MklComponentsCatalog.SymbolicSgdLogisticRegression Metoda

Odwołanie

Definicja

Przestrzeń nazw:: Microsoft.ML

Zestaw:: Microsoft.ML.Mkl.Components.dll

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

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

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

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

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

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

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

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

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

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

Ważne

Niektóre informacje odnoszą się do produktu w wersji wstępnej, który może zostać znacząco zmodyfikowany przed wydaniem. Firma Microsoft nie udziela żadnych gwarancji, jawnych lub domniemanych, w odniesieniu do informacji podanych w tym miejscu.

Przeciążenia

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Utwórz SymbolicSgdLogisticRegressionBinaryTrainer za pomocą opcji zaawansowanych, które przewidują cel przy użyciu liniowego modelu klasyfikacji binarnej wytrenowanego na danych etykiet logicznych. Spadek gradientu stochastycznego (SGD) to algorytm iteracyjny, który optymalizuje rozróżnialną funkcję obiektywną. Zrównoleglizuje usługę SymbolicSgdLogisticRegressionBinaryTrainer SGD przy użyciu wykonywania symbolicznego.

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

Utwórz SymbolicSgdLogisticRegressionBinaryTrainerobiekt , który przewiduje element docelowy przy użyciu liniowego modelu klasyfikacji binarnej wytrenowanego na danych etykiet logicznych. Spadek gradientu stochastycznego (SGD) to algorytm iteracyjny, który optymalizuje rozróżnialną funkcję obiektywną. Zrównoleglizuje usługę SymbolicSgdLogisticRegressionBinaryTrainer SGD przy użyciu wykonywania symbolicznego.

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

Parametry

catalog: BinaryClassificationCatalog.BinaryClassificationTrainers

Element BinaryClassificationCatalog.

options: SymbolicSgdLogisticRegressionBinaryTrainer.Options

Zaawansowane opcje algorytmu. Zobacz: SymbolicSgdLogisticRegressionBinaryTrainer.Options.

Zwraca

SymbolicSgdLogisticRegressionBinaryTrainer

Przykłady

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());
        }
    }
}

Dotyczy

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

Parametry

catalog: BinaryClassificationCatalog.BinaryClassificationTrainers

Element BinaryClassificationCatalog.

labelColumnName: String

Nazwa kolumny etykiety. Dane kolumny muszą mieć wartość Boolean.

featureColumnName: String

Nazwa kolumny funkcji. Dane kolumn muszą być znanym wektorem .Single

numberOfIterations: Int32

Liczba iteracji treningowych.

Zwraca

SymbolicSgdLogisticRegressionBinaryTrainer

Przykłady

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());
        }
    }
}

Dotyczy

Udostępnij za pośrednictwem

MklComponentsCatalog.SymbolicSgdLogisticRegression Metoda

Definicja

Przeciążenia

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Parametry

Zwraca

Przykłady

Dotyczy

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

Parametry

Zwraca

Przykłady

Dotyczy

Dodatkowe zasoby