#include <mart.h>

Inheritance diagram for quickrank::learning::forests::Mart:

Public Member Functions
	Mart (size_t ntrees, double shrinkage, size_t nthresholds, size_t ntreeleaves, size_t minleafsupport, size_t valid_iterations)
	Initializes a new Mart instance with the given learning parameters. More...

	Mart (const pugi::xml_document &model)
	Generates a LTR_Algorithm instance from a previously saved XML model. More...

virtual	~Mart ()

virtual void	learn (std::shared_ptr< data::Dataset > training_dataset, std::shared_ptr< data::Dataset > validation_dataset, std::shared_ptr< metric::ir::Metric > training_metric, size_t partial_save, const std::string output_basename)
	Start the learning process. More...

virtual Score	score_document (const Feature *d) const
	Returns the score by the current ranker. More...

virtual std::shared_ptr< std::vector< Score > >	partial_scores_document (const Feature *d) const
	Returns the partial scores of a given document, tree. More...

virtual void	print_additional_stats (void) const
	Print additional statistics. More...

virtual std::string	name () const
	Returns the name of the ranker. More...

virtual bool	update_weights (std::shared_ptr< std::vector< double >> weights)
	Update the weights for the ensemble models (only). More...

virtual std::shared_ptr< std::vector< double > >	get_weights () const
	Return the weights for the ensemble models (only). More...

Public Member Functions inherited from quickrank::learning::LTR_Algorithm
	LTR_Algorithm ()

	LTR_Algorithm (const pugi::xml_document &model)
	Generates a LTR_Algorithm instance from a previously saved XML model. More...

virtual	~LTR_Algorithm ()

	LTR_Algorithm (const LTR_Algorithm &other)=delete
	Avoid inefficient copy constructor. More...

LTR_Algorithm &	operator= (const LTR_Algorithm &)=delete
	Avoid inefficient copy assignment. More...

virtual void	score_dataset (std::shared_ptr< data::Dataset > dataset, Score *scores) const
	Given and input dateset, the current ranker generates scores for each instance and store the in the scores vector. More...

virtual void	save (std::string model_filename, int suffix=-1) const
	Save the current model to the output_file. More...

Static Public Attributes
static const std::string	NAME_ = "MART"

Protected Member Functions
virtual void	init (std::shared_ptr< data::VerticalDataset > training_dataset)
	Prepares private data structures before training takes place. More...

virtual void	clear (size_t num_features)
	De-allocates private data structure after training has taken place. More...

virtual void	compute_pseudoresponses (std::shared_ptr< data::VerticalDataset > training_dataset, metric::ir::Metric *metric)
	Computes pseudo responses. More...

virtual std::unique_ptr< RegressionTree >	fit_regressor_on_gradient (std::shared_ptr< data::VerticalDataset > training_dataset)
	Fits a regression tree on the gradient given by the pseudo residuals. More...

virtual void	update_modelscores (std::shared_ptr< data::Dataset > dataset, Score scores, RegressionTree tree)
	Updates scores with the last learnt regression tree. More...

virtual void	update_modelscores (std::shared_ptr< data::VerticalDataset > dataset, Score scores, RegressionTree tree)

virtual pugi::xml_document *	get_xml_model () const
	Return the xml model representing the current object. More...

Protected Attributes
float **	thresholds_ = NULL

size_t *	thresholds_size_ = NULL

double *	scores_on_training_ = NULL

quickrank::Score *	scores_on_validation_ = NULL

size_t	validation_bestmodel_ = 0

double *	pseudoresponses_ = NULL

Ensemble	ensemble_model_

size_t	ntrees_

double	shrinkage_

size_t	nthresholds_

size_t	nleaves_

size_t	minleafsupport_

size_t	valid_iterations_

size_t **	sortedsid_ = NULL

size_t	sortedsize_ = 0

RTRootHistogram *	hist_ = NULL

Private Member Functions
virtual std::ostream &	put (std::ostream &os) const
	Prints the description of Algorithm, including its parameters. More...

Friends
std::ostream &	operator<< (std::ostream &os, const Mart &a)
	The output stream operator. More...

Additional Inherited Members
Static Public Member Functions inherited from quickrank::learning::LTR_Algorithm
static std::shared_ptr< LTR_Algorithm >	load_model_from_file (std::string model_filename)
	Load a model from a given XML file. More...

Constructor & Destructor Documentation

quickrank::learning::forests::Mart::Mart	(	size_t	ntrees,
		double	shrinkage,
		size_t	nthresholds,
		size_t	ntreeleaves,
		size_t	minleafsupport,
		size_t	valid_iterations
	)

inline

Initializes a new Mart instance with the given learning parameters.

Parameters

ntrees	Maximum number of trees.
shrinkage	Learning rate.
nthresholds	Number of bins in discretization. 0 means no discretization.
ntreeleaves	Maximum number of leaves in each tree.
minleafsupport	Minimum number of instances in each leaf.
valid_iterations	Early stopping if no improvement after iterations on the validation set.

quickrank::learning::forests::Mart::Mart ( const pugi::xml_document & model )

Generates a LTR_Algorithm instance from a previously saved XML model.

virtual quickrank::learning::forests::Mart::~Mart ( )

inlinevirtual

Member Function Documentation

void quickrank::learning::forests::Mart::clear ( size_t num_features )

protectedvirtual

De-allocates private data structure after training has taken place.

Reimplemented in quickrank::learning::forests::LambdaMart.

void quickrank::learning::forests::Mart::compute_pseudoresponses	(	std::shared_ptr< data::VerticalDataset >	training_dataset,
		metric::ir::Metric *	metric
	)

protectedvirtual

Computes pseudo responses.

Parameters

training_dataset	The training data.
metric	The metric to be optimized.

Reimplemented in quickrank::learning::forests::LambdaMart.

std::unique_ptr< RegressionTree > quickrank::learning::forests::Mart::fit_regressor_on_gradient ( std::shared_ptr< data::VerticalDataset > training_dataset )

protectedvirtual

Fits a regression tree on the gradient given by the pseudo residuals.

Parameters

training_dataset The dataset used for training

Todo:: TODO: memory management of regression tree is wrong!!!

Reimplemented in quickrank::learning::forests::LambdaMart, quickrank::learning::forests::ObliviousLambdaMart, and quickrank::learning::forests::ObliviousMart.

virtual std::shared_ptr<std::vector<double> > quickrank::learning::forests::Mart::get_weights ( ) const

inlinevirtual

Return the weights for the ensemble models (only).

Default implementation will do nothing (default for non ensemble models).

Reimplemented from quickrank::learning::LTR_Algorithm.

pugi::xml_document * quickrank::learning::forests::Mart::get_xml_model ( ) const

protectedvirtual

Return the xml model representing the current object.

Implements quickrank::learning::LTR_Algorithm.

Reimplemented in quickrank::learning::forests::ObliviousMart, and quickrank::learning::forests::ObliviousLambdaMart.

void quickrank::learning::forests::Mart::init ( std::shared_ptr< data::VerticalDataset > training_dataset )

protectedvirtual

Prepares private data structures before training takes place.

Reimplemented in quickrank::learning::forests::LambdaMart.

void quickrank::learning::forests::Mart::learn	(	std::shared_ptr< data::Dataset >	training_dataset,
		std::shared_ptr< data::Dataset >	validation_dataset,
		std::shared_ptr< metric::ir::Metric >	training_metric,
		size_t	partial_save,
		const std::string	output_basename
	)

virtual

Start the learning process.

Implements quickrank::learning::LTR_Algorithm.

virtual std::string quickrank::learning::forests::Mart::name ( ) const

inlinevirtual

Returns the name of the ranker.

Implements quickrank::learning::LTR_Algorithm.

Reimplemented in quickrank::learning::forests::LambdaMart, quickrank::learning::forests::ObliviousLambdaMart, and quickrank::learning::forests::ObliviousMart.

virtual std::shared_ptr<std::vector<Score> > quickrank::learning::forests::Mart::partial_scores_document ( const Feature * d ) const

inlinevirtual

Returns the partial scores of a given document, tree.

Parameters

d	is a pointer to the document to be evaluated
next_fx_offset	The offset to the next feature in the data representation.

Note: Each algorithm has a different implementation.

Reimplemented from quickrank::learning::LTR_Algorithm.

void quickrank::learning::forests::Mart::print_additional_stats ( void ) const

virtual

Print additional statistics.

At the moment this include only number of comparisons for tree-based algorithms.

Reimplemented from quickrank::learning::LTR_Algorithm.

std::ostream & quickrank::learning::forests::Mart::put ( std::ostream & os ) const

privatevirtual

Prints the description of Algorithm, including its parameters.

Implements quickrank::learning::LTR_Algorithm.

Reimplemented in quickrank::learning::forests::ObliviousLambdaMart, and quickrank::learning::forests::ObliviousMart.

virtual Score quickrank::learning::forests::Mart::score_document ( const Feature * d ) const

inlinevirtual

Returns the score by the current ranker.

Parameters

d	Document to be scored.

Implements quickrank::learning::LTR_Algorithm.

void quickrank::learning::forests::Mart::update_modelscores	(	std::shared_ptr< data::Dataset >	dataset,
		Score *	scores,
		RegressionTree *	tree
	)

protectedvirtual

Updates scores with the last learnt regression tree.

Parameters

dataset	Dataset to be scored.
scores	Scores vector to be updated.
tree	Last regression tree leartn.

void quickrank::learning::forests::Mart::update_modelscores	(	std::shared_ptr< data::VerticalDataset >	dataset,
		Score *	scores,
		RegressionTree *	tree
	)

protectedvirtual

bool quickrank::learning::forests::Mart::update_weights ( std::shared_ptr< std::vector< double >> weights )

virtual

Update the weights for the ensemble models (only).

Default implementation will do nothing (default for non ensemble models).

Reimplemented from quickrank::learning::LTR_Algorithm.

Friends And Related Function Documentation

std::ostream& operator<<	(	std::ostream &	os,
		const Mart &	a
	)

friend

The output stream operator.

Member Data Documentation

Ensemble quickrank::learning::forests::Mart::ensemble_model_

protected

RTRootHistogram* quickrank::learning::forests::Mart::hist_ = NULL

protected

size_t quickrank::learning::forests::Mart::minleafsupport_

protected

const std::string quickrank::learning::forests::Mart::NAME_ = "MART"

static

size_t quickrank::learning::forests::Mart::nleaves_

protected

size_t quickrank::learning::forests::Mart::nthresholds_

protected

size_t quickrank::learning::forests::Mart::ntrees_

protected

double* quickrank::learning::forests::Mart::pseudoresponses_ = NULL

protected

double* quickrank::learning::forests::Mart::scores_on_training_ = NULL

protected

quickrank::Score* quickrank::learning::forests::Mart::scores_on_validation_ = NULL

protected

double quickrank::learning::forests::Mart::shrinkage_

protected

size_t** quickrank::learning::forests::Mart::sortedsid_ = NULL

protected

size_t quickrank::learning::forests::Mart::sortedsize_ = 0

protected

float** quickrank::learning::forests::Mart::thresholds_ = NULL

protected

size_t* quickrank::learning::forests::Mart::thresholds_size_ = NULL

protected

size_t quickrank::learning::forests::Mart::valid_iterations_

protected

size_t quickrank::learning::forests::Mart::validation_bestmodel_ = 0

protected

The documentation for this class was generated from the following files:

include/learning/forests/mart.h
src/learning/forests/mart.cc

Public Member Functions

Static Public Attributes

Protected Member Functions

Protected Attributes

Private Member Functions

Friends

Additional Inherited Members

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation

Member Data Documentation