NormalizationCatalog.NormalizeMinMax Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.Transforms.dll

Paquete:

Microsoft.ML v3.0.1

Sobrecargas

NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	Cree un NormalizingEstimatorobjeto , que normaliza en función de los valores mínimo y máximo observados de los datos.
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)	Cree un NormalizingEstimatorobjeto , que normaliza en función de los valores mínimo y máximo observados de los datos.

NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)

Cree un NormalizingEstimatorobjeto , que normaliza en función de los valores mínimo y máximo observados de los datos.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMinMax (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool fixZero = true);

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

<Extension()>
Public Function NormalizeMinMax (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true) 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 Singlede datos o Double 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.

maximumExampleCount

Int64

Número máximo de ejemplos usados para entrenar el normalizador.

fixZero

Boolean

Si se va a asignar cero a cero, conservando la sparsidad.

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
{
    class NormalizeMinMaxMulticolumn
    {
        public static void Example()
        {
            var mlContext = new MLContext();
            var samples = new List<DataPoint>()
            {
                new DataPoint()
                {
                    Features = new float[4] { 1, 1, 3, 0 },
                    Features2 = new float[3] { 1, 2, 3 }
                },
                new DataPoint()
                {
                    Features = new float[4] { 2, 2, 2, 0 },
                    Features2 = new float[3] { 3, 4, 5 }
                },
                new DataPoint()
                {
                    Features = new float[4] { 0, 0, 1, 0 },
                    Features2 = new float[3] { 6, 7, 8 }
                },
                new DataPoint()
                {
                    Features = new float[4] {-1,-1,-1, 1 },
                    Features2 = new float[3] { 9, 0, 4 }
                }
            };

            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);

            var columnPair = new[]
            {
                new InputOutputColumnPair("Features"),
                new InputOutputColumnPair("Features2")
            };

            // NormalizeMinMax normalize rows by finding min and max values in each
            // row slot and setting projection of min value to 0 and max to 1 and
            // everything else to values in between.
            var normalize = mlContext.Transforms.NormalizeMinMax(columnPair,
                fixZero: false);

            // Normalize rows by finding min and max values in each row slot, but
            // make sure zero values remain zero after normalization. Helps
            // preserve sparsity. That is, to help maintain very little non-zero elements.
            var normalizeFixZero = mlContext.Transforms.NormalizeMinMax(columnPair,
                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();
            var column2 = transformedData.GetColumn<float[]>("Features2").ToArray();

            for (int i = 0; i < column.Length; i++)
                Console.WriteLine(string.Join(", ", column[i].Select(x => x
                .ToString("f4"))) + "\t\t" +
                string.Join(", ", column2[i].Select(x => x.ToString("f4"))));

            // Expected output:
            // Features                                Features2  
            // 0.6667, 0.6667, 1.0000, 0.0000          0.0000, 0.2857, 0.0000
            // 1.0000, 1.0000, 0.7500, 0.0000          0.2500, 0.5714, 0.4000
            // 0.3333, 0.3333, 0.5000, 0.0000          0.6250, 1.0000, 1.0000
            // 0.0000, 0.0000, 0.0000, 1.0000          1.0000, 0.0000, 0.2000

            var columnFixZero = fixZeroData.GetColumn<float[]>("Features").ToArray();
            var column2FixZero = fixZeroData.GetColumn<float[]>("Features2").ToArray();

            Console.WriteLine(Environment.NewLine);

            for (int i = 0; i < column.Length; i++)
                Console.WriteLine(string.Join(", ", columnFixZero[i].Select(x => x
                .ToString("f4"))) + "\t\t" +
                string.Join(", ", column2FixZero[i].Select(x => x.ToString("f4"))));

            // Expected output:
            // Features                                Features2  
            // 0.5000, 0.5000, 1.0000, 0.0000          0.1111, 0.2857, 0.3750
            // 1.0000, 1.0000, 0.6667, 0.0000          0.3333, 0.5714, 0.6250
            // 0.0000, 0.0000, 0.3333, 0.0000          0.6667, 1.0000, 1.0000
            // -0.5000, -0.5000, -0.3333, 1.0000       1.0000, 0.0000, 0.5000

            // Get transformation parameters. Since we have multiple columns
            // we need to pass index of InputOutputColumnPair.
            var transformParams = normalizeTransform.GetNormalizerModelParameters(0)
                as AffineNormalizerModelParameters<ImmutableArray<float>>;

            var transformParams2 = normalizeTransform.GetNormalizerModelParameters(1)
                as AffineNormalizerModelParameters<ImmutableArray<float>>;

            Console.WriteLine(Environment.NewLine);

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

            Console.WriteLine(" y = (x - (" + (transformParams.Offset.Length == 0 ?
                0 : transformParams.Offset[1]) + ")) * " + transformParams
                .Scale[1]);

            // Expected output:
            //  The 1-index value in resulting array would be produce by:
            //  y = (x - (-1)) * 0.3333333
        }

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

            [VectorType(3)]
            public float[] Features2 { get; set; }
        }
    }
}

NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)

Cree un NormalizingEstimatorobjeto , que normaliza en función de los valores mínimo y máximo observados de los datos.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMinMax (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool fixZero = true);

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

<Extension()>
Public Function NormalizeMinMax (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true) 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 en null, el valor de outputColumnName se usará como origen. El tipo de datos de esta columna debe ser Single, Double o un vector de tamaño conocido de esos tipos.

maximumExampleCount

Int64

Número máximo de ejemplos usados para entrenar el normalizador.

fixZero

Boolean

Si se va a asignar cero a cero, conservando la sparsidad.

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 NormalizeMinMax
    {
        public static void Example()
        {
            var mlContext = new MLContext();
            var samples = new List<DataPoint>()
            {
                new DataPoint(){ Features = new float[4] { 1, 1, 3, 0} },
                new DataPoint(){ Features = new float[4] { 2, 2, 2, 0} },
                new DataPoint(){ Features = new float[4] { 0, 0, 1, 0} },
                new DataPoint(){ Features = new float[4] {-1,-1,-1, 1} }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // NormalizeMinMax normalize rows by finding min and max values in each
            // row slot and setting projection of min value to 0 and max to 1 and
            // everything else to values in between.
            var normalize = mlContext.Transforms.NormalizeMinMax("Features",
                fixZero: false);

            // Normalize rows by finding min and max values in each row slot, but
            // make sure zero values remain zero after normalization. Helps
            // preserve sparsity. That is, to help maintain very little non-zero elements.
            var normalizeFixZero = mlContext.Transforms.NormalizeMinMax("Features",
                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:
            //  0.6667, 0.6667, 1.0000, 0.0000
            //  1.0000, 1.0000, 0.7500, 0.0000
            //  0.3333, 0.3333, 0.5000, 0.0000
            //  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:
            //  0.5000, 0.5000, 1.0000, 0.0000
            //  1.0000, 1.0000, 0.6667, 0.0000
            //  0.0000, 0.0000, 0.3333, 0.0000
            // -0.5000,-0.5000,-0.3333, 1.0000

            // 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 AffineNormalizerModelParameters<ImmutableArray<float>>;

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

            Console.WriteLine(" y = (x - (" + (transformParams.Offset.Length == 0 ?
                0 : transformParams.Offset[1]) + ")) * " + transformParams
                .Scale[1]);
            // Expected output:
            //  The 1-index value in resulting array would be produce by: 
            //  y = (x - (-1)) * 0.3333333
        }

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