TransformExtensionsCatalog.Concatenate Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v3.0.1

Buat ColumnConcatenatingEstimator, yang menggabungkan satu atau beberapa kolom input ke dalam kolom output baru.

public static Microsoft.ML.Transforms.ColumnConcatenatingEstimator Concatenate (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, params string[] inputColumnNames);

static member Concatenate : Microsoft.ML.TransformsCatalog * string * string[] -> Microsoft.ML.Transforms.ColumnConcatenatingEstimator

<Extension()>
Public Function Concatenate (catalog As TransformsCatalog, outputColumnName As String, ParamArray inputColumnNames As String()) As ColumnConcatenatingEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnNames. Jenis data kolom ini akan menjadi vektor dari jenis data kolom input.

inputColumnNames

String[]

Nama kolom yang akan digabungkan. Estimator ini beroperasi melalui jenis data apa pun kecuali jenis kunci. Jika lebih dari satu kolom yang disediakan, semuanya harus memiliki jenis data yang sama.

Mengembalikan

ColumnConcatenatingEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class Concatenate
    {
        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(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f,
                    3.1f }, Feature3 = 1 },

                new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f,
                    3.2f }, Feature3 = 2 },

                new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f,
                    3.3f }, Feature3 = 3 },

                new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f,
                    3.4f }, Feature3 = 4 },

                new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f,
                    3.5f }, Feature3 = 5 },

                new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f,
                    3.6f }, Feature3 = 6 },
            };

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

            // A pipeline for concatenating the "Feature1", "Feature2" and
            // "Feature3" columns together into a vector that will be the Features
            // column. Concatenation is necessary because trainers take feature
            // vectors as inputs.
            //
            // Please note that the "Feature3" column is converted from int32 to
            // float using the ConvertType. The Concatenate requires all columns to
            // be of same type.
            var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3",
                outputKind: DataKind.Single)
                .Append(mlContext.Transforms.Concatenate("Features", new[]
                    { "Feature1", "Feature2", "Feature3" }));

            // The transformed data.
            var transformedData = pipeline.Fit(dataview).Transform(dataview);

            // Now let's take a look at what this concatenation did.
            // We can extract the newly created column as an IEnumerable of
            // TransformedData.
            var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, reuseRowObject: false);

            // And we can write out a few rows
            Console.WriteLine($"Features column obtained post-transformation.");
            foreach (var featureRow in featuresColumn)
                Console.WriteLine(string.Join(" ", featureRow.Features));

            // Expected output:
            //  Features column obtained post-transformation.
            //  0.1 1.1 2.1 3.1 1
            //  0.2 1.2 2.2 3.2 2
            //  0.3 1.3 2.3 3.3 3
            //  0.4 1.4 2.4 3.4 4
            //  0.5 1.5 2.5 3.5 5
            //  0.6 1.6 2.6 3.6 6
        }

        private class InputData
        {
            public float Feature1;
            [VectorType(3)]
            public float[] Feature2;
            public int Feature3;
        }

        private sealed class TransformedData
        {
            public float[] Features { get; set; }
        }
    }
}