gemseo / algos / doe

Show 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.

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.

minimum_dimension: int = 1

The minimum dimension of the parameter space.

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

Bases: DriverLibrary

Abstract class to use for DOE library link See DriverLibrary.

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:


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.


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


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

Return type:


DESIGN_ALGO_NAME = 'Design algorithm'
DIMENSION = 'dimension'
EVAL_JAC = 'eval_jac'
LEVEL_KEYWORD = 'levels'
N_PROCESSES = 'n_processes'
N_SAMPLES = 'n_samples'
SAMPLES_TAG = 'samples'
SEED = 'seed'
WAIT_TIME_BETWEEN_SAMPLES = 'wait_time_between_samples'
algo_name: str | None

The name of the algorithm used currently.

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: OptimizationProblem

The optimization problem the driver library is bonded to.

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