StandardTrainersCatalog.LbfgsLogisticRegression 方法

  • 参考

定义

命名空间:
Microsoft.ML
程序集:
Microsoft.ML.StandardTrainers.dll
包:
Microsoft.ML v3.0.1
包:
Microsoft.ML v1.0.0
包:
Microsoft.ML v1.1.0
包:
Microsoft.ML v1.2.0
包:
Microsoft.ML v1.3.1
包:
Microsoft.ML v1.4.0
包:
Microsoft.ML v1.5.5
包:
Microsoft.ML v1.6.0
包:
Microsoft.ML v1.7.0
包:
Microsoft.ML v2.0.0

重载

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)

使用高级选项创建 LbfgsLogisticRegressionBinaryTrainer ，该选项使用通过布尔标签数据训练的线性二元分类模型预测目标。
LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

创建 LbfgsLogisticRegressionBinaryTrainer，它使用通过布尔标签数据训练的线性二元分类模型预测目标。

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)

使用高级选项创建 LbfgsLogisticRegressionBinaryTrainer ，该选项使用通过布尔标签数据训练的线性二元分类模型预测目标。

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

参数

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

二元分类目录训练器对象。

options
LbfgsLogisticRegressionBinaryTrainer.Options

算法的高级参数。

返回

LbfgsLogisticRegressionBinaryTrainer

示例

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());
        }
    }
}

适用于

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

创建 LbfgsLogisticRegressionBinaryTrainer，它使用通过布尔标签数据训练的线性二元分类模型预测目标。

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

参数

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

二元分类目录训练器对象。

labelColumnName
String

标签列的名称。 列数据必须是 Boolean。

featureColumnName
String

功能列的名称。 列数据必须是已知大小的向量 Single。

exampleWeightColumnName
String

示例权重列的名称 (可选) 。

l1Regularization
Single

L1 正则化 超参数。 较高的值往往会导致更稀疏的模型。

l2Regularization
Single

正则化的 L2 权重。

optimizationTolerance
Single

优化器收敛的阈值。

historySize
Int32

的 LbfgsLogisticRegressionBinaryTrainer内存大小 。 低=更快，不太准确。

enforceNonNegativity
Boolean

强制实施非负权重。

返回

LbfgsLogisticRegressionBinaryTrainer

示例

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());
        }
    }
}

适用于