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Metric

ConstraintViolation

API referenceSource
Multivariate

Average absolute constraint violations for interval predictions.

Quickstart

python
from sktime.performance_metrics.forecasting.probabilistic import ConstraintViolation

estimator = ConstraintViolation(multioutput='uniform_average', score_average=True, coverage=None)

Parameters(3)

multioutput‘uniform_average’ (default), 1D array-like, or ‘raw_values’

Whether and how to aggregate metric for multivariate (multioutput) data.

  • If 'uniform_average' (default), errors of all outputs are averaged with uniform weight.

  • If 1D array-like, errors are averaged across variables, with values used as averaging weights (same order).

  • If 'raw_values', does not average across variables (outputs), per-variable errors are returned.

score_averagebool, optional, default = True

specifies whether scores for each coverage value should be averaged.

  • If True, metric/loss is averaged over all coverages present in y_pred.

  • If False, metric/loss is not averaged over coverages.

coverage (optional)float, list of float, or 1D array-like, default=None

nominal coverage to evaluate metric at. Can be specified if no explicit coverages are present in the direct use of the metric, for instance in benchmarking via evaluate, or tuning via ForecastingGridSearchCV.