#include <ltr_algorithm.h>

Inheritance diagram for quickrank::learning::LTR_Algorithm:

Public Member Functions
	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 std::string	name () const =0
	Returns the name of the ranker. More...

virtual void	learn (std::shared_ptr< data::Dataset > training_dataset, std::shared_ptr< data::Dataset > validation_dataset, std::shared_ptr< metric::ir::Metric > metric, size_t partial_save, const std::string model_filename)=0
	Executes the learning process. 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 Score	score_document (const Feature *d) const =0
	Returns the score of a given document. More...

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

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

virtual pugi::xml_document *	get_xml_model () const =0
	Return the xml model representing the current object. More...

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

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

Private Member Functions
virtual std::ostream &	put (std::ostream &os) const =0
	Prints the description of Algorithm, including its parameters. More...

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

Constructor & Destructor Documentation

quickrank::learning::LTR_Algorithm::LTR_Algorithm ( )

inline

quickrank::learning::LTR_Algorithm::LTR_Algorithm ( const pugi::xml_document & model )

Generates a LTR_Algorithm instance from a previously saved XML model.

virtual quickrank::learning::LTR_Algorithm::~LTR_Algorithm ( )

inlinevirtual

quickrank::learning::LTR_Algorithm::LTR_Algorithm ( const LTR_Algorithm & other )

delete

Avoid inefficient copy constructor.

Member Function Documentation

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

inlinevirtual

Return the weights for the ensemble models (only).

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

Reimplemented in quickrank::learning::forests::Rankboost, quickrank::learning::forests::Mart, quickrank::learning::linear::LineSearch, and quickrank::learning::linear::CoordinateAscent.

virtual pugi::xml_document* quickrank::learning::LTR_Algorithm::get_xml_model ( ) const

pure virtual

Return the xml model representing the current object.

Implemented in quickrank::learning::forests::Mart, quickrank::learning::forests::Rankboost, quickrank::learning::linear::LineSearch, quickrank::learning::CustomLTR, quickrank::learning::linear::CoordinateAscent, quickrank::learning::forests::ObliviousMart, and quickrank::learning::forests::ObliviousLambdaMart.

virtual void quickrank::learning::LTR_Algorithm::learn	(	std::shared_ptr< data::Dataset >	training_dataset,
		std::shared_ptr< data::Dataset >	validation_dataset,
		std::shared_ptr< metric::ir::Metric >	metric,
		size_t	partial_save,
		const std::string	model_filename
	)

pure virtual

Executes the learning process.

Parameters

training_dataset	The training dataset.
validation_dataset	The validation training dataset.
metric	The metric to be optimized.
partial_save	Allows to save a partial model every given number of iterations.
model_filename	The file where the model, and the partial models, are saved.

Implemented in quickrank::learning::forests::Rankboost, quickrank::learning::linear::LineSearch, quickrank::learning::CustomLTR, quickrank::learning::linear::CoordinateAscent, and quickrank::learning::forests::Mart.

std::shared_ptr< LTR_Algorithm > quickrank::learning::LTR_Algorithm::load_model_from_file ( std::string model_filename )

static

Load a model from a given XML file.

Parameters

model_filename The input file name.

virtual std::string quickrank::learning::LTR_Algorithm::name ( ) const

pure virtual

Returns the name of the ranker.

Implemented in quickrank::learning::forests::Rankboost, quickrank::learning::forests::Mart, quickrank::learning::forests::LambdaMart, quickrank::learning::forests::ObliviousLambdaMart, quickrank::learning::forests::ObliviousMart, quickrank::learning::CustomLTR, quickrank::learning::linear::CoordinateAscent, and quickrank::learning::linear::LineSearch.

LTR_Algorithm& quickrank::learning::LTR_Algorithm::operator= ( const LTR_Algorithm & )

delete

Avoid inefficient copy assignment.

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

inlinevirtual

Returns the partial score of a given document, tree by 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 in quickrank::learning::forests::Rankboost, and quickrank::learning::forests::Mart.

virtual void quickrank::learning::LTR_Algorithm::print_additional_stats ( void ) const

inlinevirtual

Print additional statistics.

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

Reimplemented in quickrank::learning::forests::Mart.

virtual std::ostream& quickrank::learning::LTR_Algorithm::put ( std::ostream & os ) const

privatepure virtual

Prints the description of Algorithm, including its parameters.

Implemented in quickrank::learning::forests::Rankboost, quickrank::learning::forests::Mart, quickrank::learning::linear::LineSearch, quickrank::learning::linear::CoordinateAscent, quickrank::learning::CustomLTR, quickrank::learning::forests::ObliviousLambdaMart, and quickrank::learning::forests::ObliviousMart.

void quickrank::learning::LTR_Algorithm::save	(	std::string	model_filename,
		int	suffix = `-1`
	)		const

virtual

Save the current model to the output_file.

Parameters

model_filename	The output file name.
suffix	The suffix used to identify partial model saves.

void quickrank::learning::LTR_Algorithm::score_dataset	(	std::shared_ptr< data::Dataset >	dataset,
		Score *	scores
	)		const

virtual

Given and input dateset, the current ranker generates scores for each instance and store the in the scores vector.

Parameters

dataset	The dataset to be scored.
scores	The vector where scores are stored.

Note: Before scoring it invokes the function preprocess_dataset. Usually this does not need to be overridden.

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

pure virtual

Returns the score of a given document.

Parameters

d	is a pointer to the document to be evaluated

Note: Each algorithm has a different implementation.

Implemented in quickrank::learning::forests::Rankboost, quickrank::learning::linear::LineSearch, quickrank::learning::CustomLTR, quickrank::learning::linear::CoordinateAscent, and quickrank::learning::forests::Mart.

virtual bool quickrank::learning::LTR_Algorithm::update_weights ( std::shared_ptr< std::vector< double >> weights )

inlinevirtual

Update the weights for the ensemble models (only).

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

Reimplemented in quickrank::learning::forests::Mart, quickrank::learning::linear::LineSearch, and quickrank::learning::linear::CoordinateAscent.

Friends And Related Function Documentation

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

friend

The output stream operator.

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

include/learning/ltr_algorithm.h
src/learning/ltr_algorithm.cc

Public Member Functions

Static Public Member Functions

Private Member Functions

Friends

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation