04b10c6b55f64ec4a01e8a09fcb2f44760486e8c,ludwig/experiment.py,,experiment,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,50

Before Change

    )
    if is_on_master():
        logging.info("Training set: {0}".format(training_set.size))
        logging.info("Validation set: {0}".format(validation_set.size))
        logging.info("Test set: {0}".format(test_set.size))

    // update model definition with metadata properties
    update_model_definition_with_metadata(model_definition, train_set_metadata)

After Change

    )
    if is_on_master():
        logging.info("Training set: {0}".format(training_set.size))
        if validation_set is not None:
            logging.info("Validation set: {0}".format(validation_set.size))
        if test_set is not None:
            logging.info("Test set: {0}".format(test_set.size))

    // update model definition with metadata properties
    update_model_definition_with_metadata(model_definition, train_set_metadata)

    // run the experiment
    model, training_results = train(
        training_set=training_set,
        validation_set=validation_set,
        test_set=test_set,
        model_definition=model_definition,
        save_path=model_dir,
        model_load_path=model_load_path,
        resume=model_resume_path is not None,
        skip_save_progress_weights=skip_save_progress_weights,
        gpus=gpus,
        gpu_fraction=gpu_fraction,
        use_horovod=use_horovod,
        random_seed=random_seed,
        debug=debug
    )
    (
        train_trainset_stats,
        train_valisest_stats,
        train_testset_stats
    ) = training_results

    if is_on_master():
        // save train set metadata
        save_json(
            os.path.join(
                model_dir,
                TRAIN_SET_METADATA_FILE_NAME
            ),
            train_set_metadata
        )

    // grab the results of the model with highest validation test performance
    validation_field = model_definition["training"]["validation_field"]
    validation_measure = model_definition["training"]["validation_measure"]
    validation_field_result = train_valisest_stats[validation_field]

    best_function = get_best_function(validation_measure)

    // print the results of the model with highest validation test performance
    if is_on_master():
        if validation_set is not None:
            // max or min depending on the measure
            
            epoch_best_vali_measure, best_vali_measure = best_function(
                enumerate(validation_field_result[validation_measure]),
                key=lambda pair: pair[1]
            )
            logging.info("Best validation model epoch: {0}".format(
                epoch_best_vali_measure + 1)
            )
            logging.info(
                "Best validation model {0} on validation set {1}: {2}".format(
                    validation_measure,
                    validation_field,
                    best_vali_measure)
            )
        
            if test_set is not None:
                best_vali_measure_epoch_test_measure = train_testset_stats[
                    validation_field
                ][validation_measure][epoch_best_vali_measure]
                logging.info(
                    "Best validation model {0} on test set {1}: {2}".format(
                        validation_measure,
                        validation_field,
                        best_vali_measure_epoch_test_measure
                    )
                )

    // save training statistics
    if is_on_master():
        save_json(
            training_stats_fn,
            {

Instances