/// <summary>
/// FastTree <see cref="RankingCatalog"/>.
/// 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="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="groupId">The groupId column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="options">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The Score output column indicating the predicted value.</returns>
public static Scalar <float> FastTree <TVal>(this RankingCatalog.RankingTrainers catalog,
Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights,
FastTreeRankingTrainer.Options options,
Action <FastTreeRankingModelParameters> onFit = null)
{
Contracts.CheckValueOrNull(options);
CheckUserValues(label, features, weights, onFit);
var rec = new TrainerEstimatorReconciler.Ranker <TVal>(
(env, labelName, featuresName, groupIdName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.RowGroupColumnName = groupIdName;
options.ExampleWeightColumnName = weightsName;
var trainer = new FastTreeRankingTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, groupId, weights);
return(rec.Score);
}
/// <summary>
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="groupId">The groupId column.</param>
/// <param name="weights">The weights column.</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">Number of iterations.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
public static Scalar <float> LightGbm <TVal>(this RankingCatalog.RankingTrainers catalog,
Scalar <float> label,
Vector <float> features,
Key <uint, TVal> groupId,
Scalar <float> weights = null,
int?numberOfLeaves = null,
int?minimumExampleCountPerLeaf = null,
double?learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations,
Action <LightGbmRankingModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations, onFit);
Contracts.CheckValue(groupId, nameof(groupId));
var rec = new TrainerEstimatorReconciler.Ranker <TVal>(
(env, labelName, featuresName, groupIdName, weightsName) =>
{
var trainer = new LightGbmRankingTrainer(env, labelName, featuresName, groupIdName, weightsName, numberOfLeaves,
minimumExampleCountPerLeaf, learningRate, numberOfIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, groupId, weights);
return(rec.Score);
}
/// <summary>
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="ctx">The <see cref="RankingContext"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="groupId">The groupId column.</param>
/// <param name="weights">The weights 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>
/// <param name="advancedSettings">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
public static Scalar <float> LightGbm <TVal>(this RankingContext.RankingTrainers ctx,
Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null,
Action <LightGbmRankingModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit);
Contracts.CheckValue(groupId, nameof(groupId));
var rec = new TrainerEstimatorReconciler.Ranker <TVal>(
(env, labelName, featuresName, groupIdName, weightsName) =>
{
var trainer = new LightGbmRankingTrainer(env, labelName, featuresName, groupIdName, weightsName, numLeaves,
minDataPerLeaf, learningRate, numBoostRound, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, groupId, weights);
return(rec.Score);
}
/// <summary>
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="LightGbmRankingTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RankingCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="groupId">The groupId column.</param>
/// <param name="weights">The weights column.</param>
/// <param name="options">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
public static Scalar <float> LightGbm <TVal>(this RankingCatalog.RankingTrainers catalog,
Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights,
Options options,
Action <LightGbmRankingModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, options, onFit);
Contracts.CheckValue(groupId, nameof(groupId));
var rec = new TrainerEstimatorReconciler.Ranker <TVal>(
(env, labelName, featuresName, groupIdName, weightsName) =>
{
options.LabelColumn = labelName;
options.FeatureColumn = featuresName;
options.GroupIdColumn = groupIdName;
options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight);
var trainer = new LightGbmRankingTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, groupId, weights);
return(rec.Score);
}
/// <summary>
/// FastTree <see cref="RankingContext"/>.
/// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
/// </summary>
/// <param name="ctx">The <see cref="RegressionContext"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features 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 a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="advancedSettings">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The Score output column indicating the predicted value.</returns>
public static Scalar <float> FastTree <TVal>(this RankingContext.RankingTrainers ctx,
Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates,
Action <FastTreeRankingTrainer.Arguments> advancedSettings = null,
Action <FastTreeRankingModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.Ranker <TVal>(
(env, labelName, featuresName, groupIdName, weightsName) =>
{
var trainer = new FastTreeRankingTrainer(env, labelName, featuresName, groupIdName, weightsName, numLeaves,
numTrees, minDatapointsInLeaves, learningRate, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, groupId, weights);
return(rec.Score);
}
/// <summary>
/// FastTree <see cref="RankingCatalog"/>.
/// 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="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="groupId">The groupId column.</param>
/// <param name="weights">The optional weights column.</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 a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way;
/// it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The Score output column indicating the predicted value.</returns>
public static Scalar <float> FastTree <TVal>(this RankingCatalog.RankingTrainers catalog,
Scalar <float> label, Vector <float> features, Key <uint, TVal> groupId, Scalar <float> weights = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minimumExampleCountPerLeaf = Defaults.MinimumExampleCountPerLeaf,
double learningRate = Defaults.LearningRate,
Action <FastTreeRankingModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate, onFit);
var rec = new TrainerEstimatorReconciler.Ranker <TVal>(
(env, labelName, featuresName, groupIdName, weightsName) =>
{
var trainer = new FastTreeRankingTrainer(env, labelName, featuresName, groupIdName, weightsName, numberOfLeaves,
numberOfTrees, minimumExampleCountPerLeaf, learningRate);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, groupId, weights);
return(rec.Score);
}
