MklComponentsCatalog.SymbolicSgdLogisticRegression Methode

Referenz

Definition

Namespace:: Microsoft.ML

Assembly:: Microsoft.ML.Mkl.Components.dll

Paket:: Microsoft.ML.Mkl.Components v3.0.1

Paket:: Microsoft.ML.Mkl.Components v1.0.0

Paket:: Microsoft.ML.Mkl.Components v1.1.0

Paket:: Microsoft.ML.Mkl.Components v1.2.0

Paket:: Microsoft.ML.Mkl.Components v1.3.1

Paket:: Microsoft.ML.Mkl.Components v1.4.0

Paket:: Microsoft.ML.Mkl.Components v1.5.5

Paket:: Microsoft.ML.Mkl.Components v1.6.0

Paket:: Microsoft.ML.Mkl.Components v1.7.0

Paket:: Microsoft.ML.Mkl.Components v2.0.0

Wichtig

Einige Informationen beziehen sich auf Vorabversionen, die vor dem Release ggf. grundlegend überarbeitet werden. Microsoft übernimmt hinsichtlich der hier bereitgestellten Informationen keine Gewährleistungen, seien sie ausdrücklich oder konkludent.

Überlädt

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Erstellen Sie SymbolicSgdLogisticRegressionBinaryTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines linearen binären Klassifizierungsmodells vorhersagen, das über boolesche Bezeichnungsdaten trainiert wurde. Stochastischer Farbverlaufsabstieg (SGD) ist ein iterativer Algorithmus, der eine differenzierbare objektive Funktion optimiert. Die SymbolicSgdLogisticRegressionBinaryTrainer parallelisiert SGD mit symbolischer Ausführung.

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)

Erstellen Sie SymbolicSgdLogisticRegressionBinaryTrainerein Ziel, das ein Ziel mithilfe eines linearen binären Klassifizierungsmodells angibt, das über boolesche Bezeichnungsdaten trainiert wurde. Stochastischer Farbverlaufsabstieg (SGD) ist ein iterativer Algorithmus, der eine differenzierbare objektive Funktion optimiert. Die SymbolicSgdLogisticRegressionBinaryTrainer parallelisiert SGD mit symbolischer Ausführung.

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

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

static member SymbolicSgdLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options -> Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer

<Extension()>
Public Function SymbolicSgdLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SymbolicSgdLogisticRegressionBinaryTrainer.Options) As SymbolicSgdLogisticRegressionBinaryTrainer

Parameter

catalog: BinaryClassificationCatalog.BinaryClassificationTrainers

Das BinaryClassificationCatalog.

options: SymbolicSgdLogisticRegressionBinaryTrainer.Options

Erweiterte Optionen für Algorithmus. Siehe SymbolicSgdLogisticRegressionBinaryTrainer.Options.

Gibt zurück

SymbolicSgdLogisticRegressionBinaryTrainer

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 SymbolicSgdLogisticRegressionWithOptions
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree at
        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
        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 SymbolicSgdLogisticRegressionBinaryTrainer.Options()
            {
                LearningRate = 0.2f,
                NumberOfIterations = 10,
                NumberOfThreads = 1,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SymbolicSgdLogisticRegression(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: 0.72
            //   AUC: 0.81
            //   F1 Score: 0.66
            //   Negative Precision: 0.68
            //   Negative Recall: 0.87
            //   Positive Precision: 0.80
            //   Positive Recall: 0.56
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      133 |      105 | 0.5588
            //    negative ||       34 |      228 | 0.8702
            //             ||======================
            //   Precision ||   0.7964 |   0.6847 |
        }

        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());
        }
    }
}

Gilt für:

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)

public static Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", int numberOfIterations = 50);

static member SymbolicSgdLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * int -> Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer

<Extension()>
Public Function SymbolicSgdLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional numberOfIterations As Integer = 50) As SymbolicSgdLogisticRegressionBinaryTrainer

Parameter

catalog: BinaryClassificationCatalog.BinaryClassificationTrainers

Das BinaryClassificationCatalog.

labelColumnName: String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen sein Boolean.

featureColumnName: String

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

numberOfIterations: Int32

Anzahl der Trainingsiterationen.

Gibt zurück

SymbolicSgdLogisticRegressionBinaryTrainer

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 SymbolicSgdLogisticRegression
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree at
        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
        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
                .SymbolicSgdLogisticRegression();

            // 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: True

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.69
            //   AUC: 0.76
            //   F1 Score: 0.68
            //   Negative Precision: 0.72
            //   Negative Recall: 0.66
            //   Positive Precision: 0.66
            //   Positive Recall: 0.71
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      196 |       42 | 0.8235
            //    negative ||       42 |      220 | 0.8397
            //             ||======================
            //   Precision ||   0.8235 |   0.8397 |
        }

        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());
        }
    }
}

Gilt für:

Freigeben über

MklComponentsCatalog.SymbolicSgdLogisticRegression Methode

Definition

Überlädt

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Parameter

Gibt zurück

Beispiele

Gilt für:

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)

Parameter

Gibt zurück

Beispiele

Gilt für:

Zusätzliche Ressourcen