ExplainabilityCatalog.CalculateFeatureContribution 메서드

정의

네임스페이스:

Microsoft.ML

어셈블리:

Microsoft.ML.Data.dll

패키지:

Microsoft.ML v2.0.0

오버로드

CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)	입력 벡터의 FeatureContributionCalculatingEstimator 각 기능에 대한 모델별 기여 점수를 계산하는 값을 만듭니다.
CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog, ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters, TCalibrator>>, Int32, Int32, Boolean)	입력 벡터의 FeatureContributionCalculatingEstimator 각 기능에 대한 모델별 기여 점수를 계산하는 값을 만듭니다. 보정된 모델을 지원합니다.

CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)

입력 벡터의 FeatureContributionCalculatingEstimator 각 기능에 대한 모델별 기여 점수를 계산하는 값을 만듭니다.

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

매개 변수

catalog

TransformsCatalog

변환 카탈로그입니다.

predictionTransformer

ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>

기능 기여 계산을 지원하고 점수 매기기에도 사용되는 A ISingleFeaturePredictionTransformer<TModel> 입니다.

numberOfPositiveContributions

Int32

가장 높은 크기에서 가장 낮은 크기로 정렬된 보고서에 대한 긍정적인 기여도의 수입니다. 긍정적인 기여 numberOfPositiveContributions를 가진 기능이 적은 경우 나머지는 0으로 반환됩니다.

numberOfNegativeContributions

Int32

보고서에 대한 부정 기여 횟수로, 가장 높은 크기에서 가장 낮은 크기로 정렬됩니다. 부정적인 기여 numberOfNegativeContributions를 가진 기능이 적은 경우 나머지는 0으로 반환됩니다.

normalize

Boolean

기능 기여를 [-1, 1] 간격으로 정규화해야 하는지 여부입니다.

반환

FeatureContributionCalculatingEstimator

예제

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;
            }
        }
    }
}

CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog, ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters, TCalibrator>>, Int32, Int32, Boolean)

입력 벡터의 FeatureContributionCalculatingEstimator 각 기능에 대한 모델별 기여 점수를 계산하는 값을 만듭니다. 보정된 모델을 지원합니다.

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

형식 매개 변수

TModelParameters

TCalibrator

매개 변수

catalog

TransformsCatalog

변환 카탈로그입니다.

predictionTransformer

ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters,TCalibrator>>

기능 기여 계산을 지원하고 점수 매기기에도 사용되는 A ISingleFeaturePredictionTransformer<TModel> 입니다.

numberOfPositiveContributions

Int32

가장 높은 크기에서 가장 낮은 크기로 정렬된 보고서에 대한 긍정적인 기여도의 수입니다. 긍정적인 기여 numberOfPositiveContributions를 가진 기능이 적은 경우 나머지는 0으로 반환됩니다.

numberOfNegativeContributions

Int32

보고서에 대한 부정 기여 횟수로, 가장 높은 크기에서 가장 낮은 크기로 정렬됩니다. 부정적인 기여 numberOfNegativeContributions를 가진 기능이 적은 경우 나머지는 0으로 반환됩니다.

normalize

Boolean

기능 기여를 [-1, 1] 간격으로 정규화해야 하는지 여부입니다.

반환

FeatureContributionCalculatingEstimator

예제

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));
    }
}