gemseo / algos / doe

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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.

Parameters:

  • 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, Serializable

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.

Parameters:

  • variables_space (DesignSpace | int) – Either the variables space to be sampled or its dimension.

  • 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. If the value provided in variables_space is the dimension, the samples will be generated in the unit hypercube whatever the value of unit_sampling.

    By default it is set to False.

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

Returns:

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

Return type:

RealArray

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

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

Parameters:

  • eval_jac (bool) –

    Whether to evaluate the Jacobian function.

    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.

  • use_database (bool) –

    Whether to store the evaluations in the database.

    By default it is set to True.

  • callbacks (Iterable[CallbackType]) –

    The functions to be evaluated after each call to OptimizationProblem.evaluate_functions(); to be called as callback(index, (output, jacobian)).

    By default it is set to ().

Return type:

None

Warning

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_samples(doe_output_file)[source]

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

Parameters:

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

Return type:

None

DESIGN_ALGO_NAME = 'Design algorithm'
DIMENSION = 'dimension'
EVAL_JAC = 'eval_jac'
LEVEL_KEYWORD = 'levels'
N_PROCESSES = 'n_processes'
N_SAMPLES = 'n_samples'
PHIP_CRITERIA = 'phi^p'
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.

lock: RLock

The lock protecting database storage in multiprocessing.

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: RealArray

The design vector samples in the design space.

The design space variable types stored as dtype metadata.

To access those in the unit hypercube, use unit_samples.

property seed: int

The default seed used for reproducibility reasons.

unit_samples: RealArray

The design vector samples projected in the unit hypercube.

In the case of a design space of dimension \(d\), the unit hypercube is \([0,1]^d\).

To access those in the design space, use samples.

gemseo.algos.doe.doe_library.DOELibraryOutputType

The type of the output value in an input-output sample.

gemseo.algos.doe.doe_library.CallbackType(*args, **kwargs)

The type of a callback function in the context of a .

alias of Callable[[int, tuple[dict[str, Union[float, ndarray]], dict[str, ndarray]]], Any]

gemseo.algos.doe.doe_library.DOELibraryOptionType(*args, **kwargs)

The type of a DOE algorithm option.

alias of Union[str, float, int, bool, list[str], ndarray, Iterable[Callable[[int, Any], Any]]]

Examples using DOELibrary

Change the seed of a DOE

Change the seed of a DOE

Scaling

Scaling