FeatureSelectionCatalog.SelectFeaturesBasedOnCount Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v2.0.0

Overload

SelectFeaturesBasedOnCount(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], Int64)	Buat CountFeatureSelectingEstimator, yang memilih slot yang jumlah nilai non-defaultnya lebih besar dari atau sama dengan ambang batas.
SelectFeaturesBasedOnCount(TransformsCatalog+FeatureSelectionTransforms, String, String, Int64)	Buat CountFeatureSelectingEstimator, yang memilih slot yang jumlah nilai non-defaultnya lebih besar dari atau sama dengan ambang batas.

SelectFeaturesBasedOnCount(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], Int64)

Buat CountFeatureSelectingEstimator, yang memilih slot yang jumlah nilai non-defaultnya lebih besar dari atau sama dengan ambang batas.

public static Microsoft.ML.Transforms.CountFeatureSelectingEstimator SelectFeaturesBasedOnCount (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, long count = 1);

static member SelectFeaturesBasedOnCount : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * Microsoft.ML.InputOutputColumnPair[] * int64 -> Microsoft.ML.Transforms.CountFeatureSelectingEstimator

<Extension()>
Public Function SelectFeaturesBasedOnCount (catalog As TransformsCatalog.FeatureSelectionTransforms, columns As InputOutputColumnPair(), Optional count As Long = 1) As CountFeatureSelectingEstimator

Parameter

catalog

TransformsCatalog.FeatureSelectionTransforms

Katalog transformasi.

columns

InputOutputColumnPair[]

Menentukan nama kolom untuk menerapkan transformasi. Estimator ini beroperasi melalui vektor atau skalar jenis data numerik, teks, atau kunci. Jenis data kolom output akan sama dengan jenis data kolom input.

count

Int64

Jika jumlah nilai non-default untuk slot lebih besar dari atau sama dengan ambang batas ini dalam data pelatihan, slot dipertahankan.

Mengembalikan

CountFeatureSelectingEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class SelectFeaturesBasedOnCountMultiColumn
    {
        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();

            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var rawData = GetData();

            // Printing the columns of the input data. 
            Console.WriteLine($"NumericVector             StringVector");
            foreach (var item in rawData)
                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.
                    NumericVector), string.Join(",", item.StringVector));

            // NumericVector             StringVector
            // 4,NaN,6                   A,WA,Male
            // 4,5,6                     A,,Female
            // 4,5,6                     A,NY,
            // 4,NaN,NaN                 A,,Male

            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // We will use the SelectFeaturesBasedOnCount transform estimator, to
            // retain only those slots which have at least 'count' non-default
            // values per slot.

            // Multi column example. This pipeline transform two columns using the
            // provided parameters.
            var pipeline = mlContext.Transforms.FeatureSelection
                .SelectFeaturesBasedOnCount(new InputOutputColumnPair[] { new
                InputOutputColumnPair("NumericVector"), new InputOutputColumnPair(
                "StringVector") }, count: 3);

            var transformedData = pipeline.Fit(data).Transform(data);

            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, true);

            // Printing the columns of the transformed data. 
            Console.WriteLine($"NumericVector             StringVector");
            foreach (var item in convertedData)
                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
                    .NumericVector), string.Join(",", item.StringVector));

            // NumericVector             StringVector
            // 4,6                       A,Male
            // 4,6                       A,Female
            // 4,6                       A,
            // 4,NaN                     A,Male
        }

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

            public string[] StringVector { get; set; }
        }

        public class InputData
        {
            [VectorType(3)]
            public float[] NumericVector { get; set; }

            [VectorType(3)]
            public string[] StringVector { get; set; }
        }

        /// <summary>
        /// Returns a few rows of data.
        /// </summary>
        public static IEnumerable<InputData> GetData()
        {
            var data = new List<InputData>
            {
                new InputData
                {
                    NumericVector = new float[] { 4, float.NaN, 6 },
                    StringVector = new string[] { "A", "WA", "Male"}
                },
                new InputData
                {
                    NumericVector = new float[] { 4, 5, 6 },
                    StringVector = new string[] { "A", "", "Female"}
                },
                new InputData
                {
                    NumericVector = new float[] { 4, 5, 6 },
                    StringVector = new string[] { "A", "NY", null}
                },
                new InputData
                {
                    NumericVector = new float[] { 4, float.NaN, float.NaN },
                    StringVector = new string[] { "A", null, "Male"}
                }
            };
            return data;
        }
    }
}

SelectFeaturesBasedOnCount(TransformsCatalog+FeatureSelectionTransforms, String, String, Int64)

Buat CountFeatureSelectingEstimator, yang memilih slot yang jumlah nilai non-defaultnya lebih besar dari atau sama dengan ambang batas.

public static Microsoft.ML.Transforms.CountFeatureSelectingEstimator SelectFeaturesBasedOnCount (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, string outputColumnName, string inputColumnName = default, long count = 1);

static member SelectFeaturesBasedOnCount : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * string * string * int64 -> Microsoft.ML.Transforms.CountFeatureSelectingEstimator

<Extension()>
Public Function SelectFeaturesBasedOnCount (catalog As TransformsCatalog.FeatureSelectionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional count As Long = 1) As CountFeatureSelectingEstimator

Parameter

catalog

TransformsCatalog.FeatureSelectionTransforms

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan sama dengan jenis data kolom input.

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi melalui vektor atau skalar jenis data numerik, teks, atau kunci.

count

Int64

Jika jumlah nilai non-default untuk slot lebih besar dari atau sama dengan ambang batas ini dalam data pelatihan, slot dipertahankan.

Mengembalikan

CountFeatureSelectingEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class SelectFeaturesBasedOnCount
    {
        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();

            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var rawData = GetData();

            // Printing the columns of the input data. 
            Console.WriteLine($"NumericVector             StringVector");
            foreach (var item in rawData)
                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
                    .NumericVector), string.Join(",", item.StringVector));

            // NumericVector             StringVector
            // 4,NaN,6                   A,WA,Male
            // 4,5,6                     A,,Female
            // 4,5,6                     A,NY,
            // 4,0,NaN                   A,,Male

            var data = mlContext.Data.LoadFromEnumerable(rawData);

            // We will use the SelectFeaturesBasedOnCount to retain only those slots
            // which have at least 'count' non-default and non-missing values per
            // slot.
            var pipeline =
                mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(
                    outputColumnName: "NumericVector", count: 3) // Usage on numeric 
                                                                 // column.
                .Append(mlContext.Transforms.FeatureSelection
                .SelectFeaturesBasedOnCount(outputColumnName: "StringVector",
                count: 3)); // Usage on text column.

            var transformedData = pipeline.Fit(data).Transform(data);

            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, true);

            // Printing the columns of the transformed data. 
            Console.WriteLine($"NumericVector             StringVector");
            foreach (var item in convertedData)
                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.
                    NumericVector), string.Join(",", item.StringVector));

            // NumericVector             StringVector
            // 4,6                       A,Male
            // 4,6                       A,Female
            // 4,6                       A,
            // 4,NaN                     A,Male
        }

        public class TransformedData
        {
            public float[] NumericVector { get; set; }

            public string[] StringVector { get; set; }
        }

        public class InputData
        {
            [VectorType(3)]
            public float[] NumericVector { get; set; }

            [VectorType(3)]
            public string[] StringVector { get; set; }
        }

        /// <summary>
        /// Return a few rows of data.
        /// </summary>
        public static IEnumerable<InputData> GetData()
        {
            var data = new List<InputData>
            {
                new InputData
                {
                    NumericVector = new float[] { 4, float.NaN, 6 },
                    StringVector = new string[] { "A", "WA", "Male"}
                },
                new InputData
                {
                    NumericVector = new float[] { 4, 5, 6 },
                    StringVector = new string[] { "A", string.Empty, "Female"}
                },
                new InputData
                {
                    NumericVector = new float[] { 4, 5, 6 },
                    StringVector = new string[] { "A", "NY", null}
                },
                new InputData
                {
                    NumericVector = new float[] { 4, 0, float.NaN },
                    StringVector = new string[] { "A", null, "Male"}
                }
            };
            return data;
        }
    }
}