Metric

PinballLoss

Pinball loss aka quantile loss for quantile/interval predictions.

Quickstart

python

from sktime.performance_metrics.forecasting.probabilistic import PinballLoss

estimator = PinballLoss(multioutput='uniform_average', score_average=True, alpha=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 quantile should be averaged.
If True, metric/loss is averaged over all quantiles present in y_pred.
If False, metric/loss is not averaged over quantiles.
alpha (optional)float, list of float, or 1D array-like, default=None: quantiles to evaluate metric at. Can be specified if no explicit quantiles are present in the direct use of the metric, for instance in benchmarking via evaluate, or tuning via ForecastingGridSearchCV.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from sktime.performance_metrics.forecasting.probabilistic import PinballLoss
>>> y_true = pd. Series ([3, - 0.5, 2, 7, 2 ])
>>> y_pred = pd. DataFrame ({
... ('Quantiles', 0.05): [1.25, 0, 1, 4, 0.625 ],
... ('Quantiles', 0.5): [2.5, 0, 2, 8, 1.25 ],
... ('Quantiles', 0.95): [3.75, 0, 3, 12, 1.875 ],
... })
>>> pl = PinballLoss ()
>>> pl (y_true, y_pred) np.float64(0.1791666666666667)
>>> pl = PinballLoss (score_average = False)
>>> pl (y_true, y_pred). to_numpy () array([0.16625, 0.275, 0.09625])
>>> y_true = pd. DataFrame ({
... "Quantiles1": [3, - 0.5, 2, 7, 2 ],
... "Quantiles2": [4, 0.5, 3, 8, 3 ],
... })
>>> y_pred = pd. DataFrame ({
... ('Quantiles1', 0.05): [1.5, - 1, 1, 4, 0.65 ],
... ('Quantiles1', 0.5): [2.5, 0, 2, 8, 1.25 ],
... ('Quantiles1', 0.95): [3.5, 4, 3, 12, 1.85 ],
... ('Quantiles2', 0.05): [2.5, 0, 2, 8, 1.25 ],
... ('Quantiles2', 0.5): [5.0, 1, 4, 16, 2.5 ],
... ('Quantiles2', 0.95): [7.5, 2, 6, 24, 3.75 ],
... })
>>> pl = PinballLoss (multioutput = 'raw_values')
>>> pl (y_true, y_pred). to_numpy () array([0.16233333, 0.465 ])
>>> pl = PinballLoss (multioutput = np. array ([0.3, 0.7 ]))
>>> pl (y_true, y_pred) np.float64(0.3742000000000001)