NormalizationCatalog.NormalizeMinMax Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Package:

Microsoft.ML v3.0.1

Overloads

NormalizeMinMax(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.
NormalizeMinMax(TransformsCatalog, String, String, Int64, Boolean)	Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.

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

Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.

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

Parameters

catalog

TransformsCatalog

The transform catalog

columns

InputOutputColumnPair[]

The pairs of input and output columns. The input columns must be of data type Single, Double or a known-sized vector of those types. The data type for the output column will be the same as the associated input column.

maximumExampleCount

Int64

Maximum number of examples used to train the normalizer.

fixZero

Boolean

Whether to map zero to zero, preserving sparsity.

Returns

NormalizingEstimator

Examples

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)

Create a NormalizingEstimator, which normalizes based on the observed minimum and maximum values of the data.

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

Parameters

catalog

TransformsCatalog

The transform catalog

outputColumnName

String

Name of the column resulting from the transformation of inputColumnName. The data type on this column is the same as the input column.

inputColumnName

String

Name of the column to transform. If set to null, the value of the outputColumnName will be used as source. The data type on this column should be Single, Double or a known-sized vector of those types.

maximumExampleCount

Int64

Maximum number of examples used to train the normalizer.

fixZero

Boolean

Whether to map zero to zero, preserving sparsity.

Returns

NormalizingEstimator

Examples

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; }
        }
    }
}