StandardTrainersCatalog.Sdca Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.StandardTrainers.dll

Paquete:

Microsoft.ML v3.0.1

Sobrecargas

Sdca(RegressionCatalog+RegressionTrainers, SdcaRegressionTrainer+Options)	Cree SdcaRegressionTrainer con opciones avanzadas, que predice un destino mediante un modelo de regresión lineal.
Sdca(RegressionCatalog+RegressionTrainers, String, String, String, ISupportSdcaRegressionLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)	Cree SdcaRegressionTrainer, que predice un destino mediante un modelo de regresión lineal.

Sdca(RegressionCatalog+RegressionTrainers, SdcaRegressionTrainer+Options)

Cree SdcaRegressionTrainer con opciones avanzadas, que predice un destino mediante un modelo de regresión lineal.

public static Microsoft.ML.Trainers.SdcaRegressionTrainer Sdca (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.SdcaRegressionTrainer.Options options);

static member Sdca : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.SdcaRegressionTrainer.Options -> Microsoft.ML.Trainers.SdcaRegressionTrainer

<Extension()>
Public Function Sdca (catalog As RegressionCatalog.RegressionTrainers, options As SdcaRegressionTrainer.Options) As SdcaRegressionTrainer

Parámetros

catalog

RegressionCatalog.RegressionTrainers

Objeto instructor del catálogo de regresión.

options

SdcaRegressionTrainer.Options

Opciones del instructor.

Devoluciones

SdcaRegressionTrainer

Ejemplos

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class SdcaWithOptions
    {
        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 SdcaRegressionTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Make the convergence tolerance tighter. It effectively leads to
                // more training iterations.
                ConvergenceTolerance = 0.02f,
                // Increase the maximum number of passes over training data. Similar
                // to ConvergenceTolerance, this value specifics the hard iteration
                // limit on the training algorithm.
                MaximumNumberOfIterations = 30,
                // Increase learning rate for bias.
                BiasLearningRate = 0.1f
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.Sdca(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(5, 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();

            // Look at 5 predictions for the Label, side by side with the actual
            // Label for comparison.
            foreach (var p in predictions)
                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");

            // Expected output:
            //   Label: 0.985, Prediction: 0.927
            //   Label: 0.155, Prediction: 0.062
            //   Label: 0.515, Prediction: 0.439
            //   Label: 0.566, Prediction: 0.500
            //   Label: 0.096, Prediction: 0.078

            // Evaluate the overall metrics
            var metrics = mlContext.Regression.Evaluate(transformedTestData);
            PrintMetrics(metrics);

            // Expected output:
            //   Mean Absolute Error: 0.05
            //   Mean Squared Error: 0.00
            //   Root Mean Squared Error: 0.06
            //   RSquared: 0.97 (closer to 1 is better. The worst case is 0)
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                float label = (float)random.NextDouble();
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => x + (float)random.NextDouble()).ToArray()
                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public float Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public float Label { get; set; }
            // Predicted score from the trainer.
            public float Score { get; set; }
        }

        // Print some evaluation metrics to regression problems.
        private static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);

            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}

Sdca(RegressionCatalog+RegressionTrainers, String, String, String, ISupportSdcaRegressionLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Cree SdcaRegressionTrainer, que predice un destino mediante un modelo de regresión lineal.

public static Microsoft.ML.Trainers.SdcaRegressionTrainer Sdca (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, Microsoft.ML.Trainers.ISupportSdcaRegressionLoss lossFunction = default, float? l2Regularization = default, float? l1Regularization = default, int? maximumNumberOfIterations = default);

static member Sdca : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * Microsoft.ML.Trainers.ISupportSdcaRegressionLoss * Nullable<single> * Nullable<single> * Nullable<int> -> Microsoft.ML.Trainers.SdcaRegressionTrainer

<Extension()>
Public Function Sdca (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional lossFunction As ISupportSdcaRegressionLoss = Nothing, Optional l2Regularization As Nullable(Of Single) = Nothing, Optional l1Regularization As Nullable(Of Single) = Nothing, Optional maximumNumberOfIterations As Nullable(Of Integer) = Nothing) As SdcaRegressionTrainer

Parámetros

catalog

RegressionCatalog.RegressionTrainers

Objeto instructor del catálogo de regresión.

labelColumnName

String

Nombre de la columna de etiquetas. Los datos de columna deben ser Single

featureColumnName

String

Nombre de la columna de característica. Los datos de columna deben ser un vector de tamaño conocido de Single

exampleWeightColumnName

String

Nombre de la columna de peso de ejemplo (opcional).

lossFunction

ISupportSdcaRegressionLoss

La función de pérdida minimizada en el proceso de entrenamiento. El uso de, por ejemplo, su valor predeterminado SquaredLoss conduce a un entrenador al menos cuadrado.

l2Regularization

Nullable<Single>

Peso L2 para regularización.

l1Regularization

Nullable<Single>

Hiperparámetros de regularización L1. Los valores más altos tienden a dar lugar a un modelo más disperso.

maximumNumberOfIterations

Nullable<Int32>

Número máximo de pases que se van a realizar a través de los datos.

Devoluciones

SdcaRegressionTrainer

Ejemplos

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class Sdca
    {
        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.Regression.Trainers.Sdca(
                labelColumnName: nameof(DataPoint.Label),
                featureColumnName: nameof(DataPoint.Features));

            // 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(5, 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();

            // Look at 5 predictions for the Label, side by side with the actual
            // Label for comparison.
            foreach (var p in predictions)
                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");

            // Expected output:
            //   Label: 0.985, Prediction: 0.960
            //   Label: 0.155, Prediction: 0.072
            //   Label: 0.515, Prediction: 0.455
            //   Label: 0.566, Prediction: 0.500
            //   Label: 0.096, Prediction: 0.079

            // Evaluate the overall metrics
            var metrics = mlContext.Regression.Evaluate(transformedTestData);
            PrintMetrics(metrics);

            // Expected output:
            //   Mean Absolute Error: 0.05
            //   Mean Squared Error: 0.00
            //   Root Mean Squared Error: 0.06
            //   RSquared: 0.97 (closer to 1 is better. The worst case is 0)
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            for (int i = 0; i < count; i++)
            {
                float label = (float)random.NextDouble();
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => x + (float)random.NextDouble()).ToArray()
                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public float Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public float Label { get; set; }
            // Predicted score from the trainer.
            public float Score { get; set; }
        }

        // Print some evaluation metrics to regression problems.
        private static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);

            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}