TransformExtensionsCatalog.SelectColumns Yöntem

Tanım

Ad Alanı:

Microsoft.ML

Bütünleştirilmiş Kod:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v2.0.0

Aşırı Yüklemeler

SelectColumns(TransformsCatalog, String[])	Belirli bir ColumnSelectingEstimatorsütun listesini bir içinde tutan ve diğerlerini düşüren bir IDataView oluşturun.
SelectColumns(TransformsCatalog, String[], Boolean)	Belirli bir ColumnSelectingEstimatorsütun listesini bir içinde tutan ve diğerlerini düşüren bir IDataView oluşturun.

SelectColumns(TransformsCatalog, String[])

Belirli bir ColumnSelectingEstimatorsütun listesini bir içinde tutan ve diğerlerini düşüren bir IDataView oluşturun.

public static Microsoft.ML.Transforms.ColumnSelectingEstimator SelectColumns (this Microsoft.ML.TransformsCatalog catalog, params string[] columnNames);

static member SelectColumns : Microsoft.ML.TransformsCatalog * string[] -> Microsoft.ML.Transforms.ColumnSelectingEstimator

<Extension()>
Public Function SelectColumns (catalog As TransformsCatalog, ParamArray columnNames As String()) As ColumnSelectingEstimator

Parametreler

catalog

TransformsCatalog

Dönüşümün kataloğu.

columnNames

String[]

Tutulacak sütun adları dizisi.

Döndürülenler

ColumnSelectingEstimator

Örnekler

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

namespace Samples.Dynamic
{
    public static class SelectColumns
    {
        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(){ Age = 21, Gender = "Male", Education = "BS",
                    ExtraColumn = 1 },

                new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
                    ExtraColumn = 2 },

                new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
                    ExtraColumn = 3 },

                new InputData(){ Age = 22, Gender = "Male", Education = "BS",
                    ExtraColumn = 4 },

                new InputData(){ Age = 23, Gender = "Female", Education = "MS",
                    ExtraColumn = 5 },

                new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
                    ExtraColumn = 6 },
            };

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

            // Select a subset of columns to keep.
            var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");

            // Now we can transform the data and look at the output to confirm the
            // behavior of SelectColumns. Don't forget that this operation doesn't
            // actually evaluate data until we read the data below, as
            // transformations are lazy in ML.NET.
            var transformedData = pipeline.Fit(dataview).Transform(dataview);

            // Print the number of columns in the schema
            Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
                $" in the dataset.");

            // Expected output:
            //  There are 2 columns in the dataset.

            // We can extract the newly created column as an IEnumerable of
            // TransformedData, 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($"Age and Educations columns obtained " +
                $"post-transformation.");

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

            // Expected output:
            //  Age and Educations columns obtained post-transformation.
            //  Age: 21 Education: BS
            //  Age: 23 Education: MBA
            //  Age: 28 Education: PhD
            //  Age: 22 Education: BS
            //  Age: 23 Education: MS
            //  Age: 27 Education: PhD
        }

        private class InputData
        {
            public int Age { get; set; }
            public string Gender { get; set; }
            public string Education { get; set; }
            public float ExtraColumn { get; set; }
        }

        private class TransformedData
        {
            public int Age { get; set; }
            public string Education { get; set; }
        }
    }
}

SelectColumns(TransformsCatalog, String[], Boolean)

Belirli bir ColumnSelectingEstimatorsütun listesini bir içinde tutan ve diğerlerini düşüren bir IDataView oluşturun.

public static Microsoft.ML.Transforms.ColumnSelectingEstimator SelectColumns (this Microsoft.ML.TransformsCatalog catalog, string[] columnNames, bool keepHidden);

static member SelectColumns : Microsoft.ML.TransformsCatalog * string[] * bool -> Microsoft.ML.Transforms.ColumnSelectingEstimator

<Extension()>
Public Function SelectColumns (catalog As TransformsCatalog, columnNames As String(), keepHidden As Boolean) As ColumnSelectingEstimator

Parametreler

catalog

TransformsCatalog

Dönüşümün kataloğu.

columnNames

String[]

Tutulacak sütun adları dizisi.

keepHidden

Boolean

Gizli true sütunları tutar ve false gizli sütunları kaldırır. Hata ayıklama amacıyla bir işlem hattı girişlerinin işlem hattının çıkışlarıyla nasıl eşlendiğini anlamak gerektiğinde, gizli sütunları bırakmak yerine saklamak önerilir.

Döndürülenler

ColumnSelectingEstimator

Örnekler

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

namespace Samples.Dynamic
{
    public static class SelectColumns
    {
        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(){ Age = 21, Gender = "Male", Education = "BS",
                    ExtraColumn = 1 },

                new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
                    ExtraColumn = 2 },

                new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
                    ExtraColumn = 3 },

                new InputData(){ Age = 22, Gender = "Male", Education = "BS",
                    ExtraColumn = 4 },

                new InputData(){ Age = 23, Gender = "Female", Education = "MS",
                    ExtraColumn = 5 },

                new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
                    ExtraColumn = 6 },
            };

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

            // Select a subset of columns to keep.
            var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");

            // Now we can transform the data and look at the output to confirm the
            // behavior of SelectColumns. Don't forget that this operation doesn't
            // actually evaluate data until we read the data below, as
            // transformations are lazy in ML.NET.
            var transformedData = pipeline.Fit(dataview).Transform(dataview);

            // Print the number of columns in the schema
            Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
                $" in the dataset.");

            // Expected output:
            //  There are 2 columns in the dataset.

            // We can extract the newly created column as an IEnumerable of
            // TransformedData, 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($"Age and Educations columns obtained " +
                $"post-transformation.");

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

            // Expected output:
            //  Age and Educations columns obtained post-transformation.
            //  Age: 21 Education: BS
            //  Age: 23 Education: MBA
            //  Age: 28 Education: PhD
            //  Age: 22 Education: BS
            //  Age: 23 Education: MS
            //  Age: 27 Education: PhD
        }

        private class InputData
        {
            public int Age { get; set; }
            public string Gender { get; set; }
            public string Education { get; set; }
            public float ExtraColumn { get; set; }
        }

        private class TransformedData
        {
            public int Age { get; set; }
            public string Education { get; set; }
        }
    }
}