Aracılığıyla paylaş

StandardTrainersCatalog.LbfgsLogisticRegression Yöntem

  • Başvuru

Tanım

Ad Alanı:
Microsoft.ML
Bütünleştirilmiş Kod:
Microsoft.ML.StandardTrainers.dll
Paket:
Microsoft.ML v3.0.1
Paket:
Microsoft.ML v1.0.0
Paket:
Microsoft.ML v1.1.0
Paket:
Microsoft.ML v1.2.0
Paket:
Microsoft.ML v1.3.1
Paket:
Microsoft.ML v1.4.0
Paket:
Microsoft.ML v1.5.5
Paket:
Microsoft.ML v1.6.0
Paket:
Microsoft.ML v1.7.0
Paket:
Microsoft.ML v2.0.0

Aşırı Yüklemeler

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)

Boole etiket verileri üzerinde eğitilen doğrusal ikili sınıflandırma modelini kullanarak hedefi tahmin eden gelişmiş seçeneklerle oluşturun LbfgsLogisticRegressionBinaryTrainer .
LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Boole etiket verileri üzerinde eğitilen doğrusal ikili sınıflandırma modelini kullanarak hedefi tahmin eden öğesini oluşturun LbfgsLogisticRegressionBinaryTrainer.

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)

Boole etiket verileri üzerinde eğitilen doğrusal ikili sınıflandırma modelini kullanarak hedefi tahmin eden gelişmiş seçeneklerle oluşturun LbfgsLogisticRegressionBinaryTrainer .

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

Parametreler

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

İkili sınıflandırma kataloğu eğitmen nesnesi.

options
LbfgsLogisticRegressionBinaryTrainer.Options

Algoritmaya yönelik gelişmiş bağımsız değişkenler.

Döndürülenler

LbfgsLogisticRegressionBinaryTrainer

Örnekler

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 LbfgsLogisticRegressionWithOptions
    {
        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 LbfgsLogisticRegressionBinaryTrainer.Options()
            {
                MaximumNumberOfIterations = 100,
                OptimizationTolerance = 1e-8f,
                L2Regularization = 0.01f
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .LbfgsLogisticRegression(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: True
            //   Label: False, Prediction: True
            //   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: 0.87
            //   AUC: 0.96
            //   F1 Score: 0.87
            //   Negative Precision: 0.89
            //   Negative Recall: 0.87
            //   Positive Precision: 0.86
            //   Positive Recall: 0.88
            //   Log Loss: 0.37
            //   Log Loss Reduction: 0.63
            //   Entropy: 1.00
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      210 |       28 | 0.8824
            //    negative ||       35 |      227 | 0.8664
            //             ||======================
            //   Precision ||   0.8571 |   0.8902 |
        }

        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());
        }
    }
}

Şunlara uygulanır

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Boole etiket verileri üzerinde eğitilen doğrusal ikili sınıflandırma modelini kullanarak hedefi tahmin eden öğesini oluşturun LbfgsLogisticRegressionBinaryTrainer.

public static Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer LbfgsLogisticRegression (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers 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 LbfgsLogisticRegression : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * single * single * single * int * bool -> Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer
<Extension()>
Public Function LbfgsLogisticRegression (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, 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 LbfgsLogisticRegressionBinaryTrainer

Parametreler

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

İkili sınıflandırma kataloğu eğitmen nesnesi.

labelColumnName
String

Etiket sütununun adı. Sütun verileri olmalıdır Boolean.

featureColumnName
String

Özellik sütununun adı. Sütun verileri bilinen boyutlu bir vektör Singleolmalıdır.

exampleWeightColumnName
String

Örnek ağırlık sütununun adı (isteğe bağlı).

l1Regularization
Single

L1 düzenlileştirme hiperparametresi. Daha yüksek değerler daha seyrek modele yol açma eğilimindedir.

l2Regularization
Single

Düzenlileştirme için L2 ağırlığı.

optimizationTolerance
Single

İyileştirici yakınsama eşiği.

historySize
Int32

için LbfgsLogisticRegressionBinaryTrainerbellek boyutu. Düşük=daha hızlı, daha az doğru.

enforceNonNegativity
Boolean

Negatif olmayan ağırlıkları zorunlu kılma.

Döndürülenler

LbfgsLogisticRegressionBinaryTrainer

Örnekler

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class LbfgsLogisticRegression
    {
        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
                .LbfgsLogisticRegression();

            // 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: True
            //   Label: False, Prediction: True
            //   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: 0.88
            //   AUC: 0.96
            //   F1 Score: 0.87
            //   Negative Precision: 0.90
            //   Negative Recall: 0.87
            //   Positive Precision: 0.86
            //   Positive Recall: 0.89
            //   Log Loss: 0.38
            //   Log Loss Reduction: 0.62
            //   Entropy: 1.00
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      212 |       26 | 0.8908
            //    negative ||       35 |      227 | 0.8664
            //             ||======================
            //   Precision ||   0.8583 |   0.8972 |
        }

        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());
        }
    }
}

Şunlara uygulanır