FeatureSelectionCatalog.SelectFeaturesBasedOnCount 方法
定義
重要
部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。
多載
SelectFeaturesBasedOnCount(TransformsCatalog+FeatureSelectionTransforms, InputOutputColumnPair[], Int64)
建立 ， CountFeatureSelectingEstimator 這會選取非預設值計數大於或等於閾值的位置。
public static Microsoft.ML.Transforms.CountFeatureSelectingEstimator SelectFeaturesBasedOnCount (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, long count = 1);static member SelectFeaturesBasedOnCount : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * Microsoft.ML.InputOutputColumnPair[] * int64 -> Microsoft.ML.Transforms.CountFeatureSelectingEstimator<Extension()>
Public Function SelectFeaturesBasedOnCount (catalog As TransformsCatalog.FeatureSelectionTransforms, columns As InputOutputColumnPair(), Optional count As Long = 1) As CountFeatureSelectingEstimator參數
轉換的目錄。
- columns
- InputOutputColumnPair[]
指定要套用轉換的資料行名稱。 此估算器會透過數值、文字或索引鍵資料類型的向量或純量運作。 輸出資料行的資料類型會與輸入資料行的資料類型相同。
- count
- Int64
如果位置的非預設值計數大於或等於定型資料中的這個臨界值，則會保留該位置。
傳回
範例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class SelectFeaturesBasedOnCountMultiColumn
{
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($"NumericVector StringVector");
foreach (var item in rawData)
Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.
NumericVector), string.Join(",", item.StringVector));
// NumericVector StringVector
// 4,NaN,6 A,WA,Male
// 4,5,6 A,,Female
// 4,5,6 A,NY,
// 4,NaN,NaN A,,Male
var data = mlContext.Data.LoadFromEnumerable(rawData);
// We will use the SelectFeaturesBasedOnCount transform estimator, to
// retain only those slots which have at least 'count' non-default
// values per slot.
// Multi column example. This pipeline transform two columns using the
// provided parameters.
var pipeline = mlContext.Transforms.FeatureSelection
.SelectFeaturesBasedOnCount(new InputOutputColumnPair[] { new
InputOutputColumnPair("NumericVector"), new InputOutputColumnPair(
"StringVector") }, count: 3);
var transformedData = pipeline.Fit(data).Transform(data);
var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, true);
// Printing the columns of the transformed data.
Console.WriteLine($"NumericVector StringVector");
foreach (var item in convertedData)
Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
.NumericVector), string.Join(",", item.StringVector));
// NumericVector StringVector
// 4,6 A,Male
// 4,6 A,Female
// 4,6 A,
// 4,NaN A,Male
}
private class TransformedData
{
public float[] NumericVector { get; set; }
public string[] StringVector { get; set; }
}
public class InputData
{
[VectorType(3)]
public float[] NumericVector { get; set; }
[VectorType(3)]
public string[] StringVector { get; set; }
}
/// <summary>
/// Returns a few rows of data.
/// </summary>
public static IEnumerable<InputData> GetData()
{
var data = new List<InputData>
{
new InputData
{
NumericVector = new float[] { 4, float.NaN, 6 },
StringVector = new string[] { "A", "WA", "Male"}
},
new InputData
{
NumericVector = new float[] { 4, 5, 6 },
StringVector = new string[] { "A", "", "Female"}
},
new InputData
{
NumericVector = new float[] { 4, 5, 6 },
StringVector = new string[] { "A", "NY", null}
},
new InputData
{
NumericVector = new float[] { 4, float.NaN, float.NaN },
StringVector = new string[] { "A", null, "Male"}
}
};
return data;
}
}
}
適用於
SelectFeaturesBasedOnCount(TransformsCatalog+FeatureSelectionTransforms, String, String, Int64)
建立 ， CountFeatureSelectingEstimator 這會選取非預設值計數大於或等於閾值的位置。
public static Microsoft.ML.Transforms.CountFeatureSelectingEstimator SelectFeaturesBasedOnCount (this Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms catalog, string outputColumnName, string inputColumnName = default, long count = 1);static member SelectFeaturesBasedOnCount : Microsoft.ML.TransformsCatalog.FeatureSelectionTransforms * string * string * int64 -> Microsoft.ML.Transforms.CountFeatureSelectingEstimator<Extension()>
Public Function SelectFeaturesBasedOnCount (catalog As TransformsCatalog.FeatureSelectionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional count As Long = 1) As CountFeatureSelectingEstimator參數
轉換的目錄。
- outputColumnName
- String
轉換所產生的 inputColumnName 資料行名稱。
此資料行的資料類型會與輸入資料行的資料類型相同。
- inputColumnName
- String
要轉換的資料行名稱。 如果設定為 null ，則會將 的值 outputColumnName 當做來源使用。
此估算器會透過數值、文字或索引鍵資料類型的向量或純量運作。
- count
- Int64
如果位置的非預設值計數大於或等於定型資料中的這個臨界值，則會保留該位置。
傳回
範例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class SelectFeaturesBasedOnCount
{
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($"NumericVector StringVector");
foreach (var item in rawData)
Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
.NumericVector), string.Join(",", item.StringVector));
// NumericVector StringVector
// 4,NaN,6 A,WA,Male
// 4,5,6 A,,Female
// 4,5,6 A,NY,
// 4,0,NaN A,,Male
var data = mlContext.Data.LoadFromEnumerable(rawData);
// We will use the SelectFeaturesBasedOnCount to retain only those slots
// which have at least 'count' non-default and non-missing values per
// slot.
var pipeline =
mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(
outputColumnName: "NumericVector", count: 3) // Usage on numeric
// column.
.Append(mlContext.Transforms.FeatureSelection
.SelectFeaturesBasedOnCount(outputColumnName: "StringVector",
count: 3)); // Usage on text column.
var transformedData = pipeline.Fit(data).Transform(data);
var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, true);
// Printing the columns of the transformed data.
Console.WriteLine($"NumericVector StringVector");
foreach (var item in convertedData)
Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.
NumericVector), string.Join(",", item.StringVector));
// NumericVector StringVector
// 4,6 A,Male
// 4,6 A,Female
// 4,6 A,
// 4,NaN A,Male
}
public class TransformedData
{
public float[] NumericVector { get; set; }
public string[] StringVector { get; set; }
}
public class InputData
{
[VectorType(3)]
public float[] NumericVector { get; set; }
[VectorType(3)]
public string[] StringVector { get; set; }
}
/// <summary>
/// Return a few rows of data.
/// </summary>
public static IEnumerable<InputData> GetData()
{
var data = new List<InputData>
{
new InputData
{
NumericVector = new float[] { 4, float.NaN, 6 },
StringVector = new string[] { "A", "WA", "Male"}
},
new InputData
{
NumericVector = new float[] { 4, 5, 6 },
StringVector = new string[] { "A", string.Empty, "Female"}
},
new InputData
{
NumericVector = new float[] { 4, 5, 6 },
StringVector = new string[] { "A", "NY", null}
},
new InputData
{
NumericVector = new float[] { 4, 0, float.NaN },
StringVector = new string[] { "A", null, "Male"}
}
};
return data;
}
}
}