TransformExtensionsCatalog.CopyColumns Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Erstellen Sie eine ColumnCopyingEstimator, die die Daten aus der spalte kopiert, die in inputColumnName einer neuen Spalte angegeben ist: outputColumnName

public static Microsoft.ML.Transforms.ColumnCopyingEstimator CopyColumns (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName);

static member CopyColumns : Microsoft.ML.TransformsCatalog * string * string -> Microsoft.ML.Transforms.ColumnCopyingEstimator

<Extension()>
Public Function CopyColumns (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String) As ColumnCopyingEstimator

Parameter

catalog

TransformsCatalog

Der Katalog der Transformation.

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName. Der Datentyp dieser Spalte entspricht dem Datentyp der Eingabespalte.

inputColumnName

String

Name der Spalte, aus der die Daten kopiert werden sollen. Dieser Schätzer wird über jeden Datentyp betrieben.

Gibt zurück

ColumnCopyingEstimator

Beispiele

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

namespace Samples.Dynamic
{
    public static class CopyColumns
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Create a small dataset as an IEnumerable.
            var samples = new List<InputData>()
            {
                new InputData(){ ImageId = 1, Features = new [] { 1.0f, 1.0f,
                    1.0f } },

                new InputData(){ ImageId = 2, Features = new [] { 2.0f, 2.0f,
                    2.0f } },

                new InputData(){ ImageId = 3, Features = new [] { 3.0f, 3.0f,
                    3.0f } },

                new InputData(){ ImageId = 4, Features = new [] { 4.0f, 4.0f,
                    4.0f } },

                new InputData(){ ImageId = 5, Features = new [] { 5.0f, 5.0f,
                    5.0f } },

                new InputData(){ ImageId = 6, Features = new [] { 6.0f, 6.0f,
                    6.0f } },
            };

            // Convert training data to IDataView.
            var dataview = mlContext.Data.LoadFromEnumerable(samples);

            // CopyColumns is commonly used to rename columns.
            // For example, if you want to train towards ImageId, and your trainer
            // expects a "Label" column, you can use CopyColumns to rename ImageId
            // to Label. Technically, the ImageId column still exists, but it won't
            // be materialized unless you actually need it somewhere (e.g. if you
            // were to save the transformed data without explicitly dropping the
            // column). This is a general property of IDataView's lazy evaluation.
            var pipeline = mlContext.Transforms.CopyColumns("Label", "ImageId");

            // Now we can transform the data and look at the output to confirm the
            // behavior of CopyColumns. Don't forget that this operation doesn't
            // actually evaluate data until we read the data below.
            var transformedData = pipeline.Fit(dataview).Transform(dataview);

            // We can extract the newly created column as an IEnumerable of
            // SampleInfertDataTransformed, the class we define below.
            var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, reuseRowObject: false);

            // And finally, we can write out the rows of the dataset, looking at the
            // columns of interest.
            Console.WriteLine($"Label and ImageId columns obtained " +
                $"post-transformation.");

            foreach (var row in rowEnumerable)
                Console.WriteLine($"Label: {row.Label} ImageId: {row.ImageId}");

            // Expected output:
            // ImageId and Label columns obtained post-transformation.
            //  Label: 1 ImageId: 1
            //  Label: 2 ImageId: 2
            //  Label: 3 ImageId: 3
            //  Label: 4 ImageId: 4
            //  Label: 5 ImageId: 5
            //  Label: 6 ImageId: 6
        }

        private class InputData
        {
            public int ImageId { get; set; }
            public float[] Features { get; set; }
        }

        private class TransformedData : InputData
        {
            public int Label { get; set; }
        }
    }
}