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StandardTrainersCatalog.LinearSvm Metodo

  • Riferimento

Definizione

Spazio dei nomi:
Microsoft.ML
Assembly:
Microsoft.ML.StandardTrainers.dll
Pacchetto:
Microsoft.ML v3.0.1
Pacchetto:
Microsoft.ML v1.0.0
Pacchetto:
Microsoft.ML v1.1.0
Pacchetto:
Microsoft.ML v1.2.0
Pacchetto:
Microsoft.ML v1.3.1
Pacchetto:
Microsoft.ML v1.4.0
Pacchetto:
Microsoft.ML v1.5.5
Pacchetto:
Microsoft.ML v1.6.0
Pacchetto:
Microsoft.ML v1.7.0
Pacchetto:
Microsoft.ML v2.0.0

Overload

LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)

Creare LinearSvmTrainer con opzioni avanzate, che stimano una destinazione usando un modello di classificazione binaria lineare sottoposto a training sui dati delle etichette booleane.
LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32)

Creare LinearSvmTrainer, che stima una destinazione usando un modello di classificazione binaria lineare sottoposto a training sui dati delle etichette booleane.

LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)

Creare LinearSvmTrainer con opzioni avanzate, che stimano una destinazione usando un modello di classificazione binaria lineare sottoposto a training sui dati delle etichette booleane.

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

Parametri

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Oggetto di training del catalogo di classificazione binaria.

options
LinearSvmTrainer.Options

Opzioni del formatore.

Restituisce

LinearSvmTrainer

Esempio

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());
        }
    }
}

Si applica a

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

Creare LinearSvmTrainer, che stima una destinazione usando un modello di classificazione binaria lineare sottoposto a training sui dati delle etichette booleane.

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

Parametri

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Oggetto di training del catalogo di classificazione binaria.

labelColumnName
String

Nome della colonna dell'etichetta. I dati della colonna devono essere Boolean.

featureColumnName
String

Nome della colonna di funzionalità. I dati della colonna devono essere un vettore di dimensioni note di Single.

exampleWeightColumnName
String

Nome della colonna peso di esempio (facoltativo).

numberOfIterations
Int32

Numero di iterazioni di training.

Restituisce

LinearSvmTrainer

Esempio

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());
        }
    }
}

Si applica a