StandardTrainersCatalog.LdSvm Methode

  • Referenz

Definition

Namespace:
Microsoft.ML
Assembly:
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

Überlädt

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)

Erstellen Sie LdSvmTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines lokalen Deep SVM-Modells voraussagen.
LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Boolean, Boolean)

Erstellen Sie LdSvmTrainerein Ziel, das ein Ziel mithilfe eines lokalen Deep SVM-Modells vorhersagt.

LdSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LdSvmTrainer+Options)

Erstellen Sie LdSvmTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines lokalen Deep SVM-Modells voraussagen.

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

Parameter

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Das BinaryClassificationCatalog.

options
LdSvmTrainer.Options

Traineroptionen.

Gibt zurück

LdSvmTrainer

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 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());
        }
    }
}

Gilt für:

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

Erstellen Sie LdSvmTrainerein Ziel, das ein Ziel mithilfe eines lokalen Deep SVM-Modells vorhersagt.

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

Parameter

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Das BinaryClassificationCatalog.

labelColumnName
String

Der Name der Bezeichnungsspalte.

featureColumnName
String

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

exampleWeightColumnName
String

Der Name der Beispielgewichtsspalte (optional).

numberOfIterations
Int32

Anzahl der Iterationen.

treeDepth
Int32

Die Tiefe eines lokalen Deep SVM-Baums.

useBias
Boolean

Gibt an, ob das Modell einen Verzerrungsbegriff aufweisen sollte.

useCachedData
Boolean

Gibt an, ob wir über die Daten mit einem Cache iterieren sollten.

Gibt zurück

LdSvmTrainer

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 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());
        }
    }
}

Gilt für: