gemseo / algos / doe

Hide inherited members

doe_library module

Base DOE library.

class gemseo.algos.doe.doe_library.DOEAlgorithmDescription(algorithm_name, internal_algorithm_name, library_name='', description='', website='', handle_integer_variables=True, require_gradient=False, minimum_dimension=1)[source]

Bases: DriverDescription

The description of a DOE algorithm.

  • algorithm_name (str) –

  • internal_algorithm_name (str) –

  • library_name (str) –

    By default it is set to “”.

  • description (str) –

    By default it is set to “”.

  • website (str) –

    By default it is set to “”.

  • handle_integer_variables (bool) –

    By default it is set to True.

  • require_gradient (bool) –

    By default it is set to False.

  • minimum_dimension (int) –

    By default it is set to 1.

algorithm_name: str

The name of the algorithm in GEMSEO.

description: str = ''

A description of the algorithm.

handle_integer_variables: bool = True

Whether the optimization algorithm handles integer variables.

internal_algorithm_name: str

The name of the algorithm in the wrapped library.

library_name: str = ''

The name of the wrapped library.

minimum_dimension: int = 1

The minimum dimension of the parameter space.

require_gradient: bool = False

Whether the optimization algorithm requires the gradient.

website: str = ''

The website of the wrapped library or algorithm.

class gemseo.algos.doe.doe_library.DOELibrary[source]

Bases: DriverLibrary

Abstract class to use for DOE library link See DriverLibrary.

Constructor Abstract class.

class ApproximationMode(value)

Bases: StrEnum

The approximation derivation modes.

COMPLEX_STEP = 'complex_step'

The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.

FINITE_DIFFERENCES = 'finite_differences'

The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.

class DifferentiationMethod(value)

Bases: StrEnum

The differentiation methods.

COMPLEX_STEP = 'complex_step'
FINITE_DIFFERENCES = 'finite_differences'
USER_GRAD = 'user'
compute_doe(variables_space, size=None, unit_sampling=False, **options)[source]

Compute a design of experiments (DOE) in a variables space.

  • variables_space (DesignSpace) – The variables space to be sampled.

  • size (int | None) – The size of the DOE. If None, the size is deduced from the options.

  • unit_sampling (bool) –

    Whether to sample in the unit hypercube.

    By default it is set to False.

  • **options (DOELibraryOptionType) – The options of the DOE algorithm.


The design of experiments whose rows are the samples and columns the variables.

Return type:



Deactivate the progress bar.

Return type:



Check the existence of an option.


option_name (str) – The name of the option.


Whether the option exists.

Return type:


ensure_bounds(orig_func, normalize=True)

Project the design vector onto the design space before execution.

  • orig_func – The original function.

  • normalize (bool) –

    Whether to use the normalized design space.

    By default it is set to True.


A function calling the original function with the input data projected onto the design space.

evaluate_samples(eval_jac=False, n_processes=1, wait_time_between_samples=0.0)[source]

Evaluate all the functions of the optimization problem at the samples.

  • eval_jac (bool) –

    Whether to evaluate the Jacobian.

    By default it is set to False.

  • n_processes (int) –

    The maximum simultaneous number of processes used to parallelize the execution.

    By default it is set to 1.

  • wait_time_between_samples (float) –

    The time to wait between each sample evaluation, in seconds.

    By default it is set to 0.0.

Return type:



This class relies on multiprocessing features when n_processes > 1, it is therefore necessary to protect its execution with an if __name__ == '__main__': statement when working on Windows.

execute(problem, algo_name=None, eval_obs_jac=False, skip_int_check=False, **options)

Execute the driver.

  • problem (OptimizationProblem) – The problem to be solved.

  • algo_name (str | None) – The name of the algorithm. If None, use the algo_name attribute which may have been set by the factory.

  • eval_obs_jac (bool) –

    Whether to evaluate the Jacobian of the observables.

    By default it is set to False.

  • skip_int_check (bool) –

    Whether to skip the integer variable handling check of the selected algorithm.

    By default it is set to False.

  • **options (DriverLibOptionType) – The options for the algorithm.


The optimization result.


ValueError – If algo_name was not either set by the factory or given as an argument.

Return type:



Export the samples generated by DOE library to a CSV file.


doe_output_file (Path | str) – The path to the output file.

Return type:



Filter the algorithms capable of solving the problem.


problem (Any) – The problem to be solved.


The names of the algorithms adapted to this problem.

Return type:



Finalize the iteration observer.

Return type:


get_optimum_from_database(message=None, status=None)

Retrieve the optimum from the database and build an optimization.

Return type:



Return x0 and bounds.


normalize_ds – Whether to normalize the input variables that are not integers, according to the normalization policy of the design space.


The current value, the lower bounds and the upper bounds.

init_iter_observer(max_iter, message='...')

Initialize the iteration observer.

It will handle the stopping criterion and the logging of the progress bar.

  • max_iter (int) – The maximum number of iterations.

  • message (str) –

    The message to display at the beginning.

    By default it is set to “…”.


ValueError – If max_iter is lower than one.

Return type:



Initialize the options’ grammar.


algo_name (str) – The name of the algorithm.

Return type:


classmethod is_algorithm_suited(algorithm_description, problem)

Check if an algorithm is suited to a problem according to its description.

  • algorithm_description (AlgorithmDescription) – The description of the algorithm.

  • problem (Any) – The problem to be solved.


Whether the algorithm is suited to the problem.

Return type:



Iterate the progress bar, implement the stop criteria.


x_vect (ndarray | None) – The design variables values. If None, use the values of the last iteration.


MaxTimeReached – If the elapsed time is greater than the maximum execution time.

Return type:



Check if a driver requires the gradient.


driver_name (str) – The name of the driver.


Whether the driver requires the gradient.

Return type:


DESIGN_ALGO_NAME = 'Design algorithm'
DIMENSION = 'dimension'
EQ_TOLERANCE = 'eq_tolerance'
EVAL_JAC = 'eval_jac'
EVAL_OBS_JAC_OPTION = 'eval_obs_jac'
INEQ_TOLERANCE = 'ineq_tolerance'
LEVEL_KEYWORD = 'levels'
LIBRARY_NAME: ClassVar[str | None] = None

The name of the interfaced library.

MAX_TIME = 'max_time'
NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'
N_PROCESSES = 'n_processes'
N_SAMPLES = 'n_samples'
OPTIONS_DIR: Final[str] = 'options'

The name of the directory containing the files of the grammars of the options.

OPTIONS_MAP: dict[str, str] = {}

The names of the options in GEMSEO mapping to those in the wrapped library.

ROUND_INTS_OPTION = 'round_ints'
SAMPLES_TAG = 'samples'
SEED = 'seed'
USE_DATABASE_OPTION = 'use_database'
WAIT_TIME_BETWEEN_SAMPLES = 'wait_time_between_samples'
activate_progress_bar: ClassVar[bool] = True

Whether to activate the progress bar in the optimization log.

algo_name: str | None

The name of the algorithm used currently.

property algorithms: list[str]

The available algorithms.

descriptions: dict[str, AlgorithmDescription]

The description of the algorithms contained in the library.

eval_jac: bool

Whether to evaluate the Jacobian.

internal_algo_name: str | None

The internal name of the algorithm used currently.

It typically corresponds to the name of the algorithm in the wrapped library if any.

opt_grammar: JSONGrammar | None

The grammar defining the options of the current algorithm.

problem: Any | None

The problem to be solved.

samples: ndarray

The input samples with the design space variable types stored as dtype metadata.

seed: int

The seed to be used for reproducibility reasons.

This seed is initialized at 0 and each call to execute() increments it before using it.

unit_samples: ndarray

The input samples transformed in \([0,1]\).

Examples using DOELibrary

Change the seed of a DOE

Change the seed of a DOE