CADETProcess.optimization.NEHVI

class CADETProcess.optimization.NEHVI(early_stopping_improvement_window, early_stopping_improvement_bar, n_init_evals, n_max_evals, seed, progress_frequency, x_tol, f_tol, cv_bounds_tol, cv_lineqcon_tol, cv_lincon_tol, cv_nonlincon_tol, n_max_iter, similarity_tol, parallelization_backend)

Noisy expected hypervolume improvement multi-objective algorithm.

Attributes:

aggregated_parameters

dict: Aggregated parameters of the instance.

cv_bounds_tol

cv_lincon_tol

cv_lineqcon_tol

cv_nonlincon_tol

early_stopping_improvement_bar

early_stopping_improvement_window

f_tol

missing_parameters

list: Parameters that are required but not set.

n_cores

int: Proxy to the number of cores used by the parallelization backend.

n_init_evals

n_max_evals

n_max_iter

options

dict: Optimizer options.

parallelization_backend

parameters

dict: Parameters of the instance.

polynomial_parameters

dict: Polynomial parameters of the instance.

progress_frequency

required_parameters

list: Parameters that have no default value.

seed

similarity_tol

sized_parameters

dict: Sized parameters of the instance.

specific_options

dict: Optimizer spcific options.

x_tol

Methods

check_optimization_problem(optimization_problem)	Check if problem is configured correctly and supported by the optimizer.
check_required_parameters()	Verify if all required parameters are set.
check_x0(optimization_problem, x0)	Check the initial guess x0 for an optimization problem.
load_results(checkpoint_path[, ...])	Load optimization results from a checkpoint file.
optimize(optimization_problem[, x0, ...])	Solve OptimizationProblem.
run_final_processing()	Run post processing at the end of the optimization.
run_post_processing(X_transformed, ...[, ...])	Run post-processing of generation.
train_model()	Train model.