DataOperationsCatalog.TrainTestSplit Metoda

Definice

Obor názvů:

Microsoft.ML

Sestavení:

Microsoft.ML.Data.dll

Balíček:

Microsoft.ML v2.0.0

Datovou sadu rozdělte do trénovací sady a testovací sady podle daného zlomku. samplingKeyColumnName Respektuje, jestli je k dispozici.

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

Parametry

data

IDataView

Datová sada, která se má rozdělit.

testFraction

Double

Zlomek dat, která se mají přejít do testovací sady.

samplingKeyColumnName

String

Název sloupce, který se má použít pro seskupení řádků. Pokud dva příklady sdílejí stejnou hodnotu samplingKeyColumnName, jsou zaručené, že se zobrazí ve stejné podmnožině (trénování nebo testování). Dá se použít k zajištění úniku štítků z trénovací sady do testovací sady. Všimněte si, že při provádění experimentu samplingKeyColumnName řazení musí být sloupec GroupId. Pokud null se neprovedou žádné seskupení řádků.

seed

Nullable<Int32>

Seed pro generátor náhodných čísel použitý k výběru řádků pro rozdělení trén-test.

Návraty

DataOperationsCatalog.TrainTestData

Příklady

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}");
        }
    }
}