MklComponentsCatalog.SymbolicSgdLogisticRegression Metoda

  • Reference

Definice

Obor názvů:
Microsoft.ML
Sestavení:
Microsoft.ML.Mkl.Components.dll
Balíček:
Microsoft.ML.Mkl.Components v3.0.1
Balíček:
Microsoft.ML.Mkl.Components v1.0.0
Balíček:
Microsoft.ML.Mkl.Components v1.1.0
Balíček:
Microsoft.ML.Mkl.Components v1.2.0
Balíček:
Microsoft.ML.Mkl.Components v1.3.1
Balíček:
Microsoft.ML.Mkl.Components v1.4.0
Balíček:
Microsoft.ML.Mkl.Components v1.5.5
Balíček:
Microsoft.ML.Mkl.Components v1.6.0
Balíček:
Microsoft.ML.Mkl.Components v1.7.0
Balíček:
Microsoft.ML.Mkl.Components v2.0.0

Přetížení

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Vytvořte SymbolicSgdLogisticRegressionBinaryTrainer s pokročilými možnostmi, které predikují cíl pomocí lineárního binárního klasifikačního modelu trénovaného na základě logických dat popisků. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci. Paralelizuje SymbolicSgdLogisticRegressionBinaryTrainer SGD pomocí symbolického spuštění.
SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)

Vytvořte SymbolicSgdLogisticRegressionBinaryTrainer, který predikuje cíl pomocí lineárního binárního klasifikačního modelu natrénovaného pomocí logických dat popisků. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci. Paralelizuje SymbolicSgdLogisticRegressionBinaryTrainer SGD pomocí symbolického spuštění.

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Vytvořte SymbolicSgdLogisticRegressionBinaryTrainer s pokročilými možnostmi, které predikují cíl pomocí lineárního binárního klasifikačního modelu trénovaného na základě logických dat popisků. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci. Paralelizuje SymbolicSgdLogisticRegressionBinaryTrainer SGD pomocí symbolického spuštění.

public static Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options options);
static member SymbolicSgdLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options -> Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer
<Extension()>
Public Function SymbolicSgdLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As SymbolicSgdLogisticRegressionBinaryTrainer.Options) As SymbolicSgdLogisticRegressionBinaryTrainer

Parametry

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Hodnota BinaryClassificationCatalog

options
SymbolicSgdLogisticRegressionBinaryTrainer.Options

Pokročilé možnosti algoritmu. Viz třída SymbolicSgdLogisticRegressionBinaryTrainer.Options.

Návraty

SymbolicSgdLogisticRegressionBinaryTrainer

Příklady

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class SymbolicSgdLogisticRegressionWithOptions
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree at
        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
        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 SymbolicSgdLogisticRegressionBinaryTrainer.Options()
            {
                LearningRate = 0.2f,
                NumberOfIterations = 10,
                NumberOfThreads = 1,
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .SymbolicSgdLogisticRegression(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(500, 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();

            // Print 5 predictions.
            foreach (var p in predictions.Take(5))
                Console.WriteLine($"Label: {p.Label}, "
                    + $"Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: True, Prediction: False
            //   Label: False, Prediction: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: False

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.72
            //   AUC: 0.81
            //   F1 Score: 0.66
            //   Negative Precision: 0.68
            //   Negative Recall: 0.87
            //   Positive Precision: 0.80
            //   Positive Recall: 0.56
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      133 |      105 | 0.5588
            //    negative ||       34 |      228 | 0.8702
            //             ||======================
            //   Precision ||   0.7964 |   0.6847 |
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = randomFloat() > 0.5f;
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    // For data points with false label, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(x => x ? randomFloat() : randomFloat() +
                        0.1f).ToArray()

                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public bool Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public bool Label { get; set; }
            // Predicted label from the trainer.
            public bool PredictedLabel { get; set; }
        }

        // Pretty-print BinaryClassificationMetrics objects.
        private static void PrintMetrics(BinaryClassificationMetrics metrics)
        {
            Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
            Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
            Console.WriteLine($"Negative Precision: " +
                $"{metrics.NegativePrecision:F2}");

            Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
            Console.WriteLine($"Positive Precision: " +
                $"{metrics.PositivePrecision:F2}");

            Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

Platí pro

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32)

Vytvořte SymbolicSgdLogisticRegressionBinaryTrainer, který predikuje cíl pomocí lineárního binárního klasifikačního modelu natrénovaného pomocí logických dat popisků. Stochastic gradientní sestup (SGD) je iterativní algoritmus, který optimalizuje odlišnou objektivní funkci. Paralelizuje SymbolicSgdLogisticRegressionBinaryTrainer SGD pomocí symbolického spuštění.

public static Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", int numberOfIterations = 50);
static member SymbolicSgdLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * int -> Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer
<Extension()>
Public Function SymbolicSgdLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional numberOfIterations As Integer = 50) As SymbolicSgdLogisticRegressionBinaryTrainer

Parametry

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Hodnota BinaryClassificationCatalog

labelColumnName
String

Název sloupce popisku Data sloupce musí být Boolean.

featureColumnName
String

Název sloupce funkce. Data ve sloupci musí být vektorem známé velikosti Single.

numberOfIterations
Int32

Počet iterací trénování

Návraty

SymbolicSgdLogisticRegressionBinaryTrainer

Příklady

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class SymbolicSgdLogisticRegression
    {
        // This example requires installation of additional NuGet package for 
        // Microsoft.ML.FastTree at
        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
        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.BinaryClassification.Trainers
                .SymbolicSgdLogisticRegression();

            // 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(500, 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();

            // Print 5 predictions.
            foreach (var p in predictions.Take(5))
                Console.WriteLine($"Label: {p.Label}, "
                    + $"Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: True, Prediction: False
            //   Label: False, Prediction: False
            //   Label: True, Prediction: True
            //   Label: True, Prediction: True
            //   Label: False, Prediction: True

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

            PrintMetrics(metrics);

            // Expected output:
            //   Accuracy: 0.69
            //   AUC: 0.76
            //   F1 Score: 0.68
            //   Negative Precision: 0.72
            //   Negative Recall: 0.66
            //   Positive Precision: 0.66
            //   Positive Recall: 0.71
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      196 |       42 | 0.8235
            //    negative ||       42 |      220 | 0.8397
            //             ||======================
            //   Precision ||   0.8235 |   0.8397 |
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = randomFloat() > 0.5f;
                yield return new DataPoint
                {
                    Label = label,
                    // Create random features that are correlated with the label.
                    // For data points with false label, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(x => x ? randomFloat() : randomFloat() +
                        0.1f).ToArray()

                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public bool Label { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public bool Label { get; set; }
            // Predicted label from the trainer.
            public bool PredictedLabel { get; set; }
        }

        // Pretty-print BinaryClassificationMetrics objects.
        private static void PrintMetrics(BinaryClassificationMetrics metrics)
        {
            Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
            Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
            Console.WriteLine($"Negative Precision: " +
                $"{metrics.NegativePrecision:F2}");

            Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
            Console.WriteLine($"Positive Precision: " +
                $"{metrics.PositivePrecision:F2}");

            Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

Platí pro