StandardTrainersCatalog.OneVersusAll<TModel> Méthode

Définition

Espace de noms:

Microsoft.ML

Assembly:

Microsoft.ML.StandardTrainers.dll

Paquet:

Microsoft.ML v3.0.1

Créez un , qui prédit une OneVersusAllTrainercible multiclasse à l’aide d’une stratégie un-contre-all avec l’estimateur de classification binaire spécifié par binaryEstimator.

public static Microsoft.ML.Trainers.OneVersusAllTrainer OneVersusAll<TModel> (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.ITrainerEstimator<Microsoft.ML.Data.BinaryPredictionTransformer<TModel>,TModel> binaryEstimator, string labelColumnName = "Label", bool imputeMissingLabelsAsNegative = false, Microsoft.ML.IEstimator<Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Calibrators.ICalibrator>> calibrator = default, int maximumCalibrationExampleCount = 1000000000, bool useProbabilities = true) where TModel : class;

static member OneVersusAll : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.ITrainerEstimator<Microsoft.ML.Data.BinaryPredictionTransformer<'Model>, 'Model (requires 'Model : null)> * string * bool * Microsoft.ML.IEstimator<Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Calibrators.ICalibrator>> * int * bool -> Microsoft.ML.Trainers.OneVersusAllTrainer (requires 'Model : null)

<Extension()>
Public Function OneVersusAll(Of TModel As Class) (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, binaryEstimator As ITrainerEstimator(Of BinaryPredictionTransformer(Of TModel), TModel), Optional labelColumnName As String = "Label", Optional imputeMissingLabelsAsNegative As Boolean = false, Optional calibrator As IEstimator(Of ISingleFeaturePredictionTransformer(Of ICalibrator)) = Nothing, Optional maximumCalibrationExampleCount As Integer = 1000000000, Optional useProbabilities As Boolean = true) As OneVersusAllTrainer

Paramètres de type

TModel

Type du modèle. Ce paramètre de type est généralement déduit automatiquement à partir de binaryEstimator.

Paramètres

catalog

MulticlassClassificationCatalog.MulticlassClassificationTrainers

Objet de formateur de catalogue de classification multiclasse.

binaryEstimator

ITrainerEstimator<BinaryPredictionTransformer<TModel>,TModel>

Instance d’un binaire ITrainerEstimator<TTransformer,TModel> utilisé comme formateur de base.

labelColumnName

String

Nom de la colonne d’étiquette.

imputeMissingLabelsAsNegative

Boolean

S’il faut traiter les étiquettes manquantes comme ayant des étiquettes négatives, au lieu de les conserver manquantes.

calibrator

IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>

Le calibrar. Si un calibrar n’est pas explicitement fourni, il est par défaut Microsoft.ML.Calibrators.PlattCalibratorTrainer

maximumCalibrationExampleCount

Int32

Nombre d’instances pour entraîner le calibrar.

useProbabilities

Boolean

Utilisez des probabilités (par rapport aux sorties brutes) pour identifier la catégorie de score supérieur.

Retours

OneVersusAllTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class OneVersusAll
    {
        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("Label")
                // Apply OneVersusAll multiclass meta trainer on top of
                // binary trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .OneVersusAll(
                mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));

            // 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);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data
                .CreateEnumerable<Prediction>(transformedTestData,
                reuseRowObject: false).ToList();

            // Look at 5 predictions
            foreach (var p in predictions.Take(5))
                Console.WriteLine($"Label: {p.Label}, " +
                    $"Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 2
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 2

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

            PrintMetrics(metrics);

            // Expected output:
            //   Micro Accuracy: 0.90
            //   Macro Accuracy: 0.90
            //   Log Loss: 0.36
            //   Log Loss Reduction: 0.68

            //   Confusion table
            //             ||========================
            //   PREDICTED ||     0 |     1 |     2 | Recall
            //   TRUTH     ||========================
            //           0 ||   152 |     0 |     8 | 0.9500
            //           1 ||     0 |   168 |     9 | 0.9492
            //           2 ||    17 |    15 |   131 | 0.8037
            //             ||========================
            //   Precision ||0.8994 |0.9180 |0.8851 |
        }

        // Generates random uniform doubles in [-0.5, 0.5)
        // range with labels 1, 2 or 3.
        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, 4);
                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; }
        }

        // Pretty-print MulticlassClassificationMetrics objects.
        public static void PrintMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
            Console.WriteLine(
                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");

            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

Remarques

Dans une stratégie un/toutes, un algorithme de classification binaire est utilisé pour entraîner un classifieur pour chaque classe, ce qui distingue cette classe de toutes les autres classes. La prédiction est ensuite effectuée en exécutant ces classifieurs binaires et en choisissant la prédiction avec le score de confiance le plus élevé.