Metrics 0.2.0 classificationreport compute

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ClassificationReport.Compute

Builds the report: one row per class, the accuracy, and two or three averaged rows.

public static ClassificationReport Compute(ConfusionMatrix cm, IReadOnlyList<string> targetNames = null, ZeroDivision zeroDivision = ZeroDivision.Zero)
public static ClassificationReport Compute(ReadOnlySpan<int> yTrue, ReadOnlySpan<int> yPred, IReadOnlyList<string> targetNames = null, ZeroDivision zeroDivision = ZeroDivision.Zero, ReadOnlySpan<int> labels = default, ReadOnlySpan<double> sampleWeight = default)

Parameters — cm is a matrix already counted, or pass yTrue and yPred instead. targetNames puts readable names on the rows, one per label and in label order; leave it null and the rows are named by the label value. zeroDivision decides what an undefined per-class score becomes. labels fixes the label set and its order, and sampleWeight gives each sample its own weight.

Returns — a ClassificationReport, whose Classes list holds one ClassRow per label in the matrix's label order, and whose MacroAverage, WeightedAverage and possibly MicroAverage hold the averaged rows. Accuracy and TotalSupport are on the report itself.

Exceptions — ArgumentNullException when cm is null; ArgumentException when targetNames has a different length from the label set, or the label spans disagree in length or are empty.

Example — a three-way triage, with names on the classes.

using Lodestar.Metrics;

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

ClassificationReport report = ClassificationReport.Compute(yTrue, yPred, ["urgent", "normal", "spam"]);
double spamF1 = report.Classes[2].F1;   // => 0.8
double accuracy = report.Accuracy;      // => 0.7142…

Remarks — this is the thing to reach for when you are looking rather than monitoring. One call gives every per-class score at once, so it replaces four calls to Precision.PerClass and its siblings and counts the matrix once instead of four times. ToText then renders it exactly as Python prints it, which makes a C# result and a Python result comparable by eye rather than by transcription.

MicroAverage is null almost always, and that is the interesting part. scikit-learn prints an accuracy row normally and swaps in a micro avg row when an explicit label subset has left some samples out — because then the diagonal over the total is no longer accuracy over the dataset. This reproduces that rule exactly: the property is non-null precisely when labels was given and something fell outside it.

The trap is targetNames: it is positional, matched to the label set by index and not by value. If labels is omitted the order is the sorted union of both inputs, so names written in the order the classes occur in your data will be silently attached to the wrong rows. Pass labels explicitly whenever you pass targetNames, or sort the names yourself.

Applies to — net10.0, netstandard2.0.

See also — ClassificationReport.ToText, ConfusionMatrix.Compute, Precision.PerClass, the Python equivalence table.