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StandardTrainersCatalog.SgdNonCalibrated Método

  • Referência

Definição

Namespace:
Microsoft.ML
Assembly:
Microsoft.ML.StandardTrainers.dll
Pacote:
Microsoft.ML v3.0.1
Pacote:
Microsoft.ML v1.0.0
Pacote:
Microsoft.ML v1.1.0
Pacote:
Microsoft.ML v1.2.0
Pacote:
Microsoft.ML v1.3.1
Pacote:
Microsoft.ML v1.4.0
Pacote:
Microsoft.ML v1.5.5
Pacote:
Microsoft.ML v1.6.0
Pacote:
Microsoft.ML v1.7.0
Pacote:
Microsoft.ML v2.0.0

Sobrecargas

SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdNonCalibratedTrainer+Options)

Crie SgdNonCalibratedTrainer com opções avançadas, que prevê um destino usando um modelo de classificação linear. SGD (descendente de gradiente estocástico) é um algoritmo iterativo que otimiza uma função de objetivo diferencial.
SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, IClassificationLoss, Int32, Double, Single)

Criar SgdNonCalibratedTrainer, que prevê um destino usando um modelo de classificação linear. SGD (descendente de gradiente estocástico) é um algoritmo iterativo que otimiza uma função de objetivo diferencial.

SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdNonCalibratedTrainer+Options)

Crie SgdNonCalibratedTrainer com opções avançadas, que prevê um destino usando um modelo de classificação linear. SGD (descendente de gradiente estocástico) é um algoritmo iterativo que otimiza uma função de objetivo diferencial.

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

Parâmetros

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

O objeto de treinador do catálogo de classificação binária.

options
SgdNonCalibratedTrainer.Options

Opções de treinador.

Retornos

SgdNonCalibratedTrainer

Exemplos

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 SgdNonCalibratedWithOptions
    {
        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 SgdNonCalibratedTrainer.Options
            {
                LearningRate = 0.01,
                NumberOfIterations = 10,
                L2Regularization = 1e-7f
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SgdNonCalibrated(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
                .EvaluateNonCalibrated(transformedTestData);

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.59
            //   AUC: 0.61
            //   F1 Score: 0.41
            //   Negative Precision: 0.57
            //   Negative Recall: 0.85
            //   Positive Precision: 0.64
            //   Positive Recall: 0.30
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      137 |      101 | 0.5756
            //    negative ||      118 |      144 | 0.5496
            //             ||======================
            //   Precision ||   0.5373 |   0.5878 |
        }

        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());
        }
    }
}

Aplica-se a

SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, IClassificationLoss, Int32, Double, Single)

Criar SgdNonCalibratedTrainer, que prevê um destino usando um modelo de classificação linear. SGD (descendente de gradiente estocástico) é um algoritmo iterativo que otimiza uma função de objetivo diferencial.

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

Parâmetros

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

O objeto de treinador do catálogo de classificação binária.

labelColumnName
String

O nome da coluna de rótulo ou variável dependente. Os dados da coluna devem ser Boolean.

featureColumnName
String

Os recursos ou variáveis independentes. Os dados da coluna devem ser um vetor de tamanho conhecido de Single.

exampleWeightColumnName
String

O nome da coluna de peso de exemplo (opcional).

lossFunction
IClassificationLoss

A função de perda minimizada no processo de treinamento. O uso, por exemplo, HingeLoss leva a um treinador de máquina de vetor de suporte.

numberOfIterations
Int32

O número máximo de passagens pelo conjunto de dados de treinamento; definido como 1 para simular o aprendizado online.

learningRate
Double

A taxa de aprendizado inicial usada pelo SGD.

l2Regularization
Single

O peso L2 para regularização.

Retornos

SgdNonCalibratedTrainer

Exemplos

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

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

            // 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
                .EvaluateNonCalibrated(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 ||       76 |      162 | 0.3193
            //    negative ||       42 |      220 | 0.8397
            //             ||======================
            //   Precision ||   0.6441 |   0.5759 |
        }

        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());
        }
    }
}

Aplica-se a