Metrics 0.3.0 recall perclass

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Recall.PerClass

Recall for every class, in label order.

public static double[] PerClass(ConfusionMatrix cm, ZeroDivision zeroDivision = ZeroDivision.Zero)
public static double[] PerClass(ReadOnlySpan<int> yTrue, ReadOnlySpan<int> yPred, ZeroDivision zeroDivision = ZeroDivision.Zero, ReadOnlySpan<int> labels = default, ReadOnlySpan<double> sampleWeight = default)

Parameters — cm is a matrix already counted, or pass yTrue and yPred. zeroDivision decides an undefined per-class score, labels fixes the label set and its order, and sampleWeight weights the samples.

Returns — a fresh double[], one entry per label, in the matrix's label order.

Exceptions — ArgumentNullException when cm is null; ArgumentException when the label spans disagree in length or are empty.

Example — the triage found every sample of class 1 and two thirds of class 2.

using Lodestar.Metrics;

int[] yTrue = [0, 0, 1, 1, 2, 2, 2];
int[] yPred = [0, 1, 1, 1, 2, 2, 0];

double[] perClass = Recall.PerClass(yTrue, yPred);
double normal = perClass[1];   // => 1
double spam = perClass[2];     // => 0.6666…

Remarks — this is the same set of numbers as the diagonal of a Normalization.True confusion matrix, and their unweighted mean is BalancedAccuracy.Score. Which of the three shapes you reach for is a matter of what you are about to do with it: an array to assert on, a matrix to draw, or one number to report.

The trap is the denominator on a restricted matrix. This divides by scikit-learn's true_sum, counted over every observed label including ones an explicit labels subset excluded from the view, where BalancedAccuracy.Score's ConfusionMatrix overload divides by the row sum inside the view. The two agree whenever nothing was dropped, and give different numbers when something was.

Applies to — net10.0, netstandard2.0.

See also — Recall.Score, Precision.PerClass, BalancedAccuracy.Score, ConfusionMatrix.ToArray, the Python equivalence table.