NormalizationCatalog.NormalizeBinning Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v2.0.0

Überlädt

NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)	Erstellen Sie eine NormalizingEstimator, die normalisiert, indem Sie die Daten in Bins mit gleicher Dichte zuweisen.
NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32)	Erstellen Sie eine NormalizingEstimator, die normalisiert, indem Sie die Daten in Bins mit gleicher Dichte zuweisen.

NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)

Erstellen Sie eine NormalizingEstimator, die normalisiert, indem Sie die Daten in Bins mit gleicher Dichte zuweisen.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeBinning (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool fixZero = true, int maximumBinCount = 1024);

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

<Extension()>
Public Function NormalizeBinning (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional maximumBinCount As Integer = 1024) As NormalizingEstimator

Parameter

catalog

TransformsCatalog

Der Transformationskatalog

columns

InputOutputColumnPair[]

Die Paare der Eingabe- und Ausgabespalten. Die Eingabespalten müssen vom Datentyp SingleDouble oder einen bekannten Vektor dieser Typen sein. Der Datentyp für die Ausgabespalte entspricht der zugeordneten Eingabespalte.

maximumExampleCount

Int64

Maximale Anzahl von Beispielen, die zum Trainieren des Normalizers verwendet werden.

fixZero

Boolean

Ob Null bis Null zugeordnet werden soll, wobei die Sparsität beibehalten wird.

maximumBinCount

Int32

Maximale Anzahl von Bins (Leistung von 2 empfohlen).

Gibt zurück

NormalizingEstimator

Beispiele

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 NormalizeBinningMulticolumn
    {
        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},
                    Features2 = 1 },

                new DataPoint(){ Features = new float[4] { 6, 2, 2, 0},
                    Features2 = 4 },

                new DataPoint(){ Features = new float[4] { 4, 0, 1, 0},
                    Features2 = 1 },

                new DataPoint(){ Features = new float[4] { 2,-1,-1, 1},
                    Features2 = 2 }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // NormalizeBinning normalizes the data by constructing equidensity bins
            // and produce output based on to which bin the original value belongs.
            var normalize = mlContext.Transforms.NormalizeBinning(new[]{
                new InputOutputColumnPair("Features"),
                new InputOutputColumnPair("Features2"),
                },
                maximumBinCount: 4, fixZero: false);

            // 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 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" + column2[i]);
            // Expected output:
            //
            //  Features                            Feature2
            //  1.0000, 0.6667, 1.0000, 0.0000          0
            //  0.6667, 1.0000, 0.6667, 0.0000          1
            //  0.3333, 0.3333, 0.3333, 0.0000          0
            //  0.0000, 0.0000, 0.0000, 1.0000          0.5
        }

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

            public float Features2 { get; set; }
        }
    }
}

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

Erstellen Sie eine NormalizingEstimator, die normalisiert, indem Sie die Daten in Bins mit gleicher Dichte zuweisen.

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

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

<Extension()>
Public Function NormalizeBinning (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional maximumBinCount As Integer = 1024) As NormalizingEstimator

Parameter

catalog

TransformsCatalog

Der Transformationskatalog

outputColumnName

String

Name der Spalte, die aus der Transformation von inputColumnName. Der Datentyp in dieser Spalte entspricht der Eingabespalte.

inputColumnName

String

Name der zu transformierenden Spalte. nullWenn festgelegt auf , wird der Wert des outputColumnName Werts als Quelle verwendet. Der Datentyp dieser Spalte sollte oder ein bekannter Vektor dieser Typen seinSingleDouble.

maximumExampleCount

Int64

Maximale Anzahl von Beispielen, die zum Trainieren des Normalizers verwendet werden.

fixZero

Boolean

Ob Null bis Null zugeordnet werden soll, wobei die Sparsität beibehalten wird.

maximumBinCount

Int32

Maximale Anzahl von Bins (Leistung von 2 empfohlen).

Gibt zurück

NormalizingEstimator

Beispiele

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 NormalizeBinning
    {
        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} },
                new DataPoint(){ Features = new float[4] { 6, 2, 2, 0} },
                new DataPoint(){ Features = new float[4] { 4, 0, 1, 0} },
                new DataPoint(){ Features = new float[4] { 2,-1,-1, 1} }
            };
            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var data = mlContext.Data.LoadFromEnumerable(samples);
            // NormalizeBinning normalizes the data by constructing equidensity bins
            // and produce output based on 
            // to which bin the original value belongs.
            var normalize = mlContext.Transforms.NormalizeBinning("Features",
                maximumBinCount: 4, fixZero: false);

            // NormalizeBinning normalizes the data by constructing equidensity bins
            // 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.NormalizeBinning("Features",
                maximumBinCount: 4, 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, 0.6667, 1.0000, 0.0000
            //  0.6667, 1.0000, 0.6667, 0.0000
            //  0.3333, 0.3333, 0.3333, 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:
            //  1.0000, 0.3333, 1.0000, 0.0000
            //  0.6667, 0.6667, 0.6667, 0.0000
            //  0.3333, 0.0000, 0.3333, 0.0000
            //  0.0000, -0.3333, 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>>;

            var density = transformParams.Density[0];
            var offset = (transformParams.Offset.Length == 0 ? 0 : transformParams
                .Offset[0]);

            Console.WriteLine($"The 0-index value in resulting array would be " +
                $"produce by: y = (Index(x) / {density}) - {offset}");

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

            Console.WriteLine("Bins upper bounds are: " + string.Join(" ",
                transformParams.UpperBounds[0]));
            // Expected output:
            //  The 0-index value in resulting array would be produce by: y = (Index(x) / 3) - 0
            //  Where Index(x) is the index of the bin to which x belongs
            //  Bins upper bounds are: 3 5 7 ∞

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

            density = fixZeroParams.Density[1];
            offset = (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams
                .Offset[1]);

            Console.WriteLine($"The 0-index value in resulting array would be " +
                $"produce by: y = (Index(x) / {density}) - {offset}");

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

            Console.WriteLine("Bins upper bounds are: " + string.Join(" ",
                fixZeroParams.UpperBounds[1]));
            // Expected output:
            //  The 0-index value in resulting array would be produce by: y = (Index(x) / 3) - 0.3333333
            //  Where Index(x) is the index of the bin to which x belongs
            //  Bins upper bounds are: -0.5 0.5 1.5 ∞
        }

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