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NormalizationCatalog.NormalizeRobustScaling Metodo

Definizione

Overload

NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)

Creare un NormalizingEstimatoroggetto , che normalizza l'uso di statistiche affidabili per i outlier centrando i dati intorno a 0 (rimuovendo il mediano) e ridimensiona i dati in base all'intervallo quantile (impostazione predefinita all'intervallo interquartile).

NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)

Creare un NormalizingEstimatoroggetto , che normalizza l'uso di statistiche affidabili per i outlier centrando i dati intorno a 0 (rimuovendo il mediano) e ridimensiona i dati in base all'intervallo quantile (impostazione predefinita all'intervallo interquartile).

NormalizeRobustScaling(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, UInt32, UInt32)

Creare un NormalizingEstimatoroggetto , che normalizza l'uso di statistiche affidabili per i outlier centrando i dati intorno a 0 (rimuovendo il mediano) e ridimensiona i dati in base all'intervallo quantile (impostazione predefinita all'intervallo interquartile).

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeRobustScaling (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool centerData = true, uint quantileMin = 25, uint quantileMax = 75);
static member NormalizeRobustScaling : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * int64 * bool * uint32 * uint32 -> Microsoft.ML.Transforms.NormalizingEstimator
<Extension()>
Public Function NormalizeRobustScaling (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional centerData As Boolean = true, Optional quantileMin As UInteger = 25, Optional quantileMax As UInteger = 75) As NormalizingEstimator

Parametri

catalog
TransformsCatalog

Catalogo di trasformazione

columns
InputOutputColumnPair[]

Coppie di colonne di input e output. Le colonne di input devono essere di tipo di SingleDouble dati o un vettore di dimensioni note di tali tipi. Il tipo di dati per la colonna di output sarà uguale alla colonna di input associata.

maximumExampleCount
Int64

Numero massimo di esempi usati per eseguire il training del normalizzatore.

centerData
Boolean

Indica se il centro dei dati intorno a 0 rimuove il mediano. Viene impostato come predefinito true.

quantileMin
UInt32

Quantile min usato per ridimensionare i dati. Il valore predefinito è 25.

quantileMax
UInt32

Quantile max usato per ridimensionare i dati. Il valore predefinito è 75.

Restituisce

Esempio

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; }
        }
    }
}

Si applica a

NormalizeRobustScaling(TransformsCatalog, String, String, Int64, Boolean, UInt32, UInt32)

Creare un NormalizingEstimatoroggetto , che normalizza l'uso di statistiche affidabili per i outlier centrando i dati intorno a 0 (rimuovendo il mediano) e ridimensiona i dati in base all'intervallo quantile (impostazione predefinita all'intervallo interquartile).

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeRobustScaling (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool centerData = true, uint quantileMin = 25, uint quantileMax = 75);
static member NormalizeRobustScaling : Microsoft.ML.TransformsCatalog * string * string * int64 * bool * uint32 * uint32 -> Microsoft.ML.Transforms.NormalizingEstimator
<Extension()>
Public Function NormalizeRobustScaling (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional centerData As Boolean = true, Optional quantileMin As UInteger = 25, Optional quantileMax As UInteger = 75) As NormalizingEstimator

Parametri

catalog
TransformsCatalog

Catalogo di trasformazione

outputColumnName
String

Nome della colonna risultante dalla trasformazione di inputColumnName. Il tipo di dati in questa colonna corrisponde alla colonna di input.

inputColumnName
String

Nome della colonna da trasformare. Se impostato su null, il valore dell'oggetto outputColumnName verrà usato come origine. Il tipo di dati in questa colonna deve essere Singleo Double un vettore di dimensioni note di tali tipi.

maximumExampleCount
Int64

Numero massimo di esempi usati per eseguire il training del normalizzatore.

centerData
Boolean

Indica se il centro dei dati intorno a 0 rimuovendo il mediano. Viene impostato come predefinito true.

quantileMin
UInt32

Quantile min usato per ridimensionare i dati. Il valore predefinito è 25.

quantileMax
UInt32

Quantile max usato per ridimensionare i dati. Il valore predefinito è 75.

Restituisce

Esempio

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; }
        }
    }
}

Si applica a