Metrics 0.3.0 d2pinball score

Lodestar.Metrics 0.3.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

D2Pinball.Score

The fraction of pinball loss explained — sklearn.metrics.d2_pinball_score.

public static double Score(ReadOnlySpan<double> yTrue, ReadOnlySpan<double> yPred, double alpha = 0.5, int outputCount = 1, ReadOnlySpan<double> sampleWeight = default, ReadOnlySpan<double> outputWeights = default, ZeroDivision zeroDivision = ZeroDivision.NaN)

Parameters — yTrue and yPred are the true and predicted values, row-major when there is more than one output. alpha is the quantile being scored, in [0, 1], 0.5 by default. outputCount is how many outputs each row holds. sampleWeight is one weight per row, not per value. outputWeights is a weight per output, multioutput=[…]; omit it for multioutput="uniform_average". zeroDivision decides the answer for fewer than two samples.

Returns — double. 1 for a perfect prediction, 0 for one no better than the best constant, and negative below that. A column whose truth never varies contributes 0.

Exceptions — ArgumentException when a length disagrees with the shape, the input is empty, or it holds a non-finite value. ArgumentOutOfRangeException when outputCount is below one, or alpha is outside [0, 1] — NaN included. UndefinedMetricException when there are fewer than two samples and zeroDivision is ZeroDivision.Throw.

Example — the same four samples read at three quantiles.

using Lodestar.Metrics;

double[] truth = [1.0, 2.0, 3.0, 4.0];
double[] predicted = [1.5, 2.5, 2.0, 4.5];

double median = D2Pinball.Score(truth, predicted);  // => 0.375

using Lodestar.Metrics;

double[] truth = [1.0, 2.0, 3.0, 4.0];
double[] predicted = [1.5, 2.5, 2.0, 4.5];

double upper = D2Pinball.Score(truth, predicted, alpha: 0.9);  // => -0.75…

The upper quantile scores below zero: these predictions are a decent median and a poor 90th percentile, which is the distinction the metric exists to draw.

Remarks — the denominator is the loss of predicting the weighted quantile of the truth at the same alpha. Which of the two candidate order statistics that quantile takes cannot be observed here: the two differ exactly where the quantile is ambiguous, and the pinball loss is flat across that interval — measured over four fixtures at five alphas each, both readings give the same score every time.

Applies to — net10.0, netstandard2.0.

See also — D2Pinball.PerOutput, D2AbsoluteError.Score, PinballLoss.Score, the Python equivalence table.