FeatureSelectionCatalog.SelectFeaturesBasedOnMutualInformation Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Pacchetto:

Microsoft.ML v2.0.0

Overload

SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], String, Int32, Int32)	Creare un MutualInformationFeatureSelectingEstimatoroggetto , che seleziona gli slot principali k in tutte le colonne specificate ordinate in base alle relative informazioni reciproche con la colonna etichetta.
SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, String, String, String, Int32, Int32)	Creare un MutualInformationFeatureSelectingEstimatoroggetto , che seleziona gli slot principali k in tutte le colonne specificate ordinate in base alle relative informazioni reciproche con la colonna etichetta.

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

Creare un MutualInformationFeatureSelectingEstimatoroggetto , che seleziona gli slot principali k in tutte le colonne specificate ordinate in base alle relative informazioni reciproche con la colonna etichetta.

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

Parametri

catalog

TransformsCatalog.FeatureSelectionTransforms

Catalogo della trasformazione.

columns

InputOutputColumnPair[]

Specifica i nomi delle colonne di input per la trasformazione e i rispettivi nomi di colonna di output.

labelColumnName

String

Nome della colonna dell'etichetta.

slotsInOutput

Int32

Numero massimo di slot da mantenere nell'output. Il numero di slot da mantenere viene acquisito in tutte le colonne di input.

numberOfBins

Int32

Numero massimo di contenitori usati per approssimare le informazioni reciproche tra ogni colonna di input e la colonna etichetta. Potenza di 2 consigliata.

Restituisce

MutualInformationFeatureSelectingEstimator

Esempio

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)

Creare un MutualInformationFeatureSelectingEstimatoroggetto , che seleziona gli slot principali k in tutte le colonne specificate ordinate in base alle relative informazioni reciproche con la colonna etichetta.

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

Parametri

catalog

TransformsCatalog.FeatureSelectionTransforms

Catalogo della trasformazione.

outputColumnName

String

Nome della colonna risultante dalla trasformazione di inputColumnName.

inputColumnName

String

Nome della colonna da trasformare. Se impostato su null, il valore di outputColumnName verrà usato come origine.

labelColumnName

String

Nome della colonna dell'etichetta.

slotsInOutput

Int32

Numero massimo di slot da mantenere nell'output. Il numero di slot da mantenere viene acquisito in tutte le colonne di input.

numberOfBins

Int32

Numero massimo di contenitori usati per approssimare le informazioni reciproche tra ogni colonna di input e la colonna etichetta. Potenza di 2 consigliata.

Restituisce

MutualInformationFeatureSelectingEstimator

Esempio

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;
        }
    }
}