gemseo / algos / doe

lib_pydoe module¶

PyDOE algorithms wrapper.

class gemseo.algos.doe.lib_pydoe.PyDOE[source]

Bases: DOELibrary

PyDOE optimization library interface See DOELibrary.

class ApproximationMode(value)

Bases: StrEnum

The approximation derivation modes.

CENTERED_DIFFERENCES = 'centered_differences'

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

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.

CENTERED_DIFFERENCES = 'centered_differences'
COMPLEX_STEP = 'complex_step'
FINITE_DIFFERENCES = 'finite_differences'
clear_listeners()

Remove the listeners from the database.

Return type:

None

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

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

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 (bool) –

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, use_database=True, callbacks=())

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.

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 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)

Return the optimization result from the database.

Return type:

OptimizationResult

get_x0_and_bounds_vects(normalize_ds, as_dict=False)

Return the initial design variable values and their lower and upper bounds.

Parameters:
• normalize_ds (bool) – Whether to normalize the design variables.

• as_dict (bool) –

Whether to return dictionaries instead of NumPy arrays.

By default it is set to False.

Returns:

The initial values of the design variables, their lower bounds, and their upper bounds.

Return type:

tuple[ndarray, ndarray, ndarray] | tuple[dict[str, ndarray], dict[str, ndarray], dict[str, ndarray]]

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 of the progress bar status.

By default it is set to “”.

Raises:

ValueError – If max_iter is lower than 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

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)

Iterate the progress bar, implement the stop criteria.

Parameters:

x_vect (ndarray) – The design variables values.

Raises:

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

Return type:

None

Check if a driver requires the gradient.

Parameters:

driver_name (str) – The name of the driver.

Returns:

Whether the driver requires the gradient.

Return type:

bool

ALGO_LIST: ClassVar[list[str]] = ['fullfact', 'ff2n', 'pbdesign', 'bbdesign', 'ccdesign', 'lhs']
ALPHA_KEYWORD = 'alpha'
CENTER_BB_KEYWORD = 'center_bb'
CENTER_CC_KEYWORD = 'center_cc'
CRITERION_KEYWORD = 'criterion'
DESC_LIST: ClassVar[list[str]] = ['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'
DIMENSION = 'dimension'
EQ_TOLERANCE = 'eq_tolerance'
EVAL_JAC = 'eval_jac'
EVAL_OBS_JAC_OPTION = 'eval_obs_jac'
FACE_KEYWORD = 'face'
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: ClassVar[str | Path] = 'options'

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

OPTIONS_MAP: ClassVar[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'
USE_DATABASE_OPTION = 'use_database'
USE_ONE_LINE_PROGRESS_BAR: ClassVar[bool] = False

Whether to use a one line progress bar.

WAIT_TIME_BETWEEN_SAMPLES = 'wait_time_between_samples'
WEB_LIST: ClassVar[list[str]] = ['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: 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.