FeatureSelectionCatalog.SelectFeaturesBasedOnMutualInformation 方法
定义
重要
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重载
SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], String, Int32, Int32)
创建一个 MutualInformationFeatureSelectingEstimator，它选择所有指定列的顶部 k 槽，其相互信息与标签列排序。
public static Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMutualInformation (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, string labelColumnName = "Label", int slotsInOutput = 1000, int numberOfBins = 256);static member SelectFeaturesBasedOnMutualInformation : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * Microsoft.ML.InputOutputColumnPair[] * string * int * int -> Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator<Extension()>
Public Function SelectFeaturesBasedOnMutualInformation (catalog As TransformsCatalog.FeatureSelectionTransforms, columns As InputOutputColumnPair(), Optional labelColumnName As String = "Label", Optional slotsInOutput As Integer = 1000, Optional numberOfBins As Integer = 256) As MutualInformationFeatureSelectingEstimator参数
转换的目录。
- columns
- InputOutputColumnPair[]
指定转换的输入列的名称及其各自的输出列名称。
- labelColumnName
- String
标签列的名称。
- slotsInOutput
- Int32
输出中要保留的最大槽数。 要保留的槽数在所有输入列中都采用。
- numberOfBins
- Int32
用于在每个输入列和标签列之间近似相互信息的箱数上限。 建议使用 2 的电源。
返回
示例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class SelectFeaturesBasedOnMutualInformationMultiColumn
{
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();
// Get a small dataset as an IEnumerable and convert it to an IDataView.
var rawData = GetData();
// Printing the columns of the input data.
Console.WriteLine($"NumericVectorA NumericVectorB");
foreach (var item in rawData)
Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
.NumericVectorA), string.Join(",", item.NumericVectorB));
// NumericVectorA NumericVectorB
// 4,0,6 7,8,9
// 0,5,7 7,9,0
// 4,0,6 7,8,9
// 0,5,7 7,8,0
var data = mlContext.Data.LoadFromEnumerable(rawData);
// We define a MutualInformationFeatureSelectingEstimator that selects
// the top k slots in a feature vector based on highest mutual
// information between that slot and a specified label.
// Multi column example : This pipeline transform two columns using the
// provided parameters.
var pipeline = mlContext.Transforms.FeatureSelection
.SelectFeaturesBasedOnMutualInformation(new InputOutputColumnPair[]
{ new InputOutputColumnPair("NumericVectorA"), new
InputOutputColumnPair("NumericVectorB") }, labelColumnName: "Label",
slotsInOutput: 4);
var transformedData = pipeline.Fit(data).Transform(data);
var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, true);
// Printing the columns of the transformed data.
Console.WriteLine($"NumericVectorA NumericVectorB");
foreach (var item in convertedData)
Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
.NumericVectorA), string.Join(",", item.NumericVectorB));
// NumericVectorA NumericVectorB
// 4,0,6 9
// 0,5,7 0
// 4,0,6 9
// 0,5,7 0
}
private class TransformedData
{
public float[] NumericVectorA { get; set; }
public float[] NumericVectorB { get; set; }
}
public class NumericData
{
public bool Label;
[VectorType(3)]
public float[] NumericVectorA { get; set; }
[VectorType(3)]
public float[] NumericVectorB { get; set; }
}
/// <summary>
/// Returns a few rows of numeric data.
/// </summary>
public static IEnumerable<NumericData> GetData()
{
var data = new List<NumericData>
{
new NumericData
{
Label = true,
NumericVectorA = new float[] { 4, 0, 6 },
NumericVectorB = new float[] { 7, 8, 9 },
},
new NumericData
{
Label = false,
NumericVectorA = new float[] { 0, 5, 7 },
NumericVectorB = new float[] { 7, 9, 0 },
},
new NumericData
{
Label = true,
NumericVectorA = new float[] { 4, 0, 6 },
NumericVectorB = new float[] { 7, 8, 9 },
},
new NumericData
{
Label = false,
NumericVectorA = new float[] { 0, 5, 7 },
NumericVectorB = new float[] { 7, 8, 0 },
}
};
return data;
}
}
}
适用于
SelectFeaturesBasedOnMutualInformation(TransformsCatalog+FeatureSelectionTransforms, String, String, String, Int32, Int32)
创建一个 MutualInformationFeatureSelectingEstimator，它选择所有指定列的顶部 k 槽，其相互信息与标签列排序。
public static Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMutualInformation (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, string outputColumnName, string inputColumnName = default, string labelColumnName = "Label", int slotsInOutput = 1000, int numberOfBins = 256);static member SelectFeaturesBasedOnMutualInformation : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * string * string * string * int * int -> Microsoft.ML.Transforms.MutualInformationFeatureSelectingEstimator<Extension()>
Public Function SelectFeaturesBasedOnMutualInformation (catalog As TransformsCatalog.FeatureSelectionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional labelColumnName As String = "Label", Optional slotsInOutput As Integer = 1000, Optional numberOfBins As Integer = 256) As MutualInformationFeatureSelectingEstimator参数
转换的目录。
- outputColumnName
- String
由转换 inputColumnName生成的列的名称。
- inputColumnName
- String
要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source.
- labelColumnName
- String
标签列的名称。
- slotsInOutput
- Int32
输出中要保留的最大槽数。 要保留的槽数在所有输入列中都采用。
- numberOfBins
- Int32
用于在每个输入列和标签列之间近似相互信息的箱数上限。 建议使用 2 的电源。
返回
示例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class SelectFeaturesBasedOnMutualInformation
{
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();
// Get a small dataset as an IEnumerable and convert it to an IDataView.
var rawData = GetData();
// Printing the columns of the input data.
Console.WriteLine($"Label NumericVector");
foreach (var item in rawData)
Console.WriteLine("{0,-25} {1,-25}", item.Label, string.Join(",",
item.NumericVector));
// Label NumericVector
// True 4,0,6
// False 0,5,7
// True 4,0,6
// False 0,5,7
var data = mlContext.Data.LoadFromEnumerable(rawData);
// We define a MutualInformationFeatureSelectingEstimator that selects
// the top k slots in a feature vector based on highest mutual
// information between that slot and a specified label.
var pipeline = mlContext.Transforms.FeatureSelection
.SelectFeaturesBasedOnMutualInformation(outputColumnName:
"NumericVector", labelColumnName: "Label", slotsInOutput: 2);
// The pipeline can then be trained, using .Fit(), and the resulting
// transformer can be used to transform data.
var transformedData = pipeline.Fit(data).Transform(data);
var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, true);
// Printing the columns of the transformed data.
Console.WriteLine($"NumericVector");
foreach (var item in convertedData)
Console.WriteLine("{0,-25}", string.Join(",", item.NumericVector));
// NumericVector
// 4,0
// 0,5
// 4,0
// 0,5
}
public class TransformedData
{
public float[] NumericVector { get; set; }
}
public class NumericData
{
public bool Label;
[VectorType(3)]
public float[] NumericVector { get; set; }
}
/// <summary>
/// Returns a few rows of numeric data.
/// </summary>
public static IEnumerable<NumericData> GetData()
{
var data = new List<NumericData>
{
new NumericData
{
Label = true,
NumericVector = new float[] { 4, 0, 6 },
},
new NumericData
{
Label = false,
NumericVector = new float[] { 0, 5, 7 },
},
new NumericData
{
Label = true,
NumericVector = new float[] { 4, 0, 6 },
},
new NumericData
{
Label = false,
NumericVector = new float[] { 0, 5, 7 },
}
};
return data;
}
}
}