Development build. This page describes main, not a released package. The latest published Lodestar.Preprocessing is 0.2.0 — read its documentation.

Home › Preprocessing › Splitting

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.

Every splitter is scikit-learn's, fold for fold and index for index, replayed from tests/oracles/preprocessing_splitters.json and preprocessing_splitters_seeded.json. A shuffled one takes scikit-learn's own random_state, or a permutation — the splitting index has why both.

Applies to — net10.0, netstandard2.0.

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