StandardTrainersCatalog.AveragedPerceptron Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.StandardTrainers.dll

Paket:

Microsoft.ML v3.0.1

Überlädt

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options)	Erstellen Sie eine AveragedPerceptronTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines linearen binären Klassifizierungsmodells vorgibt, das über boolesche Bezeichnungsdaten trainiert wurde.
AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32)	Erstellen Sie ein AveragedPerceptronTrainerZiel, das ein Ziel mithilfe eines linearen binären Klassifizierungsmodells vorgibt, das über boolesche Bezeichnungsdaten trainiert wurde.

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options)

Erstellen Sie eine AveragedPerceptronTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines linearen binären Klassifizierungsmodells vorgibt, das über boolesche Bezeichnungsdaten trainiert wurde.

public static Microsoft.ML.Trainers.AveragedPerceptronTrainer AveragedPerceptron (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options options);

static member AveragedPerceptron : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options -> Microsoft.ML.Trainers.AveragedPerceptronTrainer

<Extension()>
Public Function AveragedPerceptron (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As AveragedPerceptronTrainer.Options) As AveragedPerceptronTrainer

Parameter

catalog

BinaryClassificationCatalog.BinaryClassificationTrainers

Das Binärklassifizierungskatalogobjekt.

options

AveragedPerceptronTrainer.Options

Traineroptionen.

Gibt zurück

AveragedPerceptronTrainer

Beispiele

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 AveragedPerceptronWithOptions
    {
        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 AveragedPerceptronTrainer.Options
            {
                LossFunction = new SmoothedHingeLoss(),
                LearningRate = 0.1f,
                LazyUpdate = false,
                RecencyGain = 0.1f,
                NumberOfIterations = 10
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .AveragedPerceptron(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: True
            //   Label: False, Prediction: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.89
            //   AUC: 0.96
            //   F1 Score: 0.88
            //   Negative Precision: 0.87
            //   Negative Recall: 0.92
            //   Positive Precision: 0.91
            //   Positive Recall: 0.85
            //
            // TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      151 |       87 | 0.6345
            //    negative ||       53 |      209 | 0.7977
            //             ||======================
            //   Precision ||   0.7402 |   0.7061 |
        }

        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() +
                        0.1f).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());
        }
    }
}

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32)

Erstellen Sie ein AveragedPerceptronTrainerZiel, das ein Ziel mithilfe eines linearen binären Klassifizierungsmodells vorgibt, das über boolesche Bezeichnungsdaten trainiert wurde.

public static Microsoft.ML.Trainers.AveragedPerceptronTrainer AveragedPerceptron (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", Microsoft.ML.Trainers.IClassificationLoss lossFunction = default, float learningRate = 1, bool decreaseLearningRate = false, float l2Regularization = 0, int numberOfIterations = 10);

static member AveragedPerceptron : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * Microsoft.ML.Trainers.IClassificationLoss * single * bool * single * int -> Microsoft.ML.Trainers.AveragedPerceptronTrainer

<Extension()>
Public Function AveragedPerceptron (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional lossFunction As IClassificationLoss = Nothing, Optional learningRate As Single = 1, Optional decreaseLearningRate As Boolean = false, Optional l2Regularization As Single = 0, Optional numberOfIterations As Integer = 10) As AveragedPerceptronTrainer

Parameter

catalog

BinaryClassificationCatalog.BinaryClassificationTrainers

Das Binärklassifizierungskatalogobjekt.

labelColumnName

String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen Booleansein.

featureColumnName

String

Der Name der Featurespalte. Die Spaltendaten müssen ein bekannter Vektor von Single.

lossFunction

IClassificationLoss

Die Verlustfunktion wird im Schulungsprozess minimiert. Wenn null, HingeLoss würde verwendet und zu einem durchschnittlichen Perceptron-Trainer führen.

learningRate

Single

Die anfängliche Lernrate, die von SGD verwendet wird.

decreaseLearningRate

Boolean

true um den Fortschritt der learningRate Iterationen zu verringern; andernfalls false. Der Standardwert ist false.

l2Regularization

Single

Die L2-Gewichtung für die Regularisierung.

numberOfIterations

Int32

Anzahl der Übergänge durch das Schulungsdatensatz.

Gibt zurück

AveragedPerceptronTrainer

Beispiele

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class AveragedPerceptron
    {
        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
                .AveragedPerceptron();

            // 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: True
            //   Label: False, Prediction: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: False
            //   Label: False, Prediction: False

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.72
            //   AUC: 0.79
            //   F1 Score: 0.68
            //   Negative Precision: 0.71
            //   Negative Recall: 0.80
            //   Positive Precision: 0.74
            //   Positive Recall: 0.63
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      151 |       87 | 0.6345
            //    negative ||       53 |      209 | 0.7977
            //             ||======================
            //   Precision ||   0.7402 |   0.7061 |
        }

        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() +
                        0.1f).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());
        }
    }
}