TransformExtensionsCatalog.CopyColumns Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Data.dll

Paquete:

Microsoft.ML v3.0.1

Cree un ColumnCopyingEstimatorobjeto , que copia los datos de la columna especificada en inputColumnName en una nueva columna: 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

Parámetros

catalog

TransformsCatalog

Catálogo de la transformación.

outputColumnName

String

Nombre de la columna resultante de la transformación de inputColumnName. El tipo de datos de esta columna será el mismo que el de la columna de entrada.

inputColumnName

String

Nombre de la columna de la que se van a copiar los datos. Este estimador opera sobre cualquier tipo de datos.

Devoluciones

ColumnCopyingEstimator

Ejemplos

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; }
        }
    }
}