Lodestar.Metrics 0.2.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

ClassificationReport.ToText

Renders the table the way sklearn.metrics.classification_report prints it, to the character.

public string ToText(int digits = 2)

Parameters — digits is how many decimal places the three score columns carry, scikit-learn's digits. Two by default, which is what it prints unasked.

Returns — string: a header line, a blank line, one line per class, a blank line, the accuracy or micro-average row, the two averaged rows, and a trailing newline.

Exceptions — ArgumentOutOfRangeException when digits is negative.

Example — the macro-average line of the report above.

using System;
using System.Linq;
using Lodestar.Metrics;

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

string table = ClassificationReport.Compute(yTrue, yPred).ToText();
string header = table.Split('\n')[0].Trim();   // => precision    recall  f1-score   support

Remarks — the reason this renders text at all, rather than leaving formatting to the caller, is that a migration is usually checked by putting the two outputs side by side. Column widths, the blank lines, the right-alignment and the integer-versus-float rendering of the support column are all scikit-learn's, so a diff of the two files is empty rather than noisy.

Two things are not identical, and both are stated rather than hidden. A report built with ZeroDivision.NaN renders .NET's NaN where Python writes nan — the numbers match, the eight characters do not. And the support column switches between integer and float formatting on a rule that keys off whether any sample anywhere was predicted correctly, not off whether accuracy is zero; the two differ when a label subset is in play, and the reasoning is in decision 0031.

The trap is treating this as a data format. It is aligned for a human eye, columns can run together when a target name is long, and nothing here parses it back. Read Classes and the average rows if you want the numbers.

Applies to — net10.0, netstandard2.0.

See also — ClassificationReport.Compute, ClassificationReport.ToString, decision 0031, the Python equivalence table.