DataOperationsCatalog.CrossValidationSplit Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Data.dll

Paquete:

Microsoft.ML v2.0.0

Divida el conjunto de datos en plegamientos de validación cruzada del conjunto de entrenamiento y el conjunto de pruebas. Respeta el samplingKeyColumnName si se proporciona.

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)

Parámetros

data

IDataView

Conjunto de datos que se va a dividir.

numberOfFolds

Int32

Número de plegamientos de validación cruzada.

samplingKeyColumnName

String

Nombre de una columna que se va a usar para agrupar filas. Si dos ejemplos comparten el mismo valor de samplingKeyColumnName, se garantiza que aparecen en el mismo subconjunto (entrenamiento o prueba). Esto se puede usar para garantizar que no se filte ninguna etiqueta del tren al conjunto de pruebas. Tenga en cuenta que al realizar un experimento de clasificación, debe samplingKeyColumnName ser la columna GroupId. Si null no se realizará ninguna agrupación de filas.

seed

Nullable<Int32>

Inicialización del generador de números aleatorios usado para seleccionar filas para plegamientos de validación cruzada.

Devoluciones

IReadOnlyList<TrainTestData>

Ejemplos

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}");
        }
    }
}