StandardTrainersCatalog.Sdca Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.StandardTrainers.dll

Pacchetto:

Microsoft.ML v2.0.0

Overload

Sdca(RegressionCatalog+RegressionTrainers, SdcaRegressionTrainer+Options)	Creare SdcaRegressionTrainer con opzioni avanzate, che stimano una destinazione usando un modello di regressione lineare.
Sdca(RegressionCatalog+RegressionTrainers, String, String, String, ISupportSdcaRegressionLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)	Creare SdcaRegressionTrainer, che stima una destinazione usando un modello di regressione lineare.

Sdca(RegressionCatalog+RegressionTrainers, SdcaRegressionTrainer+Options)

Creare SdcaRegressionTrainer con opzioni avanzate, che stimano una destinazione usando un modello di regressione lineare.

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

Parametri

catalog

RegressionCatalog.RegressionTrainers

Oggetto di training del catalogo di regressione.

options

SdcaRegressionTrainer.Options

Opzioni del trainer.

Restituisce

SdcaRegressionTrainer

Esempio

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>)

Creare SdcaRegressionTrainer, che stima una destinazione usando un modello di regressione lineare.

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

Parametri

catalog

RegressionCatalog.RegressionTrainers

Oggetto di training del catalogo di regressione.

labelColumnName

String

Nome della colonna etichetta. I dati della colonna devono essere Single

featureColumnName

String

Nome della colonna di funzionalità. I dati della colonna devono essere un vettore di dimensioni note Single

exampleWeightColumnName

String

Nome della colonna di peso di esempio (facoltativo).

lossFunction

ISupportSdcaRegressionLoss

La funzione di perdita ridotta al minimo nel processo di training. Usando, ad esempio, il suo valore predefinito SquaredLoss porta a un allenatore minimo quadrato.

l2Regularization

Nullable<Single>

Peso L2 per la regolarizzazione.

l1Regularization

Nullable<Single>

Iperparametro di regolarizzazione L1. I valori più elevati tendono a portare a un modello più sparse.

maximumNumberOfIterations

Nullable<Int32>

Numero massimo di passaggi da eseguire sui dati.

Restituisce

SdcaRegressionTrainer

Esempio

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);
        }
    }
}