MulticlassClassificationMetrics.PerClassLogLoss Proprietà

Definizione

Spazio dei nomi:

Microsoft.ML.Data

Assembly:

Microsoft.ML.Data.dll

Pacchetto:

Microsoft.ML v2.0.0

Ottiene la perdita di log del classificatore per ogni classe. La perdita di log misura le prestazioni di un classificatore rispetto alla quantità di probabilità stimate divergenti rispetto all'etichetta di classe reale. La perdita di log inferiore indica un modello migliore. Un modello perfetto, che stima una probabilità pari a 1 per la classe true, avrà una perdita di log pari a 0.

public System.Collections.Generic.IReadOnlyList<double> PerClassLogLoss { get; }

member this.PerClassLogLoss : System.Collections.Generic.IReadOnlyList<double>

Public ReadOnly Property PerClassLogLoss As IReadOnlyList(Of Double)

Valore della proprietà

IReadOnlyList<Double>

Esempio

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class LogLossPerClass
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define the trainer.
            var pipeline =
                // Convert the string labels into key types.
                mlContext.Transforms.Conversion
                .MapValueToKey(nameof(DataPoint.Label))
                // Apply a multiclass trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .LightGbm());

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use different random seed to make it different
            // from training data.
            var testData = mlContext.Data
                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Evaluate the overall metrics
            var metrics = mlContext.MulticlassClassification
                .Evaluate(transformedTestData);

            // Find the original label values.
            VBuffer<uint> keys = default;
            transformedTestData.Schema["PredictedLabel"].GetKeyValues(ref keys);
            var originalLabels = keys.DenseValues().ToArray();
            for (var i = 0; i < originalLabels.Length; i++)
                Console.WriteLine($"LogLoss for label " +
                    $"{originalLabels[i]}: {metrics.PerClassLogLoss[i]:F4}");

            // Expected output:
            //   LogLoss for label 7: 0.2578
            //   LogLoss for label 8: 0.2504
            //   LogLoss for label 2: 0.1121
            //   LogLoss for label 9: 0.2229
            //   LogLoss for label 6: 0.1737
            //   LogLoss for label 1: 0.2645
            //   LogLoss for label 3: 0.2235
            //   LogLoss for label 5: 0.1128
            //   LogLoss for label 4: 0.1442
        }

        // Generates data points with random features and labels 1 to 9.
        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)(random.NextDouble() - 0.5);
            for (int i = 0; i < count; i++)
            {
                // Generate Labels that are integers 1, 2 or 3
                var label = random.Next(1, 10);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features that are correlated with the label.
                    // The feature values are slightly increased by adding a
                    // constant multiple of label.
                    Features = Enumerable.Repeat(label, 20)
                        .Select(x => randomFloat() + label * 0.2f).ToArray()

                };
            }
        }

        // Example with label and 20 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public uint Label { get; set; }
            [VectorType(20)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public uint Label { get; set; }
            // Predicted label from the trainer.
            public uint PredictedLabel { get; set; }
        }
    }
}

Commenti

La metrica log-loss viene calcolata come $-\frac{1}{m} \sum_{i=1}^m \log(p_i)$, dove $m$ è il numero di istanze nel set di test. $p_i$ è la probabilità restituita dal classificatore se l'istanza appartiene alla classe e 1 meno la probabilità restituita dal classificatore se l'istanza non appartiene alla classe .