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StandardTrainersCatalog.LinearSvm Метод

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Определение

Пространство имен:
Microsoft.ML
Сборка:
Microsoft.ML.StandardTrainers.dll
Пакет:
Microsoft.ML v3.0.1
Пакет:
Microsoft.ML v1.0.0
Пакет:
Microsoft.ML v1.1.0
Пакет:
Microsoft.ML v1.2.0
Пакет:
Microsoft.ML v1.3.1
Пакет:
Microsoft.ML v1.4.0
Пакет:
Microsoft.ML v1.5.5
Пакет:
Microsoft.ML v1.6.0
Пакет:
Microsoft.ML v1.7.0
Пакет:
Microsoft.ML v2.0.0

Перегрузки

LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)

Создавайте LinearSvmTrainer с помощью дополнительных параметров, которые прогнозируют целевой объект с помощью модели линейной двоичной классификации, обученной по данным логических меток.
LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32)

Создание LinearSvmTrainer, которое прогнозирует целевой объект с помощью модели линейной двоичной классификации, обученной по данным логических меток.

LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)

Создавайте LinearSvmTrainer с помощью дополнительных параметров, которые прогнозируют целевой объект с помощью модели линейной двоичной классификации, обученной по данным логических меток.

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

Параметры

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Объект обучения каталога двоичной классификации.

options
LinearSvmTrainer.Options

Параметры тренера.

Возвращаемое значение

LinearSvmTrainer

Примеры

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 LinearSvmWithOptions
    {
        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 LinearSvmTrainer.Options
            {
                BatchSize = 10,
                PerformProjection = true,
                NumberOfIterations = 10
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .LinearSvm(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: True
            //   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: 0.85
            //   AUC: 0.95
            //   F1 Score: 0.86
            //   Negative Precision: 0.91
            //   Negative Recall: 0.80
            //   Positive Precision: 0.80
            //   Positive Recall: 0.92
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      218 |       20 | 0.9160
            //    negative ||       53 |      209 | 0.7977
            //             ||======================
            //   Precision ||   0.8044 |   0.9127 |
        }

        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());
        }
    }
}

Применяется к

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

Создание LinearSvmTrainer, которое прогнозирует целевой объект с помощью модели линейной двоичной классификации, обученной по данным логических меток.

public static Microsoft.ML.Trainers.LinearSvmTrainer LinearSvm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfIterations = 1);
static member LinearSvm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int -> Microsoft.ML.Trainers.LinearSvmTrainer
<Extension()>
Public Function LinearSvm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfIterations As Integer = 1) As LinearSvmTrainer

Параметры

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Объект обучения каталога двоичной классификации.

labelColumnName
String

Имя столбца меток. Данные столбца должны быть Boolean.

featureColumnName
String

Имя столбца компонента. Данные столбца должны быть вектором известного Singleразмера.

exampleWeightColumnName
String

Имя примера столбца веса (необязательно).

numberOfIterations
Int32

Количество итераций обучения.

Возвращаемое значение

LinearSvmTrainer

Примеры

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

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

            // 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: True

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.73
            //   AUC: 0.83
            //   F1 Score: 0.75
            //   Negative Precision: 0.84
            //   Negative Recall: 0.60
            //   Positive Precision: 0.66
            //   Positive Recall: 0.87
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      208 |       30 | 0.8739
            //   negative ||      106 |      156 | 0.5954
            //             ||======================
            //   Precision ||   0.6624 |   0.8387 |
        }

        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());
        }
    }
}

Применяется к