Aracılığıyla paylaş

StandardTrainersCatalog.LdSvm Yöntem

  • Başvuru

Tanım

Ad Alanı:
Microsoft.ML
Bütünleştirilmiş Kod:
Microsoft.ML.StandardTrainers.dll
Paket:
Microsoft.ML v3.0.1
Paket:
Microsoft.ML v1.5.5
Paket:
Microsoft.ML v1.6.0
Paket:
Microsoft.ML v1.7.0
Paket:
Microsoft.ML v2.0.0

Aşırı Yüklemeler

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)

Yerel Derin SVM modeli kullanarak hedefi tahmin eden gelişmiş seçeneklerle oluşturun LdSvmTrainer .
LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Boolean, Boolean)

Yerel Derin SVM modeli kullanarak hedefi tahmin eden öğesini oluşturun LdSvmTrainer.

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)

Yerel Derin SVM modeli kullanarak hedefi tahmin eden gelişmiş seçeneklerle oluşturun LdSvmTrainer .

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

Parametreler

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

BinaryClassificationCatalog.

options
LdSvmTrainer.Options

Eğitmen seçenekleri.

Döndürülenler

LdSvmTrainer

Örnekler

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 LdSvmWithOptions
    {
        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 LdSvmTrainer.Options
            {
                TreeDepth = 5,
                NumberOfIterations = 10000,
                Sigma = 0.1f,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .LdSvm(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: 0.80
            //   AUC: 0.89
            //   F1 Score: 0.79
            //   Negative Precision: 0.81
            //   Negative Recall: 0.81
            //   Positive Precision: 0.79
            //   Positive Recall: 0.79

            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      189 |       49 | 0.7941
            //    negative ||       50 |      212 | 0.8092
            //             ||======================
            //   Precision ||   0.7908 |   0.8123 |
        }

        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());
        }
    }
}

Şunlara uygulanır

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Boolean, Boolean)

Yerel Derin SVM modeli kullanarak hedefi tahmin eden öğesini oluşturun LdSvmTrainer.

public static Microsoft.ML.Trainers.LdSvmTrainer LdSvm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfIterations = 15000, int treeDepth = 3, bool useBias = true, bool useCachedData = true);
static member LdSvm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int * int * bool * bool -> Microsoft.ML.Trainers.LdSvmTrainer
<Extension()>
Public Function LdSvm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfIterations As Integer = 15000, Optional treeDepth As Integer = 3, Optional useBias As Boolean = true, Optional useCachedData As Boolean = true) As LdSvmTrainer

Parametreler

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

BinaryClassificationCatalog.

labelColumnName
String

Etiket sütununun adı.

featureColumnName
String

Özellik sütununun adı. Sütun verileri bilinen boyutlu bir vektör Singleolmalıdır.

exampleWeightColumnName
String

Örnek ağırlık sütununun adı (isteğe bağlı).

numberOfIterations
Int32

Yineleme sayısı.

treeDepth
Int32

Yerel Derin SVM ağacının derinliği.

useBias
Boolean

Modelin sapma terimine sahip olup olmadığını gösterir.

useCachedData
Boolean

Önbellek kullanarak veriler üzerinde yineleme yapmamız gerekip gerekmediğini gösterir.

Döndürülenler

LdSvmTrainer

Örnekler

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

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

            // 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: 0.82
            // AUC: 0.85
            // F1 Score: 0.81
            // Negative Precision: 0.82
            // Negative Recall: 0.82
            // Positive Precision: 0.81
            // Positive Recall: 0.81

            // TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            // Confusion table
            //           ||======================
            // PREDICTED || positive | negative | Recall
            // TRUTH     ||======================
            //  positive ||      192 |       46 | 0.8067
            //  negative ||       46 |      216 | 0.8244
            //           ||======================
            // Precision ||   0.8067 |   0.8244 |
        }

        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());
        }
    }
}

Şunlara uygulanır