MklComponentsCatalog.Ols Metoda

Definicja

Przestrzeń nazw:

Microsoft.ML

Zestaw:

Microsoft.ML.Mkl.Components.dll

Pakiet:

Microsoft.ML.Mkl.Components v3.0.1

Przeciążenia

Ols(RegressionCatalog+RegressionTrainers, OlsTrainer+Options)	Utwórz OlsTrainer za pomocą opcji zaawansowanych, które przewidują cel przy użyciu modelu regresji liniowej.
Ols(RegressionCatalog+RegressionTrainers, String, String, String)	Utwórz OlsTrainerobiekt , który przewiduje cel przy użyciu modelu regresji liniowej.

Ols(RegressionCatalog+RegressionTrainers, OlsTrainer+Options)

Utwórz OlsTrainer za pomocą opcji zaawansowanych, które przewidują cel przy użyciu modelu regresji liniowej.

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

static member Ols : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.OlsTrainer.Options -> Microsoft.ML.Trainers.OlsTrainer

<Extension()>
Public Function Ols (catalog As RegressionCatalog.RegressionTrainers, options As OlsTrainer.Options) As OlsTrainer

Parametry

catalog

RegressionCatalog.RegressionTrainers

Element RegressionCatalog.

options

OlsTrainer.Options

Zaawansowane opcje algorytmu. Zobacz: OlsTrainer.Options.

Zwraca

OlsTrainer

Przykłady

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 OlsWithOptions
    {
        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 OlsTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // Larger values leads to smaller (closer to zero) model parameters.
                L2Regularization = 0.1f,
                // Whether to compute standard error and other statistics of model
                // parameters.
                CalculateStatistics = false
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.Ols(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: 0.960
            //   Label: 0.155, Prediction: 0.075
            //   Label: 0.515, Prediction: 0.456
            //   Label: 0.566, Prediction: 0.499
            //   Label: 0.096, Prediction: 0.080

            // 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);
        }
    }
}

Ols(RegressionCatalog+RegressionTrainers, String, String, String)

Utwórz OlsTrainerobiekt , który przewiduje cel przy użyciu modelu regresji liniowej.

public static Microsoft.ML.Trainers.OlsTrainer Ols (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default);

static member Ols : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string -> Microsoft.ML.Trainers.OlsTrainer

<Extension()>
Public Function Ols (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing) As OlsTrainer

Parametry

catalog

RegressionCatalog.RegressionTrainers

Element RegressionCatalog.

labelColumnName

String

Nazwa kolumny etykiety. Dane kolumny muszą mieć wartość Single.

featureColumnName

String

Nazwa kolumny funkcji. Dane kolumn muszą być znanym wektorem .Single

exampleWeightColumnName

String

Nazwa przykładowej kolumny wagi (opcjonalnie).

Zwraca

OlsTrainer

Przykłady

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class Ols
    {
        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.Ols(
                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: 0.961
            //   Label: 0.155, Prediction: 0.072
            //   Label: 0.515, Prediction: 0.455
            //   Label: 0.566, Prediction: 0.499
            //   Label: 0.096, Prediction: 0.080

            // 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);
        }
    }
}