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StandardTrainersCatalog.SgdCalibrated Méthode

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Définition

Espace de noms:
Microsoft.ML
Assembly:
Microsoft.ML.StandardTrainers.dll
Paquet:
Microsoft.ML v3.0.1
Paquet:
Microsoft.ML v1.0.0
Paquet:
Microsoft.ML v1.1.0
Paquet:
Microsoft.ML v1.2.0
Paquet:
Microsoft.ML v1.3.1
Paquet:
Microsoft.ML v1.4.0
Paquet:
Microsoft.ML v1.5.5
Paquet:
Microsoft.ML v1.6.0
Paquet:
Microsoft.ML v1.7.0
Paquet:
Microsoft.ML v2.0.0

Surcharges

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdCalibratedTrainer+Options)

Créez SgdCalibratedTrainer avec des options avancées, ce qui prédit une cible à l’aide d’un modèle de classification linéaire. La descente de gradient stochastique (SGD) est un algorithme itératif qui optimise une fonction objective différentiable.
SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Double, Single)

Créez SgdCalibratedTrainer, qui prédit une cible à l’aide d’un modèle de classification linéaire. La descente de gradient stochastique (SGD) est un algorithme itératif qui optimise une fonction objective différentiable.

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdCalibratedTrainer+Options)

Créez SgdCalibratedTrainer avec des options avancées, ce qui prédit une cible à l’aide d’un modèle de classification linéaire. La descente de gradient stochastique (SGD) est un algorithme itératif qui optimise une fonction objective différentiable.

public static Microsoft.ML.Trainers.SgdCalibratedTrainer SgdCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SgdCalibratedTrainer.Options options);
static member SgdCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SgdCalibratedTrainer.Options -> Microsoft.ML.Trainers.SgdCalibratedTrainer
<Extension()>
Public Function SgdCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SgdCalibratedTrainer.Options) As SgdCalibratedTrainer

Paramètres

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Objet formateur de catalogue de classification binaire.

options
SgdCalibratedTrainer.Options

Options de formateur.

Retours

SgdCalibratedTrainer

Exemples

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class SgdCalibratedWithOptions
    {
        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. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define trainer options.
            var options = new SgdCalibratedTrainer.Options()
            {
                // Make the convergence tolerance tighter.
                ConvergenceTolerance = 5e-5,
                // Increase the maximum number of passes over training data.
                NumberOfIterations = 30,
                // Give the instances of the positive class slightly more weight.
                PositiveInstanceWeight = 1.2f,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SgdCalibrated(options);

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use different random seed to make it different
            // from training data.
            var testData = mlContext.Data
                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data
                .CreateEnumerable<Prediction>(transformedTestData,
                reuseRowObject: false).ToList();

            // Print 5 predictions.
            foreach (var p in predictions.Take(5))
                Console.WriteLine($"Label: {p.Label}, "
                    + $"Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: True, Prediction: False
            //   Label: False, Prediction: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

            // Evaluate the overall metrics.
            var metrics = mlContext.BinaryClassification
                .Evaluate(transformedTestData);

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.60
            //   AUC: 0.65
            //   F1 Score: 0.50
            //   Negative Precision: 0.59
            //   Negative Recall: 0.74
            //   Positive Precision: 0.61
            //   Positive Recall: 0.43
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      184 |       54 | 0.7731
            //    negative ||      156 |      106 | 0.4046
            //             ||======================
            //   Precision ||   0.5412 |   0.6625 |
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = randomFloat() > 0.5f;
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    // For data points with false label, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(x => x ? randomFloat() : randomFloat() +
                        0.03f).ToArray()

                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public bool Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public bool Label { get; set; }
            // Predicted label from the trainer.
            public bool PredictedLabel { get; set; }
        }

        // Pretty-print BinaryClassificationMetrics objects.
        private static void PrintMetrics(BinaryClassificationMetrics metrics)
        {
            Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
            Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
            Console.WriteLine($"Negative Precision: " +
                $"{metrics.NegativePrecision:F2}");

            Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
            Console.WriteLine($"Positive Precision: " +
                $"{metrics.PositivePrecision:F2}");

            Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

S’applique à

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Double, Single)

Créez SgdCalibratedTrainer, qui prédit une cible à l’aide d’un modèle de classification linéaire. La descente de gradient stochastique (SGD) est un algorithme itératif qui optimise une fonction objective différentiable.

public static Microsoft.ML.Trainers.SgdCalibratedTrainer SgdCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfIterations = 20, double learningRate = 0.01, float l2Regularization = 1E-06);
static member SgdCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int * double * single -> Microsoft.ML.Trainers.SgdCalibratedTrainer
<Extension()>
Public Function SgdCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfIterations As Integer = 20, Optional learningRate As Double = 0.01, Optional l2Regularization As Single = 1E-06) As SgdCalibratedTrainer

Paramètres

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Objet formateur de catalogue de classification binaire.

labelColumnName
String

Nom de la colonne d’étiquette ou variable dépendante. Les données de colonne doivent être Boolean.

featureColumnName
String

Fonctionnalités ou variables indépendantes. Les données de colonne doivent être un vecteur de taille connue de Single.

exampleWeightColumnName
String

Nom de l’exemple de colonne de poids (facultatif).

numberOfIterations
Int32

Nombre maximal de passes par le jeu de données d’entraînement ; défini sur 1 pour simuler l’apprentissage en ligne.

learningRate
Double

Taux d’apprentissage initial utilisé par SGD.

l2Regularization
Single

Poids L2 pour la régularisation.

Retours

SgdCalibratedTrainer

Exemples

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class SgdCalibrated
    {
        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. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SgdCalibrated();

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use different random seed to make it different
            // from training data.
            var testData = mlContext.Data
                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data
                .CreateEnumerable<Prediction>(transformedTestData,
                reuseRowObject: false).ToList();

            // Print 5 predictions.
            foreach (var p in predictions.Take(5))
                Console.WriteLine($"Label: {p.Label}, "
                    + $"Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: True, Prediction: False
            //   Label: False, Prediction: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

            // Evaluate the overall metrics.
            var metrics = mlContext.BinaryClassification
                .Evaluate(transformedTestData);

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.60
            //   AUC: 0.63
            //   F1 Score: 0.43
            //   Negative Precision: 0.58
            //   Negative Recall: 0.85
            //   Positive Precision: 0.66
            //   Positive Recall: 0.32
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||       77 |      161 | 0.3235
            //    negative ||       43 |      219 | 0.8359
            //             ||======================
            //   Precision ||   0.6417 |   0.5763 |
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = randomFloat() > 0.5f;
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    // For data points with false label, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(x => x ? randomFloat() : randomFloat() +
                        0.03f).ToArray()

                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public bool Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public bool Label { get; set; }
            // Predicted label from the trainer.
            public bool PredictedLabel { get; set; }
        }

        // Pretty-print BinaryClassificationMetrics objects.
        private static void PrintMetrics(BinaryClassificationMetrics metrics)
        {
            Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
            Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
            Console.WriteLine($"Negative Precision: " +
                $"{metrics.NegativePrecision:F2}");

            Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
            Console.WriteLine($"Positive Precision: " +
                $"{metrics.PositivePrecision:F2}");

            Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

S’applique à