def fit_trial_split(self, trial_split: TrialSplit,
train_data_container: DataContainer,
validation_data_container: DataContainer,
context: ExecutionContext) -> TrialSplit:
"""
Train pipeline using the training data container.
Track training, and validation metrics for each epoch.
:param train_data_container: train data container
:param validation_data_container: validation data container
:param trial_split: trial to execute
:param context: execution context
:return: executed trial
"""
for i in range(self.epochs):
context.logger.info('epoch {}/{}'.format(i + 1, self.epochs))
trial_split = trial_split.fit_trial_split(
train_data_container.copy(),
context.copy().set_execution_phase(ExecutionPhase.TRAIN))
y_pred_train = trial_split.predict_with_pipeline(
train_data_container.copy(),
context.copy().set_execution_phase(ExecutionPhase.VALIDATION))
y_pred_val = trial_split.predict_with_pipeline(
validation_data_container.copy(),
context.copy().set_execution_phase(ExecutionPhase.VALIDATION))
if self.callbacks.call(
trial_split=trial_split,
epoch_number=i,
total_epochs=self.epochs,
input_train=train_data_container,
pred_train=y_pred_train,
input_val=validation_data_container,
pred_val=y_pred_val,
context=context.copy().set_execution_phase(
ExecutionPhase.VALIDATION),
is_finished_and_fitted=False,
):
break
# Saves the metrics
trial_split.save_parent_trial()
return trial_split
def fit_trial_split(
self,
trial_split: TrialSplit,
train_data_container: DataContainer,
validation_data_container: DataContainer,
context: ExecutionContext
) -> TrialSplit:
"""
Train pipeline using the training data container.
Track training, and validation metrics for each epoch.
:param train_data_container: train data container
:param validation_data_container: validation data container
:param trial_split: trial to execute
:param context: execution context
:return: executed trial
"""
early_stopping = False
for i in range(self.epochs):
self.print_func('\nepoch {}/{}'.format(i + 1, self.epochs))
trial_split = trial_split.fit_trial_split(train_data_container.copy(), context)
y_pred_train = trial_split.predict_with_pipeline(train_data_container.copy(), context)
y_pred_val = trial_split.predict_with_pipeline(validation_data_container.copy(), context)
if self.callbacks.call(
trial=trial_split,
epoch_number=i,
total_epochs=self.epochs,
input_train=train_data_container,
pred_train=y_pred_train,
input_val=validation_data_container,
pred_val=y_pred_val,
is_finished_and_fitted=early_stopping
):
break
return trial_split