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