NormalizationCatalog.NormalizeMeanVariance Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Package:

Microsoft.ML v3.0.1

Overloads

NormalizeMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.
NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean)	Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.

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

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMeanVariance (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool fixZero = true, bool useCdf = false);

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

<Extension()>
Public Function NormalizeMeanVariance (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional useCdf As Boolean = false) 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.

useCdf

Boolean

Whether to use CDF as the output.

Returns

NormalizingEstimator

NormalizeMeanVariance(TransformsCatalog, String, String, Int64, Boolean, Boolean)

Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the data.

public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeMeanVariance (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool fixZero = true, bool useCdf = false);

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

<Extension()>
Public Function NormalizeMeanVariance (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional useCdf As Boolean = false) 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.

useCdf

Boolean

Whether to use CDF as the output.

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 NormalizeMeanVariance
    {
        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] { 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);
            // NormalizeMeanVariance normalizes the data based on the computed mean
            // and variance of the data. Uses Cumulative distribution function as
            // output.
            var normalize = mlContext.Transforms.NormalizeMeanVariance("Features",
                useCdf: true);

            // NormalizeMeanVariance normalizes the data based on the computed mean
            // and variance of the data.
            var normalizeNoCdf = mlContext.Transforms.NormalizeMeanVariance(
                "Features", useCdf: 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 normalizeNoCdfTransform = normalizeNoCdf.Fit(data);
            var noCdfData = normalizeNoCdfTransform.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:
            //  0.6726, 0.6726, 0.8816, 0.2819
            //  0.9101, 0.9101, 0.6939, 0.2819
            //  0.3274, 0.3274, 0.4329, 0.2819
            //  0.0899, 0.0899, 0.0641, 0.9584


            var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
            foreach (var row in columnFixZero)
                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
                    "f4"))));
            // Expected output:
            //  0.8165, 0.8165, 1.5492, 0.0000
            //  1.6330, 1.6330, 1.0328, 0.0000
            //  0.0000, 0.0000, 0.5164, 0.0000
            // -0.8165,-0.8165,-0.5164, 2.0000

            // 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 CdfNormalizerModelParameters<
                ImmutableArray<float>>;

            Console.WriteLine($"The 1-index value in resulting array would " +
                $"be produce by:");

            Console.WriteLine(" y = 0.5* (1 + ERF((x- " + transformParams.Mean[1] +
                ") / (" + transformParams.StandardDeviation[1] + " * sqrt(2)))");
            // ERF is https://en.wikipedia.org/wiki/Error_function.
            // Expected output:
            //  The 1-index value in resulting array would be produce by:
            //  y = 0.5 * (1 + ERF((x - 0.5) / (1.118034 * sqrt(2)))

            var noCdfParams = normalizeNoCdfTransform
                .GetNormalizerModelParameters(0) as
                AffineNormalizerModelParameters<ImmutableArray<float>>;

            var offset = noCdfParams.Offset.Length == 0 ? 0 : noCdfParams.Offset[1];
            var scale = noCdfParams.Scale[1];
            Console.WriteLine($"Values for slot 1 would be transformed by " +
                $"applying y = (x - ({offset})) * {scale}");
            // Expected output:
            // The 1-index value in resulting array would be produce by: y = (x - (0)) * 0.8164966
        }

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