opennn::InputsSelection Class Reference

Abstract base class for input feature selection methods. More...

#include <inputs_selection.h>

Inheritance diagram for opennn::InputsSelection:

Public Types
enum class	StoppingCondition {   MaximumTime , SelectionErrorGoal , MaximumInputs , MaximumEpochs ,   MaximumSelectionFailures }
	Reasons that can terminate an input-selection run. More...

Public Member Functions
	InputsSelection (TrainingStrategy *training_strategy=nullptr)
	Constructs the selector.
virtual	~InputsSelection ()=default
	Virtual destructor.
const TrainingStrategy *	get_training_strategy () const
	Read-only access to the bound training strategy.
bool	has_training_strategy () const
	Whether a training strategy has been bound.
bool	get_display () const
	Whether progress should be printed to stdout.
virtual Index	get_minimum_inputs_number () const =0
	Lower bound on the number of selected inputs in any candidate.
virtual Index	get_maximum_inputs_number () const =0
	Upper bound on the number of selected inputs in any candidate.
void	set (TrainingStrategy *new_training_strategy)
	Re-initializes the selector by setting its training strategy.
void	set_trials_number (const Index new_trials_number)
	Sets the number of training trials per candidate (mean is used).
void	set_display (bool new_display)
	Toggles per-iteration progress printing.
void	set_validation_error_goal (const float new_validation_error_goal)
	Sets the validation error goal.
void	set_maximum_epochs (const Index new_maximum_epochs)
	Sets the maximum training epochs per evaluation.
void	set_maximum_validation_failures (const Index new_maximum_validation_failures)
	Sets the maximum number of consecutive validation-error increases tolerated.
void	set_maximum_time (const float new_maximum_time)
	Sets the maximum wall-clock selection time.
void	check () const
	Validates the bound configuration; throws if anything is missing.
virtual InputsSelectionResults	perform_input_selection ()=0
	Runs the selection algorithm.
string	get_name () const
	Canonical name of the selector (set by subclasses).
virtual void	from_JSON (const JsonDocument &)=0
	Loads selector hyperparameters from a parsed JSON document.
virtual void	to_JSON (JsonWriter &) const =0
	Writes selector hyperparameters to a streaming JSON writer.
void	save (const filesystem::path &) const
	Saves the selector configuration to a file.
void	load (const filesystem::path &)
	Loads the selector configuration from a file.
virtual void	print () const
	Prints a human-readable summary of the selector to stdout.

Protected Attributes
TrainingStrategy *	training_strategy = nullptr
	Training strategy used to evaluate candidate subsets; not owned.
Index	trials_number = 1
	Number of independent training runs averaged per candidate.
bool	display = true
	Whether progress should be printed to stdout during selection.
float	validation_error_goal
	Validation error goal; selection stops when reached.
Index	maximum_epochs
	Maximum training epochs per evaluation.
Index	maximum_validation_failures = 100
	Maximum consecutive validation-error increases tolerated.
float	maximum_time
	Maximum wall-clock selection time in seconds.
string	name
	Canonical name of the selector (set by subclasses).

Detailed Description

Abstract base class for input feature selection methods.

Holds a pointer to a TrainingStrategy used to evaluate candidate input subsets, the common stopping criteria (epoch budget, time budget, validation goal, validation failure budget) and trial averaging. Subclasses implement perform_input_selection() to drive the search.

Member Enumeration Documentation

StoppingCondition

enum class opennn::InputsSelection::StoppingCondition

strong

Reasons that can terminate an input-selection run.

Enumerator
MaximumTime	Configured time budget exhausted.
SelectionErrorGoal	Validation error reached the configured goal.
MaximumInputs	Maximum number of input variables reached.
MaximumEpochs	Configured epoch budget exhausted.
MaximumSelectionFailures	Validation error increased too many times.

Constructor & Destructor Documentation

InputsSelection()

opennn::InputsSelection::InputsSelection

(

TrainingStrategy *

training_strategy = nullptr

)

Constructs the selector.

Parameters

training_strategy

Training strategy used to evaluate candidate subsets.

~InputsSelection()

virtual opennn::InputsSelection::~InputsSelection ( )

virtualdefault

Virtual destructor.

Member Function Documentation

check()

void opennn::InputsSelection::check

(

)

const

Validates the bound configuration; throws if anything is missing.

from_JSON()

virtual void opennn::InputsSelection::from_JSON ( const JsonDocument & )

pure virtual

Loads selector hyperparameters from a parsed JSON document.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

get_display()

bool opennn::InputsSelection::get_display ( ) const

inline

Whether progress should be printed to stdout.

get_maximum_inputs_number()

virtual Index opennn::InputsSelection::get_maximum_inputs_number ( ) const

pure virtual

Upper bound on the number of selected inputs in any candidate.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

get_minimum_inputs_number()

virtual Index opennn::InputsSelection::get_minimum_inputs_number ( ) const

pure virtual

Lower bound on the number of selected inputs in any candidate.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

get_name()

string opennn::InputsSelection::get_name ( ) const

inline

Canonical name of the selector (set by subclasses).

get_training_strategy()

const TrainingStrategy * opennn::InputsSelection::get_training_strategy ( ) const

inline

Read-only access to the bound training strategy.

has_training_strategy()

bool opennn::InputsSelection::has_training_strategy ( ) const

inline

Whether a training strategy has been bound.

load()

void opennn::InputsSelection::load

(

const filesystem::path &

)

Loads the selector configuration from a file.

Receives the source path.

perform_input_selection()

virtual InputsSelectionResults opennn::InputsSelection::perform_input_selection ( )

pure virtual

Runs the selection algorithm.

Returns

Best-of-run input subset and supporting statistics.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

print()

virtual void opennn::InputsSelection::print ( ) const

inlinevirtual

Prints a human-readable summary of the selector to stdout.

save()

void opennn::InputsSelection::save

(

const filesystem::path &

)

const

Saves the selector configuration to a file.

Receives the destination path.

set()

void opennn::InputsSelection::set ( TrainingStrategy * new_training_strategy )

inline

Re-initializes the selector by setting its training strategy.

Parameters

new_training_strategy

Strategy used to evaluate candidates.

set_display()

void opennn::InputsSelection::set_display ( bool new_display )

inline

Toggles per-iteration progress printing.

Parameters

new_display

True to print progress to stdout.

set_maximum_epochs()

void opennn::InputsSelection::set_maximum_epochs ( const Index new_maximum_epochs )

inline

Sets the maximum training epochs per evaluation.

Parameters

new_maximum_epochs

Epoch budget for each candidate's training.

set_maximum_time()

void opennn::InputsSelection::set_maximum_time ( const float new_maximum_time )

inline

Sets the maximum wall-clock selection time.

Parameters

new_maximum_time

Time budget in seconds.

set_maximum_validation_failures()

void opennn::InputsSelection::set_maximum_validation_failures ( const Index new_maximum_validation_failures )

inline

Sets the maximum number of consecutive validation-error increases tolerated.

Parameters

new_maximum_validation_failures

Failure budget for early stopping.

set_trials_number()

void opennn::InputsSelection::set_trials_number ( const Index new_trials_number )

inline

Sets the number of training trials per candidate (mean is used).

Parameters

new_trials_number

Number of independent training runs averaged.

set_validation_error_goal()

void opennn::InputsSelection::set_validation_error_goal ( const float new_validation_error_goal )

inline

Sets the validation error goal.

Parameters

new_validation_error_goal

Selection stops when validation error reaches this value.

to_JSON()

virtual void opennn::InputsSelection::to_JSON ( JsonWriter & ) const

pure virtual

Writes selector hyperparameters to a streaming JSON writer.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

Member Data Documentation

display

bool opennn::InputsSelection::display = true

protected

Whether progress should be printed to stdout during selection.

maximum_epochs

Index opennn::InputsSelection::maximum_epochs

protected

Maximum training epochs per evaluation.

maximum_time

float opennn::InputsSelection::maximum_time

protected

Maximum wall-clock selection time in seconds.

maximum_validation_failures

Index opennn::InputsSelection::maximum_validation_failures = 100

protected

Maximum consecutive validation-error increases tolerated.

name

string opennn::InputsSelection::name

protected

Canonical name of the selector (set by subclasses).

training_strategy

TrainingStrategy* opennn::InputsSelection::training_strategy = nullptr

protected

Training strategy used to evaluate candidate subsets; not owned.

trials_number

Index opennn::InputsSelection::trials_number = 1

protected

Number of independent training runs averaged per candidate.

validation_error_goal

float opennn::InputsSelection::validation_error_goal

protected

Validation error goal; selection stops when reached.