ExplainabilityCatalog.CalculateFeatureContribution メソッド
定義
重要
一部の情報は、リリース前に大きく変更される可能性があるプレリリースされた製品に関するものです。 Microsoft は、ここに記載されている情報について、明示または黙示を問わず、一切保証しません。
オーバーロード
CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)
入力ベクトルの FeatureContributionCalculatingEstimator 各特徴に対してモデル固有の寄与度スコアを計算する作成。
public static Microsoft.ML.Transforms.FeatureContributionCalculatingEstimator CalculateFeatureContribution (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Trainers.ICalculateFeatureContribution> predictionTransformer, int numberOfPositiveContributions = 10, int numberOfNegativeContributions = 10, bool normalize = true);static member CalculateFeatureContribution : Microsoft.ML.TransformsCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Trainers.ICalculateFeatureContribution> * int * int * bool -> Microsoft.ML.Transforms.FeatureContributionCalculatingEstimator<Extension()>
Public Function CalculateFeatureContribution (catalog As TransformsCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of ICalculateFeatureContribution), Optional numberOfPositiveContributions As Integer = 10, Optional numberOfNegativeContributions As Integer = 10, Optional normalize As Boolean = true) As FeatureContributionCalculatingEstimatorパラメーター
- catalog
- TransformsCatalog
変換カタログ。
- predictionTransformer
- ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>
特徴 ISingleFeaturePredictionTransformer<TModel> 寄与度計算をサポートし、スコアリングにも使用されます。
- numberOfPositiveContributions
- Int32
レポートへの肯定的な寄与の数。最大の大きさから最も小さい大きさに並べ替えられます。
正の寄与 numberOfPositiveContributionsを持つ特徴が少ない場合、残りはゼロとして返されることに注意してください。
- numberOfNegativeContributions
- Int32
レポートに対する負の寄与の数。最大の大きさから最も小さい大きさに並べ替えられます。
負の寄与 numberOfNegativeContributionsを持つ特徴が少ない場合、残りはゼロとして返されることに注意してください。
- normalize
- Boolean
特徴の寄与度を [-1, 1] 間隔に正規化する必要があるかどうか。
戻り値
例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class CalculateFeatureContribution
{
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 a small dataset.
var samples = GenerateData();
// Convert training data to IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Create a pipeline to concatenate the features into a feature vector
// and normalize it.
var transformPipeline = mlContext.Transforms.Concatenate("Features",
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) })
.Append(mlContext.Transforms.NormalizeMeanVariance("Features"));
// Fit the pipeline.
var transformer = transformPipeline.Fit(data);
// Transform the data.
var transformedData = transformer.Transform(data);
// Define a linear trainer.
var linearTrainer = mlContext.Regression.Trainers.Ols();
// Now we train the model and score it on the transformed data.
var linearModel = linearTrainer.Fit(transformedData);
// Print the model parameters.
Console.WriteLine($"Linear Model Parameters");
Console.WriteLine("Bias: " + linearModel.Model.Bias + " Feature1: " +
linearModel.Model.Weights[0] + " Feature2: " + linearModel.Model
.Weights[1]);
// Define a feature contribution calculator for all the features, and
// don't normalize the contributions.These are "trivial estimators" and
// they don't need to fit to the data, so we can feed a subset.
var simpleScoredDataset = linearModel.Transform(mlContext.Data
.TakeRows(transformedData, 1));
var linearFeatureContributionCalculator = mlContext.Transforms
.CalculateFeatureContribution(linearModel, normalize: false).Fit(
simpleScoredDataset);
// Create a transformer chain to describe the entire pipeline.
var scoringPipeline = transformer.Append(linearModel).Append(
linearFeatureContributionCalculator);
// Create the prediction engine to get the features extracted from the
// text.
var predictionEngine = mlContext.Model.CreatePredictionEngine<Data,
ScoredData>(scoringPipeline);
// Convert the text into numeric features.
var prediction = predictionEngine.Predict(samples.First());
// Write out the prediction, with contributions.
// Note that for the linear model, the feature contributions for a
// feature in an example is the feature-weight*feature-value.
// The total prediction is thus the bias plus the feature contributions.
Console.WriteLine("Label: " + prediction.Label + " Prediction: " +
prediction.Score);
Console.WriteLine("Feature1: " + prediction.Features[0] +
" Feature2: " + prediction.Features[1]);
Console.WriteLine("Feature Contributions: " + prediction
.FeatureContributions[0] + " " + prediction
.FeatureContributions[1]);
// Expected output:
// Linear Model Parameters
// Bias: -0.007505796 Feature1: 1.536963 Feature2: 3.031206
// Label: 1.55184 Prediction: 1.389091
// Feature1: -0.5053467 Feature2: 0.7169741
// Feature Contributions: -0.7766994 2.173296
}
private class Data
{
public float Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
private class ScoredData : Data
{
public float Score { get; set; }
public float[] Features { get; set; }
public float[] FeatureContributions { 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;
}
}
}
}
適用対象
CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog, ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters, TCalibrator>>, Int32, Int32, Boolean)
入力ベクトルの FeatureContributionCalculatingEstimator 各特徴に対してモデル固有の寄与度スコアを計算する作成。 校正済みモデルをサポートします。
public static Microsoft.ML.Transforms.FeatureContributionCalculatingEstimator CalculateFeatureContribution<TModelParameters,TCalibrator> (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<TModelParameters,TCalibrator>> predictionTransformer, int numberOfPositiveContributions = 10, int numberOfNegativeContributions = 10, bool normalize = true) where TModelParameters : class, Microsoft.ML.Trainers.ICalculateFeatureContribution where TCalibrator : class, Microsoft.ML.Calibrators.ICalibrator;static member CalculateFeatureContribution : Microsoft.ML.TransformsCatalog * Microsoft.ML.ISingleFeaturePredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<'ModelParameters, 'Calibrator>> * int * int * bool -> Microsoft.ML.Transforms.FeatureContributionCalculatingEstimator (requires 'ModelParameters : null and 'ModelParameters :> Microsoft.ML.Trainers.ICalculateFeatureContribution and 'Calibrator : null and 'Calibrator :> Microsoft.ML.Calibrators.ICalibrator)<Extension()>
Public Function CalculateFeatureContribution(Of TModelParameters As {Class, ICalculateFeatureContribution}, TCalibrator As {Class, ICalculateFeatureContribution}) (catalog As TransformsCatalog, predictionTransformer As ISingleFeaturePredictionTransformer(Of CalibratedModelParametersBase(Of TModelParameters, TCalibrator)), Optional numberOfPositiveContributions As Integer = 10, Optional numberOfNegativeContributions As Integer = 10, Optional normalize As Boolean = true) As FeatureContributionCalculatingEstimator型パラメーター
- TModelParameters
- TCalibrator
パラメーター
- catalog
- TransformsCatalog
変換カタログ。
- predictionTransformer
- ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters,TCalibrator>>
特徴 ISingleFeaturePredictionTransformer<TModel> 寄与度計算をサポートし、スコアリングにも使用されます。
- numberOfPositiveContributions
- Int32
レポートへの肯定的な寄与の数。最大の大きさから最も小さい大きさに並べ替えられます。
正の寄与 numberOfPositiveContributionsを持つ特徴が少ない場合、残りはゼロとして返されることに注意してください。
- numberOfNegativeContributions
- Int32
レポートに対する負の寄与の数。最大の大きさから最も小さい大きさに並べ替えられます。
負の寄与 numberOfNegativeContributionsを持つ特徴が少ない場合、残りはゼロとして返されることに注意してください。
- normalize
- Boolean
特徴の寄与度を [-1, 1] 間隔に正規化する必要があるかどうか。
戻り値
例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class CalculateFeatureContributionCalibrated
{
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();
// Create a small dataset.
var samples = GenerateData();
// Convert training data to IDataView.
var data = mlContext.Data.LoadFromEnumerable(samples);
// Create a pipeline to concatenate the features into a feature vector
// and normalize it.
var transformPipeline = mlContext.Transforms.Concatenate("Features",
new string[] { nameof(Data.Feature1), nameof(Data.Feature2) })
.Append(mlContext.Transforms.NormalizeMeanVariance("Features"));
// Fit the pipeline.
var transformer = transformPipeline.Fit(data);
// Transform the data.
var transformedData = transformer.Transform(data);
// Define a linear trainer.
var linearTrainer = mlContext.BinaryClassification.Trainers
.SdcaLogisticRegression();
// Now we train the model and score it on the transformed data.
var linearModel = linearTrainer.Fit(transformedData);
// Print the model parameters.
Console.WriteLine($"Linear Model Parameters");
Console.WriteLine("Bias: {0} Feature1: {1} Feature2: {2}",
linearModel.Model.SubModel.Bias,
linearModel.Model.SubModel.Weights[0],
linearModel.Model.SubModel.Weights[1]);
// Define a feature contribution calculator for all the features, and
// don't normalize the contributions. These are "trivial estimators" and
// they don't need to fit to the data, so we can feed a subset.
var simpleScoredDataset = linearModel.Transform(mlContext.Data
.TakeRows(transformedData, 1));
var linearFeatureContributionCalculator = mlContext.Transforms
.CalculateFeatureContribution(linearModel, normalize: false)
.Fit(simpleScoredDataset);
// Create a transformer chain to describe the entire pipeline.
var scoringPipeline = transformer.Append(linearModel)
.Append(linearFeatureContributionCalculator);
// Create the prediction engine to get the features extracted from the
// text.
var predictionEngine = mlContext.Model.CreatePredictionEngine<Data,
ScoredData>(scoringPipeline);
// Convert the text into numeric features.
var prediction = predictionEngine.Predict(samples.First());
// Write out the prediction, with contributions.
// Note that for the linear model, the feature contributions for a
// feature in an example is the feature-weight*feature-value. The total
// prediction is thus the bias plus the feature contributions.
Console.WriteLine("Label: " + prediction.Label + " Prediction-Score: " +
prediction.Score + " Prediction-Probability: " + prediction
.Probability);
Console.WriteLine("Feature1: " + prediction.Features[0] + " Feature2: "
+ prediction.Features[1]);
Console.WriteLine("Feature Contributions: " + prediction
.FeatureContributions[0] + " " + prediction
.FeatureContributions[1]);
// Expected output:
// Linear Model Parameters
// Bias: 0.003757346 Feature1: 9.070082 Feature2: 17.7816
// Label: True Prediction-Score: 8.169167 Prediction-Probability: 0.9997168
// Feature1: -0.5053467 Feature2: 0.7169741
// Feature Contributions: -4.583536 12.74894
}
private class Data
{
public bool Label { get; set; }
public float Feature1 { get; set; }
public float Feature2 { get; set; }
}
private class ScoredData : Data
{
public float Score { get; set; }
public float Probability { get; set; }
public float[] Features { get; set; }
public float[] FeatureContributions { 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 Boolean label with noise.
var value = 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));
}
}