DataOperationsCatalog.CrossValidationSplit Yöntem

Tanım

Ad Alanı:

Microsoft.ML

Bütünleştirilmiş Kod:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Veri kümesini, tren kümesi ve test kümesinin çapraz doğrulama katlarına bölün. samplingKeyColumnName Sağlananlara saygı gösterir.

public System.Collections.Generic.IReadOnlyList<Microsoft.ML.DataOperationsCatalog.TrainTestData> CrossValidationSplit (Microsoft.ML.IDataView data, int numberOfFolds = 5, string samplingKeyColumnName = default, int? seed = default);

member this.CrossValidationSplit : Microsoft.ML.IDataView * int * string * Nullable<int> -> System.Collections.Generic.IReadOnlyList<Microsoft.ML.DataOperationsCatalog.TrainTestData>

Public Function CrossValidationSplit (data As IDataView, Optional numberOfFolds As Integer = 5, Optional samplingKeyColumnName As String = Nothing, Optional seed As Nullable(Of Integer) = Nothing) As IReadOnlyList(Of DataOperationsCatalog.TrainTestData)

Parametreler

data

IDataView

Bölünecek veri kümesi.

numberOfFolds

Int32

Çapraz doğrulama katlama sayısı.

samplingKeyColumnName

String

Satırları gruplandırmak için kullanılacak sütunun adı. İki örnek aynı değerini samplingKeyColumnNamepaylaşıyorsa, bunların aynı alt kümede (eğitim veya test) görüneceği garanti edilir. Bu, trenden test kümesine etiket sızıntısı olmamasını sağlamak için kullanılabilir. Derecelendirme Denemesi gerçekleştirirken GroupId samplingKeyColumnName sütunu olması gerektiğini unutmayın. Satır gruplandırma gerçekleştirilmeyecekse null .

seed

Nullable<Int32>

Çapraz doğrulama katlamaları için satırları seçmek için kullanılan rastgele sayı oluşturucunun tohumu.

Döndürülenler

IReadOnlyList<TrainTestData>

Örnekler

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

namespace Samples.Dynamic
{
    /// <summary>
    /// Sample class showing how to use CrossValidationSplit.
    /// </summary>
    public static class CrossValidationSplit
    {
        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);

            // Cross validation splits your data randomly into set of "folds", and
            // creates groups of Train and Test sets, where for each group, one fold
            // is the Test and the rest of the folds the Train. So below, we specify
            // Group column as the column containing the sampling keys. If we pass
            // that column to cross validation it would be used to break data into
            // certain chunks.
            var folds = mlContext.Data
                .CrossValidationSplit(dataview, numberOfFolds: 3,
                samplingKeyColumnName: "Group");

            var trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[0].TrainSet,
                reuseRowObject: false);

            var testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[0].TestSet,
                reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);

            // The data in the Train split.
            // [Group, 1], [Features, 0.8173254]
            // [Group, 2], [Features, 0.7680227]
            // [Group, 1], [Features, 0.2060332]
            // [Group, 2], [Features, 0.5588848]
            // [Group, 1], [Features, 0.4421779]
            // [Group, 2], [Features, 0.9775497]
            // 
            // The data in the Test split.
            // [Group, 0], [Features, 0.7262433]
            // [Group, 0], [Features, 0.5581612]
            // [Group, 0], [Features, 0.9060271]
            // [Group, 0], [Features, 0.2737045]

            trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[1].TrainSet,
                reuseRowObject: false);

            testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[1].TestSet,
                reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);
            // The data in the Train split.
            // [Group, 0], [Features, 0.7262433]
            // [Group, 2], [Features, 0.7680227]
            // [Group, 0], [Features, 0.5581612]
            // [Group, 2], [Features, 0.5588848]
            // [Group, 0], [Features, 0.9060271]
            // [Group, 2], [Features, 0.9775497]
            // [Group, 0], [Features, 0.2737045]
            // 
            // The data in the Test split.
            // [Group, 1], [Features, 0.8173254]
            // [Group, 1], [Features, 0.2060332]
            // [Group, 1], [Features, 0.4421779]

            trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[2].TrainSet,
                reuseRowObject: false);

            testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[2].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, 0.5581612]
            // [Group, 1], [Features, 0.2060332]
            // [Group, 0], [Features, 0.9060271]
            // [Group, 1], [Features, 0.4421779]
            // [Group, 0], [Features, 0.2737045]
            // 
            // The data in the Test split.
            // [Group, 2], [Features, 0.7680227]
            // [Group, 2], [Features, 0.5588848]
            // [Group, 2], [Features, 0.9775497]

            // Example of a split without specifying a sampling key column.
            folds = mlContext.Data.CrossValidationSplit(dataview, numberOfFolds: 3);
            trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[0].TrainSet,
                reuseRowObject: false);

            testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[0].TestSet,
                reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);
            // The data in the Train split.
            // [Group, 0], [Features, 0.7262433]
            // [Group, 1], [Features, 0.8173254]
            // [Group, 2], [Features, 0.7680227]
            // [Group, 0], [Features, 0.5581612]
            // [Group, 1], [Features, 0.2060332]
            // [Group, 1], [Features, 0.4421779]
            // [Group, 2], [Features, 0.9775497]
            // [Group, 0], [Features, 0.2737045]
            // 
            // The data in the Test split.
            // [Group, 2], [Features, 0.5588848]
            // [Group, 0], [Features, 0.9060271]

            trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[1].TrainSet,
                reuseRowObject: false);

            testSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[1].TestSet,
                reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);
            // The data in the Train split.
            // [Group, 2], [Features, 0.7680227]
            // [Group, 0], [Features, 0.5581612]
            // [Group, 1], [Features, 0.2060332]
            // [Group, 2], [Features, 0.5588848]
            // [Group, 0], [Features, 0.9060271]
            // [Group, 1], [Features, 0.4421779]
            // 
            // The data in the Test split.
            // [Group, 0], [Features, 0.7262433]
            // [Group, 1], [Features, 0.8173254]
            // [Group, 2], [Features, 0.9775497]
            // [Group, 0], [Features, 0.2737045]

            trainSet = mlContext.Data
                .CreateEnumerable<DataPoint>(folds[2].TrainSet,
                reuseRowObject: false);

            testSet = mlContext.Data.CreateEnumerable<DataPoint>(folds[2].TestSet,
                reuseRowObject: false);

            PrintPreviewRows(trainSet, testSet);
            // The data in the Train split.
            // [Group, 0], [Features, 0.7262433]
            // [Group, 1], [Features, 0.8173254]
            // [Group, 2], [Features, 0.5588848]
            // [Group, 0], [Features, 0.9060271]
            // [Group, 2], [Features, 0.9775497]
            // [Group, 0], [Features, 0.2737045]
            // 
            // The data in the Test split.
            // [Group, 2], [Features, 0.7680227]
            // [Group, 0], [Features, 0.5581612]
            // [Group, 1], [Features, 0.2060332]
            // [Group, 1], [Features, 0.4421779]
        }

        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 % 3,

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

        // Example with features 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}");
        }
    }
}