LightGbmExtensions.LightGbm Method
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Overloads
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<Extension()>
Public Function LightGbm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, options As LightGbmBinaryTrainer.Options) As LightGbmBinaryTrainerParameters
- 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());
}
}
}
Applies to
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<Extension()>
Public Function LightGbm (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As LightGbmMulticlassTrainer.Options) As LightGbmMulticlassTrainerParameters
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());
}
}
}
Applies to
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<Extension()>
Public Function LightGbm (catalog As RankingCatalog.RankingTrainers, options As LightGbmRankingTrainer.Options) As LightGbmRankingTrainerParameters
- catalog
- RankingCatalog.RankingTrainers
The RankingCatalog.
- 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()));
}
}
}
Applies to
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<Extension()>
Public Function LightGbm (catalog As RegressionCatalog.RegressionTrainers, options As LightGbmRegressionTrainer.Options) As LightGbmRegressionTrainerParameters
The RegressionCatalog.
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 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);
}
}
}
Applies to
LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, Stream, String)
Create LightGbmBinaryTrainer from a pre-trained LightGBM model, 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, System.IO.Stream lightGbmModel, string featureColumnName = "Features");static member LightGbm : Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer<Extension()>
Public Function LightGbm (catalog As BinaryClassificationCatalog.BinaryClassificationTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmBinaryTrainerParameters
- featureColumnName
- String
The name of the feature column. The column data must be a known-sized vector of Single.
Returns
Applies to
LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, Stream, String)
Create LightGbmMulticlassTrainer from a pre-trained LightGBM model, 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, System.IO.Stream lightGbmModel, string featureColumnName = "Features");static member LightGbm : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer<Extension()>
Public Function LightGbm (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmMulticlassTrainerParameters
- featureColumnName
- String
The name of the feature column. The column data must be a known-sized vector of Single.
Returns
Applies to
LightGbm(RankingCatalog+RankingTrainers, Stream, String)
Create LightGbmRankingTrainer from a pre-trained LightGBM model, 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, System.IO.Stream lightGbmModel, string featureColumnName = "Features");static member LightGbm : Microsoft.ML.RankingCatalog.RankingTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer<Extension()>
Public Function LightGbm (catalog As RankingCatalog.RankingTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmRankingTrainerParameters
- catalog
- RankingCatalog.RankingTrainers
The RankingCatalog.
- featureColumnName
- String
The name of the feature column. The column data must be a known-sized vector of Single.
Returns
Applies to
LightGbm(RegressionCatalog+RegressionTrainers, Stream, String)
Create LightGbmRegressionTrainer from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree regression.
public static Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer LightGbm (this Microsoft.ML.RegressionCatalog.RegressionTrainers catalog, System.IO.Stream lightGbmModel, string featureColumnName = "Features");static member LightGbm : Microsoft.ML.RegressionCatalog.RegressionTrainers * System.IO.Stream * string -> Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer<Extension()>
Public Function LightGbm (catalog As RegressionCatalog.RegressionTrainers, lightGbmModel As Stream, Optional featureColumnName As String = "Features") As LightGbmRegressionTrainerParameters
The RegressionCatalog.
- featureColumnName
- String
The name of the feature column. The column data must be a known-sized vector of Single.
Returns
Applies to
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 = 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 LightGbmBinaryTrainerParameters
- 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).
The minimal number of data points required to form a new tree leaf.
- 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());
}
}
}
Applies to
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 = 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 LightGbmMulticlassTrainerParameters
- 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).
The minimal number of data points required to form a new tree leaf.
- 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());
}
}
}
Applies to
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 = 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 LightGbmRegressionTrainerParameters
The RegressionCatalog.
- 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).
The minimal number of data points required to form a new tree leaf.
- 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 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);
}
}
}
Applies to
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 = 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 LightGbmRankingTrainerParameters
- catalog
- RankingCatalog.RankingTrainers
The RankingCatalog.
- 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).
The minimal number of data points required to form a new tree leaf.
- 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()));
}
}
}