DataOperationsCatalog.CrossValidationSplit Methode
Definition
Wichtig
Einige Informationen beziehen sich auf Vorabversionen, die vor dem Release ggf. grundlegend überarbeitet werden. Microsoft übernimmt hinsichtlich der hier bereitgestellten Informationen keine Gewährleistungen, seien sie ausdrücklich oder konkludent.
Teilen Sie das Dataset in Kreuzüberprüfungsfalten von Train-Set und Testsatz.
Respektiert die samplingKeyColumnName sofern angegeben.
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)Parameter
- data
- IDataView
Das zu teilende Dataset.
- numberOfFolds
- Int32
Anzahl der Kreuzüberprüfungsfalten.
- 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 für den Zufallszahlengenerator, der zum Auswählen von Zeilen für Kreuzüberprüfungsfalten verwendet wird.
Gibt zurück
Beispiele
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}");
}
}
}