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Cross-validation and train/test splits over row indices.

public static class Splitters

Example — three stratified folds over twelve rows in three classes.

using Lodestar.Preprocessing;

// Six rows of class 0, three of class 1, three of class 2.
int[] labels = [0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2];

IReadOnlyList<FoldSplit> folds = Splitters.StratifiedKFold(labels, foldCount: 3);

string held = string.Join(",", folds[0].TestIndices);  // => 0,1,6,9
string fit = string.Join(",", folds[0].TrainIndices);  // => 2,3,4,5,7,8,10,11

Remarks — every fold above holds two rows of class 0 and one of each other class, which is what stratifying buys: Splitters.KFold on the same twelve rows would hand fold 0 four rows of class 0 and nothing else.

The unshuffled splitters are scikit-learn's, fold for fold and index for index, replayed from tests/oracles/preprocessing_splitters.json. The shuffled ones take the permutation as an argument rather than a seed — the splitting index has why, and which shuffled splits a permutation reproduces.

Applies to — net10.0, netstandard2.0.

See also — FoldSplit, TrainTestSplit, the splitting index, the Python equivalence table.