NormalizationCatalog.NormalizeBinning 方法

定义

命名空间:

Microsoft.ML

程序集:

Microsoft.ML.Transforms.dll

包:

Microsoft.ML v3.0.1

重载

NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)	创建一个 NormalizingEstimator，通过将数据分配到密度相等的箱来规范化。
NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32)	创建一个 NormalizingEstimator，通过将数据分配到密度相等的箱来规范化。

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

创建一个 NormalizingEstimator，通过将数据分配到密度相等的箱来规范化。

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

参数

catalog

TransformsCatalog

转换目录

columns

InputOutputColumnPair[]

输入和输出列对。 输入列必须是数据类型 Single， Double 或者是这些类型的已知大小的向量。 输出列的数据类型将与关联的输入列相同。

maximumExampleCount

Int64

用于训练规范化器的最大示例数。

fixZero

Boolean

是否将零映射到零，保留稀疏。

maximumBinCount

Int32

建议的 2 个) (最大箱数。

返回

NormalizingEstimator

示例

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)

创建一个 NormalizingEstimator，通过将数据分配到密度相等的箱来规范化。

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

参数

catalog

TransformsCatalog

转换目录

outputColumnName

String

由转换 inputColumnName生成的列的名称。 此列上的数据类型与输入列相同。

inputColumnName

String

要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source. 此列上的数据类型应为SingleDouble已知大小的向量，或这些类型的已知大小向量。

maximumExampleCount

Int64

用于训练规范化器的最大示例数。

fixZero

Boolean

是否将零映射到零，保留稀疏。

maximumBinCount

Int32

建议的 2 个) (最大箱数。

返回

NormalizingEstimator

示例

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; }
        }
    }
}