PermutationFeatureImportanceExtensions.PermutationFeatureImportanceNonCalibrated 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。
二元分類的 PFI) (排列特徵重要性。
public static System.Collections.Immutable.ImmutableDictionary<string,Microsoft.ML.Data.BinaryClassificationMetricsStatistics> PermutationFeatureImportanceNonCalibrated(this Microsoft.ML.BinaryClassificationCatalog catalog, Microsoft.ML.ITransformer model, Microsoft.ML.IDataView data, string labelColumnName = "Label", bool useFeatureWeightFilter = false, int? numberOfExamplesToUse = default, int permutationCount = 1);static member PermutationFeatureImportanceNonCalibrated : Microsoft.ML.BinaryClassificationCatalog * Microsoft.ML.ITransformer * Microsoft.ML.IDataView * string * bool * Nullable<int> * int -> System.Collections.Immutable.ImmutableDictionary<string, Microsoft.ML.Data.BinaryClassificationMetricsStatistics><Extension()>
Public Function PermutationFeatureImportanceNonCalibrated (catalog As BinaryClassificationCatalog, 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, BinaryClassificationMetricsStatistics)參數
- catalog
- BinaryClassificationCatalog
二元分類目錄。
- model
- ITransformer
要評估特徵重要性的模型。
- data
- IDataView
評估資料集。
- useFeatureWeightFilter
- Boolean
使用功能權數預先篩選功能。
- permutationCount
- Int32
要執行的排列數目。
傳回
字典會將每個特徵對應至其每個特徵的「貢獻」到分數。
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
PFI) (排列特徵重要性是一種技術，可用來判斷定型機器學習模型中特徵的全域重要性。 PFI 是 Breiman 在隨機樹系檔 10 第 10 節 (Breiman 所動機的簡單但功能強大的技術。 「隨機樹系」。 Machine Learning， 2001.) PFI 方法的優點是它與模型無關，它適用于任何可評估的模型，而且可以使用任何資料集，而不只是定型集，來計算特徵重要性計量。
PFI 的運作方式是採用已加上標籤的資料集、選擇特徵，並將該功能的值分散到所有範例中，讓每個範例現在都有特徵的隨機值，以及所有其他特徵的原始值。 評估計量 (例如 AUC) ，然後計算此修改資料集的評估計量變更，並計算原始資料集中的評估計量變更。 評估計量的變更愈大，功能對模型而言愈重要。 PFI 的運作方式是跨模型的所有功能執行此排列分析，逐一執行。
在此實作中，PFI 會計算每個功能之所有可能二元分類評估計量的變更，並 ImmutableArray 傳回 物件的 BinaryClassificationMetrics 。 如需使用這些結果來分析模型特徵重要性的範例，請參閱下列範例。