Sdílet prostřednictvím

StandardTrainersCatalog.SgdCalibrated Metoda

  • Reference

Definice

Obor názvů:
Microsoft.ML
Sestavení:
Microsoft.ML.StandardTrainers.dll
Balíček:
Microsoft.ML v3.0.1
Balíček:
Microsoft.ML v1.0.0
Balíček:
Microsoft.ML v1.1.0
Balíček:
Microsoft.ML v1.2.0
Balíček:
Microsoft.ML v1.3.1
Balíček:
Microsoft.ML v1.4.0
Balíček:
Microsoft.ML v1.5.5
Balíček:
Microsoft.ML v1.6.0
Balíček:
Microsoft.ML v1.7.0
Balíček:
Microsoft.ML v2.0.0

Přetížení

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdCalibratedTrainer+Options)

Vytvářejte SgdCalibratedTrainer s pokročilými možnostmi, které predikují cíl pomocí modelu lineární klasifikace. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci.
SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Double, Single)

Vytvoření SgdCalibratedTrainer, které predikuje cíl pomocí modelu lineární klasifikace. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci.

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdCalibratedTrainer+Options)

Vytvářejte SgdCalibratedTrainer s pokročilými možnostmi, které predikují cíl pomocí modelu lineární klasifikace. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci.

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

Parametry

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Objekt trenéra katalogu binární klasifikace.

options
SgdCalibratedTrainer.Options

Možnosti trenéra.

Návraty

SgdCalibratedTrainer

Příklady

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 SgdCalibratedWithOptions
    {
        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 SgdCalibratedTrainer.Options()
            {
                // Make the convergence tolerance tighter.
                ConvergenceTolerance = 5e-5,
                // Increase the maximum number of passes over training data.
                NumberOfIterations = 30,
                // Give the instances of the positive class slightly more weight.
                PositiveInstanceWeight = 1.2f,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SgdCalibrated(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: 0.60
            //   AUC: 0.65
            //   F1 Score: 0.50
            //   Negative Precision: 0.59
            //   Negative Recall: 0.74
            //   Positive Precision: 0.61
            //   Positive Recall: 0.43
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      184 |       54 | 0.7731
            //    negative ||      156 |      106 | 0.4046
            //             ||======================
            //   Precision ||   0.5412 |   0.6625 |
        }

        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() +
                        0.03f).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());
        }
    }
}

Platí pro

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Double, Single)

Vytvoření SgdCalibratedTrainer, které predikuje cíl pomocí modelu lineární klasifikace. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci.

public static Microsoft.ML.Trainers.SgdCalibratedTrainer SgdCalibrated (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfIterations = 20, double learningRate = 0.01, float l2Regularization = 1E-06);
static member SgdCalibrated : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * int * double * single -> Microsoft.ML.Trainers.SgdCalibratedTrainer
<Extension()>
Public Function SgdCalibrated (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfIterations As Integer = 20, Optional learningRate As Double = 0.01, Optional l2Regularization As Single = 1E-06) As SgdCalibratedTrainer

Parametry

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Objekt trenéra katalogu binární klasifikace.

labelColumnName
String

Název sloupce popisku nebo závislé proměnné. Data sloupce musí být Boolean.

featureColumnName
String

Funkce nebo nezávislé proměnné. Data ve sloupci musí být vektorem známé velikosti Single.

exampleWeightColumnName
String

Název ukázkového sloupce hmotnosti (volitelné).

numberOfIterations
Int32

Maximální počet průchodů trénovací datovou sadou; nastavte na 1 pro simulaci online učení.

learningRate
Double

Počáteční míra učení používaná SGD.

l2Regularization
Single

Hmotnost L2 pro regularizaci.

Návraty

SgdCalibratedTrainer

Příklady

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

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

            // 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: 0.60
            //   AUC: 0.63
            //   F1 Score: 0.43
            //   Negative Precision: 0.58
            //   Negative Recall: 0.85
            //   Positive Precision: 0.66
            //   Positive Recall: 0.32
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||       77 |      161 | 0.3235
            //    negative ||       43 |      219 | 0.8359
            //             ||======================
            //   Precision ||   0.6417 |   0.5763 |
        }

        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() +
                        0.03f).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());
        }
    }
}

Platí pro