gemseo / algos / doe

lib_pydoe module

PyDOE algorithms wrapper.

Classes:

PyDOE()

PyDOE optimization library interface See DOELibrary.

Functions:

set_seed

seed(self, seed=None)

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

Bases: gemseo.algos.doe.doe_lib.DOELibrary

PyDOE optimization library interface See DOELibrary.

Return type

None

lib_dict

The properties of the algorithm in terms of requirements on the properties of the problem to be solved.

Type

Dict[str, Dict[str, Union[str, bool]]]

algo_name

The name of the algorithm used currently.

Type

str

internal_algo_name

The name of the algorithm used currently as defined in the used library.

Type

str

problem

The problem to be solved.

Type

Any

Constructor Abstract class.

Attributes:

ALGO_LIST

ALPHA_KEYWORD

CENTER_BB_KEYWORD

CENTER_CC_KEYWORD

COMPLEX_STEP_METHOD

CRITERION_KEYWORD

DESCRIPTION

DESC_LIST

DESIGN_ALGO_NAME

DIFFERENTIATION_METHODS

DIMENSION

EQ_TOLERANCE

EVAL_JAC

FACE_KEYWORD

FINITE_DIFF_METHOD

HANDLE_EQ_CONS

HANDLE_INEQ_CONS

INEQ_TOLERANCE

INTERNAL_NAME

ITERATION_KEYWORD

LEVEL_KEYWORD

LIB

MAX_DS_SIZE_PRINT

MAX_TIME

MIN_DIMS

NORMALIZE_DESIGN_SPACE_OPTION

N_PROCESSES

N_SAMPLES

OPTIONS_DIR

OPTIONS_MAP

PHIP_CRITERIA

POSITIVE_CONSTRAINTS

PROBLEM_TYPE

PYDOE_2LEVELFACT

PYDOE_2LEVELFACT_DESC

PYDOE_2LEVELFACT_WEB

PYDOE_BBDESIGN

PYDOE_BBDESIGN_DESC

PYDOE_BBDESIGN_WEB

PYDOE_CCDESIGN

PYDOE_CCDESIGN_DESC

PYDOE_CCDESIGN_WEB

PYDOE_DOC

PYDOE_FULLFACT

PYDOE_FULLFACT_DESC

PYDOE_FULLFACT_WEB

PYDOE_LHS

PYDOE_LHS_DESC

PYDOE_LHS_WEB

PYDOE_PBDESIGN

PYDOE_PBDESIGN_DESC

PYDOE_PBDESIGN_WEB

REQUIRE_GRAD

ROUND_INTS_OPTION

SAMPLES_TAG

SEED

USER_DEFINED_GRADIENT

USE_DATABASE_OPTION

WAIT_TIME_BETWEEN_SAMPLES

WEBSITE

WEB_LIST

algorithms

The available algorithms.

Methods:

compute_doe(variables_space[, size, ...])

Compute a design of experiments (DOE) in a variables space.

compute_phip_criteria(samples[, power])

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

deactivate_progress_bar()

Deactivate the progress bar.

driver_has_option(option_key)

Check if the option key exists.

ensure_bounds(orig_func[, normalize])

Project the design vector onto the design space before execution.

evaluate_samples([eval_jac, n_processes, ...])

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

execute(problem[, algo_name])

Executes the driver.

export_samples(doe_output_file)

Export samples generated by DOE library to a csv file.

filter_adapted_algorithms(problem)

Filter the algorithms capable of solving the problem.

finalize_iter_observer()

Finalize the iteration observer.

get_optimum_from_database([message, status])

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.

init_iter_observer(max_iter, message)

Initialize the iteration observer.

init_options_grammar(algo_name)

Initialize the options grammar.

is_algo_requires_grad(algo_name)

Returns True if the algorithm requires a gradient evaluation.

is_algorithm_suited(algo_charact, problem)

Check if the algorithm is suited to the problem according to its characteristics.

new_iteration_callback([x_vect])

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.

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'
DESCRIPTION = 'description'
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'
FACE_KEYWORD = 'face'
FINITE_DIFF_METHOD = 'finite_differences'
HANDLE_EQ_CONS = 'handle_equality_constraints'
HANDLE_INEQ_CONS = 'handle_inequality_constraints'
INEQ_TOLERANCE = 'ineq_tolerance'
INTERNAL_NAME = 'internal_algo_name'
ITERATION_KEYWORD = 'iterations'
LEVEL_KEYWORD = 'levels'
LIB = 'lib'
MAX_DS_SIZE_PRINT = 40
MAX_TIME = 'max_time'
MIN_DIMS = 'min_dims'
NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'
N_PROCESSES = 'n_processes'
N_SAMPLES = 'n_samples'
OPTIONS_DIR = 'options'
OPTIONS_MAP = {}
PHIP_CRITERIA = 'phi^p'
POSITIVE_CONSTRAINTS = 'positive_constraints'
PROBLEM_TYPE = 'problem_type'
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'
REQUIRE_GRAD = 'require_grad'
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'
WEBSITE = 'website'
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']
property algorithms

The available algorithms.

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

Compute a design of experiments (DOE) in a variables space.

Parameters
  • variables_space (gemseo.algos.design_space.DesignSpace) – The variables space to be sampled.

  • size (Optional[int]) –

    The size of the DOE. If None, the size is deduced from the options.

    By default it is set to None.

  • unit_sampling (bool) –

    Whether to sample in the unit hypercube.

    By default it is set to False.

  • **options (Union[str, float, int, bool, List[str], numpy.ndarray]) – The options of the DOE algorithm.

Returns

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

Return type

numpy.ndarray

static compute_phip_criteria(samples, power=10.0)

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

See Morris & Mitchell, Exploratory designs for computational experiments, 1995.

Parameters
  • samples – design variables list

  • power

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

    By default it is set to 10.0.

deactivate_progress_bar()

Deactivate the progress bar.

Return type

None

driver_has_option(option_key)

Check if the option key exists.

Parameters

option_key (str) – The name of the option.

Returns

Whether the option is in the grammar.

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

    if True, use the normalized design space

    By default it is set to True.

Returns

the wrapped function

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

    Whether to evaluate the jacobian.

    By default it is set to False.

  • n_processes

    The number of processes used to evaluate the samples.

    By default it is set to 1.

  • wait_time_between_samples

    The time to wait between each sample evaluation, in seconds.

    By default it is set to 0.0.

execute(problem, algo_name=None, **options)

Executes the driver.

Parameters
  • problem – the problem to be solved

  • algo_name

    name of the algorithm if None, use self.algo_name which may have been set by the factory (Default value = None)

    By default it is set to None.

  • options – the options dict for the algorithm

export_samples(doe_output_file)

Export samples generated by DOE library to a csv file.

Parameters

doe_output_file (string) – export file name

filter_adapted_algorithms(problem)

Filter the algorithms capable of solving the problem.

Parameters

problem (Any) – The opt_problem to be solved.

Returns

The list of adapted algorithms names.

Return type

bool

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.

Parameters
  • message

    Default value = None)

    By default it is set to None.

  • status

    Default value = None)

    By default it is set to None.

get_x0_and_bounds_vects(normalize_ds)

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

Parameters

normalize_ds – if True, normalizes all input vars that are not integers, according to design space normalization policy

Returns

x, lower bounds, 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.

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

gemseo.core.grammars.json_grammar.JSONGrammar

is_algo_requires_grad(algo_name)

Returns True if the algorithm requires a gradient evaluation.

Parameters

algo_name – name of the algorithm

static is_algorithm_suited(algo_charact, problem)[source]

Check if the algorithm is suited to the problem according to its characteristics.

Parameters
  • algo_charact (Mapping[str, DOELibrary.DOELibraryOptionType]) – The algorithm characteristics.

  • problem (OptimizationProblem) – The optimization 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 (Optional[numpy.ndarray]) –

The design variables values. If None, use the values of the last iteration.

By default it is set to None.

Raises

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

Return type

None

gemseo.algos.doe.lib_pydoe.set_seed()

seed(self, seed=None)

Reseed a legacy MT19937 BitGenerator

Notes

This is a convenience, legacy function.

The best practice is to not reseed a BitGenerator, rather to recreate a new one. This method is here for legacy reasons. This example demonstrates best practice.

>>> from numpy.random import MT19937
>>> from numpy.random import RandomState, SeedSequence
>>> rs = RandomState(MT19937(SeedSequence(123456789)))
# Later, you want to restart the stream
>>> rs = RandomState(MT19937(SeedSequence(987654321)))