TransformExtensionsCatalog.SelectColumns Méthode

Définition

Espace de noms:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Paquet:

Microsoft.ML v3.0.1

Surcharges

SelectColumns(TransformsCatalog, String[])	Créez un ColumnSelectingEstimator, qui conserve une liste donnée de colonnes dans un IDataView et supprime les autres.
SelectColumns(TransformsCatalog, String[], Boolean)	Créez un ColumnSelectingEstimator, qui conserve une liste donnée de colonnes dans un IDataView et supprime les autres.

SelectColumns(TransformsCatalog, String[])

Créez un ColumnSelectingEstimator, qui conserve une liste donnée de colonnes dans un IDataView et supprime les autres.

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

Paramètres

catalog

TransformsCatalog

Catalogue de la transformation.

columnNames

String[]

Tableau de noms de colonnes à conserver.

Retours

ColumnSelectingEstimator

Exemples

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)

Créez un ColumnSelectingEstimator, qui conserve une liste donnée de colonnes dans un IDataView et supprime les autres.

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

Paramètres

catalog

TransformsCatalog

Catalogue de la transformation.

columnNames

String[]

Tableau de noms de colonnes à conserver.

keepHidden

Boolean

Si true elle conserve les colonnes masquées et false supprime les colonnes masquées. La conservation des colonnes masquées, au lieu de les supprimer, est recommandée lorsqu’il est nécessaire de comprendre comment les entrées d’un mappage de pipeline aux sorties du pipeline, à des fins de débogage.

Retours

ColumnSelectingEstimator

Exemples

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; }
        }
    }
}