NormalizationCatalog.NormalizeSupervisedBinning Método
Definición
Importante
Parte de la información hace referencia a la versión preliminar del producto, que puede haberse modificado sustancialmente antes de lanzar la versión definitiva. Microsoft no otorga ninguna garantía, explícita o implícita, con respecto a la información proporcionada aquí.
Sobrecargas
| NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32) |
Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la |
| NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32) |
Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la |
NormalizeSupervisedBinning(TransformsCatalog, InputOutputColumnPair[], String, Int64, Boolean, Int32, Int32)
Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la labelColumnName columna.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeSupervisedBinning (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns, string labelColumnName = "Label", long maximumExampleCount = 1000000000, bool fixZero = true, int maximumBinCount = 1024, int mininimumExamplesPerBin = 10);static member NormalizeSupervisedBinning : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] * string * int64 * bool * int * int -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeSupervisedBinning (catalog As TransformsCatalog, columns As InputOutputColumnPair(), Optional labelColumnName As String = "Label", Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional maximumBinCount As Integer = 1024, Optional mininimumExamplesPerBin As Integer = 10) As NormalizingEstimatorParámetros
- catalog
- TransformsCatalog
Catálogo de transformación
- columns
- InputOutputColumnPair[]
Pares de columnas de entrada y salida. Las columnas de entrada deben ser de tipo SingleDouble de datos o un vector de tamaño conocido de esos tipos. El tipo de datos de la columna de salida será el mismo que la columna de entrada asociada.
- labelColumnName
- String
Nombre de la columna de etiqueta para la discretización supervisada.
- maximumExampleCount
- Int64
Número máximo de ejemplos usados para entrenar el normalizador.
- fixZero
- Boolean
Si se asigna cero a cero, conservando la sparsidad.
- maximumBinCount
- Int32
Número máximo de contenedores (potencia de 2 recomendados).
- mininimumExamplesPerBin
- Int32
Número mínimo de ejemplos por contenedor.
Devoluciones
Se aplica a
NormalizeSupervisedBinning(TransformsCatalog, String, String, String, Int64, Boolean, Int32, Int32)
Cree un NormalizingEstimatorobjeto , que normaliza asignando los datos a intervalos en función de la correlación con la labelColumnName columna.
public static Microsoft.ML.Transforms.NormalizingEstimator NormalizeSupervisedBinning (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, string labelColumnName = "Label", long maximumExampleCount = 1000000000, bool fixZero = true, int maximumBinCount = 1024, int mininimumExamplesPerBin = 10);static member NormalizeSupervisedBinning : Microsoft.ML.TransformsCatalog * string * string * string * int64 * bool * int * int -> Microsoft.ML.Transforms.NormalizingEstimator<Extension()>
Public Function NormalizeSupervisedBinning (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional labelColumnName As String = "Label", Optional maximumExampleCount As Long = 1000000000, Optional fixZero As Boolean = true, Optional maximumBinCount As Integer = 1024, Optional mininimumExamplesPerBin As Integer = 10) As NormalizingEstimatorParámetros
- catalog
- TransformsCatalog
Catálogo de transformación
- outputColumnName
- String
Nombre de la columna resultante de la transformación de inputColumnName.
El tipo de datos de esta columna es el mismo que la columna de entrada.
- inputColumnName
- String
Nombre de la columna que se va a transformar. Si se establece nullen , el valor de outputColumnName se usará como origen.
El tipo de datos de esta columna debe ser Singleo Double un vector de tamaño conocido de esos tipos.
- labelColumnName
- String
Nombre de la columna de etiqueta para la discretización supervisada.
- maximumExampleCount
- Int64
Número máximo de ejemplos usados para entrenar el normalizador.
- fixZero
- Boolean
Si se asigna cero a cero, conservando la sparsidad.
- maximumBinCount
- Int32
Número máximo de contenedores (potencia de 2 recomendados).
- mininimumExamplesPerBin
- Int32
Número mínimo de ejemplos por contenedor.
Devoluciones
Ejemplos
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 NormalizeSupervisedBinning
{
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},
Bin ="Bin1" },
new DataPoint(){ Features = new float[4] { 6, 2, 2, 1},
Bin ="Bin2" },
new DataPoint(){ Features = new float[4] { 5, 3, 0, 2},
Bin ="Bin2" },
new DataPoint(){ Features = new float[4] { 4,-8, 1, 3},
Bin ="Bin3" },
new DataPoint(){ Features = new float[4] { 2,-5,-1, 4},
Bin ="Bin3" }
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Let's transform "Bin" column from string to key.
data = mlContext.Transforms.Conversion.MapValueToKey("Bin").Fit(data)
.Transform(data);
// NormalizeSupervisedBinning normalizes the data by constructing bins
// based on correlation with the label column and produce output based
// on to which bin original value belong.
var normalize = mlContext.Transforms.NormalizeSupervisedBinning(
"Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1,
fixZero: false);
// NormalizeSupervisedBinning normalizes the data by constructing bins
// based on correlation with the label column 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.NormalizeSupervisedBinning(
"Features", labelColumnName: "Bin", mininimumExamplesPerBin: 1,
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, 0.5000, 1.0000, 0.0000
// 0.5000, 1.0000, 0.0000, 0.5000
// 0.5000, 1.0000, 0.0000, 0.5000
// 0.0000, 0.0000, 0.0000, 1.0000
// 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, 0.0000, 1.0000, 0.0000
// 0.5000, 0.5000, 0.0000, 0.5000
// 0.5000, 0.5000, 0.0000, 0.5000
// 0.0000,-0.5000, 0.0000, 1.0000
// 0.0000,-0.5000, 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>>;
Console.WriteLine($"The 1-index value in resulting array would be " +
$"produce by:");
Console.WriteLine("y = (Index(x) / " + transformParams.Density[0] +
") - " + (transformParams.Offset.Length == 0 ? 0 : transformParams
.Offset[0]));
Console.WriteLine("Where Index(x) is the index of the bin to which " +
"x belongs");
Console.WriteLine("Bins upper borders are: " + string.Join(" ",
transformParams.UpperBounds[0]));
// Expected output:
// The 1-index value in resulting array would be produce by:
// y = (Index(x) / 2) - 0
// Where Index(x) is the index of the bin to which x belongs
// Bins upper bounds are: 4.5 7 ∞
var fixZeroParams = normalizeFixZeroTransform
.GetNormalizerModelParameters(0) as BinNormalizerModelParameters<
ImmutableArray<float>>;
Console.WriteLine($"The 1-index value in resulting array would be " +
$"produce by:");
Console.WriteLine(" y = (Index(x) / " + fixZeroParams.Density[1] +
") - " + (fixZeroParams.Offset.Length == 0 ? 0 : fixZeroParams
.Offset[1]));
Console.WriteLine("Where Index(x) is the index of the bin to which x " +
"belongs");
Console.WriteLine("Bins upper borders are: " + string.Join(" ",
fixZeroParams.UpperBounds[1]));
// Expected output:
// The 1-index value in resulting array would be produce by:
// y = (Index(x) / 2) - 0.5
// Where Index(x) is the index of the bin to which x belongs
// Bins upper bounds are: -2 1.5 ∞
}
private class DataPoint
{
[VectorType(4)]
public float[] Features { get; set; }
public string Bin { get; set; }
}
}
}