abstract class, defines interface for Regressor implementations (mainly to be used in wrappers) More...

#include <regressor.hpp>

Inheritance diagram for FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >:

Public Types
typedef std::shared_ptr< DATAACCESSOR >	PDataAccessor
	shared pointer to the data accessor supplying train/test blocks
typedef std::shared_ptr< SUBSET > const	PSubset
	shared pointer to the (fixed, non-reassignable) feature subset being evaluated
typedef DATAACCESSOR::PPattern	PPattern
	one data sample as exposed by DATAACCESSOR (a raw pointer to its feature vector)

Public Member Functions
virtual bool	predict (RETURNTYPE &depvar, const PPattern &pattern)=0
	Predicts the dependent variable of one pattern into `depvar` (see the dependent-variable convention in the class documentation). Returns false when no prediction can be made at all - the regressor has not been trained, or the implementation hit an unrecoverable condition; false aborts the enclosing test loop.
virtual bool	train (const PDataAccessor da, const PSubset sub)=0
virtual bool	test (RETURNTYPE &result, const PDataAccessor da)=0
	Scores the designated test data with the regressor's own default measure, oriented higher-is-better (a hit rate under the class-label convention, a negated prediction error otherwise). Returns false if the test part is empty or predict() failed.
virtual bool	test_metric (RETURNTYPE &result, const PDataAccessor da, const Metric_Spec< RETURNTYPE > &spec)
Regressor *	clone () const override=0
Regressor *	sharing_clone () const override
Regressor *	stateless_clone () const override
Public Member Functions inherited from FST::Clonable
bool	is_clone () const
bool	is_sharing () const
	true if this instance is a sharing_clone() result (referencing, not owning, the source's structures)
bool	is_sharing_clone () const
	true if this instance is BOTH a clone and sharing – i.e. specifically a sharing_clone() result, not a clone()/stateless_clone() one
virtual std::ostream &	print (std::ostream &os) const
	writes a human-readable identification of the object; base default writes nothing

Additional Inherited Members
Protected Member Functions inherited from FST::Clonable
void	set_cloned (const bool cloned=true)
	marks/unmarks this instance as a clone; call from clone()/stateless_clone() overrides (Clonable_Impl does this automatically)
void	set_sharing (const bool sharing=true)
	marks/unmarks this instance as sharing (see is_sharing())
void	set_sharing_cloned (const bool cloned=true, const bool sharing=true)
	sets both flags at once; call from sharing_clone() overrides (Clonable_Impl does this automatically)

Detailed Description

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>
class FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >

abstract class, defines interface for Regressor implementations (mainly to be used in wrappers)

The counterpart of Classifier for the regression pipeline; the same lifecycle applies - train() first, then test*()/predict(), with a data accessor exposing the same features in the same order. Two regressors are shipped: Regressor_Weighted_Neighbours (distance-weighted nearest neighbours) and Regressor_Ridge (closed-form L2-regularized least squares).

Note: DEPENDENT-VARIABLE CONVENTION. The dependent variable predicted here is by default the CLASS LABEL of the sample, not a data column - "regression simulating classification", which is what makes a regressor usable as a feature-selection criterion on ordinary classification data. Concrete regressors express this with the sentinel index -1 (set_dependent_index(-1)), their default; a non-negative index selects an actual data column instead.; Every value produced for a criterion is oriented HIGHER IS BETTER, as FST4 searches maximize: test() returns a hit rate under the class-label convention and a NEGATED error otherwise, and the regression metrics of the metric palette are r2, neg-rmse and neg-mae (see Metric_Spec).

Member Function Documentation

◆ clone()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

Regressor * FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::clone ( ) const

overridepure virtual

create 1:1 independent clone of the current object. The caller owns the returned object. Families that cannot support a given clone kind implement it to throw fst_error; Clonable_Impl's CLONE_INHERIT slot defers to such a body.

Implements FST::Clonable.

Implemented in FST::Clonable_Impl< Regressor_Ridge< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >, and FST::Clonable_Impl< Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >, Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >.

◆ predict()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual bool FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::predict	(	RETURNTYPE &	depvar,
		const PPattern &	pattern )

pure virtual

Predicts the dependent variable of one pattern into depvar (see the dependent-variable convention in the class documentation). Returns false when no prediction can be made at all - the regressor has not been trained, or the implementation hit an unrecoverable condition; false aborts the enclosing test loop.

Implemented in FST::Regressor_Ridge< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >.

◆ sharing_clone()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

Regressor * FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::sharing_clone ( ) const

inlineoverridevirtual

create equivalent clone of the current object, permitting read-only access to structures in the source object (allows referencing instead of copying of large memory structures). may be faster and save space but requires more caution with respect to concurrency Use example: Data_Accessor memory data representation cloning

Implements FST::Clonable.

◆ stateless_clone()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

Regressor * FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::stateless_clone ( ) const

inlineoverridevirtual

create clone of the current object, ignoring internal temporary structures to save speed. Does not replicate exact object state. The clone must be used carefully in a way that ensures internal structure re-initialization Use example: Data_Splitter cloning or Classifier_LIBSVM cloning due to inability to clone external structures defined in LibSVM

Implements FST::Clonable.

◆ test()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual bool FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::test	(	RETURNTYPE &	result,
		const PDataAccessor	da )

pure virtual

Scores the designated test data with the regressor's own default measure, oriented higher-is-better (a hit rate under the class-label convention, a negated prediction error otherwise). Returns false if the test part is empty or predict() failed.

Implemented in FST::Regressor_Ridge< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >.

◆ test_metric()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual bool FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::test_metric	(	RETURNTYPE &	result,
		const PDataAccessor	da,
		const Metric_Spec< RETURNTYPE > &	spec )

inlinevirtual

evaluates the designated test data under the given regression metric (r2/neg-rmse/neg-mae); the default spec is served by test() itself. Classification metrics do not apply to a regression stack and make this return false. The dependent-variable convention is the concrete regressor's - see the class documentation.

Reimplemented in FST::Regressor_Ridge< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, and FST::Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >.

◆ train()

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class DATAACCESSOR>

virtual bool FST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::train	(	const PDataAccessor	da,
		const PSubset	sub )

pure virtual

learns from designated training part of data

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

/Users/psms/dev/devtest1/_src_criteria/regressor.hpp

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ clone()

◆ predict()

◆ sharing_clone()

◆ stateless_clone()

◆ test()

◆ test_metric()

◆ train()