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LightGbmCatalog.cs
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// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using Microsoft.ML.Data;
using Microsoft.ML.Runtime;
using Microsoft.ML.Trainers.LightGbm;
namespace Microsoft.ML
{
/// <summary>
/// LightGBM extension methods.
/// </summary>
public static class LightGbmExtensions
{
/// <summary>
/// Predict a target using a gradient boosting decision tree regression model trained with the <see cref="LightGbmRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfLeaves">The maximum number of leaves in one tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points required to form a new tree leaf.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int? numberOfLeaves = null,
int? minimumExampleCountPerLeaf = null,
double? learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRegressionTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree regression model trained with the <see cref="LightGbmRegressionTrainer"/> and advanced options.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRegressionTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRegressionTrainer(env, options);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree binary classification model trained with the <see cref="LightGbmBinaryClassificationTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfLeaves">The maximum number of leaves in one tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points required to form a new tree leaf.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmBinaryClassificationTrainer LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int? numberOfLeaves = null,
int? minimumExampleCountPerLeaf = null,
double? learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmBinaryClassificationTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree binary classification model trained with the <see cref="LightGbmBinaryClassificationTrainer"/> and advanced options.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmBinaryClassificationTrainer LightGbm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmBinaryClassificationTrainer(env, options);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree ranking model trained with the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="rowGroupColumnName">The name of the group column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfLeaves">The maximum number of leaves in one tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points required to form a new tree leaf.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string rowGroupColumnName = DefaultColumnNames.GroupId,
string exampleWeightColumnName = null,
int? numberOfLeaves = null,
int? minimumExampleCountPerLeaf = null,
double? learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRankingTrainer(env, labelColumnName, featureColumnName, rowGroupColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree ranking model trained with the <see cref="LightGbmRankingTrainer"/> and advanced options.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainers catalog,
Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRankingTrainer(env, options);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree multiclass classification model trained with the <see cref="LightGbmMulticlassClassificationTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="MulticlassClassificationCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfLeaves">The maximum number of leaves in one tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points required to form a new tree leaf.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmMulticlassClassificationTrainer LightGbm(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int? numberOfLeaves = null,
int? minimumExampleCountPerLeaf = null,
double? learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmMulticlassClassificationTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations);
}
/// <summary>
/// Predict a target using a gradient boosting decision tree multiclass classification model trained with the <see cref="LightGbmMulticlassClassificationTrainer"/> and advanced options.
/// </summary>
/// <param name="catalog">The <see cref="MulticlassClassificationCatalog"/>.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmMulticlassClassificationTrainer LightGbm(this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmMulticlassClassificationTrainer(env, options);
}
}
}