StandardTrainersCatalog.LbfgsPoissonRegression Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.StandardTrainers.dll

Paket:

Microsoft.ML v3.0.1

Überlädt

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)	Erstellen Sie LbfgsPoissonRegressionTrainer erweiterte Optionen, die ein Ziel mithilfe eines linearen Regressionsmodells vorhersagen.
LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, String, String, String, Single, Single, Single, Int32, Boolean)	Erstellen Sie LbfgsPoissonRegressionTrainerein Ziel, das ein Ziel mithilfe eines linearen Regressionsmodells vorhersagt.

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)

Erstellen Sie LbfgsPoissonRegressionTrainer erweiterte Optionen, die ein Ziel mithilfe eines linearen Regressionsmodells vorhersagen.

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

Das Regressionskatalog-Trainerobjekt.

options

LbfgsPoissonRegressionTrainer.Options

Traineroptionen.

Gibt zurück

LbfgsPoissonRegressionTrainer

Beispiele

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)

Erstellen Sie LbfgsPoissonRegressionTrainerein Ziel, das ein Ziel mithilfe eines linearen Regressionsmodells vorhersagt.

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

Das Regressionskatalog-Trainerobjekt.

labelColumnName

String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen sein Single.

featureColumnName

String

Der Name der Featurespalte. Die Spaltendaten müssen ein bekannter Vektor von Single.

exampleWeightColumnName

String

Der Name der Beispielgewichtungsspalte (optional).

l1Regularization

Single

Der L1-Normalisierungs-Hyperparameter . Höhere Werte führen tendenziell zu einem sparsameren Modell.

l2Regularization

Single

Das L2-Gewicht für die Regularisierung.

optimizationTolerance

Single

Schwellenwert für die Optimierungskonvergenz.

historySize

Int32

Anzahl der vorherigen Iterationen, die sich für die Schätzung des Hessischen erinnern. Niedrigere Werte bedeuten schneller, aber weniger genaue Schätzungen.

enforceNonNegativity

Boolean

Erzwingen sie nicht negative Gewichtungen.

Gibt zurück

LbfgsPoissonRegressionTrainer

Beispiele

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);
        }
    }
}