StandardTrainersCatalog.LbfgsPoissonRegression Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.StandardTrainers.dll

Paquete:

Microsoft.ML v2.0.0

Sobrecargas

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)	Cree LbfgsPoissonRegressionTrainer con opciones avanzadas, que predice un destino mediante un modelo de regresión lineal.
LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, String, String, String, Single, Single, Single, Int32, Boolean)	Cree LbfgsPoissonRegressionTrainer, que predice un destino mediante un modelo de regresión lineal.

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)

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

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

static member LbfgsPoissonRegression : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer.Options -> Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer

<Extension()>
Public Function LbfgsPoissonRegression (catalog As RegressionCatalog.RegressionTrainers, options As LbfgsPoissonRegressionTrainer.Options) As LbfgsPoissonRegressionTrainer

Parámetros

catalog

RegressionCatalog.RegressionTrainers

Objeto instructor del catálogo de regresión.

options

LbfgsPoissonRegressionTrainer.Options

Opciones de entrenador.

Devoluciones

LbfgsPoissonRegressionTrainer

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 LbfgsPoissonRegressionWithOptions
    {
        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 LbfgsPoissonRegressionTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Reduce optimization tolerance to speed up training at the cost of
                // accuracy.
                OptimizationTolerance = 1e-4f,
                // Decrease history size to speed up training at the cost of
                // accuracy.
                HistorySize = 30,
                // Specify scale for initial weights.
                InitialWeightsDiameter = 0.2f
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.LbfgsPoissonRegression(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: 1.110
            //   Label: 0.155, Prediction: 0.169
            //   Label: 0.515, Prediction: 0.400
            //   Label: 0.566, Prediction: 0.415
            //   Label: 0.096, Prediction: 0.169

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

            // Expected output:
            //   Mean Absolute Error: 0.10
            //   Mean Squared Error: 0.01
            //   Root Mean Squared Error: 0.11
            //   RSquared: 0.89 (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);
        }
    }
}

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

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

public static Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer LbfgsPoissonRegression (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, float l1Regularization = 1, float l2Regularization = 1, float optimizationTolerance = 1E-07, int historySize = 20, bool enforceNonNegativity = false);

static member LbfgsPoissonRegression : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * single * single * single * int * bool -> Microsoft.ML.Trainers.LbfgsPoissonRegressionTrainer

<Extension()>
Public Function LbfgsPoissonRegression (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional l1Regularization As Single = 1, Optional l2Regularization As Single = 1, Optional optimizationTolerance As Single = 1E-07, Optional historySize As Integer = 20, Optional enforceNonNegativity As Boolean = false) As LbfgsPoissonRegressionTrainer

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).

l1Regularization

Single

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

l2Regularization

Single

Peso L2 para regularización.

optimizationTolerance

Single

Umbral para la convergencia del optimizador.

historySize

Int32

Número de iteraciones anteriores que recordar para calcular el hessiano. Los valores inferiores significan estimaciones más rápidas pero menos precisas.

enforceNonNegativity

Boolean

Aplicar pesos no negativos.

Devoluciones

LbfgsPoissonRegressionTrainer

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 LbfgsPoissonRegression
    {
        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.
                LbfgsPoissonRegression(
                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: 1.109
            //   Label: 0.155, Prediction: 0.171
            //   Label: 0.515, Prediction: 0.400
            //   Label: 0.566, Prediction: 0.417
            //   Label: 0.096, Prediction: 0.172

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

            // Expected output:
            //   Mean Absolute Error: 0.07
            //   Mean Squared Error: 0.01
            //   Root Mean Squared Error: 0.08
            //   RSquared: 0.93 (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);
        }
    }
}