Metric

ConstraintViolation

Average absolute constraint violations for interval predictions.

Applies to interval predictions.

Should be used together with EmpiricalCoverage if reported.

Up to a constant, PinballLoss is a weighted sum of ConstraintViolation and EmpiricalCoverage.

For an interval prediction \(I = [a, b]\) and a ground truth value \(y\), the constraint violation loss is defined as

\[\begin{split}L(y, I):= \begin{cases} a - y, & \text{if } y < a \\ y - b, & \text{if } y > b \\ 0, & \text{otherwise} \end{cases}\end{split}\]

evaluate computes the average test sample loss.
evaluate_by_index produces the loss sample by test data point.
multivariate controls averaging over variables.
score_average controls averaging over quantiles/intervals.

Schnellstart

python

from sktime.performance_metrics.forecasting.probabilistic import ConstraintViolation

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

Parameter(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.