FeatureSelectionCatalog.SelectFeaturesBasedOnMutualInformation Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Transforms.dll

Paquete:

Microsoft.ML v2.0.0

Sobrecargas

SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], String, Int32, Int32)	Cree un MutualInformationFeatureSelectingEstimatorobjeto , que selecciona las k ranuras principales en todas las columnas especificadas ordenadas por su información mutua con la columna de etiqueta.
SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, String, String, String, Int32, Int32)	Cree un MutualInformationFeatureSelectingEstimatorobjeto , que selecciona las k ranuras principales en todas las columnas especificadas ordenadas por su información mutua con la columna de etiqueta.

SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], String, Int32, Int32)

Cree un MutualInformationFeatureSelectingEstimatorobjeto , que selecciona las k ranuras principales en todas las columnas especificadas ordenadas por su información mutua con la columna de etiqueta.

public static Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMutualInformation (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, string labelColumnName = "Label", int slotsInOutput = 1000, int numberOfBins = 256);

static member SelectFeaturesBasedOnMutualInformation : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * Microsoft.ML.InputOutputColumnPair[] * string * int * int -> Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator

<Extension()>
Public Function SelectFeaturesBasedOnMutualInformation (catalog As TransformsCatalog.FeatureSelectionTransforms, columns As InputOutputColumnPair(), Optional labelColumnName As String = "Label", Optional slotsInOutput As Integer = 1000, Optional numberOfBins As Integer = 256) As MutualInformationFeatureSelectingEstimator

Parámetros

catalog

TransformsCatalog.FeatureSelectionTransforms

Catálogo de la transformación.

columns

InputOutputColumnPair[]

Especifica los nombres de las columnas de entrada para la transformación y sus respectivos nombres de columna de salida.

labelColumnName

String

Nombre de la columna de etiquetas.

slotsInOutput

Int32

Número máximo de ranuras que se conservarán en la salida. El número de ranuras que se van a conservar se toma en todas las columnas de entrada.

numberOfBins

Int32

Número máximo de contenedores utilizados para aproximar la información mutua entre cada columna de entrada y la columna de etiqueta. Potencia de 2 recomendada.

Devoluciones

MutualInformationFeatureSelectingEstimator

Ejemplos

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

namespace Samples.Dynamic
{
    public static class SelectFeaturesBasedOnMutualInformationMultiColumn
    {
        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($"NumericVectorA            NumericVectorB");
            foreach (var item in rawData)
                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
                    .NumericVectorA), string.Join(",", item.NumericVectorB));

            // NumericVectorA              NumericVectorB
            // 4,0,6                       7,8,9
            // 0,5,7                       7,9,0
            // 4,0,6                       7,8,9
            // 0,5,7                       7,8,0

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

            // We define a MutualInformationFeatureSelectingEstimator that selects
            // the top k slots in a feature vector based on highest mutual
            // information between that slot and a specified label. 

            // Multi column example : This pipeline transform two columns using the
            // provided parameters.
            var pipeline = mlContext.Transforms.FeatureSelection
                .SelectFeaturesBasedOnMutualInformation(new InputOutputColumnPair[]
                { new InputOutputColumnPair("NumericVectorA"), new
                InputOutputColumnPair("NumericVectorB") }, labelColumnName: "Label",
                slotsInOutput: 4);

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

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

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

            // NumericVectorA              NumericVectorB
            // 4,0,6                       9
            // 0,5,7                       0
            // 4,0,6                       9
            // 0,5,7                       0
        }

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

            public float[] NumericVectorB { get; set; }
        }

        public class NumericData
        {
            public bool Label;

            [VectorType(3)]
            public float[] NumericVectorA { get; set; }

            [VectorType(3)]
            public float[] NumericVectorB { get; set; }
        }

        /// <summary>
        /// Returns a few rows of numeric data.
        /// </summary>
        public static IEnumerable<NumericData> GetData()
        {
            var data = new List<NumericData>
            {
                new NumericData
                {
                    Label = true,
                    NumericVectorA = new float[] { 4, 0, 6 },
                    NumericVectorB = new float[] { 7, 8, 9 },
                },
                new NumericData
                {
                    Label = false,
                    NumericVectorA = new float[] { 0, 5, 7 },
                    NumericVectorB = new float[] { 7, 9, 0 },
                },
                new NumericData
                {
                    Label = true,
                    NumericVectorA = new float[] { 4, 0, 6 },
                    NumericVectorB = new float[] { 7, 8, 9 },
                },
                new NumericData
                {
                    Label = false,
                    NumericVectorA = new float[] { 0, 5, 7 },
                    NumericVectorB = new float[] { 7, 8, 0 },
                }
            };
            return data;
        }
    }
}

SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, String, String, String, Int32, Int32)

Cree un MutualInformationFeatureSelectingEstimatorobjeto , que selecciona las k ranuras principales en todas las columnas especificadas ordenadas por su información mutua con la columna de etiqueta.

public static Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMutualInformation (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, string outputColumnName, string inputColumnName = default, string labelColumnName = "Label", int slotsInOutput = 1000, int numberOfBins = 256);

static member SelectFeaturesBasedOnMutualInformation : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * string * string * string * int * int -> Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator

<Extension()>
Public Function SelectFeaturesBasedOnMutualInformation (catalog As TransformsCatalog.FeatureSelectionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional labelColumnName As String = "Label", Optional slotsInOutput As Integer = 1000, Optional numberOfBins As Integer = 256) As MutualInformationFeatureSelectingEstimator

Parámetros

catalog

TransformsCatalog.FeatureSelectionTransforms

Catálogo de la transformación.

outputColumnName

String

Nombre de la columna resultante de la transformación de inputColumnName.

inputColumnName

String

Nombre de columna que se va a transformar. Si se establece en null, el valor de outputColumnName se usará como origen.

labelColumnName

String

Nombre de la columna de etiquetas.

slotsInOutput

Int32

Número máximo de ranuras que se conservarán en la salida. El número de ranuras que se van a conservar se toma en todas las columnas de entrada.

numberOfBins

Int32

Número máximo de contenedores utilizados para aproximar la información mutua entre cada columna de entrada y la columna de etiqueta. Potencia de 2 recomendada.

Devoluciones

MutualInformationFeatureSelectingEstimator

Ejemplos

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

namespace Samples.Dynamic
{
    public static class SelectFeaturesBasedOnMutualInformation
    {
        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($"Label             NumericVector");
            foreach (var item in rawData)
                Console.WriteLine("{0,-25} {1,-25}", item.Label, string.Join(",",
                    item.NumericVector));

            // Label                       NumericVector
            // True                        4,0,6
            // False                       0,5,7
            // True                        4,0,6
            // False                       0,5,7

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

            // We define a MutualInformationFeatureSelectingEstimator that selects
            // the top k slots in a feature vector based on highest mutual
            // information between that slot and a specified label. 
            var pipeline = mlContext.Transforms.FeatureSelection
                .SelectFeaturesBasedOnMutualInformation(outputColumnName:
                "NumericVector", labelColumnName: "Label", slotsInOutput: 2);

            // The pipeline can then be trained, using .Fit(), and the resulting
            // transformer can be used to transform data. 
            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");
            foreach (var item in convertedData)
                Console.WriteLine("{0,-25}", string.Join(",", item.NumericVector));

            // NumericVector
            // 4,0
            // 0,5
            // 4,0
            // 0,5
        }

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

        public class NumericData
        {
            public bool Label;

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

        /// <summary>
        /// Returns a few rows of numeric data.
        /// </summary>
        public static IEnumerable<NumericData> GetData()
        {
            var data = new List<NumericData>
            {
                new NumericData
                {
                    Label = true,
                    NumericVector = new float[] { 4, 0, 6 },
                },
                new NumericData
                {
                    Label = false,
                    NumericVector = new float[] { 0, 5, 7 },
                },
                new NumericData
                {
                    Label = true,
                    NumericVector = new float[] { 4, 0, 6 },
                },
                new NumericData
                {
                    Label = false,
                    NumericVector = new float[] { 0, 5, 7 },
                }
            };
            return data;
        }
    }
}