def evaluate_trainer()

in main.py [0:0]

• 39 lines of code
• 5 McCabe index (conditional complexity)

def evaluate_trainer(trainer, args, datasets, is_trnsf, name, **kwargs):
    """Evaluate trainers on the train and test dataset."""

    logger.info(f"Evaluating {name} ...")

    if is_trnsf:
        evaluator = eval_trnsf
    else:
        evaluator = eval_corr_gen if args.is_correlation else eval_clf

    chckpnt_paths = get_chckpnt_paths(args, name)
    tensorboard_dir = get_tensorboard_paths(args, name)
    trainers = {"best": trainer}

    is_append = False
    for epoch, trainer in trainers.items():
        # Test Evaluation
        eval_trainer_log(
            trainer,
            datasets["test"],
            args.csv_score_pattern,
            chckpnt_paths,
            dict(args.hyperparameters),
            evaluator=evaluator,
            tensorboard_dir=tensorboard_dir,
            epoch=epoch,
            is_append=is_append,
            **kwargs,
        )
        # only create the field for the first time you log
        is_append = True

        # evaluation should be made on the training without any addition (e.g. anti generalization)
        train_data = datasets.get("train_unmodified", datasets["train"])

        # Train Evaluation
        eval_trainer_log(
            trainer,
            train_data,
            args.csv_score_pattern,
            chckpnt_paths,
            dict(args.hyperparameters),
            evaluator=evaluator,
            tensorboard_dir=None,
            is_append=is_append,
            mode="train",
            file_clf_rep="train_" + FILE_CLF_REP,
            epoch=epoch,
            **kwargs,
        )