StandardTrainersCatalog.LbfgsPoissonRegression Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.StandardTrainers.dll

Paket:

Microsoft.ML v3.0.1

Overload

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)	Buat LbfgsPoissonRegressionTrainer menggunakan opsi tingkat lanjut, yang memprediksi target menggunakan model regresi linier.
LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, String, String, String, Single, Single, Single, Int32, Boolean)	Buat LbfgsPoissonRegressionTrainer, yang memprediksi target menggunakan model regresi linier.

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)

Buat LbfgsPoissonRegressionTrainer menggunakan opsi tingkat lanjut, yang memprediksi target menggunakan model regresi linier.

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

Parameter

catalog

RegressionCatalog.RegressionTrainers

Objek pelatih katalog regresi.

options

LbfgsPoissonRegressionTrainer.Options

Opsi pelatih.

Mengembalikan

LbfgsPoissonRegressionTrainer

Contoh

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)

Buat LbfgsPoissonRegressionTrainer, yang memprediksi target menggunakan model regresi linier.

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

Parameter

catalog

RegressionCatalog.RegressionTrainers

Objek pelatih katalog regresi.

labelColumnName

String

Nama kolom label. Data kolom harus Single.

featureColumnName

String

Nama kolom fitur. Data kolom harus merupakan vektor berukuran besar yang diketahui dari Single.

exampleWeightColumnName

String

Nama kolom berat contoh (opsional).

l1Regularization

Single

Hiperparameter regularisasi L1. Nilai yang lebih tinggi akan cenderung mengarah ke model yang lebih jarang.

l2Regularization

Single

Berat L2 untuk regularisasi.

optimizationTolerance

Single

Ambang batas untuk konvergensi pengoptimal.

historySize

Int32

Jumlah iterasi sebelumnya yang perlu diingat untuk memperkirakan Hessian. Nilai yang lebih rendah berarti perkiraan yang lebih cepat tetapi kurang akurat.

enforceNonNegativity

Boolean

Memberlakukan bobot non-negatif.

Mengembalikan

LbfgsPoissonRegressionTrainer

Contoh

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);
        }
    }
}