ExplainabilityCatalog.CalculateFeatureContribution Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Data.dll

Paket:

Microsoft.ML v2.0.0

Overload

CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)	FeatureContributionCalculatingEstimator Buat yang menghitung skor kontribusi khusus model untuk setiap fitur vektor input.
CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog, ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters, TCalibrator>>, Int32, Int32, Boolean)	FeatureContributionCalculatingEstimator Buat yang menghitung skor kontribusi khusus model untuk setiap fitur vektor input. Mendukung model yang dikalibrasi.

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

FeatureContributionCalculatingEstimator Buat yang menghitung skor kontribusi khusus model untuk setiap fitur vektor input.

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

Parameter

catalog

TransformsCatalog

Katalog transformasi.

predictionTransformer

ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>

ISingleFeaturePredictionTransformer<TModel> yang mendukung Perhitungan Kontribusi Fitur, dan yang juga akan digunakan untuk penilaian.

numberOfPositiveContributions

Int32

Jumlah kontribusi positif untuk dilaporkan, diurutkan dari besaran tertinggi ke besaran terendah. Perhatikan bahwa jika ada lebih sedikit fitur dengan kontribusi positif daripada numberOfPositiveContributions, sisanya akan dikembalikan sebagai nol.

numberOfNegativeContributions

Int32

Jumlah kontribusi negatif untuk dilaporkan, diurutkan dari besaran tertinggi ke besaran terendah. Perhatikan bahwa jika ada lebih sedikit fitur dengan kontribusi negatif daripada numberOfNegativeContributions, sisanya akan dikembalikan sebagai nol.

normalize

Boolean

Apakah kontribusi fitur harus dinormalisasi ke interval [-1, 1].

Mengembalikan

FeatureContributionCalculatingEstimator

Contoh

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)

FeatureContributionCalculatingEstimator Buat yang menghitung skor kontribusi khusus model untuk setiap fitur vektor input. Mendukung model yang dikalibrasi.

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

Jenis parameter

TModelParameters

TCalibrator

Parameter

catalog

TransformsCatalog

Katalog transformasi.

predictionTransformer

ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters,TCalibrator>>

ISingleFeaturePredictionTransformer<TModel> yang mendukung Perhitungan Kontribusi Fitur, dan yang juga akan digunakan untuk penilaian.

numberOfPositiveContributions

Int32

Jumlah kontribusi positif untuk dilaporkan, diurutkan dari besaran tertinggi ke besaran terendah. Perhatikan bahwa jika ada lebih sedikit fitur dengan kontribusi positif daripada numberOfPositiveContributions, sisanya akan dikembalikan sebagai nol.

numberOfNegativeContributions

Int32

Jumlah kontribusi negatif untuk dilaporkan, diurutkan dari besaran tertinggi ke besaran terendah. Perhatikan bahwa jika ada lebih sedikit fitur dengan kontribusi negatif daripada numberOfNegativeContributions, sisanya akan dikembalikan sebagai nol.

normalize

Boolean

Apakah kontribusi fitur harus dinormalisasi ke interval [-1, 1].

Mengembalikan

FeatureContributionCalculatingEstimator

Contoh

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));
    }
}