def call(self, trial_split: TrialSplit, epoch_number: int, total_epochs: int, input_train: DataContainer,
pred_train: DataContainer, input_val: DataContainer, pred_val: DataContainer, context: ExecutionContext,
is_finished_and_fitted: bool):
if self.pass_context_to_metric_function:
train_score = self.metric_function(pred_train.expected_outputs, pred_train.data_inputs, context=context)
validation_score = self.metric_function(pred_val.expected_outputs, pred_val.data_inputs, context=context)
else:
train_score = self.metric_function(pred_train.expected_outputs, pred_train.data_inputs)
validation_score = self.metric_function(pred_val.expected_outputs, pred_val.data_inputs)
trial_split.add_metric_results_train(
name=self.name,
score=train_score,
higher_score_is_better=self.higher_score_is_better,
log_metric=self.log_metrics
)
trial_split.add_metric_results_validation(
name=self.name,
score=validation_score,
higher_score_is_better=self.higher_score_is_better,
log_metric=self.log_metrics
)
return False
def call(self, trial: TrialSplit, epoch_number: int, total_epochs: int,
input_train: DataContainer, pred_train: DataContainer,
input_val: DataContainer, pred_val: DataContainer,
is_finished_and_fitted: bool):
train_score = self.metric_function(pred_train.expected_outputs,
pred_train.data_inputs)
validation_score = self.metric_function(pred_val.expected_outputs,
pred_val.data_inputs)
trial.add_metric_results_train(
name=self.name,
score=train_score,
higher_score_is_better=self.higher_score_is_better)
trial.add_metric_results_validation(
name=self.name,
score=validation_score,
higher_score_is_better=self.higher_score_is_better)
if self.print_metrics:
self.print_function('{} train: {}'.format(self.name, train_score))
self.print_function('{} validation: {}'.format(
self.name, validation_score))
return False
