using System;

public class Program

{

    public static void Main()

    {

        // Perfectly confident but wrong predictions => Infinity for log-loss

        Console.WriteLine("LogLoss of label=0, p=1.0: {0}", LogLoss(0, 1.0f));

        Console.WriteLine("LogLoss of label=1, p=0.0: {0}", LogLoss(1, 0.0f));

        // Semi-confident but wrong predictions => Normal numbers for log-loss

        Console.WriteLine("LogLoss of label=0, p=0.95: {0}", LogLoss(0, 0.95f));

        Console.WriteLine("LogLoss of label=1, p=0.05: {0}", LogLoss(0, 0.05f));

        // NaN probabilities => NaN for log-loss

        Console.WriteLine("LogLoss of label=0, p=NaN: {0}", LogLoss(0, Single.NaN));

    }

​

    // Code from ML.NET https://github.com/dotnet/machinelearning/blob/062be280a9d6de23838d4db5ad93fea9d7d0c1f6/src/Microsoft.ML.Data/Evaluators/BinaryClassifierEvaluator.cs#L663-L677

    static double LogLoss(float _label, float prob)

    {

        Double logloss;

        if (!Single.IsNaN(prob))

        {

            if (_label > 0)

            {

                // REVIEW: Should we bring back the option to use ln instead of log2?

                logloss = -Math.Log(prob, 2);

            }

            else

                logloss = -Math.Log(1.0 - prob, 2);

        }

        else

            logloss = Double.NaN;

        return logloss;

    }

}