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NormalizationCatalog.NormalizeSupervisedBinning Metoda

Definice

Přetížení

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

NormalizingEstimatorVytvořte , který normalizuje přiřazením dat do intervalů na základě korelace se sloupcemlabelColumnName.

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

NormalizingEstimatorVytvořte , který normalizuje přiřazením dat do intervalů na základě korelace se sloupcemlabelColumnName.

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

NormalizingEstimatorVytvořte , který normalizuje přiřazením dat do intervalů na základě korelace se sloupcemlabelColumnName.

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

Parametry

catalog
TransformsCatalog

Katalog transformací

columns
InputOutputColumnPair[]

Dvojice vstupních a výstupních sloupců. Vstupní sloupce musí být datového typu Singlenebo Double známého vektoru těchto typů. Datový typ pro výstupní sloupec bude stejný jako přidružený vstupní sloupec.

labelColumnName
String

Název sloupce popisku pro binning pod dohledem

maximumExampleCount
Int64

Maximální počet příkladů používaných k trénování normalizátoru

fixZero
Boolean

Zda se má mapovat nula na nulu, zachování sparsity.

maximumBinCount
Int32

Maximální počet intervalů (doporučená síla 2).

mininimumExamplesPerBin
Int32

Minimální počet příkladů na interval

Návraty

Platí pro

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

NormalizingEstimatorVytvořte , který normalizuje přiřazením dat do intervalů na základě korelace se sloupcemlabelColumnName.

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

Parametry

catalog
TransformsCatalog

Katalog transformací

outputColumnName
String

Název sloupce, který je výsledkem transformace inputColumnName. Datový typ v tomto sloupci je stejný jako vstupní sloupec.

inputColumnName
String

Název sloupce, který se má transformovat. Pokud je nastavená hodnota null, použije se jako zdroj hodnota outputColumnName . Datový typ v tomto sloupci by měl být Singlenebo Double známý vektor velikosti těchto typů.

labelColumnName
String

Název sloupce popisku pro binning pod dohledem

maximumExampleCount
Int64

Maximální počet příkladů používaných k trénování normalizátoru

fixZero
Boolean

Zda se má mapovat nula na nulu, zachování sparsity.

maximumBinCount
Int32

Maximální počet intervalů (doporučená síla 2).

mininimumExamplesPerBin
Int32

Minimální počet příkladů na interval

Návraty

Příklady

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; }
        }
    }
}

Platí pro