/// <summary>
/// Predict a target using a decision tree ranking model trained with the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="options">Advanced options to the algorithm.</param>
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>
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="options">Algorithm advanced settings.</param>
public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainers catalog,
FastTreeRankingTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeRankingTrainer(env, options));
}
/// <summary>
/// Predict a target using a decision tree ranking model trained with the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The features column.</param>
/// <param name="weights">The weights column.</param>
/// <param name="groupIdColumn">The groupId column.</param>
/// <param name="numLeaves">The number of leaves to use.</param>
/// <param name="numBoostRound">Number of iterations.</param>
/// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static LightGbmRankingTrainer LightGbm(this RankingCatalog.RankingTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string groupIdColumn = DefaultColumnNames.GroupId,
string weights = null,
int? numLeaves = null,
int? minDataPerLeaf = null,
double? learningRate = null,
int numBoostRound = Options.Defaults.NumBoostRound)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRankingTrainer(env, labelColumn, featureColumn, groupIdColumn, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound);
}
/// <summary>
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The featureColumn column.</param>
/// <param name="groupId">The groupId column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string groupId = DefaultColumnNames.GroupId,
string weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeRankingTrainer(env, labelColumn, featureColumn, groupId, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate));
}
/// <summary>
/// Predict a target using a 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 number of leaves to use.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of iterations to use.</param>
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>
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
/// </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="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numberOfLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static FastTreeRankingTrainer FastTree(this RankingCatalog.RankingTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string rowGroupColumnName = DefaultColumnNames.GroupId,
string exampleWeightColumnName = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minimumExampleCountPerLeaf = Defaults.MinimumExampleCountPerLeaf,
double learningRate = Defaults.LearningRate)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeRankingTrainer(env, labelColumnName, featureColumnName, rowGroupColumnName, exampleWeightColumnName, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate));
}
