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MultiClassRocOptions

Everything optional about RocAuc.MultiClass, in one ref struct so the spans can travel with the rest.

public readonly ref struct MultiClassRocOptions

Properties — Strategy is one-vs-rest or one-vs-one, MultiClassStrategy.OneVsRest by default. Average is Averaging.Macro or Averaging.Weighted, or Averaging.Micro under one-vs-rest (#1601), and is nullable so that default can mean macro: default(Averaging) is Averaging.Binary, which multiclass ROC-AUC refuses. Labels names the classes the score columns stand for, sorted ascending and unique; empty reads them off yTrue, which is wrong when a class is absent from it. SampleWeight weights the samples and is refused with one-vs-one over three classes or more, as scikit-learn refuses it; one or two score columns over a yTrue of two labels or one take its binary path, which never refuses them for the strategy. MaxDegreeOfParallelism is how many workers run the per-class or per-pair loop; 0 and 1 are sequential, and there is no sentinel for "all cores" — write Environment.ProcessorCount.

Example — the same scores under both strategies and both averages.

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,
];

MultiClassRocOptions weightedOptions = new() { Average = Averaging.Weighted };
MultiClassRocOptions pairwise = new() { Strategy = MultiClassStrategy.OneVsOne };

double macro = RocAuc.MultiClass(yTrue, yScore, 3);   // => 0.7824…
double weighted = RocAuc.MultiClass(yTrue, yScore, 3, weightedOptions);   // => 0.7361…
double pairs = RocAuc.MultiClass(yTrue, yScore, 3, pairwise);   // => 0.8194…

Remarks — default reproduces scikit-learn's own defaults, so the three-argument call is the one to write until you need something else. Being a ref struct is what lets Labels and SampleWeight be spans rather than arrays: build it at the call site, and do not try to store it in a field.

MaxDegreeOfParallelism is the one setting with no Python counterpart, and it is opt-in rather than automatic on purpose — the result is bit-identical at any setting, and above 1 the inputs are copied, so it is a trade a caller should make knowingly. docs/guides/performance.md has the argument.

The trap is Labels. Leaving it empty means the score columns are matched to the sorted distinct labels of yTrue, so if your model has five classes and only four of them occur in this evaluation set, the columns silently shift by one and the number that comes back is meaningless rather than wrong-looking. Pass Labels whenever the label set is fixed by the model rather than by the data.

Applies to — net10.0, netstandard2.0.

See also — RocAuc.MultiClass, MultiClassStrategy, Averaging, docs/guides/performance.md, the Python equivalence table.

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