DataOperationsCatalog.TrainTestSplit Méthode

Définition

Espace de noms:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paquet:

Microsoft.ML v3.0.1

Fractionnez le jeu de données dans le jeu d’apprentissage et le jeu de test en fonction de la fraction donnée. Respecte le samplingKeyColumnName cas échéant.

public Microsoft.ML.DataOperationsCatalog.TrainTestData TrainTestSplit (Microsoft.ML.IDataView data, double testFraction = 0.1, string samplingKeyColumnName = default, int? seed = default);

member this.TrainTestSplit : Microsoft.ML.IDataView * double * string * Nullable<int> -> Microsoft.ML.DataOperationsCatalog.TrainTestData

Public Function TrainTestSplit (data As IDataView, Optional testFraction As Double = 0.1, Optional samplingKeyColumnName As String = Nothing, Optional seed As Nullable(Of Integer) = Nothing) As DataOperationsCatalog.TrainTestData

Paramètres

data

IDataView

Jeu de données à fractionner.

testFraction

Double

Fraction de données à passer dans le jeu de test.

samplingKeyColumnName

String

Nom d’une colonne à utiliser pour regrouper les lignes. Si deux exemples partagent la même valeur samplingKeyColumnName, ils sont garantis d’apparaître dans le même sous-ensemble (entraîner ou tester). Cela peut être utilisé pour garantir qu’aucune fuite d’étiquette entre le train et le jeu de test. Notez que lors de l’exécution d’une expérience de classement, il samplingKeyColumnName doit s’agir de la colonne GroupId. Si null aucun regroupement de lignes n’est effectué.

seed

Nullable<Int32>

Valeur initiale du générateur de nombres aléatoires utilisé pour sélectionner des lignes pour le fractionnement de test d’apprentissage.

Retours

DataOperationsCatalog.TrainTestData

Exemples

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

namespace Samples.Dynamic
{
    /// <summary>
    /// Sample class showing how to use TrainTestSplit.
    /// </summary>
    public static class TrainTestSplit
    {
        public static void Example()
        {
            // Creating the ML.Net IHostEnvironment object, needed for the pipeline.
            var mlContext = new MLContext();

            // Generate some data points.
            var examples = GenerateRandomDataPoints(10);

            // Convert the examples list to an IDataView object, which is consumable
            // by ML.NET API.
            var dataview = mlContext.Data.LoadFromEnumerable(examples);

            // Leave out 10% of the dataset for testing.For some types of problems,
            // for example for ranking or anomaly detection, we must ensure that the
            // split leaves the rows with the same value in a particular column, in
            // one of the splits. So below, we specify Group column as the column
            // containing the sampling keys. Notice how keeping the rows with the
            // same value in the Group column overrides the testFraction definition. 
            var split = mlContext.Data
                .TrainTestSplit(dataview, testFraction: 0.1,
                samplingKeyColumnName: "Group");

            var trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(split.TrainSet, reuseRowObject: false);

            var testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(split.TestSet, reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);

            //  The data in the Train split.
            //  [Group, 1], [Features, 0.8173254]
            //  [Group, 1], [Features, 0.5581612]
            //  [Group, 1], [Features, 0.5588848]
            //  [Group, 1], [Features, 0.4421779]
            //  [Group, 1], [Features, 0.2737045]

            //  The data in the Test split.
            //  [Group, 0], [Features, 0.7262433]
            //  [Group, 0], [Features, 0.7680227]
            //  [Group, 0], [Features, 0.2060332]
            //  [Group, 0], [Features, 0.9060271]
            //  [Group, 0], [Features, 0.9775497]

            // Example of a split without specifying a sampling key column.
            split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.2);
            trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(split.TrainSet, reuseRowObject: false);

            testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(split.TestSet, reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);

            // The data in the Train split.
            // [Group, 0], [Features, 0.7262433]
            // [Group, 1], [Features, 0.8173254]
            // [Group, 0], [Features, 0.7680227]
            // [Group, 1], [Features, 0.5581612]
            // [Group, 0], [Features, 0.2060332]
            // [Group, 1], [Features, 0.4421779]
            // [Group, 0], [Features, 0.9775497]
            // [Group, 1], [Features, 0.2737045]

            // The data in the Test split.
            // [Group, 1], [Features, 0.5588848]
            // [Group, 0], [Features, 0.9060271]

        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                yield return new DataPoint
                {
                    Group = i % 2,

                    // Create random features that are correlated with label.
                    Features = (float)random.NextDouble()
                };
            }
        }

        // Example with label and group column. A data set is a collection of such
        // examples.
        private class DataPoint
        {
            public float Group { get; set; }

            public float Features { get; set; }
        }

        // print helper
        private static void PrintPreviewRows(IEnumerable<DataPoint> trainSet,
            IEnumerable<DataPoint> testSet)

        {

            Console.WriteLine($"The data in the Train split.");
            foreach (var row in trainSet)
                Console.WriteLine($"{row.Group}, {row.Features}");

            Console.WriteLine($"\nThe data in the Test split.");
            foreach (var row in testSet)
                Console.WriteLine($"{row.Group}, {row.Features}");
        }
    }
}