ExplainabilityCatalog.CalculateFeatureContribution Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.Data.dll

Pacchetto:

Microsoft.ML v2.0.0

Overload

CalculateFeatureContribution(TransformsCatalog, ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>, Int32, Int32, Boolean)	Creare un FeatureContributionCalculatingEstimator oggetto che calcola i punteggi di contributo specifici del modello per ogni funzionalità del vettore di input.
CalculateFeatureContribution<TModelParameters,TCalibrator>(TransformsCatalog, ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters, TCalibrator>>, Int32, Int32, Boolean)	Creare un FeatureContributionCalculatingEstimator oggetto che calcola i punteggi di contributo specifici del modello per ogni funzionalità del vettore di input. Supporta modelli calibrati.

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

Creare un FeatureContributionCalculatingEstimator oggetto che calcola i punteggi di contributo specifici del modello per ogni funzionalità del vettore di 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

Parametri

catalog

TransformsCatalog

Catalogo delle trasformazioni.

predictionTransformer

ISingleFeaturePredictionTransformer<ICalculateFeatureContribution>

Oggetto ISingleFeaturePredictionTransformer<TModel> che supporta il calcolo del contributo delle funzionalità e che verrà usato anche per l'assegnazione dei punteggi.

numberOfPositiveContributions

Int32

Numero di contributi positivi da segnalare, ordinati dalla grandezza più alta alla grandezza più bassa. Si noti che se sono presenti meno funzionalità con contributi positivi di numberOfPositiveContributions, il resto verrà restituito come zeri.

numberOfNegativeContributions

Int32

Numero di contributi negativi da segnalare, ordinati dalla grandezza più alta alla grandezza più bassa. Si noti che se sono presenti meno funzionalità con contributi negativi di numberOfNegativeContributions, il resto verrà restituito come zeri.

normalize

Boolean

Indica se i contributi di funzionalità devono essere normalizzati nell'intervallo [-1, 1].

Restituisce

FeatureContributionCalculatingEstimator

Esempio

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)

Creare un FeatureContributionCalculatingEstimator oggetto che calcola i punteggi di contributo specifici del modello per ogni funzionalità del vettore di input. Supporta modelli calibrati.

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

Parametri di tipo

TModelParameters

TCalibrator

Parametri

catalog

TransformsCatalog

Catalogo delle trasformazioni.

predictionTransformer

ISingleFeaturePredictionTransformer<CalibratedModelParametersBase<TModelParameters,TCalibrator>>

Oggetto ISingleFeaturePredictionTransformer<TModel> che supporta il calcolo del contributo delle funzionalità e che verrà usato anche per l'assegnazione dei punteggi.

numberOfPositiveContributions

Int32

Numero di contributi positivi da segnalare, ordinati dalla grandezza più alta alla grandezza più bassa. Si noti che se sono presenti meno funzionalità con contributi positivi di numberOfPositiveContributions, il resto verrà restituito come zeri.

numberOfNegativeContributions

Int32

Numero di contributi negativi da segnalare, ordinati dalla grandezza più alta alla grandezza più bassa. Si noti che se sono presenti meno funzionalità con contributi negativi di numberOfNegativeContributions, il resto verrà restituito come zeri.

normalize

Boolean

Indica se i contributi di funzionalità devono essere normalizzati nell'intervallo [-1, 1].

Restituisce

FeatureContributionCalculatingEstimator

Esempio

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));
    }
}