gemseo / algos / doe

lib_pydoe module

PyDOE algorithms wrapper.

class gemseo.algos.doe.lib_pydoe.PyDOE

Bases: DOELibrary

PyDOE optimization library interface See DOELibrary.

Constructor Abstract class.

compute_doe(variables_space, size=None, unit_sampling=False, **options)

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)

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)

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)

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)

Retrieve the optimum from the database and build an optimization.

get_x0_and_bounds_vects(normalize_ds)

Return x0 and bounds.

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)

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

ALGO_LIST = ['fullfact', 'ff2n', 'pbdesign', 'bbdesign', 'ccdesign', 'lhs']

ALPHA_KEYWORD = 'alpha'

CENTER_BB_KEYWORD = 'center_bb'

CENTER_CC_KEYWORD = 'center_cc'

COMPLEX_STEP_METHOD = 'complex_step'

CRITERION_KEYWORD = 'criterion'

DESC_LIST = ['Full-Factorial implemented in pyDOE', '2-Level Full-Factorial implemented in pyDOE', 'Plackett-Burman design implemented in pyDOE', 'Box-Behnken design implemented in pyDOE', 'Central Composite implemented in pyDOE', 'Latin Hypercube Sampling implemented in pyDOE']

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'

FACE_KEYWORD = 'face'

FINITE_DIFF_METHOD = 'finite_differences'

INEQ_TOLERANCE = 'ineq_tolerance'

ITERATION_KEYWORD = 'iterations'

LEVEL_KEYWORD = 'levels'

LIBRARY_NAME: ClassVar[str | None] = 'PyDOE'

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'

PYDOE_2LEVELFACT = 'ff2n'

PYDOE_2LEVELFACT_DESC = '2-Level Full-Factorial implemented in pyDOE'

PYDOE_2LEVELFACT_WEB = 'https://pythonhosted.org/pyDOE/factorial.html#level-full-factorial'

PYDOE_BBDESIGN = 'bbdesign'

PYDOE_BBDESIGN_DESC = 'Box-Behnken design implemented in pyDOE'

PYDOE_BBDESIGN_WEB = 'https://pythonhosted.org/pyDOE/rsm.html#box-behnken'

PYDOE_CCDESIGN = 'ccdesign'

PYDOE_CCDESIGN_DESC = 'Central Composite implemented in pyDOE'

PYDOE_CCDESIGN_WEB = 'https://pythonhosted.org/pyDOE/rsm.html#central-composite'

PYDOE_DOC = 'https://pythonhosted.org/pyDOE/'

PYDOE_FULLFACT = 'fullfact'

PYDOE_FULLFACT_DESC = 'Full-Factorial implemented in pyDOE'

PYDOE_FULLFACT_WEB = 'https://pythonhosted.org/pyDOE/factorial.html#general-full-factorial'

PYDOE_LHS = 'lhs'

PYDOE_LHS_DESC = 'Latin Hypercube Sampling implemented in pyDOE'

PYDOE_LHS_WEB = 'https://pythonhosted.org/pyDOE/randomized.html#latin-hypercube'

PYDOE_PBDESIGN = 'pbdesign'

PYDOE_PBDESIGN_DESC = 'Plackett-Burman design implemented in pyDOE'

PYDOE_PBDESIGN_WEB = 'https://pythonhosted.org/pyDOE/factorial.html#plackett-burman'

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'

WEB_LIST = ['https://pythonhosted.org/pyDOE/factorial.html#general-full-factorial', 'https://pythonhosted.org/pyDOE/factorial.html#level-full-factorial', 'https://pythonhosted.org/pyDOE/factorial.html#plackett-burman', 'https://pythonhosted.org/pyDOE/rsm.html#box-behnken', 'https://pythonhosted.org/pyDOE/rsm.html#central-composite', 'https://pythonhosted.org/pyDOE/randomized.html#latin-hypercube']

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.

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

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