DataOperationsCatalog.CrossValidationSplit Метод
Определение
Важно!
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Разделите набор данных на свертки перекрестной проверки набора обучения и тестового набора.
Уважает предоставленный samplingKeyColumnName параметр.
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)Параметры
- data
- IDataView
Набор данных для разделения.
- numberOfFolds
- Int32
Количество сверток перекрестной проверки.
- samplingKeyColumnName
- String
Имя столбца, используемого для группировки строк. Если два примера имеют одинаковое значение samplingKeyColumnName, они гарантированно будут отображаться в одном подмножестве (обучение или тестирование). Это можно использовать для обеспечения отсутствия утечки меток из поезда в тестовый набор.
Обратите внимание, что при выполнении эксперимента samplingKeyColumnName ранжирования должен быть столбец GroupId.
Если null группирование строк не будет выполнено.
Начальное значение генератора случайных чисел, используемого для выбора строк для сверток перекрестной проверки.
Возвращаемое значение
Примеры
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}");
}
}
}