def get_eval_metrics_and_feval()

in src/sagemaker_xgboost_container/algorithm_mode/train_utils.py [0:0]

• 17 lines of code
• 4 McCabe index (conditional complexity)

def get_eval_metrics_and_feval(tuning_objective_metric_param, eval_metric):
    """Return list of default xgb evaluation metrics and list of container defined metrics.

    XGB uses the 'eval_metric' parameter for the evaluation metrics supported by default, and 'feval' as an argument
    during training to validate using custom evaluation metrics. The argument 'feval' takes a function as value; the
    method returned here will be configured to run for only the metrics the user specifies.

    :param tuning_objective_metric_param: HPO metric
    :param eval_metric: list of xgb metrics to output
    :return: cleaned list of xgb supported evaluation metrics, method configured with container defined metrics,
    and tuning objective metric.
    """
    tuning_objective_metric = None
    configured_eval = None
    cleaned_eval_metrics = None

    if tuning_objective_metric_param is not None:
        tuning_objective_metric_tuple = MetricNameComponents.decode(tuning_objective_metric_param)
        tuning_objective_metric = tuning_objective_metric_tuple.metric_name.split(",")
        logging.info("Setting up HPO optimized metric to be : {}".format(tuning_objective_metric_tuple.metric_name))

    union_metrics = get_union_metrics(tuning_objective_metric, eval_metric)

    if union_metrics is not None:
        feval_metrics = get_custom_metrics(union_metrics)
        if feval_metrics:
            configured_eval = configure_feval(feval_metrics)
            cleaned_eval_metrics = list(set(union_metrics) - set(feval_metrics))
        else:
            cleaned_eval_metrics = union_metrics

    return cleaned_eval_metrics, configured_eval, tuning_objective_metric