PermutationFeatureImportanceExtensions.PermutationFeatureImportance 方法
定義
重要
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多載
PermutationFeatureImportance(MulticlassClassificationCatalog, ITransformer, IDataView, String, Boolean, Nullable<Int32>, Int32)
多分類的置換特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.MulticlassClassificationMetricsStatistics> PermutationFeatureImportance(this Microsoft.ML.MulticlassClassificationCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportance : Microsoft.ML.MulticlassClassificationCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.MulticlassClassificationMetricsStatistics><Extension()>
Public Function PermutationFeatureImportance (catalog As MulticlassClassificationCatalog, model As ITransformer, data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableDictionary(Of String, MulticlassClassificationMetricsStatistics)參數
- catalog
- MulticlassClassificationCatalog
多級分類目錄。
- model
- ITransformer
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤欄名。 欄位資料必須為 KeyDataViewType。
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
字典將每個特徵映射到其對配樂的每個特徵「貢獻」。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns =
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
var pipeline = mlContext.Transforms
.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.MulticlassClassification.Trainers
.SdcaMaximumEntropy());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.MulticlassClassification
.PermutationFeatureImportance(linearPredictor, transformedData,
permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on
// microaccuracy.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new { index, metrics.MicroAccuracy })
.OrderByDescending(feature => Math.Abs(feature.MicroAccuracy.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in MicroAccuracy\t95% Confidence in "
+ "the Mean Change in MicroAccuracy");
var microAccuracy = permutationMetrics.Select(x => x.MicroAccuracy)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
microAccuracy[i].Mean,
1.96 * microAccuracy[i].StandardError);
}
// Expected output:
//Feature Change in MicroAccuracy 95% Confidence in the Mean Change in MicroAccuracy
//Feature2 -0.1395 0.0006567
//Feature1 -0.05367 0.0006908
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the
/// label.</param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)
(bias + weight1 * data.Feature1 + weight2 * data.Feature2 +
rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如微觀準確度），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的多類別分類評估指標的變化，並回傳 a ImmutableArray 的 MulticlassClassificationMetrics 物件。 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。
適用於
PermutationFeatureImportance(RegressionCatalog, ITransformer, IDataView, String, Boolean, Nullable<Int32>, Int32)
迴歸分析中的置換特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.RegressionMetricsStatistics> PermutationFeatureImportance(this Microsoft.ML.RegressionCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportance : Microsoft.ML.RegressionCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.RegressionMetricsStatistics><Extension()>
Public Function PermutationFeatureImportance (catalog As RegressionCatalog, model As ITransformer, data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableDictionary(Of String, RegressionMetricsStatistics)參數
- catalog
- RegressionCatalog
回歸目錄。
- model
- ITransformer
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
字典將每個特徵映射到其對配樂的每個特徵「貢獻」。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Regression
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1),
nameof(Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate(
"Features",
featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Regression.Trainers.Ols());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Regression
.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on RMSE.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new
{
index,
metrics.RootMeanSquaredError
})
.OrderByDescending(feature => Math.Abs(
feature.RootMeanSquaredError.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tModel Weight\tChange in RMSE\t95%" +
"Confidence in the Mean Change in RMSE");
var rmse = permutationMetrics.Select(x => x.RootMeanSquaredError)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:G4}\t{3:G4}",
featureColumns[i],
linearPredictor.Model.Weights[i],
rmse[i].Mean,
1.96 * rmse[i].StandardError);
}
// Expected output:
// Feature Model Weight Change in RMSE 95% Confidence in the Mean Change in RMSE
// Feature2 9.00 4.009 0.008304
// Feature1 4.48 1.901 0.003351
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
yield return data;
}
}
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如R平方），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的迴歸評估指標變化，並回傳物件ImmutableArray的RegressionMetrics 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。
適用於
PermutationFeatureImportance(RankingCatalog, ITransformer, IDataView, String, String, Boolean, Nullable<Int32>, Int32)
排序特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.RankingMetricsStatistics> PermutationFeatureImportance(this Microsoft.ML.RankingCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", string rowGroupColumnName = "GroupId", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportance : Microsoft.ML.RankingCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.RankingMetricsStatistics><Extension()>
Public Function PermutationFeatureImportance (catalog As RankingCatalog, model As ITransformer, data As IDataView, Optional labelColumnName As String = "Label", Optional rowGroupColumnName As String = "GroupId", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableDictionary(Of String, RankingMetricsStatistics)參數
- catalog
- RankingCatalog
排名目錄。
- model
- ITransformer
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤欄名。 欄位資料必須是 Single 或 KeyDataViewType。
- rowGroupColumnName
- String
GroupID 欄位名稱
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
字典將每個特徵映射到其對配樂的每個特徵「貢獻」。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1), nameof(
Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate("Features",
featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.Conversion.MapValueToKey(
"GroupId"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Ranking.Trainers.FastTree());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Ranking.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on NDCG@1.
var sortedIndices = permutationMetrics.Select((metrics, index) => new
{
index,
metrics.NormalizedDiscountedCumulativeGains
})
.OrderByDescending(feature => Math.Abs(
feature.NormalizedDiscountedCumulativeGains[0].Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the" +
"Mean Change in NDCG@1");
var ndcg = permutationMetrics.Select(
x => x.NormalizedDiscountedCumulativeGains).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
ndcg[i][0].Mean,
1.96 * ndcg[i][0].StandardError);
}
// Expected output:
// Feature Change in NDCG@1 95% Confidence in the Mean Change in NDCG@1
// Feature2 -0.2421 0.001748
// Feature1 -0.0513 0.001184
}
private class Data
{
public float Label { get; set; }
public int GroupId { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
///
/// <param name="nExamples">The number of examples.</param>
///
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
///
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
///
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
///
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
///
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1,
int groupSize = 5)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
GroupId = i / groupSize,
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如 NDCG），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的排名評估指標變化，並回傳 a ImmutableArray 的 RankingMetrics 物件。 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。
適用於
PermutationFeatureImportance<TModel>(BinaryClassificationCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)
二元分類中的置換特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.BinaryClassificationMetricsStatistics> PermutationFeatureImportance<TModel>(this Microsoft.ML.BinaryClassificationCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.BinaryClassificationCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.BinaryClassificationMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As BinaryClassificationCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of BinaryClassificationMetricsStatistics)類型參數
- TModel
參數
- catalog
- BinaryClassificationCatalog
二元分類目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
每部電影對配樂的「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.BinaryClassification
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns =
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
var pipeline = mlContext.Transforms
.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.BinaryClassification.Trainers
.SdcaLogisticRegression());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.BinaryClassification
.PermutationFeatureImportance(linearPredictor, transformedData,
permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on AUC.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new { index, metrics.AreaUnderRocCurve })
.OrderByDescending(
feature => Math.Abs(feature.AreaUnderRocCurve.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tModel Weight\tChange in AUC"
+ "\t95% Confidence in the Mean Change in AUC");
var auc = permutationMetrics.Select(x => x.AreaUnderRocCurve).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:G4}\t{3:G4}",
featureColumns[i],
linearPredictor.Model.SubModel.Weights[i],
auc[i].Mean,
1.96 * auc[i].StandardError);
}
// Expected output:
// Feature Model Weight Change in AUC 95% Confidence in the Mean Change in AUC
// Feature2 35.15 -0.387 0.002015
// Feature1 17.94 -0.1514 0.0008963
}
private class Data
{
public bool Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
data.Label = Sigmoid(value) > 0.5;
yield return data;
}
}
private static double Sigmoid(double x) => 1.0 / (1.0 + Math.Exp(-1 * x));
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如AUC），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的二元分類評估指標變化，並回傳物件的 BinaryClassificationMetrics aImmutableArray。 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。
適用於
PermutationFeatureImportance<TModel>(MulticlassClassificationCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)
多分類的置換特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.MulticlassClassificationMetricsStatistics> PermutationFeatureImportance<TModel>(this Microsoft.ML.MulticlassClassificationCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.MulticlassClassificationCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.MulticlassClassificationMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As MulticlassClassificationCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of MulticlassClassificationMetricsStatistics)類型參數
- TModel
參數
- catalog
- MulticlassClassificationCatalog
多級分類目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤欄名。 欄位資料必須為 KeyDataViewType。
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
每部電影對配樂的「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.MulticlassClassification
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns =
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) };
var pipeline = mlContext.Transforms
.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.MulticlassClassification.Trainers
.SdcaMaximumEntropy());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.MulticlassClassification
.PermutationFeatureImportance(linearPredictor, transformedData,
permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on
// microaccuracy.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new { index, metrics.MicroAccuracy })
.OrderByDescending(feature => Math.Abs(feature.MicroAccuracy.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in MicroAccuracy\t95% Confidence in "
+ "the Mean Change in MicroAccuracy");
var microAccuracy = permutationMetrics.Select(x => x.MicroAccuracy)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
microAccuracy[i].Mean,
1.96 * microAccuracy[i].StandardError);
}
// Expected output:
//Feature Change in MicroAccuracy 95% Confidence in the Mean Change in MicroAccuracy
//Feature2 -0.1395 0.0006567
//Feature1 -0.05367 0.0006908
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the
/// label.</param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)
(bias + weight1 * data.Feature1 + weight2 * data.Feature2 +
rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如微觀準確度），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的多類別分類評估指標的變化，並回傳 a ImmutableArray 的 MulticlassClassificationMetrics 物件。 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。
適用於
PermutationFeatureImportance<TModel>(RegressionCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, Boolean, Nullable<Int32>, Int32)
迴歸分析中的置換特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RegressionMetricsStatistics> PermutationFeatureImportance<TModel>(this Microsoft.ML.RegressionCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.RegressionCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RegressionMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As RegressionCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of RegressionMetricsStatistics)類型參數
- TModel
參數
- catalog
- RegressionCatalog
回歸目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
每部電影對配樂的「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Regression
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1),
nameof(Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate(
"Features",
featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Regression.Trainers.Ols());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Regression
.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on RMSE.
var sortedIndices = permutationMetrics
.Select((metrics, index) => new
{
index,
metrics.RootMeanSquaredError
})
.OrderByDescending(feature => Math.Abs(
feature.RootMeanSquaredError.Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tModel Weight\tChange in RMSE\t95%" +
"Confidence in the Mean Change in RMSE");
var rmse = permutationMetrics.Select(x => x.RootMeanSquaredError)
.ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:G4}\t{3:G4}",
featureColumns[i],
linearPredictor.Model.Weights[i],
rmse[i].Mean,
1.96 * rmse[i].StandardError);
}
// Expected output:
// Feature Model Weight Change in RMSE 95% Confidence in the Mean Change in RMSE
// Feature2 9.00 4.009 0.008304
// Feature1 4.48 1.901 0.003351
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
/// <param name="nExamples">The number of examples.</param>
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1)
{
var rng = new Random(seed);
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
yield return data;
}
}
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如R平方），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的迴歸評估指標變化，並回傳物件ImmutableArray的RegressionMetrics 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。
適用於
PermutationFeatureImportance<TModel>(RankingCatalog, ISingleFeaturePredictionTransformer<TModel>, IDataView, String, String, Boolean, Nullable<Int32>, Int32)
排序特徵重要性（PFI）。
public static System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RankingMetricsStatistics> PermutationFeatureImportance<TModel>(this Microsoft.ML.RankingCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<TModel> predictionTransformer, Microsoft.ML.IDataView data, string labelColumnName = "Label", string rowGroupColumnName = "GroupId", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1) where TModel : class;static member PermutationFeatureImportance : Microsoft.ML.RankingCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> * Microsoft.ML.IDataView * string * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableArray<Microsoft.ML.Data.RankingMetricsStatistics> (requires 'Model : null)<Extension()>
Public Function PermutationFeatureImportance(Of TModel As Class) (catalog As RankingCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of TModel), data As IDataView, Optional labelColumnName As String = "Label", Optional rowGroupColumnName As String = "GroupId", Optional useFeatureWeightFilter As Boolean = false, Optional numberOfExamplesToUse As Nullable(Of Integer) = Nothing, Optional permutationCount As Integer = 1) As ImmutableArray(Of RankingMetricsStatistics)類型參數
- TModel
參數
- catalog
- RankingCatalog
排名目錄。
- predictionTransformer
- ISingleFeaturePredictionTransformer<TModel>
評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- labelColumnName
- String
標籤欄名。 欄位資料必須是 Single 或 KeyDataViewType。
- rowGroupColumnName
- String
GroupID 欄位名稱
- useFeatureWeightFilter
- Boolean
利用特徵權重來預先過濾特徵。
- permutationCount
- Int32
要執行的排列組合數量。
傳回
每部電影對配樂的「貢獻」陣列。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic.Trainers.Ranking
{
public static class PermutationFeatureImportance
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness.
var mlContext = new MLContext(seed: 1);
// Create sample data.
var samples = GenerateData();
// Load the sample data as an IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Define a training pipeline that concatenates features into a vector,
// normalizes them, and then trains a linear model.
var featureColumns = new string[] { nameof(Data.Feature1), nameof(
Data.Feature2) };
var pipeline = mlContext.Transforms.Concatenate("Features",
featureColumns)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.Transforms.Conversion.MapValueToKey(
"GroupId"))
.Append(mlContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Ranking.Trainers.FastTree());
// Fit the pipeline to the data.
var model = pipeline.Fit(data);
// Transform the dataset.
var transformedData = model.Transform(data);
// Extract the predictor.
var linearPredictor = model.LastTransformer;
// Compute the permutation metrics for the linear model using the
// normalized data.
var permutationMetrics = mlContext.Ranking.PermutationFeatureImportance(
linearPredictor, transformedData, permutationCount: 30);
// Now let's look at which features are most important to the model
// overall. Get the feature indices sorted by their impact on NDCG@1.
var sortedIndices = permutationMetrics.Select((metrics, index) => new
{
index,
metrics.NormalizedDiscountedCumulativeGains
})
.OrderByDescending(feature => Math.Abs(
feature.NormalizedDiscountedCumulativeGains[0].Mean))
.Select(feature => feature.index);
Console.WriteLine("Feature\tChange in NDCG@1\t95% Confidence in the" +
"Mean Change in NDCG@1");
var ndcg = permutationMetrics.Select(
x => x.NormalizedDiscountedCumulativeGains).ToArray();
foreach (int i in sortedIndices)
{
Console.WriteLine("{0}\t{1:G4}\t{2:G4}",
featureColumns[i],
ndcg[i][0].Mean,
1.96 * ndcg[i][0].StandardError);
}
// Expected output:
// Feature Change in NDCG@1 95% Confidence in the Mean Change in NDCG@1
// Feature2 -0.2421 0.001748
// Feature1 -0.0513 0.001184
}
private class Data
{
public float Label { get; set; }
public int GroupId { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
/// <summary>
/// Generate an enumerable of Data objects, creating the label as a simple
/// linear combination of the features.
/// </summary>
///
/// <param name="nExamples">The number of examples.</param>
///
/// <param name="bias">The bias, or offset, in the calculation of the label.
/// </param>
///
/// <param name="weight1">The weight to multiply the first feature with to
/// compute the label.</param>
///
/// <param name="weight2">The weight to multiply the second feature with to
/// compute the label.</param>
///
/// <param name="seed">The seed for generating feature values and label
/// noise.</param>
///
/// <returns>An enumerable of Data objects.</returns>
private static IEnumerable<Data> GenerateData(int nExamples = 10000,
double bias = 0, double weight1 = 1, double weight2 = 2, int seed = 1,
int groupSize = 5)
{
var rng = new Random(seed);
var max = bias + 4.5 * weight1 + 4.5 * weight2 + 0.5;
for (int i = 0; i < nExamples; i++)
{
var data = new Data
{
GroupId = i / groupSize,
Feature1 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
Feature2 = (float)(rng.Next(10) * (rng.NextDouble() - 0.5)),
};
// Create a noisy label.
var value = (float)(bias + weight1 * data.Feature1 + weight2 *
data.Feature2 + rng.NextDouble() - 0.5);
if (value < max / 3)
data.Label = 0;
else if (value < 2 * max / 3)
data.Label = 1;
else
data.Label = 2;
yield return data;
}
}
}
}
備註
置換特徵重要性（PFI）是一種用來判斷訓練機器學習模型中特徵的全局重要性的技術。 PFI 是一種簡單卻強大的技術，由 Breiman 在其隨機森林論文第 10 節中提出（Breiman 著）。「隨機森林。」機器學習，2001年。）PFI 方法的優點在於它與模型無關——可與任何可評估的模型合作——且可使用任何資料集（不僅限於訓練集）來計算特徵重要性指標。
PFI 的運作方式是將一個標記過的資料集選出一個特徵，並將該特徵的值置換到所有範例中，使每個範例的特徵值與其他特徵的原始值各有隨機值。 接著計算此修改後資料集的評估指標（例如 NDCG），並計算評估指標與原始資料集的變化。 評估指標變化越大，該特徵對模型的重要性越大。 PFI 的運作方式是對模型的所有特徵依序進行置換分析。
在此實作中，PFI 計算每個特徵所有可能的排名評估指標變化，並回傳 a ImmutableArray 的 RankingMetrics 物件。 請參考下方範例，示範如何利用這些結果分析模型的特徵重要性。