Metrics rocauc multiclass

RocAuc.MultiClass

Area under the ROC curve for more than two classes, by reducing to binary problems.

public static double MultiClass(ReadOnlySpan<int> yTrue, ReadOnlySpan<double> yScore, int classCount, MultiClassRocOptions options = default)

Parameters — yTrue holds one true label per sample. yScore holds the class probabilities row-major — sample 0's classes, then sample 1's — so its length is classCount times the sample count, and each row must sum to 1. classCount is how many classes each row scores. options carries the strategy, the averaging, the label set, the sample weights and the worker count; default is scikit-learn's own defaults, on one thread.

Returns — double in [0, 1], larger meaning a better ranking.

Exceptions — ArgumentException when any of the shape rules is broken — a length that does not match, a row that does not sum to 1, a NaN, a sample weight under one-vs-one; ArgumentOutOfRangeException when classCount is below two or MultiClassRocOptions.MaxDegreeOfParallelism is negative.

Example — six samples over three classes, one probability row each.

using Lodestar.Metrics;

int[] yTrue = [0, 1, 2, 2, 2, 1];
double[] yScore =
[
    0.6, 0.3, 0.1,
    0.3, 0.5, 0.2,
    0.2, 0.5, 0.3,
    0.1, 0.2, 0.7,
    0.4, 0.4, 0.2,
    0.2, 0.3, 0.5,
];

double auc = RocAuc.MultiClass(yTrue, yScore, 3);   // => 0.7824…

Remarks — a separate method rather than an overload of RocAuc.Score, because the two parameter lists would be indistinguishable to the C# compiler and a call like Score(y, s, 3) would stop compiling in consumer code. Everything optional lives in MultiClassRocOptions.

Three traps, and the first two are about the shape of yScore. It is probabilities, not scores: each row has to sum to 1, and the call refuses it otherwise, so a raw logit or a decision-function output has to go through a softmax first. And it is row-major — one sample's classes are contiguous — which is the transpose of what you get from a column-per-class table; there is no two-dimensional overload because a span cannot carry one.

The third is the class-to-column mapping. With Labels left empty the columns are matched to the sorted distinct labels of yTrue, so a class the model knows about but this evaluation set happens not to contain will shift every later column. Pass MultiClassRocOptions.Labels whenever the label set comes from the model rather than from the data.

The exception behaviour is worth one line for anyone raising MaxDegreeOfParallelism: the parallel path rethrows the original exception instance — same type, message and ParamName, from the lowest-numbered class or pair that failed — so a catch written against the sequential path keeps working, and no AggregateException ever escapes.

Applies to — net10.0, netstandard2.0.

See also — RocAuc.Score, MultiClassRocOptions, MultiClassStrategy, decision 0018, the Python equivalence table.