DataOperationsCatalog.TrainTestSplit Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Teilen Sie das Dataset in den Zugsatz und den Testsatz nach dem angegebenen Bruch. Respektiert die samplingKeyColumnName sofern angegeben.

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

Parameter

data

IDataView

Das zu teilende Dataset.

testFraction

Double

Der Teil der Daten, die in den Testsatz aufgenommen werden sollen.

samplingKeyColumnName

String

Name einer Spalte, die zum Gruppieren von Zeilen verwendet werden soll. Wenn zwei Beispiele den gleichen Wert des samplingKeyColumnNameWerts teilen, werden sie garantiert in derselben Teilmenge (Train oder Test) angezeigt. Dies kann verwendet werden, um sicherzustellen, dass keine Etikettenlecks vom Zug bis zum Testsatz vorhanden sind. Beachten Sie, dass beim Ausführen eines Bewertungsversuchs die samplingKeyColumnName Spalte "GroupId" sein muss. Wenn null keine Zeilengruppe ausgeführt wird.

seed

Nullable<Int32>

Seed for the random number generator used to select rows for the train-test split.

Gibt zurück

DataOperationsCatalog.TrainTestData

Beispiele

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}");
        }
    }
}