lib_pseven module¶
Wrapper for the Generic Tool for Optimization (GTOpt) of pSeven Core.
- class gemseo.algos.opt.lib_pseven.PSevenAlgorithmDescription(algorithm_name, internal_algorithm_name, library_name='pSeven', description='', website='https://datadvance.net/product/pseven/manual/', handle_integer_variables=False, require_gradient=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, problem_type='non-linear')[source]¶
Bases:
gemseo.algos.opt.opt_lib.OptimizationAlgorithmDescription
The description of an optimization algorithm from the NLopt library.
- Parameters
algorithm_name (str) –
internal_algorithm_name (str) –
library_name (str) –
By default it is set to pSeven.
description (str) –
By default it is set to .
website (str) –
By default it is set to https://datadvance.net/product/pseven/manual/.
handle_integer_variables (bool) –
By default it is set to False.
require_gradient (bool) –
By default it is set to False.
handle_equality_constraints (bool) –
By default it is set to False.
handle_inequality_constraints (bool) –
By default it is set to False.
handle_multiobjective (bool) –
By default it is set to False.
positive_constraints (bool) –
By default it is set to False.
problem_type (str) –
By default it is set to non-linear.
- Return type
None
- handle_equality_constraints: bool = False¶
Whether the optimization algorithm handles equality constraints.
- handle_inequality_constraints: bool = False¶
Whether the optimization algorithm handles inequality constraints.
- handle_integer_variables: bool = False¶
Whether the optimization algorithm handles integer variables.
- handle_multiobjective: bool = False¶
Whether the optimization algorithm handles multiple objectives.
- positive_constraints: bool = False¶
Whether the optimization algorithm requires positive constraints.
- problem_type: str = 'non-linear'¶
The type of problem (see
OptimizationProblem.AVAILABLE_PB_TYPES
).
- class gemseo.algos.opt.lib_pseven.PSevenOpt[source]¶
Bases:
gemseo.algos.opt.opt_lib.OptimizationLibrary
Interface for the Generic Tool for Optimization (GTOpt) of pSeven Core.
- Return type
None
- algorithm_handles_eqcstr(algo_name)¶
Check if an algorithm handles equality constraints.
- algorithm_handles_ineqcstr(algo_name)¶
Check if an algorithm handles inequality constraints.
- deactivate_progress_bar()¶
Deactivate the progress bar.
- Return type
None
- driver_has_option(option_name)¶
Check the existence of an option.
- 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.
- 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.
By default it is set to None.
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
- filter_adapted_algorithms(problem)¶
Filter the algorithms capable of solving the problem.
- 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_right_sign_constraints()¶
Transforms the problem constraints into their opposite sign counterpart if the algorithm requires positive constraints.
- 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
- 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
- is_algo_requires_grad(algo_name)¶
Returns True if the algorithm requires a gradient evaluation.
- Parameters
algo_name – The name of the algorithm.
- is_algo_requires_positive_cstr(algo_name)¶
Check if an algorithm requires positive constraints.
- static is_algorithm_suited(algorithm_description, problem)¶
Check if the algorithm is suited to the problem according to its description.
- Parameters
algorithm_description (gemseo.algos.opt.opt_lib.OptimizationAlgorithmDescription) – The description of the algorithm.
problem (gemseo.algos.opt_problem.OptimizationProblem) – The problem to be solved.
- Returns
Whether the algorithm is suited to the problem.
- Return type
- new_iteration_callback(x_vect=None)¶
- Raises
FtolReached – If the defined relative or absolute function tolerance is reached.
XtolReached – If the defined relative or absolute x tolerance is reached.
- Parameters
x_vect (ndarray | None) –
By default it is set to None.
- Return type
None
- COMPLEX_STEP_METHOD = 'complex_step'¶
- DIFFERENTIATION_METHODS = ['user', 'complex_step', 'finite_differences']¶
- EQ_TOLERANCE = 'eq_tolerance'¶
- EVAL_OBS_JAC_OPTION = 'eval_obs_jac'¶
- FINITE_DIFF_METHOD = 'finite_differences'¶
- F_TOL_ABS = 'ftol_abs'¶
- F_TOL_REL = 'ftol_rel'¶
- INEQ_TOLERANCE = 'ineq_tolerance'¶
- LIB_COMPUTE_GRAD = True¶
- LS_STEP_NB_MAX = 'max_ls_step_nb'¶
- LS_STEP_SIZE_MAX = 'max_ls_step_size'¶
- MAX_DS_SIZE_PRINT = 40¶
- MAX_FUN_EVAL = 'max_fun_eval'¶
- MAX_ITER = 'max_iter'¶
- MAX_TIME = 'max_time'¶
- NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'¶
- OPTIONS_DIR: Final[str] = 'options'¶
The name of the directory containing the files of the grammars of the options.
- OPTIONS_MAP: dict[str, str] = {'constraints_smoothness': 'GTOpt/ConstraintsSmoothness', 'detect_nan_clusters': 'GTOpt/DetectNaNClusters', 'deterministic': 'GTOpt/Deterministic', 'diff_scheme': 'GTOpt/DiffScheme', 'diff_step': 'GTOpt/NumDiffStepSize', 'diff_type': 'GTOpt/DiffType', 'ensure_feasibility': 'GTOpt/EnsureFeasibility', 'global_phase_intensity': 'GTOpt/GlobalPhaseIntensity', 'local_search': 'GTOpt/LocalSearch', 'log_level': 'GTOpt/LogLevel', 'max_batch_size': 'GTOpt/BatchSize', 'max_expensive_func_iter': 'GTOpt/MaximumExpensiveIterations', 'max_func_iter': 'GTOpt/MaximumIterations', 'max_threads': 'GTOpt/MaxParallel', 'objectives_smoothness': 'GTOpt/ObjectivesSmoothness', 'responses_scalability': 'GTOpt/ResponsesScalability', 'restore_analytic_func': 'GTOpt/RestoreAnalyticResponses', 'seed': 'GTOpt/Seed', 'time_limit': 'GTOpt/TimeLimit', 'verbose_log': 'GTOpt/VerboseOutput'}¶
The names of the options in GEMSEO mapping to those in the wrapped library.
- PG_TOL = 'pg_tol'¶
- ROUND_INTS_OPTION = 'round_ints'¶
- STOP_CRIT_NX = 'stop_crit_n_x'¶
- USER_DEFINED_GRADIENT = 'user'¶
- USE_DATABASE_OPTION = 'use_database'¶
- VERBOSE = 'verbose'¶
- X_TOL_ABS = 'xtol_abs'¶
- X_TOL_REL = 'xtol_rel'¶
- activate_progress_bar: ClassVar[bool] = True¶
Whether to activate the progress bar in the optimization log.
- 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.