NormalizationCatalog.NormalizeBinning 메서드
정의
중요
일부 정보는 릴리스되기 전에 상당 부분 수정될 수 있는 시험판 제품과 관련이 있습니다. Microsoft는 여기에 제공된 정보에 대해 어떠한 명시적이거나 묵시적인 보증도 하지 않습니다.
오버로드
| NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32) |
Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density. |
| NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32) |
Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density. |
NormalizeBinning(TransformsCatalog, InputOutputColumnPair[], Int64, Boolean, Int32)
Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.
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 NormalizingEstimator매개 변수
- catalog
- TransformsCatalog
변환 카탈로그
- columns
- InputOutputColumnPair[]
입력 및 출력 열 쌍입니다. 입력 열은 데이터 형식이거나 해당 형식 SingleDouble 의 알려진 크기 벡터여야 합니다. 출력 열의 데이터 형식은 연결된 입력 열과 동일합니다.
- maximumExampleCount
- Int64
normalizer를 학습시키는 데 사용되는 최대 예제 수입니다.
- fixZero
- Boolean
0에서 0으로 매핑할지 여부, 스파스를 유지합니다.
- maximumBinCount
- Int32
최대 bin 수(권장되는 2개 전원)입니다.
반환
예제
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; }
}
}
}
적용 대상
NormalizeBinning(TransformsCatalog, String, String, Int64, Boolean, Int32)
Create a NormalizingEstimator, which normalizes by assigning the data into bins with equal density.
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 NormalizingEstimator매개 변수
- catalog
- TransformsCatalog
변환 카탈로그
- outputColumnName
- String
의 변환에서 생성된 열의 inputColumnName이름입니다.
이 열의 데이터 형식은 입력 열과 동일합니다.
- inputColumnName
- String
변환할 열의 이름입니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다.
이 열의 데이터 형식은 해당 형식의 알려진 크기 벡터여야 합니다SingleDouble.
- maximumExampleCount
- Int64
normalizer를 학습시키는 데 사용되는 최대 예제 수입니다.
- fixZero
- Boolean
0에서 0으로 매핑할지 여부, 스파스를 유지합니다.
- maximumBinCount
- Int32
최대 bin 수(권장되는 2개 전원)입니다.
반환
예제
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; }
}
}
}