NormalizationCatalog.NormalizeLogMeanVariance Method
Definition
Important
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Overloads
| NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
| NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
| NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
| NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean) |
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data. |
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean)
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParameters
- 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.
- useCdf
- Boolean
Whether to use CDF as the output.
Returns
Applies to
NormalizeLogMeanVariance(TransformsCatalog, InputOutputColumnPair[], Boolean, Int64, Boolean)
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, bool fixZero, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * bool * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, columns As InputOutputColumnPair(), fixZero As Boolean, Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParameters
- 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.
- fixZero
- Boolean
Whether to map zero to zero, preserving sparsity.
- maximumExampleCount
- Int64
Maximum number of examples used to train the normalizer.
- useCdf
- Boolean
Whether to use CDF as the output.
Returns
Applies to
NormalizeLogMeanVariance(TransformsCatalog, String, String, Int64, Boolean)
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * string * string * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParameters
- 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.
- useCdf
- Boolean
Whether to use CDF as the output.
Returns
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 NormalizeLogMeanVariance
{
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[5] { 1, 1, 3, 0, float.MaxValue } },
new DataPoint(){ Features = new float[5] { 2, 2, 2, 0, float.MinValue } },
new DataPoint(){ Features = new float[5] { 0, 0, 1, 0, 0} },
new DataPoint(){ Features = new float[5] {-1,-1,-1, 1, 1} }
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// NormalizeLogMeanVariance normalizes the data based on the computed
// mean and variance of the logarithm of the data.
// Uses Cumulative distribution function as output.
var normalize = mlContext.Transforms.NormalizeLogMeanVariance(
"Features", useCdf: true);
// NormalizeLogMeanVariance normalizes the data based on the computed
// mean and variance of the logarithm of the data.
var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance(
"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:
// 0.1587, 0.1587, 0.8654, 0.0000, 0.8413
// 0.8413, 0.8413, 0.5837, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0940, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0000, 0.0000, 0.1587
var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
foreach (var row in columnFixZero)
Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
"f4"))));
// Expected output:
// 1.8854, 1.8854, 5.2970, 0.0000, 7670682000000000000000000000000000000.0000
// 4.7708, 4.7708, 3.0925, 0.0000, -7670682000000000000000000000000000000.0000
// -1.0000,-1.0000, 0.8879, 0.0000, -1.0000
// -3.8854,-3.8854,-3.5213, 0.0000, -0.9775
// 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((Math.Log(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((Math.Log(x)- 0.3465736) / (0.3465736 * 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($"The 1-index value in resulting array would be " +
$"produce by: y = (x - ({offset})) * {scale}");
// Expected output:
// The 1-index value in resulting array would be produce by: y = (x - (0.3465736)) * 2.88539
}
private class DataPoint
{
[VectorType(5)]
public float[] Features { get; set; }
}
}
}
Applies to
NormalizeLogMeanVariance(TransformsCatalog, String, Boolean, String, Int64, Boolean)
Create a NormalizingEstimator, which normalizes based on the computed mean and variance of the logarithm of the data.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeLogMeanVariance (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, bool fixZero, string inputColumnName = default, long maximumExampleCount = 1000000000, bool useCdf = true);static member NormalizeLogMeanVariance : Microsoft.ML.TransformsCatalog * string * bool * string * int64 * bool -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeLogMeanVariance (catalog As TransformsCatalog, outputColumnName As String, fixZero As Boolean, Optional inputColumnName As String = Nothing, Optional maximumExampleCount As Long = 1000000000, Optional useCdf As Boolean = true) As NormalizingEstimatorParameters
- 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.
- fixZero
- Boolean
Whether to map zero to zero, preserving sparsity.
- 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.
- useCdf
- Boolean
Whether to use CDF as the output.
Returns
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 NormalizeLogMeanVarianceFixZero
{
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[5] { 1, 1, 3, 0, float.MaxValue } },
new DataPoint(){ Features = new float[5] { 2, 2, 2, 0, float.MinValue } },
new DataPoint(){ Features = new float[5] { 0, 0, 1, 0, 0} },
new DataPoint(){ Features = new float[5] {-1,-1,-1, 1, 1} }
};
// Convert training data to IDataView, the general data type used in ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// NormalizeLogMeanVariance normalizes the data based on the computed mean and variance of the logarithm of the data.
// Uses Cumulative distribution function as output.
var normalize = mlContext.Transforms.NormalizeLogMeanVariance("Features", true, useCdf: true);
// NormalizeLogMeanVariance normalizes the data based on the computed mean and variance of the logarithm of the data.
var normalizeNoCdf = mlContext.Transforms.NormalizeLogMeanVariance("Features", true, 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:
// 0.1587, 0.1587, 0.8654, 0.0000, 0.8413
// 0.8413, 0.8413, 0.5837, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0940, 0.0000, 0.0000
// 0.0000, 0.0000, 0.0000, 0.0000, 0.1587
var columnFixZero = noCdfData.GetColumn<float[]>("Features").ToArray();
foreach (var row in columnFixZero)
Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
// Expected output:
// 2.0403, 2.0403, 4.0001, 0.0000, 5423991000000000000000000000000000000.0000
// 4.0806, 4.0806, 2.6667, 0.0000,-5423991000000000000000000000000000000.0000
// 0.0000, 0.0000, 1.3334, 0.0000, 0.0000
// -2.0403,-2.0403,-1.3334, 0.0000, 0.0159
// 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 values in the column with index 1 in the resulting array would be produced by:");
Console.WriteLine($"y = 0.5* (1 + ERF((Math.Log(x)- {transformParams.Mean[1]}) / ({transformParams.StandardDeviation[1]} * sqrt(2)))");
// ERF is https://en.wikipedia.org/wiki/Error_function.
// Expected output:
// The values in the column with index 1 in the resulting array would be produced by:
// y = 0.5 * (1 + ERF((Math.Log(x) - 0.3465736) / (0.3465736 * 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($"The values in the column with index 1 in the resulting array would be produced by: y = (x - ({offset})) * {scale}");
// Expected output:
// The values in the column with index 1 in the resulting array would be produced by: y = (x - (0)) * 2.040279
}
private class DataPoint
{
[VectorType(5)]
public float[] Features { get; set; }
}
}
}