gemseo / algos / doe

doe_lib module

Base DOE library.

class gemseo.algos.doe.doe_lib.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.

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_lib.DOELibrary[source]

Bases: DriverLib

Abstract class to use for DOE library link See DriverLib.

Constructor Abstract class.

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) – 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.

Returns:

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

Return type:

ndarray

static compute_phip_criteria(samples, power=10.0)[source]

Compute the \(\phi^p\) space-filling criterion (the smaller the better).

See [MM95].

Parameters:
  • samples (ndarray) – The samples of the input variables.

  • power (float) –

    The power \(p\) of the \(\phi^p\) criterion.

    By default it is set to 10.0.

Returns:

The \(\phi^p\) space-filling criterion.

Return type:

float

deactivate_progress_bar()

Deactivate the progress bar.

Return type:

None

driver_has_option(option_name)

Check the existence of an option.

Parameters:

option_name (str) – The name of the option.

Returns:

Whether the option exists.

Return type:

bool

ensure_bounds(orig_func, normalize=True)

Project the design vector onto the design space before execution.

Parameters:
  • orig_func – The original function.

  • normalize

    Whether to use the normalized design space.

    By default it is set to True.

Returns:

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.

Parameters:
  • 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.

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.

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

Execute the driver.

Parameters:
  • 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.

Returns:

The optimization result.

Raises:

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

Return type:

OptimizationResult

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

filter_adapted_algorithms(problem)

Filter the algorithms capable of solving the problem.

Parameters:

problem (Any) – The problem to be solved.

Returns:

The names of the algorithms adapted to this problem.

Return type:

list[str]

finalize_iter_observer()

Finalize the iteration observer.

Return type:

None

get_optimum_from_database(message=None, status=None)

Retrieves the optimum from the database and builds an optimization result object from it.

get_x0_and_bounds_vects(normalize_ds)

Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.

Parameters:

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

Returns:

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.

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

  • message (str) –

    The message to display at the beginning.

    By default it is set to “…”.

Raises:

ValueError – If the max_iter is not greater than or equal to one.

Return type:

None

init_options_grammar(algo_name)

Initialize the options’ grammar.

Parameters:

algo_name (str) – The name of the algorithm.

Return type:

JSONGrammar

is_algo_requires_grad(algo_name)

Returns True if the algorithm requires a gradient evaluation.

Parameters:

algo_name – The name of the algorithm.

classmethod is_algorithm_suited(algorithm_description, problem)

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

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

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

Returns:

Whether the algorithm is suited to the problem.

Return type:

bool

new_iteration_callback(x_vect=None)

Callback called at each new iteration, i.e. every time a design vector that is not already in the database is proposed by the optimizer.

Iterate the progress bar, implement the stop criteria.

Parameters:

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

Raises:

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

Return type:

None

COMPLEX_STEP_METHOD = 'complex_step'
DESIGN_ALGO_NAME = 'Design algorithm'
DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']
DIMENSION = 'dimension'
EQ_TOLERANCE = 'eq_tolerance'
EVAL_JAC = 'eval_jac'
EVAL_OBS_JAC_OPTION = 'eval_obs_jac'
FINITE_DIFF_METHOD = 'finite_differences'
INEQ_TOLERANCE = 'ineq_tolerance'
LEVEL_KEYWORD = 'levels'
LIBRARY_NAME: ClassVar[str | None] = None

The name of the interfaced library.

MAX_DS_SIZE_PRINT = 40
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.

PHIP_CRITERIA = 'phi^p'
ROUND_INTS_OPTION = 'round_ints'
SAMPLES_TAG = 'samples'
SEED = 'seed'
USER_DEFINED_GRADIENT = 'user'
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.

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 | None

The input samples.

seed: int

The seed to be used for replicability reasons.

It increments with each generation of samples so that repeating the generation of sets of \(N\) leads to different sets.

unit_samples: ndarray | None

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