NormalizationCatalog.NormalizeSupervisedBinning Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Transforms.dll

Paquete:

Microsoft.ML v2.0.0

Sobrecargas

NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32)	Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la labelColumnName columna.
NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32)	Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la labelColumnName columna.

NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32)

Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la labelColumnName columna.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeSupervisedBinning (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, string labelColumnName = "Label", long maximumExampleCount = 1000000000, bool fixZero = true, int maximumBinCount = 1024, int mininimumExamplesPerBin = 10);

static member NormalizeSupervisedBinning : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * string * int64 * bool * int * int -> Microsoft.ML.Transforms.NormalizingEstimator

<Extension()>
Public Function NormalizeSupervisedBinning (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional labelColumnName As String = "Label", Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional maximumBinCount As Integer = 1024, Optional mininimumExamplesPerBin As Integer = 10) As NormalizingEstimator

Parámetros

catalog

TransformsCatalog

Catálogo de transformación

columns

InputOutputColumnPair[]

Pares de columnas de entrada y salida. Las columnas de entrada deben ser de tipo SingleDouble de datos o un vector de tamaño conocido de esos tipos. El tipo de datos de la columna de salida será el mismo que la columna de entrada asociada.

labelColumnName

String

Nombre de la columna de etiqueta para la discretización supervisada.

maximumExampleCount

Int64

Número máximo de ejemplos usados para entrenar el normalizador.

fixZero

Boolean

Si se asigna cero a cero, conservando la sparsidad.

maximumBinCount

Int32

Número máximo de contenedores (potencia de 2 recomendados).

mininimumExamplesPerBin

Int32

Número mínimo de ejemplos por contenedor.

Devoluciones

NormalizingEstimator

NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32)

Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la labelColumnName columna.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeSupervisedBinning (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, string labelColumnName = "Label", long maximumExampleCount = 1000000000, bool fixZero = true, int maximumBinCount = 1024, int mininimumExamplesPerBin = 10);

static member NormalizeSupervisedBinning : Microsoft.ML.TransformsCatalog * string * string * string * int64 * bool * int * int -> Microsoft.ML.Transforms.NormalizingEstimator

<Extension()>
Public Function NormalizeSupervisedBinning (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional labelColumnName As String = "Label", Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional maximumBinCount As Integer = 1024, Optional mininimumExamplesPerBin As Integer = 10) As NormalizingEstimator

Parámetros

catalog

TransformsCatalog

Catálogo de transformación

outputColumnName

String

Nombre de la columna resultante de la transformación de inputColumnName. El tipo de datos de esta columna es el mismo que la columna de entrada.

inputColumnName

String

Nombre de la columna que se va a transformar. Si se establece nullen , el valor de outputColumnName se usará como origen. El tipo de datos de esta columna debe ser Singleo Double un vector de tamaño conocido de esos tipos.

labelColumnName

String

Nombre de la columna de etiqueta para la discretización supervisada.

maximumExampleCount

Int64

Número máximo de ejemplos usados para entrenar el normalizador.

fixZero

Boolean

Si se asigna cero a cero, conservando la sparsidad.

maximumBinCount

Int32

Número máximo de contenedores (potencia de 2 recomendados).

mininimumExamplesPerBin

Int32

Número mínimo de ejemplos por contenedor.

Devoluciones

NormalizingEstimator

Ejemplos

using System;
using System.Collections.Generic;
using System.Collections.Immutable;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using static Microsoft.ML.Transforms.NormalizingTransformer;

namespace Samples.Dynamic
{
    public class NormalizeSupervisedBinning
    {
        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();
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features = new float[4] { 8, 1, 3, 0},
                    Bin ="Bin1" },

                new DataPoint(){ Features = new float[4] { 6, 2, 2, 1},
                    Bin ="Bin2" },

                new DataPoint(){ Features = new float[4] { 5, 3, 0, 2},
                    Bin ="Bin2" },

                new DataPoint(){ Features = new float[4] { 4,-8, 1, 3},
                    Bin ="Bin3" },

                new DataPoint(){ Features = new float[4] { 2,-5,-1, 4},
                    Bin ="Bin3" }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // Let's transform "Bin" column from string to key.
            data = mlContext.Transforms.Conversion.MapValueToKey("Bin").Fit(data)
                .Transform(data);
            // NormalizeSupervisedBinning normalizes the data by constructing bins
            // based on correlation with the label column and produce output based
            // on to which bin original value belong.
            var normalize = mlContext.Transforms.NormalizeSupervisedBinning(
                "Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1,
                fixZero: false);

            // NormalizeSupervisedBinning normalizes the data by constructing bins
            // based on correlation with the label column and produce output based
            // on to which bin original value belong but make sure zero values would
            // remain zero after normalization. Helps preserve sparsity.
            var normalizeFixZero = mlContext.Transforms.NormalizeSupervisedBinning(
                "Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1,
                fixZero: true);

            // Now we can transform the data and look at the output to confirm the
            // behavior of the estimator. This operation doesn't actually evaluate
            // data until we read the data below.
            var normalizeTransform = normalize.Fit(data);
            var transformedData = normalizeTransform.Transform(data);
            var normalizeFixZeroTransform = normalizeFixZero.Fit(data);
            var fixZeroData = normalizeFixZeroTransform.Transform(data);
            var column = transformedData.GetColumn<float[]>("Features").ToArray();
            foreach (var row in column)
                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
                    "f4"))));
            // Expected output:
            //  1.0000, 0.5000, 1.0000, 0.0000
            //  0.5000, 1.0000, 0.0000, 0.5000
            //  0.5000, 1.0000, 0.0000, 0.5000
            //  0.0000, 0.0000, 0.0000, 1.0000
            //  0.0000, 0.0000, 0.0000, 1.0000

            var columnFixZero = fixZeroData.GetColumn<float[]>("Features")
                .ToArray();

            foreach (var row in columnFixZero)
                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
                    "f4"))));
            // Expected output:
            //  1.0000, 0.0000, 1.0000, 0.0000
            //  0.5000, 0.5000, 0.0000, 0.5000
            //  0.5000, 0.5000, 0.0000, 0.5000
            //  0.0000,-0.5000, 0.0000, 1.0000
            //  0.0000,-0.5000, 0.0000, 1.0000

            // Let's get transformation parameters. Since we work with only one
            // column we need to pass 0 as parameter for
            // GetNormalizerModelParameters.
            // If we have multiple columns transformations we need to pass index of
            // InputOutputColumnPair.
            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
                as BinNormalizerModelParameters<ImmutableArray<float>>;

            Console.WriteLine($"The 1-index value in resulting array would be " +
                $"produce by:");

            Console.WriteLine("y = (Index(x) / " + transformParams.Density[0] +
                ") - " + (transformParams.Offset.Length == 0 ? 0 : transformParams
                .Offset[0]));

            Console.WriteLine("Where Index(x) is the index of the bin to which " +
                "x belongs");

            Console.WriteLine("Bins upper borders are: " + string.Join(" ",
                transformParams.UpperBounds[0]));
            // Expected output:
            //  The 1-index value in resulting array would be produce by:
            //  y = (Index(x) / 2) - 0
            //  Where Index(x) is the index of the bin to which x belongs
            //  Bins upper bounds are: 4.5 7 ∞

            var fixZeroParams = normalizeFixZeroTransform
                .GetNormalizerModelParameters(0) as BinNormalizerModelParameters<
                ImmutableArray<float>>;

            Console.WriteLine($"The 1-index value in resulting array would be " +
                $"produce by:");

            Console.WriteLine(" y = (Index(x) / " + fixZeroParams.Density[1] +
                ") - " + (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams
                .Offset[1]));

            Console.WriteLine("Where Index(x) is the index of the bin to which x " +
                "belongs");

            Console.WriteLine("Bins upper borders are: " + string.Join(" ",
                fixZeroParams.UpperBounds[1]));
            // Expected output:
            //  The 1-index value in resulting array would be produce by:
            //  y = (Index(x) / 2) - 0.5
            //  Where Index(x) is the index of the bin to which x belongs
            //  Bins upper bounds are: -2 1.5 ∞
        }

        private class DataPoint
        {
            [VectorType(4)]
            public float[] Features { get; set; }

            public string Bin { get; set; }
        }
    }
}