Source code for immuneML.ml_metrics.MetricUtil

import warnings
import inspect

from immuneML.ml_metrics import ml_metrics
from sklearn import metrics as sklearn_metrics
from immuneML.ml_metrics.Metric import Metric
from immuneML.ml_methods.util.Util import Util
from immuneML.environment.Constants import Constants

[docs] class MetricUtil:
[docs] @staticmethod def get_metric_fn(metric: Metric): if hasattr(ml_metrics, metric.value): fn = getattr(ml_metrics, metric.value) else: fn = getattr(sklearn_metrics, metric.value) return fn
[docs] @staticmethod def score_for_metric(metric: Metric, predicted_y, predicted_proba_y, true_y, classes): ''' Note: when providing label classes, make sure the 'positive class' is sorted last. This sorting should be done automatically when accessing Label.values ''' fn = MetricUtil.get_metric_fn(metric) true_y, predicted_y = Util.binarize_label_classes(true_y=true_y, predicted_y=predicted_y, classes=classes) try: if metric in Metric.get_probability_based_metric_types(): predictions = predicted_proba_y if predicted_proba_y is None: warnings.warn( f"MLMethodAssessment: metric {metric} is specified, but the chosen ML method does not output " f"class probabilities. Using predicted classes instead...") predictions = predicted_y else: predictions = predicted_y if 'labels' in inspect.getfullargspec(fn).kwonlyargs or 'labels' in inspect.getfullargspec(fn).args: score = fn(true_y, predictions, labels=classes) else: score = fn(true_y, predictions) except ValueError as err: warnings.warn(f"MLMethodAssessment: score for metric {} could not be calculated." f"\nPredicted values: {predicted_y}\nTrue values: {true_y}.\nMore details: {err}", RuntimeWarning) score = Constants.NOT_COMPUTED return score