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.metric_name is None:
validation_scores = trial_split.get_validation_scores()
else :
validation_scores = trial_split.get_metric_validation_results(self.metric_name)
if len(validation_scores) > self.n_epochs_without_improvement:
higher_score_is_better = trial_split.is_higher_score_better()
if (higher_score_is_better) and \
all(validation_scores[-self.n_epochs_without_improvement] >= v for v in
validation_scores[-self.n_epochs_without_improvement:]):
return True
elif (not higher_score_is_better) and \
all(validation_scores[-self.n_epochs_without_improvement] <= v for v in
validation_scores[-self.n_epochs_without_improvement:]):
return True
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):
validation_scores = trial.get_validation_scores()
if len(validation_scores) > self.n_epochs_without_improvement:
higher_score_is_better = trial.is_higher_score_better()
if validation_scores[-1] == 0:
return False
if higher_score_is_better:
if validation_scores[-2] >= validation_scores[-1]:
self.epochs_without_improvement += 1
else:
self.epochs_without_improvement = 0
if not higher_score_is_better:
if validation_scores[-2] <= validation_scores[-1]:
self.epochs_without_improvement += 1
else:
self.epochs_without_improvement = 0
if self.epochs_without_improvement == self.n_epochs_without_improvement:
self.epochs_without_improvement = 0
return True
return False