main class for the Feat learner. More...

#include <feat.h>

Collaboration diagram for FT::Feat:

[legend]

Public Member Functions
	Feat ()
	member initializer list constructor More...

	~Feat ()
	destructor More...

void	init ()
	initialize Feat object for fitting. More...

void	set_is_fitted (bool f)
	set flag indicating whether fit has been called More...

bool	get_is_fitted ()

void	set_pop_size (int pop_size)
	set size of population More...

int	get_pop_size ()
	return population size More...

void	set_gens (int gens)
	set size of max generations More...

int	get_gens ()
	return size of max generations More...

void	set_ml (string ml)
	set ML algorithm to use More...

string	get_ml ()
	return ML algorithm string More...

void	set_classification (bool classification)
	set EProblemType for shogun More...

bool	get_classification ()
	return type of classification flag set More...

void	set_verbosity (int verbosity)
	set level of debug info More...

int	get_verbosity ()
	return current verbosity level set More...

void	set_max_stall (int max_stall)
	set maximum stall in learning, in generations More...

int	get_max_stall ()
	return maximum stall in learning, in generations More...

void	set_selection (string sel)
	set selection method More...

void	set_survival (string surv)
	set survivability More...

float	get_cross_rate ()
	return cross rate for variation More...

void	set_cross_rate (float cross_rate)
	set cross rate in variation More...

void	set_root_xo_rate (float cross_rate)
	set root xo rate in variation More...

float	get_root_xo_rate ()

vector< char >	get_otypes ()
	return program output type ('f', 'b') More...

char	get_otype ()
	return parameter otype, used to set otypes More...

void	set_otype (char ot)
	set program output type ('f', 'b') More...

void	set_functions (const vector< string > &fns)
	sets available functions based on comma-separated list. More...

vector< string >	get_functions ()

int	get_max_depth ()
	return max_depth of programs More...

void	set_max_depth (unsigned int max_depth)
	set max depth of programs More...

int	get_max_dim ()
	return max dimensionality of programs More...

void	set_max_dim (unsigned int max_dim)
	set maximum dimensionality of programs More...

void	set_random_state (int random_state)
	set dimensionality as multiple of the number of columns More...

int	get_random_state ()

int	get_random_state_ ()
	returns the actual seed determined by the input argument. More...

bool	get_erc ()
	return boolean value of erc flag More...

void	set_erc (bool erc)
	flag to set whether to use variable or constants for terminals More...

bool	get_shuffle ()
	return whether option to shuffle the data is set or not More...

void	set_shuffle (bool sh)
	flag to shuffle the input samples for train/test splits More...

float	get_split ()
	return fraction of data to use for training More...

void	set_split (float sp)
	set train fraction of dataset More...

vector< char >	get_dtypes ()
	return data types for input parameters More...

void	set_dtypes (vector< char > dtypes)
	set data types for input parameters More...

float	get_fb ()
	get feedback setting More...

void	set_fb (float fb)
	set feedback More...

string	get_logfile ()
	get name More...

void	set_logfile (string s)
	set name for files More...

string	get_scorer ()

void	set_scorer (string s)
	set scoring function More...

string	get_scorer_ ()

void	set_feature_names (string s)

string	get_feature_names ()

void	set_backprop (bool bp)
	set constant optimization options More...

bool	get_backprop ()

void	set_simplify (float s)

float	get_simplify ()

void	set_corr_delete_mutate (bool s)

bool	get_corr_delete_mutate ()

void	set_hillclimb (bool hc)

bool	get_hillclimb ()

void	set_iters (int iters)

int	get_iters ()

void	set_lr (float lr)

float	get_lr ()

int	get_batch_size ()

void	set_batch_size (int bs)

void	set_n_jobs (unsigned t)
	set number of threads More...

int	get_n_jobs ()

void	set_max_time (int time)
	set max time in seconds for fit method More...

int	get_max_time ()

void	set_use_batch ()
	set flag to use batch for training More...

void	set_residual_xo (bool res_xo=true)
	use residual crossover More...

bool	get_residual_xo ()

void	set_stagewise_xo (bool sem_xo=true)
	use stagewise crossover More...

bool	get_stagewise_xo ()

void	set_stagewise_xo_tol (int tol)

int	get_stagewise_xo_tol ()

void	set_softmax_norm (bool sftmx=true)
	use softmax More...

bool	get_softmax_norm ()

void	set_save_pop (int pp)

int	get_save_pop ()

void	set_starting_pop (string sp)

string	get_starting_pop ()

void	set_normalize (bool in)

bool	get_normalize ()

string	get_sel ()

void	set_sel (string in)

string	get_surv ()

void	set_surv (string in)

bool	get_tune_initial ()

void	set_tune_initial (bool in)

bool	get_tune_final ()

void	set_tune_final (bool in)

auto	get_objectives ()
	get objectives for multi-objective search More...

void	set_objectives (const vector< string > &obj)
	set objectives for multi-objective search More...

string	get_protected_groups ()

void	set_protected_groups (string pg)
	set protected groups for fairness More...

bool	get_val_from_arch ()

void	set_val_from_arch (bool in)

int	get_archive_size ()
	return archive size More...

int	get_max_size ()
	return max size of programs More...

int	get_num_features ()
	return number of features More...

string	get_representation ()
	return best model More...

string	get_model (bool sort=true)
	return best model, in tabular form More...

string	get_ind_eqn (bool sort, Individual &ind)
	return best model as a single line equation More...

string	get_eqn (bool sort=false)

int	get_n_params ()
	get number of parameters in best More...

int	get_dim ()
	get dimensionality of best More...

int	get_complexity ()
	get dimensionality of best More...

vector< nl::json >	get_archive (bool front)
	return population as string More...

ArrayXf	get_coefs ()
	return the coefficients or importance scores of the best model. More...

int	get_n_nodes ()
	return the number of nodes in the best model More...

LongData	get_Z (string s, int *idx, int idx_size)
	get longitudinal data from file s More...

void	fit (MatrixXf &X, VectorXf &y)
	train a model. More...

void	fit (MatrixXf &X, VectorXf &y, LongData &Z)

void	run_generation (unsigned int g, vector< size_t > survivors, DataRef &d, std::ofstream &log, float percentage, unsigned &stall_count)

VectorXf	predict (MatrixXf &X, LongData &Z)
	predict on unseen data. More...

VectorXf	predict (MatrixXf &X)

VectorXf	predict_archive (int id, MatrixXf &X)
	predict on unseen data from the whole archive More...

VectorXf	predict_archive (int id, MatrixXf &X, LongData &Z)

ArrayXXf	predict_proba_archive (int id, MatrixXf &X, LongData &Z)

ArrayXXf	predict_proba_archive (int id, MatrixXf &X)

shared_ptr< CLabels >	predict_labels (MatrixXf &X, LongData Z=LongData())
	predict on unseen data. return CLabels. More...

ArrayXXf	predict_proba (MatrixXf &X, LongData &Z)
	predict probabilities of each class. More...

ArrayXXf	predict_proba (MatrixXf &X)

MatrixXf	transform (MatrixXf &X)
	transform an input matrix using a program. More...

MatrixXf	transform (MatrixXf &X, LongData &Z)

MatrixXf	transform (MatrixXf &X, LongData Z, Individual *ind)

float	score (MatrixXf &X, const VectorXf &y, LongData Z=LongData())
	scoring function More...

nl::json	get_stats ()
	return statistics from the run as a json string More...

void	load_best_ind (string filename)
	load best_ind from file More...

void	load_population (string filename, bool justfront=false)
	load population from file, optionall just Pareto front More...

void	load (const json &j)
	load Feat state from a json string. More...

void	load_from_file (string filename)
	load Feat state from file. More...

json	save () const
	save and return a json Feat state as string. More...

void	save_to_file (string filename)
	save Feat state to file. More...

Public Attributes
bool	is_fitted
	keeps track of whether fit was called. More...

Private Member Functions
bool	update_best (const DataRef &d, bool val=false)
	updates best score More...

void	calculate_stats (const DataRef &d)
	calculate and print stats More...

void	print_stats (std::ofstream &log, float fraction)

void	log_stats (std::ofstream &log)

vector< float >	univariate_initial_model (DataRef &d, int n_feats)

void	initial_model (DataRef &d)
	method to fit inital ml model More...

void	final_model (DataRef &d)
	fits final model to best transformation More...

void	simplify_model (DataRef &d, Individual &)
	simplifies final model to best transformation More...

void	update_stall_count (unsigned &stall_count, bool updated)
	updates stall count for early stopping More...

	NLOHMANN_DEFINE_TYPE_INTRUSIVE (Feat, params, pop, selector, survivor, archive, use_arch, survival, N, min_loss, min_loss_v, best_med_score, best_complexity, str_dim, starting_pop, best_ind, is_fitted)

Private Attributes
Parameters	params
	hyperparameters of Feat More...

Timer	timer
	start time of training More...

Population	pop
	population of programs More...

Selection	selector
	selection algorithm More...

Evaluation	evaluator
	evaluation code More...

Variation	variator
	variation operators More...

Selection	survivor
	survival algorithm More...

Archive	archive
	pareto front archive More...

bool	use_arch
	internal control over use of archive More...

string	survival
	stores survival mode More...

Normalizer	N
	scales training data. More...

float	min_loss
	current best score More...

float	min_loss_v
	best validation score More...

float	best_med_score
	best median population score More...

int	best_complexity
	complexity of the best model More...

string	str_dim
	dimensionality as multiple of number of cols More...

string	starting_pop
	file with starting population More...

Individual	best_ind
	best individual More...

string	logfile
	log filename More...

int	save_pop
	controls whether pop is printed each gen More...

bool	val_from_arch
	model selection only uses Pareto front More...

float	simplify
	post-run simplification More...

Log_Stats	stats
	runtime stats More...

Detailed Description

main class for the Feat learner.

Feat optimizes feature represenations for a given machine learning algorithm. It does so by using evolutionary computation to optimize a population of programs. Each program represents a set of feature transformations.

Definition at line 72 of file feat.h.

Constructor & Destructor Documentation

◆ Feat()

FT::Feat::Feat ( )

inline

member initializer list constructor

Definition at line 101 of file feat.h.

◆ ~Feat()

FT::Feat::~Feat ( )

inline

destructor

Definition at line 103 of file feat.h.

Member Function Documentation

◆ calculate_stats()

void Feat::calculate_stats ( const DataRef & d )

private

calculate and print stats

Definition at line 1338 of file feat.cc.

◆ final_model()

void Feat::final_model ( DataRef & d )

private

fits final model to best transformation

Definition at line 770 of file feat.cc.

◆ fit() [1/2]

void Feat::fit	(	MatrixXf &	X,
		VectorXf &	y
	)

train a model.

Definition at line 668 of file feat.cc.

◆ fit() [2/2]

void Feat::fit	(	MatrixXf &	X,
		VectorXf &	y,
		LongData &	Z
	)

Input:

 X: n_features x n_samples MatrixXf of features
 y: VectorXf of labels

Output:

 updates best_estimator, hof

steps:

fit model yhat = f(X)
generate transformations Phi(X) for each individual
fit model yhat_new = f( Phi(X)) for each individual
evaluate features
selection parents
produce offspring from parents via variation
select surviving individuals from parents and offspring

< log file stream

Definition at line 46 of file feat.cc.

◆ get_archive()

vector< json > Feat::get_archive ( bool front )

return population as string

Definition at line 577 of file feat.cc.

◆ get_archive_size()

int FT::Feat::get_archive_size ( )

inline

return archive size

Definition at line 312 of file feat.h.

◆ get_backprop()

bool FT::Feat::get_backprop ( )

inline

Definition at line 228 of file feat.h.

◆ get_batch_size()

int FT::Feat::get_batch_size ( )

inline

Definition at line 245 of file feat.h.

◆ get_classification()

bool Feat::get_classification ( )

return type of classification flag set

Definition at line 428 of file feat.cc.

◆ get_coefs()

ArrayXf Feat::get_coefs ( )

return the coefficients or importance scores of the best model.

Definition at line 649 of file feat.cc.

◆ get_complexity()

int Feat::get_complexity ( )

get dimensionality of best

Definition at line 570 of file feat.cc.

◆ get_corr_delete_mutate()

bool FT::Feat::get_corr_delete_mutate ( )

inline

Definition at line 234 of file feat.h.

◆ get_cross_rate()

float Feat::get_cross_rate ( )

return cross rate for variation

Definition at line 443 of file feat.cc.

◆ get_dim()

int Feat::get_dim ( )

get dimensionality of best

Definition at line 567 of file feat.cc.

◆ get_dtypes()

vector< char > Feat::get_dtypes ( )

return data types for input parameters

add custom node into feat

return data types for input parameters

Definition at line 470 of file feat.cc.

◆ get_eqn()

string Feat::get_eqn ( bool sort = false )

Definition at line 478 of file feat.cc.

◆ get_erc()

bool Feat::get_erc ( )

return boolean value of erc flag

Definition at line 452 of file feat.cc.

◆ get_fb()

float Feat::get_fb ( )

get feedback setting

return feedback setting

Definition at line 473 of file feat.cc.

◆ get_feature_names()

string FT::Feat::get_feature_names ( )

inline

Definition at line 224 of file feat.h.

◆ get_functions()

vector<string> FT::Feat::get_functions ( )

inline

Definition at line 164 of file feat.h.

◆ get_gens()

int Feat::get_gens ( )

return size of max generations

Definition at line 422 of file feat.cc.

◆ get_hillclimb()

bool FT::Feat::get_hillclimb ( )

inline

Definition at line 237 of file feat.h.

◆ get_ind_eqn()

string Feat::get_ind_eqn	(	bool	sort,
		Individual &	ind
	)

return best model as a single line equation

Definition at line 480 of file feat.cc.

◆ get_is_fitted()

bool FT::Feat::get_is_fitted ( )

inline

Definition at line 108 of file feat.h.

◆ get_iters()

int FT::Feat::get_iters ( )

inline

Definition at line 240 of file feat.h.

◆ get_logfile()

string Feat::get_logfile ( )

get name

Definition at line 455 of file feat.cc.

◆ get_lr()

float FT::Feat::get_lr ( )

inline

Definition at line 243 of file feat.h.

◆ get_max_depth()

int Feat::get_max_depth ( )

return max_depth of programs

Definition at line 440 of file feat.cc.

◆ get_max_dim()

int Feat::get_max_dim ( )

return max dimensionality of programs

Definition at line 449 of file feat.cc.

◆ get_max_size()

int Feat::get_max_size ( )

return max size of programs

Definition at line 446 of file feat.cc.

◆ get_max_stall()

int Feat::get_max_stall ( )

return maximum stall in learning, in generations

Definition at line 431 of file feat.cc.

◆ get_max_time()

int FT::Feat::get_max_time ( )

inline

Definition at line 254 of file feat.h.

◆ get_ml()

string Feat::get_ml ( )

return ML algorithm string

Definition at line 425 of file feat.cc.

◆ get_model()

string Feat::get_model ( bool sort = true )

return best model, in tabular form

Definition at line 527 of file feat.cc.

◆ get_n_jobs()

int FT::Feat::get_n_jobs ( )

inline

Definition at line 250 of file feat.h.

◆ get_n_nodes()

int Feat::get_n_nodes ( )

return the number of nodes in the best model

Definition at line 574 of file feat.cc.

◆ get_n_params()

int Feat::get_n_params ( )

get number of parameters in best

Definition at line 564 of file feat.cc.

◆ get_normalize()

bool FT::Feat::get_normalize ( )

inline

Definition at line 281 of file feat.h.

◆ get_num_features()

int Feat::get_num_features ( )

return number of features

Definition at line 458 of file feat.cc.

◆ get_objectives()

auto FT::Feat::get_objectives ( )

inline

get objectives for multi-objective search

Definition at line 297 of file feat.h.

◆ get_otype()

char FT::Feat::get_otype ( )

inline

return parameter otype, used to set otypes

Definition at line 158 of file feat.h.

◆ get_otypes()

vector< char > Feat::get_otypes ( )

return program output type ('f', 'b')

Definition at line 434 of file feat.cc.

◆ get_pop_size()

int Feat::get_pop_size ( )

return population size

Definition at line 419 of file feat.cc.

◆ get_protected_groups()

string FT::Feat::get_protected_groups ( )

inline

Definition at line 301 of file feat.h.

◆ get_random_state()

int FT::Feat::get_random_state ( )

inline

Definition at line 181 of file feat.h.

◆ get_random_state_()

int FT::Feat::get_random_state_ ( )

inline

returns the actual seed determined by the input argument.

Definition at line 183 of file feat.h.

◆ get_representation()

string Feat::get_representation ( )

return best model

Definition at line 476 of file feat.cc.

◆ get_residual_xo()

bool FT::Feat::get_residual_xo ( )

inline

Definition at line 261 of file feat.h.

◆ get_root_xo_rate()

float FT::Feat::get_root_xo_rate ( )

inline

Definition at line 153 of file feat.h.

◆ get_save_pop()

int FT::Feat::get_save_pop ( )

inline

Definition at line 275 of file feat.h.

◆ get_scorer()

string Feat::get_scorer ( )

Definition at line 382 of file feat.cc.

◆ get_scorer_()

string Feat::get_scorer_ ( )

Definition at line 381 of file feat.cc.

◆ get_sel()

string FT::Feat::get_sel ( )

inline

Definition at line 283 of file feat.h.

◆ get_shuffle()

bool Feat::get_shuffle ( )

return whether option to shuffle the data is set or not

Definition at line 461 of file feat.cc.

◆ get_simplify()

float FT::Feat::get_simplify ( )

inline

Definition at line 231 of file feat.h.

◆ get_softmax_norm()

bool FT::Feat::get_softmax_norm ( )

inline

Definition at line 272 of file feat.h.

◆ get_split()

float Feat::get_split ( )

return fraction of data to use for training

Definition at line 464 of file feat.cc.

◆ get_stagewise_xo()

bool FT::Feat::get_stagewise_xo ( )

inline

Definition at line 265 of file feat.h.

◆ get_stagewise_xo_tol()

int FT::Feat::get_stagewise_xo_tol ( )

inline

Definition at line 268 of file feat.h.

◆ get_starting_pop()

string FT::Feat::get_starting_pop ( )

inline

Definition at line 278 of file feat.h.

◆ get_stats()

json Feat::get_stats ( )

return statistics from the run as a json string

Definition at line 1566 of file feat.cc.

◆ get_surv()

string FT::Feat::get_surv ( )

inline

Definition at line 286 of file feat.h.

◆ get_tune_final()

bool FT::Feat::get_tune_final ( )

inline

Definition at line 292 of file feat.h.

◆ get_tune_initial()

bool FT::Feat::get_tune_initial ( )

inline

Definition at line 289 of file feat.h.

◆ get_val_from_arch()

bool FT::Feat::get_val_from_arch ( )

inline

Definition at line 305 of file feat.h.

◆ get_verbosity()

int Feat::get_verbosity ( )

return current verbosity level set

Definition at line 437 of file feat.cc.

◆ get_Z()

std::map< string, std::pair< vector< ArrayXf >, vector< ArrayXf > > > Feat::get_Z	(	string	s,
		int *	idx,
		int	idx_size
	)

get longitudinal data from file s

Definition at line 657 of file feat.cc.

◆ init()

void Feat::init ( )

initialize Feat object for fitting.

Definition at line 24 of file feat.cc.

◆ initial_model()

void Feat::initial_model ( DataRef & d )

private

method to fit inital ml model

fits an ML model to the raw data as a starting point.

Definition at line 1043 of file feat.cc.

◆ load()

void Feat::load ( const json & j )

load Feat state from a json string.

Definition at line 1584 of file feat.cc.

◆ load_best_ind()

void Feat::load_best_ind ( string filename )

load best_ind from file

Definition at line 1573 of file feat.cc.

◆ load_from_file()

void Feat::load_from_file ( string filename )

load Feat state from file.

Definition at line 1597 of file feat.cc.

◆ load_population()

void Feat::load_population	(	string	filename,
		bool	justfront = `false`
	)

load population from file, optionall just Pareto front

Definition at line 1579 of file feat.cc.

◆ log_stats()

void Feat::log_stats ( std::ofstream & log )

private

Definition at line 1536 of file feat.cc.

◆ NLOHMANN_DEFINE_TYPE_INTRUSIVE()

FT::Feat::NLOHMANN_DEFINE_TYPE_INTRUSIVE	(	Feat	,
		params	,
		pop	,
		selector	,
		survivor	,
		archive	,
		use_arch	,
		survival	,
		N	,
		min_loss	,
		min_loss_v	,
		best_med_score	,
		best_complexity	,
		str_dim	,
		starting_pop	,
		best_ind	,
		is_fitted
	)

private

◆ predict() [1/2]

VectorXf Feat::predict ( MatrixXf & X )

Definition at line 1178 of file feat.cc.

◆ predict() [2/2]

VectorXf Feat::predict	(	MatrixXf &	X,
		LongData &	Z
	)

predict on unseen data.

Definition at line 1184 of file feat.cc.

◆ predict_archive() [1/2]

VectorXf Feat::predict_archive	(	int	id,
		MatrixXf &	X
	)

predict on unseen data from the whole archive

Definition at line 1195 of file feat.cc.

◆ predict_archive() [2/2]

VectorXf Feat::predict_archive	(	int	id,
		MatrixXf &	X,
		LongData &	Z
	)

Definition at line 1201 of file feat.cc.

◆ predict_labels()

shared_ptr< CLabels > Feat::predict_labels	(	MatrixXf &	X,
		LongData	Z = `LongData()`
	)

predict on unseen data. return CLabels.

Definition at line 1269 of file feat.cc.

◆ predict_proba() [1/2]

ArrayXXf Feat::predict_proba ( MatrixXf & X )

Definition at line 1289 of file feat.cc.

◆ predict_proba() [2/2]

ArrayXXf Feat::predict_proba	(	MatrixXf &	X,
		LongData &	Z
	)

predict probabilities of each class.

Definition at line 1280 of file feat.cc.

◆ predict_proba_archive() [1/2]

ArrayXXf Feat::predict_proba_archive	(	int	id,
		MatrixXf &	X
	)

Definition at line 1242 of file feat.cc.

◆ predict_proba_archive() [2/2]

ArrayXXf Feat::predict_proba_archive	(	int	id,
		MatrixXf &	X,
		LongData &	Z
	)

Definition at line 1247 of file feat.cc.

◆ print_stats()

void Feat::print_stats	(	std::ofstream &	log,
		float	fraction
	)

private

Definition at line 1431 of file feat.cc.

◆ run_generation()

void Feat::run_generation	(	unsigned int	g,
		vector< size_t >	survivors,
		DataRef &	d,
		std::ofstream &	log,
		float	percentage,
		unsigned &	stall_count
	)

Definition at line 675 of file feat.cc.

◆ save()

json Feat::save ( ) const

save and return a json Feat state as string.

Definition at line 1590 of file feat.cc.

◆ save_to_file()

void Feat::save_to_file ( string filename )

save Feat state to file.

Definition at line 1614 of file feat.cc.

◆ score()

float Feat::score	(	MatrixXf &	X,
		const VectorXf &	y,
		LongData	Z = `LongData()`
	)

scoring function

Definition at line 1331 of file feat.cc.

◆ set_backprop()

void Feat::set_backprop ( bool bp )

set constant optimization options

Definition at line 385 of file feat.cc.

◆ set_batch_size()

void Feat::set_batch_size ( int bs )

Definition at line 397 of file feat.cc.

◆ set_classification()

void Feat::set_classification ( bool classification )

set EProblemType for shogun

Definition at line 304 of file feat.cc.

◆ set_corr_delete_mutate()

void Feat::set_corr_delete_mutate ( bool s )

Definition at line 389 of file feat.cc.

◆ set_cross_rate()

void Feat::set_cross_rate ( float cross_rate )

set cross rate in variation

Definition at line 326 of file feat.cc.

◆ set_dtypes()

void Feat::set_dtypes ( vector< char > dtypes )

set data types for input parameters

Definition at line 371 of file feat.cc.

◆ set_erc()

void Feat::set_erc ( bool erc )

flag to set whether to use variable or constants for terminals

Definition at line 362 of file feat.cc.

◆ set_fb()

void Feat::set_fb ( float fb )

set feedback

Definition at line 374 of file feat.cc.

◆ set_feature_names()

void FT::Feat::set_feature_names ( string s )

inline

Definition at line 223 of file feat.h.

◆ set_functions()

void FT::Feat::set_functions ( const vector< string > & fns )

inline

sets available functions based on comma-separated list.

Definition at line 163 of file feat.h.

◆ set_gens()

void Feat::set_gens ( int gens )

set size of max generations

Definition at line 298 of file feat.cc.

◆ set_hillclimb()

void Feat::set_hillclimb ( bool hc )

Definition at line 391 of file feat.cc.

◆ set_is_fitted()

void FT::Feat::set_is_fitted ( bool f )

inline

set flag indicating whether fit has been called

Definition at line 107 of file feat.h.

◆ set_iters()

void Feat::set_iters ( int iters )

Definition at line 393 of file feat.cc.

◆ set_logfile()

void Feat::set_logfile ( string s )

set name for files

Definition at line 377 of file feat.cc.

◆ set_lr()

void Feat::set_lr ( float lr )

Definition at line 395 of file feat.cc.

◆ set_max_depth()

void Feat::set_max_depth ( unsigned int max_depth )

set max depth of programs

Definition at line 343 of file feat.cc.

◆ set_max_dim()

void Feat::set_max_dim ( unsigned int max_dim )

set maximum dimensionality of programs

Definition at line 349 of file feat.cc.

◆ set_max_stall()

void Feat::set_max_stall ( int max_stall )

set maximum stall in learning, in generations

Definition at line 313 of file feat.cc.

◆ set_max_time()

void Feat::set_max_time ( int time )

set max time in seconds for fit method

Definition at line 406 of file feat.cc.

◆ set_ml()

void Feat::set_ml ( string ml )

set ML algorithm to use

Definition at line 301 of file feat.cc.

◆ set_n_jobs()

void Feat::set_n_jobs ( unsigned t )

set number of threads

Definition at line 404 of file feat.cc.

◆ set_normalize()

void FT::Feat::set_normalize ( bool in )

inline

Definition at line 280 of file feat.h.

◆ set_objectives()

void FT::Feat::set_objectives ( const vector< string > & obj )

inline

set objectives for multi-objective search

Definition at line 299 of file feat.h.

◆ set_otype()

void Feat::set_otype ( char ot )

set program output type ('f', 'b')

Definition at line 339 of file feat.cc.

◆ set_pop_size()

void Feat::set_pop_size ( int pop_size )

set size of population

Definition at line 295 of file feat.cc.

◆ set_protected_groups()

void Feat::set_protected_groups ( string pg )

set protected groups for fairness

Definition at line 410 of file feat.cc.

◆ set_random_state()

void Feat::set_random_state ( int rs )

set dimensionality as multiple of the number of columns

set seeds for each core's random number generator

Definition at line 355 of file feat.cc.

◆ set_residual_xo()

void FT::Feat::set_residual_xo ( bool res_xo = true )

inline

use residual crossover

Definition at line 260 of file feat.h.

◆ set_root_xo_rate()

void Feat::set_root_xo_rate ( float cross_rate )

set root xo rate in variation

set root cross rate in variation

Definition at line 333 of file feat.cc.

◆ set_save_pop()

void FT::Feat::set_save_pop ( int pp )

inline

Definition at line 274 of file feat.h.

◆ set_scorer()

void Feat::set_scorer ( string s )

set scoring function

Definition at line 380 of file feat.cc.

◆ set_sel()

void FT::Feat::set_sel ( string in )

inline

Definition at line 284 of file feat.h.

◆ set_selection()

void Feat::set_selection ( string sel )

set selection method

Definition at line 316 of file feat.cc.

◆ set_shuffle()

void Feat::set_shuffle ( bool sh )

flag to shuffle the input samples for train/test splits

Definition at line 365 of file feat.cc.

◆ set_simplify()

void Feat::set_simplify ( float s )

Definition at line 387 of file feat.cc.

◆ set_softmax_norm()

void FT::Feat::set_softmax_norm ( bool sftmx = true )

inline

use softmax

Definition at line 271 of file feat.h.

◆ set_split()

void Feat::set_split ( float sp )

set train fraction of dataset

Definition at line 368 of file feat.cc.

◆ set_stagewise_xo()

void FT::Feat::set_stagewise_xo ( bool sem_xo = true )

inline

use stagewise crossover

Definition at line 264 of file feat.h.

◆ set_stagewise_xo_tol()

void FT::Feat::set_stagewise_xo_tol ( int tol )

inline

Definition at line 267 of file feat.h.

◆ set_starting_pop()

void FT::Feat::set_starting_pop ( string sp )

inline

Definition at line 277 of file feat.h.

◆ set_surv()

void FT::Feat::set_surv ( string in )

inline

Definition at line 287 of file feat.h.

◆ set_survival()

void Feat::set_survival ( string surv )

set survivability

Definition at line 319 of file feat.cc.

◆ set_tune_final()

void FT::Feat::set_tune_final ( bool in )

inline

Definition at line 293 of file feat.h.

◆ set_tune_initial()

void FT::Feat::set_tune_initial ( bool in )

inline

Definition at line 290 of file feat.h.

◆ set_use_batch()

void Feat::set_use_batch ( )

set flag to use batch for training

Definition at line 408 of file feat.cc.

◆ set_val_from_arch()

void FT::Feat::set_val_from_arch ( bool in )

inline

Definition at line 306 of file feat.h.

◆ set_verbosity()

void Feat::set_verbosity ( int verbosity )

set level of debug info

Definition at line 310 of file feat.cc.

◆ simplify_model()

void Feat::simplify_model	(	DataRef &	d,
		Individual &	ind
	)

private

simplifies final model to best transformation

Definition at line 786 of file feat.cc.

◆ transform() [1/3]

MatrixXf Feat::transform ( MatrixXf & X )

transform an input matrix using a program.

Definition at line 1142 of file feat.cc.

◆ transform() [2/3]

MatrixXf Feat::transform	(	MatrixXf &	X,
		LongData &	Z
	)

Definition at line 1147 of file feat.cc.

◆ transform() [3/3]

MatrixXf Feat::transform	(	MatrixXf &	X,
		LongData	Z,
		Individual *	ind
	)

Transforms input data according to ind or best ind, if ind is undefined.

Definition at line 1151 of file feat.cc.

◆ univariate_initial_model()

vector< float > Feat::univariate_initial_model	(	DataRef &	d,
		int	n_feats
	)

private

If there are more data variables than the max feature size can allow, we can't initialize a model in the population without some sort of feature selection. To select features we do the following: 1) fit univariate models to all features in X and store the coefficients 2) fit univariate models to all features in median(Z) and store the coefficients 3) set terminal weights according to the univariate scores 4) construct a program of dimensionality n_feats using the largest magnitude coefficients

Definition at line 979 of file feat.cc.

◆ update_best()

bool Feat::update_best	(	const DataRef &	d,
		bool	val = `false`
	)

private

updates best score

Definition at line 1296 of file feat.cc.

◆ update_stall_count()

void Feat::update_stall_count	(	unsigned &	stall_count,
		bool	updated
	)

private

updates stall count for early stopping

Definition at line 754 of file feat.cc.

Member Data Documentation

◆ archive

Archive FT::Feat::archive

private

pareto front archive

Definition at line 410 of file feat.h.

◆ best_complexity

int FT::Feat::best_complexity

private

complexity of the best model

Definition at line 418 of file feat.h.

◆ best_ind

Individual FT::Feat::best_ind

private

best individual

Definition at line 421 of file feat.h.

◆ best_med_score

float FT::Feat::best_med_score

private

best median population score

Definition at line 417 of file feat.h.

◆ evaluator

Evaluation FT::Feat::evaluator

private

evaluation code

Definition at line 407 of file feat.h.

◆ is_fitted

bool FT::Feat::is_fitted

keeps track of whether fit was called.

Definition at line 398 of file feat.h.

◆ logfile

string FT::Feat::logfile

private

log filename

Definition at line 422 of file feat.h.

◆ min_loss

float FT::Feat::min_loss

private

current best score

Definition at line 415 of file feat.h.

◆ min_loss_v

float FT::Feat::min_loss_v

private

best validation score

Definition at line 416 of file feat.h.

◆ N

Normalizer FT::Feat::N

private

scales training data.

Definition at line 413 of file feat.h.

◆ params

Parameters FT::Feat::params

private

hyperparameters of Feat

Definition at line 401 of file feat.h.

◆ pop

Population FT::Feat::pop

private

population of programs

Definition at line 405 of file feat.h.

◆ save_pop

int FT::Feat::save_pop

private

controls whether pop is printed each gen

Definition at line 423 of file feat.h.

◆ selector

Selection FT::Feat::selector

private

selection algorithm

Definition at line 406 of file feat.h.

◆ simplify

float FT::Feat::simplify

private

post-run simplification

Definition at line 425 of file feat.h.

◆ starting_pop

string FT::Feat::starting_pop

private

file with starting population

Definition at line 420 of file feat.h.

◆ stats

Log_Stats FT::Feat::stats

private

runtime stats

Definition at line 426 of file feat.h.

◆ str_dim

string FT::Feat::str_dim

private

dimensionality as multiple of number of cols

Definition at line 419 of file feat.h.

◆ survival

string FT::Feat::survival

private

stores survival mode

Definition at line 412 of file feat.h.

◆ survivor

Selection FT::Feat::survivor

private

survival algorithm

Definition at line 409 of file feat.h.

◆ timer

Timer FT::Feat::timer

private

start time of training

Definition at line 403 of file feat.h.

◆ use_arch

bool FT::Feat::use_arch

private

internal control over use of archive

Definition at line 411 of file feat.h.

◆ val_from_arch

bool FT::Feat::val_from_arch

private

model selection only uses Pareto front

Definition at line 424 of file feat.h.

◆ variator

Variation FT::Feat::variator

private

variation operators

Definition at line 408 of file feat.h.

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

Public Member Functions

Public Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ Feat()

◆ ~Feat()

Member Function Documentation

◆ calculate_stats()

◆ final_model()

◆ fit() [1/2]

◆ fit() [2/2]

◆ get_archive()

◆ get_archive_size()

◆ get_backprop()

◆ get_batch_size()

◆ get_classification()

◆ get_coefs()

◆ get_complexity()

◆ get_corr_delete_mutate()

◆ get_cross_rate()

◆ get_dim()

◆ get_dtypes()

◆ get_eqn()

◆ get_erc()

◆ get_fb()

◆ get_feature_names()

◆ get_functions()

◆ get_gens()

◆ get_hillclimb()

◆ get_ind_eqn()

◆ get_is_fitted()

◆ get_iters()

◆ get_logfile()

◆ get_lr()

◆ get_max_depth()

◆ get_max_dim()

◆ get_max_size()

◆ get_max_stall()

◆ get_max_time()

◆ get_ml()

◆ get_model()

◆ get_n_jobs()

◆ get_n_nodes()

◆ get_n_params()

◆ get_normalize()

◆ get_num_features()

◆ get_objectives()

◆ get_otype()

◆ get_otypes()

◆ get_pop_size()

◆ get_protected_groups()

◆ get_random_state()

◆ get_random_state_()

◆ get_representation()

◆ get_residual_xo()

◆ get_root_xo_rate()

◆ get_save_pop()

◆ get_scorer()

◆ get_scorer_()

◆ get_sel()

◆ get_shuffle()

◆ get_simplify()

◆ get_softmax_norm()

◆ get_split()

◆ get_stagewise_xo()

◆ get_stagewise_xo_tol()

◆ get_starting_pop()

◆ get_stats()

◆ get_surv()

◆ get_tune_final()

◆ get_tune_initial()

◆ get_val_from_arch()

◆ get_verbosity()

◆ get_Z()

◆ init()

◆ initial_model()

◆ load()

◆ load_best_ind()