LightGbmExtensions.LightGbm Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.LightGbm.dll

Paket:

Microsoft.ML.LightGbm v2.0.0

Überlädt

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)	Erstellen Sie LightGbmBinaryTrainer mit erweiterten Optionen, die ein Ziel mithilfe einer binären Klassifizierung der Entscheidungsstruktur zur Erhöhung des Farbverlaufs vorhersagen.
LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)	Erstellen Sie LightGbmMulticlassTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines Mehrklassenklassifizierungsmodells für entscheidungsstruktursteigernde Entscheidungsstruktur vorhersagen.
LightGbm(RankingCatalog+RankingTrainers, LightGbmRankingTrainer+Options)	Erstellen Sie LightGbmRankingTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines Entscheidungsbaumrangfolgemodells vorhersagen.
LightGbm(RegressionCatalog+RegressionTrainers, LightGbmRegressionTrainer+Options)	Erstellen Sie LightGbmRegressionTrainer mithilfe erweiterter Optionen, die ein Ziel mithilfe eines Regressionsmodells für entscheidungsstruktursteigernde Gradienten vorhersagen.
LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)	Erstellen Sie LightGbmBinaryTrainer, das ein Ziel mithilfe einer binären Klassifizierung der Entscheidungsstruktur zur Steigerung des Farbverlaufs vorhersagt.
LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)	Erstellen Sie LightGbmMulticlassTrainer, das ein Ziel mithilfe eines Klassifizierungsmodells für die Entscheidungsstruktur mit mehreren Klassen vorhersagt.
LightGbm(RegressionCatalog+RegressionTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)	Erstellen Sie LightGbmRegressionTrainer, das ein Ziel mithilfe eines Regressionsmodells für die Entscheidungsstruktur mit Farbverlaufssteigerung vorhersagt.
LightGbm(RankingCatalog+RankingTrainers, String, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)	Erstellen Sie LightGbmRankingTrainer, das ein Ziel mithilfe eines Entscheidungsstrukturrangfolgemodells zur Steigerung des Farbverlaufs vorhersagt.

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)

Erstellen Sie LightGbmBinaryTrainer mit erweiterten Optionen, die ein Ziel mithilfe einer binären Klassifizierung der Entscheidungsstruktur zur Erhöhung des Farbverlaufs vorhersagen.

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

Parameter

catalog

BinaryClassificationCatalog.BinaryClassificationTrainers

Das BinaryClassificationCatalog.

options

LightGbmBinaryTrainer.Options

Traineroptionen.

Gibt zurück

LightGbmBinaryTrainer

Beispiele

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());
        }
    }
}

LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)

Erstellen Sie LightGbmMulticlassTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines Mehrklassenklassifizierungsmodells für entscheidungsstruktursteigernde Entscheidungsstruktur vorhersagen.

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

Parameter

catalog

MulticlassClassificationCatalog.MulticlassClassificationTrainers

Das MulticlassClassificationCatalog.

options

LightGbmMulticlassTrainer.Options

Traineroptionen.

Gibt zurück

LightGbmMulticlassTrainer

Beispiele

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());
        }
    }
}

LightGbm(RankingCatalog+RankingTrainers, LightGbmRankingTrainer+Options)

Erstellen Sie LightGbmRankingTrainer mit erweiterten Optionen, die ein Ziel mithilfe eines Entscheidungsbaumrangfolgemodells vorhersagen.

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

Parameter

catalog

RankingCatalog.RankingTrainers

Das RankingCatalog.

options

LightGbmRankingTrainer.Options

Traineroptionen.

Gibt zurück

LightGbmRankingTrainer

Beispiele

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()));
        }
    }
}

LightGbm(RegressionCatalog+RegressionTrainers, LightGbmRegressionTrainer+Options)

Erstellen Sie LightGbmRegressionTrainer mithilfe erweiterter Optionen, die ein Ziel mithilfe eines Regressionsmodells für entscheidungsstruktursteigernde Gradienten vorhersagen.

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

Parameter

catalog

RegressionCatalog.RegressionTrainers

Das RegressionCatalog.

options

LightGbmRegressionTrainer.Options

Traineroptionen.

Gibt zurück

LightGbmRegressionTrainer

Beispiele

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);
        }
    }
}

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

Erstellen Sie LightGbmBinaryTrainer, das ein Ziel mithilfe einer binären Klassifizierung der Entscheidungsstruktur zur Steigerung des Farbverlaufs vorhersagt.

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

Parameter

catalog

BinaryClassificationCatalog.BinaryClassificationTrainers

Das BinaryClassificationCatalog.

labelColumnName

String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen sein Boolean.

featureColumnName

String

Der Name der Featurespalte. Die Spaltendaten müssen ein Vektor von bekannter Größe sein Single.

exampleWeightColumnName

String

Der Name der Beispielgewichtungsspalte (optional).

numberOfLeaves

Nullable<Int32>

Die maximale Anzahl von Blättern in einem Baum.

minimumExampleCountPerLeaf

Nullable<Int32>

Die minimale Anzahl von Datenpunkten, die zum Bilden eines neuen Baumblatts erforderlich sind.

learningRate

Nullable<Double>

Die Lernrate.

numberOfIterations

Int32

Die Anzahl der Verstärkungsiterationen. In jeder Iteration wird eine neue Struktur erstellt. Dies entspricht also der Anzahl der Strukturen.

Gibt zurück

LightGbmBinaryTrainer

Beispiele

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());
        }
    }
}

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

Erstellen Sie LightGbmMulticlassTrainer, das ein Ziel mithilfe eines Klassifizierungsmodells für die Entscheidungsstruktur mit mehreren Klassen vorhersagt.

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

Parameter

catalog

MulticlassClassificationCatalog.MulticlassClassificationTrainers

Das MulticlassClassificationCatalog.

labelColumnName

String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen sein KeyDataViewType.

featureColumnName

String

Der Name der Featurespalte. Die Spaltendaten müssen ein Vektor von bekannter Größe sein Single.

exampleWeightColumnName

String

Der Name der Beispielgewichtungsspalte (optional).

numberOfLeaves

Nullable<Int32>

Die maximale Anzahl von Blättern in einem Baum.

minimumExampleCountPerLeaf

Nullable<Int32>

Die minimale Anzahl von Datenpunkten, die zum Bilden eines neuen Baumblatts erforderlich sind.

learningRate

Nullable<Double>

Die Lernrate.

numberOfIterations

Int32

Die Anzahl der Verstärkungsiterationen. In jeder Iteration wird eine neue Struktur erstellt. Dies entspricht also der Anzahl der Strukturen.

Gibt zurück

LightGbmMulticlassTrainer

Beispiele

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());
        }
    }
}

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

Erstellen Sie LightGbmRegressionTrainer, das ein Ziel mithilfe eines Regressionsmodells für die Entscheidungsstruktur mit Farbverlaufssteigerung vorhersagt.

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

Parameter

catalog

RegressionCatalog.RegressionTrainers

Das RegressionCatalog.

labelColumnName

String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen sein Single.

featureColumnName

String

Der Name der Featurespalte. Die Spaltendaten müssen ein Vektor von bekannter Größe sein Single.

exampleWeightColumnName

String

Der Name der Beispielgewichtungsspalte (optional).

numberOfLeaves

Nullable<Int32>

Die maximale Anzahl von Blättern in einem Baum.

minimumExampleCountPerLeaf

Nullable<Int32>

Die minimale Anzahl von Datenpunkten, die zum Bilden eines neuen Baumblatts erforderlich sind.

learningRate

Nullable<Double>

Die Lernrate.

numberOfIterations

Int32

Die Anzahl der Verstärkungsiterationen. In jeder Iteration wird eine neue Struktur erstellt. Dies entspricht also der Anzahl der Strukturen.

Gibt zurück

LightGbmRegressionTrainer

Beispiele

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);
        }
    }
}

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

Erstellen Sie LightGbmRankingTrainer, das ein Ziel mithilfe eines Entscheidungsstrukturrangfolgemodells zur Steigerung des Farbverlaufs vorhersagt.

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

Parameter

catalog

RankingCatalog.RankingTrainers

Das RankingCatalog.

labelColumnName

String

Der Name der Bezeichnungsspalte. Die Spaltendaten müssen oder KeyDataViewTypeseinSingle.

featureColumnName

String

Der Name der Featurespalte. Die Spaltendaten müssen ein Vektor von bekannter Größe sein Single.

rowGroupColumnName

String

Der Name der Gruppenspalte.

exampleWeightColumnName

String

Der Name der Beispielgewichtungsspalte (optional).

numberOfLeaves

Nullable<Int32>

Die maximale Anzahl von Blättern in einem Baum.

minimumExampleCountPerLeaf

Nullable<Int32>

Die minimale Anzahl von Datenpunkten, die zum Bilden eines neuen Baumblatts erforderlich sind.

learningRate

Nullable<Double>

Die Lernrate.

numberOfIterations

Int32

Die Anzahl der Verstärkungsiterationen. In jeder Iteration wird eine neue Struktur erstellt. Dies entspricht also der Anzahl der Strukturen.

Gibt zurück

LightGbmRankingTrainer

Beispiele

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()));
        }
    }
}