LightGbmExtensions.LightGbm Method

Definition

Overloads

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)

Create LightGbmBinaryTrainer with advanced options, which predicts a target using a gradient boosting decision tree binary classification.

LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)

Create LightGbmMulticlassTrainer with advanced options, which predicts a target using a gradient boosting decision tree multiclass classification model.

LightGbm(RankingCatalog+RankingTrainers, LightGbmRankingTrainer+Options)

Create LightGbmRankingTrainer with advanced options, which predicts a target using a gradient boosting decision tree ranking model.

LightGbm(RegressionCatalog+RegressionTrainers, LightGbmRegressionTrainer+Options)

Create LightGbmRegressionTrainer using advanced options, which predicts a target using a gradient boosting decision tree regression model.

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

Create LightGbmBinaryTrainer, which predicts a target using a gradient boosting decision tree binary classification.

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

Create LightGbmMulticlassTrainer, which predicts a target using a gradient boosting decision tree multiclass classification model.

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

Create LightGbmRegressionTrainer, which predicts a target using a gradient boosting decision tree regression model.

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

Create LightGbmRankingTrainer, which predicts a target using a gradient boosting decision tree ranking model.

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)

Create LightGbmBinaryTrainer with advanced options, which predicts a target using a gradient boosting decision tree binary classification.

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

Parameters

options
LightGbmBinaryTrainer.Options

Trainer options.

Returns

Examples

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)

Create LightGbmMulticlassTrainer with advanced options, which predicts a target using a gradient boosting decision tree multiclass classification model.

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

Parameters

options
LightGbmMulticlassTrainer.Options

Trainer options.

Returns

Examples

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)

Create LightGbmRankingTrainer with advanced options, which predicts a target using a gradient boosting decision tree ranking model.

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

Parameters

options
LightGbmRankingTrainer.Options

Trainer options.

Returns

Examples

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)

Create LightGbmRegressionTrainer using advanced options, which predicts a target using a gradient boosting decision tree regression model.

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

Parameters

options
LightGbmRegressionTrainer.Options

Trainer options.

Returns

Examples

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 worest 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)

Create LightGbmBinaryTrainer, which predicts a target using a gradient boosting decision tree binary classification.

public static Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer LightGbm (this Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = null, Nullable<int> numberOfLeaves = null, Nullable<int> minimumExampleCountPerLeaf = null, Nullable<double> learningRate = null, 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 = null, Optional numberOfLeaves As Nullable(Of Integer) = null, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = null, Optional learningRate As Nullable(Of Double) = null, Optional numberOfIterations As Integer = 100) As LightGbmBinaryTrainer

Parameters

labelColumnName
String

The name of the label column. The column data must be Boolean.

featureColumnName
String

The name of the feature column. The column data must be a known-sized vector of Single.

exampleWeightColumnName
String

The name of the example weight column (optional).

numberOfLeaves
Nullable<Int32>

The maximum number of leaves in one tree.

minimumExampleCountPerLeaf
Nullable<Int32>

The minimal number of data points required to form a new tree leaf.

learningRate
Nullable<Double>

The learning rate.

numberOfIterations
Int32

The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.

Returns

Examples

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)

Create LightGbmMulticlassTrainer, which predicts a target using a gradient boosting decision tree multiclass classification model.

public static Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer LightGbm (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = null, Nullable<int> numberOfLeaves = null, Nullable<int> minimumExampleCountPerLeaf = null, Nullable<double> learningRate = null, 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 = null, Optional numberOfLeaves As Nullable(Of Integer) = null, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = null, Optional learningRate As Nullable(Of Double) = null, Optional numberOfIterations As Integer = 100) As LightGbmMulticlassTrainer

Parameters

labelColumnName
String

The name of the label column. The column data must be KeyDataViewType.

featureColumnName
String

The name of the feature column. The column data must be a known-sized vector of Single.

exampleWeightColumnName
String

The name of the example weight column (optional).

numberOfLeaves
Nullable<Int32>

The maximum number of leaves in one tree.

minimumExampleCountPerLeaf
Nullable<Int32>

The minimal number of data points required to form a new tree leaf.

learningRate
Nullable<Double>

The learning rate.

numberOfIterations
Int32

The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.

Returns

Examples

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)

Create LightGbmRegressionTrainer, which predicts a target using a gradient boosting decision tree regression model.

public static Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer LightGbm (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string exampleWeightColumnName = null, Nullable<int> numberOfLeaves = null, Nullable<int> minimumExampleCountPerLeaf = null, Nullable<double> learningRate = null, 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 = null, Optional numberOfLeaves As Nullable(Of Integer) = null, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = null, Optional learningRate As Nullable(Of Double) = null, Optional numberOfIterations As Integer = 100) As LightGbmRegressionTrainer

Parameters

labelColumnName
String

The name of the label column. The column data must be Single.

featureColumnName
String

The name of the feature column. The column data must be a known-sized vector of Single.

exampleWeightColumnName
String

The name of the example weight column (optional).

numberOfLeaves
Nullable<Int32>

The maximum number of leaves in one tree.

minimumExampleCountPerLeaf
Nullable<Int32>

The minimal number of data points required to form a new tree leaf.

learningRate
Nullable<Double>

The learning rate.

numberOfIterations
Int32

The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.

Returns

Examples

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 worest 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)

Create LightGbmRankingTrainer, which predicts a target using a gradient boosting decision tree ranking model.

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 = null, Nullable<int> numberOfLeaves = null, Nullable<int> minimumExampleCountPerLeaf = null, Nullable<double> learningRate = null, 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 = null, Optional numberOfLeaves As Nullable(Of Integer) = null, Optional minimumExampleCountPerLeaf As Nullable(Of Integer) = null, Optional learningRate As Nullable(Of Double) = null, Optional numberOfIterations As Integer = 100) As LightGbmRankingTrainer

Parameters

labelColumnName
String

The name of the label column. The column data must be Single or KeyDataViewType.

featureColumnName
String

The name of the feature column. The column data must be a known-sized vector of Single.

rowGroupColumnName
String

The name of the group column.

exampleWeightColumnName
String

The name of the example weight column (optional).

numberOfLeaves
Nullable<Int32>

The maximum number of leaves in one tree.

minimumExampleCountPerLeaf
Nullable<Int32>

The minimal number of data points required to form a new tree leaf.

learningRate
Nullable<Double>

The learning rate.

numberOfIterations
Int32

The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.

Returns

Examples

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()));
        }
    }
}

Applies to