gemseo / algos / doe

lib_openturns module¶

OpenTURNS DOE algorithms.

Classes:

OpenTURNS()

Library of OpenTURNS DOE algorithms.

class gemseo.algos.doe.lib_openturns.OpenTURNS[source]¶

Bases: gemseo.algos.doe.doe_lib.DOELibrary

Library of OpenTURNS DOE algorithms.

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:

`ANNEALING`
`CENTER_KEYWORD`
`COMPLEX_STEP_METHOD`
`CRITERIA`
`CRITERION`
`DESCRIPTION`
`DESIGN_ALGO_NAME`
`DIFFERENTIATION_METHODS`
`DIMENSION`
`DOE_SETTINGS_OPTIONS`
`EQ_TOLERANCE`
`EVAL_JAC`
`FINITE_DIFF_METHOD`
`HANDLE_EQ_CONS`
`HANDLE_INEQ_CONS`
`INEQ_TOLERANCE`
`INTERNAL_NAME`
`LEVEL_KEYWORD`
`LIB`
`MAX_DS_SIZE_PRINT`
`MAX_TIME`
`MIN_DIMS`
`NORMALIZE_DESIGN_SPACE_OPTION`
`N_PROCESSES`
`N_REPLICATES`
`N_SAMPLES`
`OPTIONS_DIR`
`OPTIONS_MAP`
`OT_AXIAL`
`OT_COMPOSITE`
`OT_FACTORIAL`
`OT_FAURE`
`OT_FULLFACT`
`OT_HALTON`
`OT_HASEL`
`OT_LHS`
`OT_LHSC`
`OT_LHSO`
`OT_MC`
`OT_RANDOM`
`OT_REVERSE_HALTON`
`OT_SOBOL`
`OT_SOBOL_INDICES`
`PHIP_CRITERIA`
`POSITIVE_CONSTRAINTS`
`PROBLEM_TYPE`
`REQUIRE_GRAD`
`ROUND_INTS_OPTION`
`SAMPLES_TAG`
`SEED`
`TEMPERATURE`
`TEMPERATURES`
`USER_DEFINED_GRADIENT`
`USE_DATABASE_OPTION`
`WAIT_TIME_BETWEEN_SAMPLES`
`WEBSITE`
`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_dict, problem)	Checks if the algorithm is suited to the problem according to its algo dict.
`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.

ANNEALING = 'annealing'¶

CENTER_KEYWORD = 'centers'¶

COMPLEX_STEP_METHOD = 'complex_step'¶

CRITERIA = {'C2': <class 'openturns.weightedexperiment.SpaceFillingC2'>, 'MinDist': <class 'openturns.weightedexperiment.SpaceFillingMinDist'>, 'PhiP': <class 'openturns.weightedexperiment.SpaceFillingPhiP'>}¶

CRITERION = 'criterion'¶

DESCRIPTION = 'description'¶

DESIGN_ALGO_NAME = 'Design algorithm'¶

DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']¶

DIMENSION = 'dimension'¶

DOE_SETTINGS_OPTIONS = ['levels', 'centers']¶

EQ_TOLERANCE = 'eq_tolerance'¶

EVAL_JAC = 'eval_jac'¶

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'¶

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_REPLICATES = 'n_replicates'¶

N_SAMPLES = 'n_samples'¶

OPTIONS_DIR = 'options'¶

OPTIONS_MAP = {}¶

OT_AXIAL = 'OT_AXIAL'¶

OT_COMPOSITE = 'OT_COMPOSITE'¶

OT_FACTORIAL = 'OT_FACTORIAL'¶

OT_FAURE = 'OT_FAURE'¶

OT_FULLFACT = 'OT_FULLFACT'¶

OT_HALTON = 'OT_HALTON'¶

OT_HASEL = 'OT_HASELGROVE'¶

OT_LHS = 'OT_LHS'¶

OT_LHSC = 'OT_LHSC'¶

OT_LHSO = 'OT_OPT_LHS'¶

OT_MC = 'OT_MONTE_CARLO'¶

OT_RANDOM = 'OT_RANDOM'¶

OT_REVERSE_HALTON = 'OT_REVERSE_HALTON'¶

OT_SOBOL = 'OT_SOBOL'¶

OT_SOBOL_INDICES = 'OT_SOBOL_INDICES'¶

PHIP_CRITERIA = 'phi^p'¶

POSITIVE_CONSTRAINTS = 'positive_constraints'¶

PROBLEM_TYPE = 'problem_type'¶

REQUIRE_GRAD = 'require_grad'¶

ROUND_INTS_OPTION = 'round_ints'¶

SAMPLES_TAG = 'samples'¶

SEED = 'seed'¶

TEMPERATURE = 'temperature'¶

TEMPERATURES = {'Geometric': <class 'openturns.weightedexperiment.GeometricProfile'>, 'Linear': <class 'openturns.weightedexperiment.LinearProfile'>}¶

USER_DEFINED_GRADIENT = 'user'¶

USE_DATABASE_OPTION = 'use_database'¶

WAIT_TIME_BETWEEN_SAMPLES = 'wait_time_between_samples'¶

WEBSITE = 'website'¶

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_dict, problem)¶

Checks if the algorithm is suited to the problem according to its algo dict.

Parameters

algo_dict – the algorithm characteristics
problem – the opt_problem to be solved

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