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LightGbmExtensions.LightGbm Méthode

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Définition

Espace de noms:
Microsoft.ML
Assembly:
Microsoft.ML.LightGbm.dll
Paquet:
Microsoft.ML.LightGbm v3.0.1
Paquet:
Microsoft.ML.LightGbm v1.0.0
Paquet:
Microsoft.ML.LightGbm v1.1.0
Paquet:
Microsoft.ML.LightGbm v1.2.0
Paquet:
Microsoft.ML.LightGbm v1.3.1
Paquet:
Microsoft.ML.LightGbm v1.4.0
Paquet:
Microsoft.ML.LightGbm v1.5.5
Paquet:
Microsoft.ML.LightGbm v1.6.0
Paquet:
Microsoft.ML.LightGbm v1.7.0
Paquet:
Microsoft.ML.LightGbm v2.0.0

Surcharges

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)

Créez LightGbmBinaryTrainer avec des options avancées, ce qui prédit une cible à l’aide d’une classification binaire d’arbre de décision boostant le gradient.
LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)

Créez LightGbmMulticlassTrainer avec des options avancées, qui prédisent une cible à l’aide d’un modèle de classification multiclasse d’arborescence de décision d’augmentation de gradient.
LightGbm(RankingCatalog+RankingTrainers, LightGbmRankingTrainer+Options)

Créez LightGbmRankingTrainer avec des options avancées, qui prédisent une cible à l’aide d’un modèle de classement d’arbre de décision d’augmentation de gradient.
LightGbm(RegressionCatalog+RegressionTrainers, LightGbmRegressionTrainer+Options)

Créez LightGbmRegressionTrainer à l’aide d’options avancées, qui prédit une cible à l’aide d’un modèle de régression d’arborescence de décision d’augmentation de gradient.
LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, Stream, String)

Créez LightGbmBinaryTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’une classification binaire d’arbre de décision d’augmentation de gradient.
LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, Stream, String)

Créez LightGbmMulticlassTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’un modèle de classification multiclasse d’arbre de décision d’augmentation de gradient.
LightGbm(RankingCatalog+RankingTrainers, Stream, String)

Créez LightGbmRankingTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’un modèle de classement d’arbre de décision d’augmentation de gradient.
LightGbm(RegressionCatalog+RegressionTrainers, Stream, String)

Créez LightGbmRegressionTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’une régression d’arbre de décision d’augmentation de gradient.
LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmBinaryTrainer, qui prédit une cible à l’aide d’une classification binaire d’arbre de décision d’augmentation de gradient.
LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmMulticlassTrainer, qui prédit une cible à l’aide d’un modèle de classification multiclasse de l’arborescence de décision de gradient.
LightGbm(RegressionCatalog+RegressionTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmRegressionTrainer, qui prédit une cible à l’aide d’un modèle de régression d’arbre de décision d’augmentation de gradient.
LightGbm(RankingCatalog+RankingTrainers, String, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmRankingTrainer, qui prédit une cible à l’aide d’un modèle de classement d’arbre de décision d’augmentation de gradient.

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)

Créez LightGbmBinaryTrainer avec des options avancées, ce qui prédit une cible à l’aide d’une classification binaire d’arbre de décision boostant le gradient.

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

Paramètres

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Le BinaryClassificationCatalog.

options
LightGbmBinaryTrainer.Options

Options d’entraînement.

Retours

LightGbmBinaryTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.BinaryClassification
{
    public static class LightGbmWithOptions
    {
        // 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 LightGbmBinaryTrainer.Options
            {
                Booster = new GossBooster.Options
                {
                    TopRate = 0.3,
                    OtherRate = 0.2
                }
            };

            // Define the trainer.
            var pipeline = mlContext.BinaryClassification.Trainers
                .LightGbm(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: 0.71
            //   AUC: 0.76
            //   F1 Score: 0.70
            //   Negative Precision: 0.73
            //   Negative Recall: 0.71
            //   Positive Precision: 0.69
            //   Positive Recall: 0.71
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      168 |       70 | 0.7059
            //    negative ||       88 |      174 | 0.6641
            //             ||======================
            //   Precision ||   0.6563 |   0.7131 |
        }

        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() +
                        0.03f).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());
        }
    }
}

S’applique à

LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)

Créez LightGbmMulticlassTrainer avec des options avancées, qui prédisent une cible à l’aide d’un modèle de classification multiclasse d’arborescence de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer LightGbm (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer.Options options);
static member LightGbm : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer.Options -> Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer
<Extension()>
Public Function LightGbm (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As LightGbmMulticlassTrainer.Options) As LightGbmMulticlassTrainer

Paramètres

catalog
MulticlassClassificationCatalog.MulticlassClassificationTrainers

Le MulticlassClassificationCatalog.

options
LightGbmMulticlassTrainer.Options

Options d’entraînement.

Retours

LightGbmMulticlassTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class LightGbmWithOptions
    {
        // 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 LightGbmMulticlassTrainer.Options
            {
                Booster = new DartBooster.Options()
                {
                    TreeDropFraction = 0.15,
                    XgboostDartMode = false
                }
            };

            // Define the trainer.
            var pipeline =
                // Convert the string labels into key types.
                mlContext.Transforms.Conversion.MapValueToKey("Label")
                // Apply LightGbm multiclass trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .LightGbm(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();

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

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 3
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 3

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

            PrintMetrics(metrics);

            // Expected output:
            //   Micro Accuracy: 0.98
            //   Macro Accuracy: 0.98
            //   Log Loss: 0.07
            //   Log Loss Reduction: 0.94

            //   Confusion table
            //             ||========================
            //   PREDICTED ||     0 |     1 |     2 | Recall
            //   TRUTH     ||========================
            //           0 ||   156 |     0 |     4 | 0.9750
            //           1 ||     0 |   171 |     6 | 0.9661
            //           2 ||     1 |     0 |   162 | 0.9939
            //             ||========================
            //   Precision ||0.9936 |1.0000 |0.9419 |
        }

        // Generates random uniform doubles in [-0.5, 0.5)
        // range with labels 1, 2 or 3.
        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)(random.NextDouble() - 0.5);
            for (int i = 0; i < count; i++)
            {
                // Generate Labels that are integers 1, 2 or 3
                var label = random.Next(1, 4);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features that are correlated with the label.
                    // The feature values are slightly increased by adding a
                    // constant multiple of label.
                    Features = Enumerable.Repeat(label, 20)
                        .Select(x => randomFloat() + label * 0.2f).ToArray()

                };
            }
        }

        // Example with label and 20 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public uint Label { get; set; }
            [VectorType(20)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print MulticlassClassificationMetrics objects.
        public static void PrintMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
            Console.WriteLine(
                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");

            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

S’applique à

LightGbm(RankingCatalog+RankingTrainers, LightGbmRankingTrainer+Options)

Créez LightGbmRankingTrainer avec des options avancées, qui prédisent une cible à l’aide d’un modèle de classement d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer LightGbm (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer.Options options);
static member LightGbm : Microsoft.ML.RankingCatalog.RankingTrainers * Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer.Options -> Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer
<Extension()>
Public Function LightGbm (catalog As RankingCatalog.RankingTrainers, options As LightGbmRankingTrainer.Options) As LightGbmRankingTrainer

Paramètres

catalog
RankingCatalog.RankingTrainers

Le RankingCatalog.

options
LightGbmRankingTrainer.Options

Options d’entraînement.

Retours

LightGbmRankingTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.Ranking
{
    public static class LightGbmWithOptions
    {
        // 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 LightGbmRankingTrainer.Options
            {
                NumberOfLeaves = 4,
                MinimumExampleCountPerGroup = 10,
                LearningRate = 0.1,
                NumberOfIterations = 2,
                Booster = new GradientBooster.Options
                {
                    FeatureFraction = 0.9
                },
                RowGroupColumnName = "GroupId"
            };

            // Define the trainer.
            var pipeline = mlContext.Ranking.Trainers.LightGbm(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);

            // Take the top 5 rows.
            var topTransformedTestData = mlContext.Data.TakeRows(
                transformedTestData, 5);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                topTransformedTestData, reuseRowObject: false).ToList();

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

            // Expected output:
            //   Label: 5, Score: 0.05836755
            //   Label: 1, Score: -0.06531862
            //   Label: 3, Score: -0.004557075
            //   Label: 3, Score: -0.009396422
            //   Label: 1, Score: -0.05871891

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

            // Expected output:
            //   DCG: @1:28.83, @2:46.36, @3:56.18
            //   NDCG: @1:0.69, @2:0.72, @3:0.74
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0, int groupSize = 10)
        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = random.Next(0, 5);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    GroupId = (uint)(i / groupSize),
                    // Create random features that are correlated with the label.
                    // For data points with larger labels, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => randomFloat() + x * 0.1f).ToArray()
                };
            }
        }

        // Example with label, groupId, and 50 feature values. A data set is a
        // collection of such examples.
        private class DataPoint
        {
            [KeyType(5)]
            public uint Label { get; set; }
            [KeyType(100)]
            public uint GroupId { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public uint Label { get; set; }
            // Score produced from the trainer.
            public float Score { get; set; }
        }

        // Pretty-print RankerMetrics objects.
        public static void PrintMetrics(RankingMetrics metrics)
        {
            Console.WriteLine("DCG: " + string.Join(", ",
                metrics.DiscountedCumulativeGains.Select(
                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
            Console.WriteLine("NDCG: " + string.Join(", ",
                metrics.NormalizedDiscountedCumulativeGains.Select(
                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
        }
    }
}

S’applique à

LightGbm(RegressionCatalog+RegressionTrainers, LightGbmRegressionTrainer+Options)

Créez LightGbmRegressionTrainer à l’aide d’options avancées, qui prédit une cible à l’aide d’un modèle de régression d’arborescence de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer LightGbm (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.Options options);
static member LightGbm : Microsoft.ML.RegressionCatalog.RegressionTrainers * Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.Options -> Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer
<Extension()>
Public Function LightGbm (catalog As RegressionCatalog.RegressionTrainers, options As LightGbmRegressionTrainer.Options) As LightGbmRegressionTrainer

Paramètres

catalog
RegressionCatalog.RegressionTrainers

Le RegressionCatalog.

options
LightGbmRegressionTrainer.Options

Options d’entraînement.

Retours

LightGbmRegressionTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class LightGbmWithOptions
    {
        // This example requires installation of additional NuGet
        // package for Microsoft.ML.LightGBM
        // at https://www.nuget.org/packages/Microsoft.ML.LightGbm/
        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 LightGbmRegressionTrainer.Options
            {
                LabelColumnName = nameof(DataPoint.Label),
                FeatureColumnName = nameof(DataPoint.Features),
                // How many leaves a single tree should have.
                NumberOfLeaves = 4,
                // Each leaf contains at least this number of training data points.
                MinimumExampleCountPerLeaf = 6,
                // The step size per update. Using a large value might reduce the
                // training time but also increase the algorithm's numerical
                // stability.
                LearningRate = 0.001,
                Booster = new Microsoft.ML.Trainers.LightGbm.GossBooster.Options()
                {
                    TopRate = 0.3,
                    OtherRate = 0.2
                }
            };

            // Define the trainer.
            var pipeline =
                mlContext.Regression.Trainers.LightGbm(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: 0.866
            //   Label: 0.155, Prediction: 0.171
            //   Label: 0.515, Prediction: 0.470
            //   Label: 0.566, Prediction: 0.476
            //   Label: 0.096, Prediction: 0.140

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

            // Expected output:
            //   Mean Absolute Error: 0.04
            //   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);
        }
    }
}

S’applique à

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, Stream, String)

Créez LightGbmBinaryTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’une classification binaire d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer LightGbm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, System.IO.Stream lightGbmModel, string featureColumnName = "Features");
static member LightGbm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer
<Extension()>
Public Function LightGbm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmBinaryTrainer

Paramètres

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Le BinaryClassificationCatalog.

lightGbmModel
Stream

Préentraîné Stream d’une inférence de fichier de modèle LightGBM

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

Retours

LightGbmBinaryTrainer

S’applique à

LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, Stream, String)

Créez LightGbmMulticlassTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’un modèle de classification multiclasse d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer LightGbm (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, System.IO.Stream lightGbmModel, string featureColumnName = "Features");
static member LightGbm : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer
<Extension()>
Public Function LightGbm (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmMulticlassTrainer

Paramètres

catalog
MulticlassClassificationCatalog.MulticlassClassificationTrainers

Le MulticlassClassificationCatalog.

lightGbmModel
Stream

Préentraîné Stream d’une inférence de fichier de modèle LightGBM

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

Retours

LightGbmMulticlassTrainer

S’applique à

LightGbm(RankingCatalog+RankingTrainers, Stream, String)

Créez LightGbmRankingTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’un modèle de classement d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer LightGbm (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, System.IO.Stream lightGbmModel, string featureColumnName = "Features");
static member LightGbm : Microsoft.ML.RankingCatalog.RankingTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer
<Extension()>
Public Function LightGbm (catalog As RankingCatalog.RankingTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmRankingTrainer

Paramètres

catalog
RankingCatalog.RankingTrainers

Le RankingCatalog.

lightGbmModel
Stream

Préentraîné Stream d’une inférence de fichier de modèle LightGBM

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

Retours

LightGbmRankingTrainer

S’applique à

LightGbm(RegressionCatalog+RegressionTrainers, Stream, String)

Créez LightGbmRegressionTrainer à partir d’un modèle LightGBM préentraîné, qui prédit une cible à l’aide d’une régression d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer LightGbm (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, System.IO.Stream lightGbmModel, string featureColumnName = "Features");
static member LightGbm : Microsoft.ML.RegressionCatalog.RegressionTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer
<Extension()>
Public Function LightGbm (catalog As RegressionCatalog.RegressionTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmRegressionTrainer

Paramètres

catalog
RegressionCatalog.RegressionTrainers

Le RegressionCatalog.

lightGbmModel
Stream

Préentraîné Stream d’une inférence de fichier de modèle LightGBM

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

Retours

LightGbmRegressionTrainer

S’applique à

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmBinaryTrainer, qui prédit une cible à l’aide d’une classification binaire d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer LightGbm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int? numberOfLeaves = default, int? minimumExampleCountPerLeaf = default, double? learningRate = default, int numberOfIterations = 100);
static member LightGbm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * string * string * string * Nullable<int> * Nullable<int> * Nullable<double> * int -> Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer
<Extension()>
Public Function LightGbm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Nullable(Of Integer) = Nothing, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = Nothing, Optional learningRate As Nullable(Of Double) = Nothing, Optional numberOfIterations As Integer = 100) As LightGbmBinaryTrainer

Paramètres

catalog
BinaryClassificationCatalog.BinaryClassificationTrainers

Le BinaryClassificationCatalog.

labelColumnName
String

Nom de la colonne d’étiquette. Les données de colonne doivent être Boolean.

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

exampleWeightColumnName
String

Nom de l’exemple de colonne de poids (facultatif).

numberOfLeaves
Nullable<Int32>

Nombre maximal de feuilles dans une arborescence.

minimumExampleCountPerLeaf
Nullable<Int32>

Nombre minimal de points de données requis pour former une nouvelle feuille d’arborescence.

learningRate
Nullable<Double>

Taux d’apprentissage.

numberOfIterations
Int32

Nombre d’itérations d’augmentation. Une nouvelle arborescence est créée dans chaque itération, ce qui équivaut au nombre d’arborescences.

Retours

LightGbmBinaryTrainer

Exemples

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

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

            // 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: 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: 0.77
            //   AUC: 0.85
            //   F1 Score: 0.76
            //   Negative Precision: 0.79
            //   Negative Recall: 0.77
            //   Positive Precision: 0.75
            //   Positive Recall: 0.77
            //
            //   TEST POSITIVE RATIO:    0.4760 (238.0/(238.0+262.0))
            //   Confusion table
            //             ||======================
            //   PREDICTED || positive | negative | Recall
            //   TRUTH     ||======================
            //    positive ||      183 |       55 | 0.7689
            //    negative ||       60 |      202 | 0.7710
            //             ||======================
            //   Precision ||   0.7531 |   0.7860 |
        }

        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() +
                        0.03f).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());
        }
    }
}

S’applique à

LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmMulticlassTrainer, qui prédit une cible à l’aide d’un modèle de classification multiclasse de l’arborescence de décision de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer LightGbm (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int? numberOfLeaves = default, int? minimumExampleCountPerLeaf = default, double? learningRate = default, int numberOfIterations = 100);
static member LightGbm : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * Nullable<int> * Nullable<int> * Nullable<double> * int -> Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer
<Extension()>
Public Function LightGbm (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Nullable(Of Integer) = Nothing, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = Nothing, Optional learningRate As Nullable(Of Double) = Nothing, Optional numberOfIterations As Integer = 100) As LightGbmMulticlassTrainer

Paramètres

catalog
MulticlassClassificationCatalog.MulticlassClassificationTrainers

Le MulticlassClassificationCatalog.

labelColumnName
String

Nom de la colonne d’étiquette. Les données de colonne doivent être KeyDataViewType.

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

exampleWeightColumnName
String

Nom de l’exemple de colonne de poids (facultatif).

numberOfLeaves
Nullable<Int32>

Nombre maximal de feuilles dans une arborescence.

minimumExampleCountPerLeaf
Nullable<Int32>

Nombre minimal de points de données requis pour former une nouvelle feuille d’arborescence.

learningRate
Nullable<Double>

Taux d’apprentissage.

numberOfIterations
Int32

Nombre d’itérations d’augmentation. Une nouvelle arborescence est créée dans chaque itération, ce qui équivaut au nombre d’arborescences.

Retours

LightGbmMulticlassTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.MulticlassClassification
{
    public static class LightGbm
    {
        // 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 =
                // Convert the string labels into key types.
                mlContext.Transforms.Conversion
                .MapValueToKey(nameof(DataPoint.Label))
                // Apply LightGbm multiclass trainer.
                .Append(mlContext.MulticlassClassification.Trainers
                .LightGbm());

            // 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();

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

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 3
            //   Label: 2, Prediction: 2
            //   Label: 3, Prediction: 3

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

            PrintMetrics(metrics);

            // Expected output:
            //   Micro Accuracy: 0.99
            //   Macro Accuracy: 0.99
            //   Log Loss: 0.05
            //   Log Loss Reduction: 0.95

            //   Confusion table
            //             ||========================
            //   PREDICTED ||     0 |     1 |     2 | Recall
            //   TRUTH     ||========================
            //           0 ||   156 |     0 |     4 | 0.9750
            //           1 ||     0 |   176 |     1 | 0.9944
            //           2 ||     1 |     0 |   162 | 0.9939
            //             ||========================
            //   Precision ||0.9936 |1.0000 |0.9701 |
        }

        // Generates random uniform doubles in [-0.5, 0.5)
        // range with labels 1, 2 or 3.
        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)

        {
            var random = new Random(seed);
            float randomFloat() => (float)(random.NextDouble() - 0.5);
            for (int i = 0; i < count; i++)
            {
                // Generate Labels that are integers 1, 2 or 3
                var label = random.Next(1, 4);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features that are correlated with the label.
                    // The feature values are slightly increased by adding a
                    // constant multiple of label.
                    Features = Enumerable.Repeat(label, 20)
                        .Select(x => randomFloat() + label * 0.2f).ToArray()

                };
            }
        }

        // Example with label and 20 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            public uint Label { get; set; }
            [VectorType(20)]
            public float[] Features { get; set; }
        }

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

        // Pretty-print MulticlassClassificationMetrics objects.
        public static void PrintMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
            Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
            Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
            Console.WriteLine(
                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");

            Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        }
    }
}

S’applique à

LightGbm(RegressionCatalog+RegressionTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmRegressionTrainer, qui prédit une cible à l’aide d’un modèle de régression d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer LightGbm (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = default, int? numberOfLeaves = default, int? minimumExampleCountPerLeaf = default, double? learningRate = default, int numberOfIterations = 100);
static member LightGbm : Microsoft.ML.RegressionCatalog.RegressionTrainers * string * string * string * Nullable<int> * Nullable<int> * Nullable<double> * int -> Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer
<Extension()>
Public Function LightGbm (catalog As RegressionCatalog.RegressionTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Nullable(Of Integer) = Nothing, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = Nothing, Optional learningRate As Nullable(Of Double) = Nothing, Optional numberOfIterations As Integer = 100) As LightGbmRegressionTrainer

Paramètres

catalog
RegressionCatalog.RegressionTrainers

Le RegressionCatalog.

labelColumnName
String

Nom de la colonne d’étiquette. Les données de colonne doivent être Single.

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

exampleWeightColumnName
String

Nom de l’exemple de colonne de poids (facultatif).

numberOfLeaves
Nullable<Int32>

Nombre maximal de feuilles dans une arborescence.

minimumExampleCountPerLeaf
Nullable<Int32>

Nombre minimal de points de données requis pour former une nouvelle feuille d’arborescence.

learningRate
Nullable<Double>

Taux d’apprentissage.

numberOfIterations
Int32

Nombre d’itérations d’augmentation. Une nouvelle arborescence est créée dans chaque itération, ce qui équivaut au nombre d’arborescences.

Retours

LightGbmRegressionTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.Regression
{
    public static class LightGbm
    {
        // This example requires installation of additional NuGet
        // package for Microsoft.ML.LightGBM
        // at https://www.nuget.org/packages/Microsoft.ML.LightGbm/
        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.
                LightGbm(
                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: 0.864
            //   Label: 0.155, Prediction: 0.164
            //   Label: 0.515, Prediction: 0.470
            //   Label: 0.566, Prediction: 0.501
            //   Label: 0.096, Prediction: 0.138

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

            // Expected output:
            //   Mean Absolute Error: 0.10
            //   Mean Squared Error: 0.01
            //   Root Mean Squared Error: 0.11
            //   RSquared: 0.89 (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);
        }
    }
}

S’applique à

LightGbm(RankingCatalog+RankingTrainers, String, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Créez LightGbmRankingTrainer, qui prédit une cible à l’aide d’un modèle de classement d’arbre de décision d’augmentation de gradient.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer LightGbm (this Microsoft.ML.RankingCatalog.RankingTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string rowGroupColumnName = "GroupId", string exampleWeightColumnName = default, int? numberOfLeaves = default, int? minimumExampleCountPerLeaf = default, double? learningRate = default, int numberOfIterations = 100);
static member LightGbm : Microsoft.ML.RankingCatalog.RankingTrainers * string * string * string * string * Nullable<int> * Nullable<int> * Nullable<double> * int -> Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer
<Extension()>
Public Function LightGbm (catalog As RankingCatalog.RankingTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional rowGroupColumnName As String = "GroupId", Optional exampleWeightColumnName As String = Nothing, Optional numberOfLeaves As Nullable(Of Integer) = Nothing, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = Nothing, Optional learningRate As Nullable(Of Double) = Nothing, Optional numberOfIterations As Integer = 100) As LightGbmRankingTrainer

Paramètres

catalog
RankingCatalog.RankingTrainers

Le RankingCatalog.

labelColumnName
String

Nom de la colonne d’étiquette. Les données de colonne doivent être Single ou KeyDataViewType.

featureColumnName
String

Nom de la colonne de fonctionnalité. Les données de colonne doivent être un vecteur de taille connue de Single.

rowGroupColumnName
String

Nom de la colonne de groupe.

exampleWeightColumnName
String

Nom de l’exemple de colonne de poids (facultatif).

numberOfLeaves
Nullable<Int32>

Nombre maximal de feuilles dans une arborescence.

minimumExampleCountPerLeaf
Nullable<Int32>

Nombre minimal de points de données requis pour former une nouvelle feuille d’arborescence.

learningRate
Nullable<Double>

Taux d’apprentissage.

numberOfIterations
Int32

Nombre d’itérations d’augmentation. Une nouvelle arborescence est créée dans chaque itération, ce qui équivaut au nombre d’arborescences.

Retours

LightGbmRankingTrainer

Exemples

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

namespace Samples.Dynamic.Trainers.Ranking
{
    public static class LightGbm
    {
        // 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.Ranking.Trainers.LightGbm();

            // 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);

            // Take the top 5 rows.
            var topTransformedTestData = mlContext.Data.TakeRows(
                transformedTestData, 5);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                topTransformedTestData, reuseRowObject: false).ToList();

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

            // Expected output:
            //   Label: 5, Score: 2.493263
            //   Label: 1, Score: -4.528436
            //   Label: 3, Score: -3.002865
            //   Label: 3, Score: -2.151812
            //   Label: 1, Score: -4.089102

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

            // Expected output:
            //   DCG: @1:41.95, @2:63.76, @3:75.97
            //   NDCG: @1:0.99, @2:0.99, @3:0.99
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0, int groupSize = 10)
        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                var label = random.Next(0, 5);
                yield return new DataPoint
                {
                    Label = (uint)label,
                    GroupId = (uint)(i / groupSize),
                    // Create random features that are correlated with the label.
                    // For data points with larger labels, the feature values are
                    // slightly increased by adding a constant.
                    Features = Enumerable.Repeat(label, 50).Select(
                        x => randomFloat() + x * 0.1f).ToArray()
                };
            }
        }

        // Example with label, groupId, and 50 feature values. A data set is a
        // collection of such examples.
        private class DataPoint
        {
            [KeyType(5)]
            public uint Label { get; set; }
            [KeyType(100)]
            public uint GroupId { get; set; }
            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label.
            public uint Label { get; set; }
            // Score produced from the trainer.
            public float Score { get; set; }
        }

        // Pretty-print RankerMetrics objects.
        public static void PrintMetrics(RankingMetrics metrics)
        {
            Console.WriteLine("DCG: " + string.Join(", ",
                metrics.DiscountedCumulativeGains.Select(
                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
            Console.WriteLine("NDCG: " + string.Join(", ",
                metrics.NormalizedDiscountedCumulativeGains.Select(
                    (d, i) => (i + 1) + ":" + d + ":F2").ToArray()));
        }
    }
}

S’applique à