StandardTrainersCatalog.OnlineGradientDescent Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

Overloads

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, String, String, IRegressionLoss, Single, Boolean, Single, Int32)	Create OnlineGradientDescentTrainer, which predicts a target using a linear regression model.
OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)	Create OnlineGradientDescentTrainer using advanced options, which predicts a target using a linear regression model.

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, String, String, IRegressionLoss, Single, Boolean, Single, Int32)

Create OnlineGradientDescentTrainer, which predicts a target using a linear regression model.

public static Microsoft.ML.Trainers.OnlineGradientDescentTrainer OnlineGradientDescent (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", Microsoft.ML.Trainers.IRegressionLoss lossFunction = default, float learningRate = 0.1, bool decreaseLearningRate = true, float l2Regularization = 0, int numberOfIterations = 1);

static member OnlineGradientDescent : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * Microsoft.ML.Trainers.IRegressionLoss * single * bool * single * int -> Microsoft.ML.Trainers.OnlineGradientDescentTrainer

<Extension()>
Public Function OnlineGradientDescent (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional lossFunction As IRegressionLoss = Nothing, Optional learningRate As Single = 0.1, Optional decreaseLearningRate As Boolean = true, Optional l2Regularization As Single = 0, Optional numberOfIterations As Integer = 1) As OnlineGradientDescentTrainer

Parameters

catalog

RegressionCatalog.RegressionTrainers

The regression catalog trainer object.

labelColumnName

String

The name of the label column. The column data must be Single.

featureColumnName

String

The name of the feature column. The column data must be a known-sized vector of Single.

lossFunction

IRegressionLoss

The loss function minimized in the training process. Using, for example, SquaredLoss leads to a least square trainer.

learningRate

Single

The initial learning rate used by SGD.

decreaseLearningRate

Boolean

Decrease learning rate as iterations progress.

l2Regularization

Single

The L2 weight for regularization.

numberOfIterations

Int32

The number of passes through the training dataset.

Returns

OnlineGradientDescentTrainer

Examples

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class OnlineGradientDescent
    {
        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.OnlineGradientDescent(
                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}");

            // This trainer is not numerically stable.
            // Please see issue #2425.

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


        }

        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);
        }
    }
}

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Create OnlineGradientDescentTrainer using advanced options, which predicts a target using a linear regression model.

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

static member OnlineGradientDescent : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.OnlineGradientDescentTrainer.Options -> Microsoft.ML.Trainers.OnlineGradientDescentTrainer

<Extension()>
Public Function OnlineGradientDescent (catalog As RegressionCatalog.RegressionTrainers, options As OnlineGradientDescentTrainer.Options) As OnlineGradientDescentTrainer

Parameters

catalog

RegressionCatalog.RegressionTrainers

The regression catalog trainer object.

options

OnlineGradientDescentTrainer.Options

Trainer options.

Returns

OnlineGradientDescentTrainer

Examples

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 OnlineGradientDescentWithOptions
    {
        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 OnlineGradientDescentTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Change the loss function.
                LossFunction = new TweedieLoss(),
                // Give an extra gain to more recent updates.
                RecencyGain = 0.1f,
                // Turn off lazy updates.
                LazyUpdate = false,
                // Specify scale for initial weights.
                InitialWeightsDiameter = 0.2f
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.OnlineGradientDescent(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}");

            // This trainer is not numerically stable.
            // Please see issue #2425.

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

            // This trainer is not numerically stable. Please see
            // issue #2425.
        }

        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);
        }
    }
}