quickrank::learning::forests::Rankboost Class Reference

This implements the RankBoost algorithm. More...

#include <rankboost.h>

Inheritance diagram for quickrank::learning::forests::Rankboost:

Public Member Functions
	Rankboost (size_t max_wr)
	Rankboost (const pugi::xml_document &model)
virtual	~Rankboost ()
	Rankboost (const Rankboost &other)=delete
	Avoid inefficient copy constructor. More...
Rankboost &	operator= (const Rankboost &)=delete
	Avoid inefficient copy assignment. More...
virtual std::string	name () const
	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)
	Executes the learning process. More...
virtual Score	score_document (const Feature *d) const
	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 scores of a given document, tree. More...
virtual pugi::xml_document *	get_xml_model () const
	Return the xml model representing the current object. More...
virtual bool	update_weights (std::vector< double > &weights)
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...
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...

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

Private Member Functions
void	init (std::shared_ptr< data::Dataset > training_dataset, std::shared_ptr< data::Dataset > validation_dataset)
void	compute_pi (std::shared_ptr< data::Dataset > dataset)
WeakRanker *	compute_weak_ranker (std::shared_ptr< data::Dataset > dataset)
void	update_d (std::shared_ptr< data::Dataset > dataset, WeakRanker *wr, float alpha)
MetricScore	compute_metric_score (std::shared_ptr< data::Dataset > dataset, std::shared_ptr< quickrank::metric::ir::Metric > scorer)
void	clean (std::shared_ptr< data::Dataset > dataset)
virtual std::ostream &	put (std::ostream &os) const
	Prints the description of Algorithm, including its parameters. More...

Private Attributes
float ***	D = NULL
float **	PI = NULL
Feature **	THETA = NULL
unsigned int *	n_theta = NULL
unsigned int ***	SDF = NULL
Score *	training_scores = NULL
Score *	validation_scores = NULL
size_t	T
size_t	best_T
bool	go_parallel
char const *	omp_schedule
WeakRanker **	weak_rankers = NULL
float *	alphas = NULL
float	best_r = 0.0
float	max_alpha = 0.0
float	r_t = 0.0
float	z_t = 1.0

Friends
std::ostream &	operator<< (std::ostream &os, const Rankboost &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...

Detailed Description

This implements the RankBoost algorithm.

Freund, Y., Iyer, R., Schapire, R. E., & Singer, Y. (2003). An efficient boosting algorithm for combining preferences. The Journal of machine learning research, 4, 933-969.

Constructor & Destructor Documentation

quickrank::learning::forests::Rankboost::Rankboost

(

size_t

max_wr

)

quickrank::learning::forests::Rankboost::Rankboost

(

const pugi::xml_document &

model

)

quickrank::learning::forests::Rankboost::~Rankboost ( )

virtual

quickrank::learning::forests::Rankboost::Rankboost ( const Rankboost &  other )

delete

Avoid inefficient copy constructor.

Member Function Documentation

void quickrank::learning::forests::Rankboost::clean ( std::shared_ptr< data::Dataset >  dataset )

private

MetricScore quickrank::learning::forests::Rankboost::compute_metric_score ( std::shared_ptr< data::Dataset >  dataset, std::shared_ptr< quickrank::metric::ir::Metric >  scorer  )

private

void quickrank::learning::forests::Rankboost::compute_pi ( std::shared_ptr< data::Dataset >  dataset )

private

WeakRanker * quickrank::learning::forests::Rankboost::compute_weak_ranker ( std::shared_ptr< data::Dataset >  dataset )

private

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

virtual

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::Rankboost::get_xml_model ( ) const

virtual

Return the xml model representing the current object.

Implements quickrank::learning::LTR_Algorithm.

void quickrank::learning::forests::Rankboost::init ( std::shared_ptr< data::Dataset >  training_dataset, std::shared_ptr< data::Dataset >  validation_dataset  )

private

void quickrank::learning::forests::Rankboost::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  )

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.

Implements quickrank::learning::LTR_Algorithm.

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

inlinevirtual

Returns the name of the ranker.

Implements quickrank::learning::LTR_Algorithm.

Rankboost& quickrank::learning::forests::Rankboost::operator= ( const Rankboost &  )

delete

Avoid inefficient copy assignment.

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

virtual

Returns the partial scores of a given document, tree.

Parameters

d	is a pointer to the document to be evaluated

Reimplemented from quickrank::learning::LTR_Algorithm.

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

privatevirtual

Prints the description of Algorithm, including its parameters.

Implements quickrank::learning::LTR_Algorithm.

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

virtual

Returns the score of a given document.

Implements quickrank::learning::LTR_Algorithm.

void quickrank::learning::forests::Rankboost::update_d ( std::shared_ptr< data::Dataset >  dataset, WeakRanker *  wr, float  alpha  )

private

bool quickrank::learning::forests::Rankboost::update_weights ( std::vector< double > &  weights )

virtual

Friends And Related Function Documentation

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

friend

The output stream operator.

Member Data Documentation

float* quickrank::learning::forests::Rankboost::alphas = NULL

private

float quickrank::learning::forests::Rankboost::best_r = 0.0

private

size_t quickrank::learning::forests::Rankboost::best_T

private

float*** quickrank::learning::forests::Rankboost::D = NULL

private

bool quickrank::learning::forests::Rankboost::go_parallel

private

float quickrank::learning::forests::Rankboost::max_alpha = 0.0

private

unsigned int* quickrank::learning::forests::Rankboost::n_theta = NULL

private

const std::string quickrank::learning::forests::Rankboost::NAME_ = "RANKBOOST"

static

char const* quickrank::learning::forests::Rankboost::omp_schedule

private

float** quickrank::learning::forests::Rankboost::PI = NULL

private

float quickrank::learning::forests::Rankboost::r_t = 0.0

private

unsigned int*** quickrank::learning::forests::Rankboost::SDF = NULL

private

size_t quickrank::learning::forests::Rankboost::T

private

Feature** quickrank::learning::forests::Rankboost::THETA = NULL

private

Score* quickrank::learning::forests::Rankboost::training_scores = NULL

private

Score* quickrank::learning::forests::Rankboost::validation_scores = NULL

private

WeakRanker** quickrank::learning::forests::Rankboost::weak_rankers = NULL

private

float quickrank::learning::forests::Rankboost::z_t = 1.0

private

---

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

• include/learning/forests/rankboost.h
• src/learning/forests/rankboost.cc