_description(
repo_trial=repo_trial,
repo_trial_split_number=repo_trial_split.split_number,
validation_splits=validation_splits,
trial_number=trial_number,
n_trial=n_trial)
context.logger.info(
'fitting trial {}'.format(trial_split_description))
repo_trial_split = self.fit_trial_split(
trial_split=repo_trial_split,
train_data_container=training_data_container,
validation_data_container=validation_data_container,
context=context)
repo_trial_split.set_success()
context.logger.info(
'success trial {}\nbest score: {} at epoch {}'.format(
trial_split_description,
repo_trial_split.get_best_validation_score(),
repo_trial_split.get_n_epochs_to_best_validation_score(
)))
return repo_trial_split
