driver_library module¶
Driver library.
A driver library aims to solve an OptimizationProblem
using a particular algorithm from a particular family of numerical methods.
This algorithm will be in charge of evaluating the objective and constraints
functions at different points of the design space, using the
DriverLibrary.execute()
method.
The most famous kinds of numerical methods to solve an optimization problem
are optimization algorithms and design of experiments (DOE). A DOE driver
browses the design space agnostically, i.e. without taking into
account the function evaluations. On the contrary, an optimization algorithm
uses this information to make the journey through design space
as relevant as possible in order to reach as soon as possible the optimum.
These families are implemented in DOELibrary
and OptimizationLibrary
.
- class gemseo.algos.driver_library.DriverDescription(algorithm_name, internal_algorithm_name, library_name='', description='', website='', handle_integer_variables=False, require_gradient=False)[source]
Bases:
AlgorithmDescription
The description of a driver.
- Parameters:
algorithm_name (str) –
internal_algorithm_name (str) –
library_name (str) –
By default it is set to “”.
description (str) –
By default it is set to “”.
website (str) –
By default it is set to “”.
handle_integer_variables (bool) –
By default it is set to False.
require_gradient (bool) –
By default it is set to False.
- algorithm_name: str
The name of the algorithm in GEMSEO.
- handle_integer_variables: bool = False
Whether the optimization algorithm handles integer variables.
- internal_algorithm_name: str
The name of the algorithm in the wrapped library.
- require_gradient: bool = False
Whether the optimization algorithm requires the gradient.
- class gemseo.algos.driver_library.DriverLibrary[source]
Bases:
AlgorithmLibrary
Abstract class for library interfaces.
Lists available methods in the library for the proposed problem to be solved.
To integrate an optimization package, inherit from this class and put your file in gemseo.algos.doe or gemseo.algo.opt packages.
- 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'
- USER_GRAD = 'user'
- deactivate_progress_bar()[source]
Deactivate the progress bar.
- Return type:
None
- ensure_bounds(orig_func, normalize=True)[source]
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.
- execute(problem, algo_name=None, eval_obs_jac=False, skip_int_check=False, **options)[source]
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:
- finalize_iter_observer()[source]
Finalize the iteration observer.
- Return type:
None
- get_optimum_from_database(message=None, status=None)[source]
Retrieve the optimum from the database and build an optimization.
- Return type:
- get_x0_and_bounds_vects(normalize_ds: bool, as_dict: Literal[False] = False) tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray] [source]
- get_x0_and_bounds_vects(normalize_ds: bool, as_dict: Literal[True] = False) tuple[dict[str, numpy.ndarray], dict[str, numpy.ndarray], dict[str, numpy.ndarray]]
Return the initial design variable values and their lower and upper bounds.
- Parameters:
normalize_ds – Whether to normalize the design variables.
as_dict – Whether to return dictionaries instead of NumPy arrays.
- Returns:
The initial values of the design variables, their lower bounds, and their upper bounds.
- init_iter_observer(max_iter, message='...')[source]
Initialize the iteration observer.
It will handle the stopping criterion and the logging of the progress bar.
- Parameters:
- Raises:
ValueError – If
max_iter
is lower than one.- Return type:
None
- new_iteration_callback(x_vect=None)[source]
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
- requires_gradient(driver_name)[source]
Check if a driver requires the gradient.
- EQ_TOLERANCE = 'eq_tolerance'
- EVAL_OBS_JAC_OPTION = 'eval_obs_jac'
- INEQ_TOLERANCE = 'ineq_tolerance'
- MAX_DS_SIZE_PRINT = 40
- MAX_TIME = 'max_time'
- NORMALIZE_DESIGN_SPACE_OPTION = 'normalize_design_space'
- ROUND_INTS_OPTION = 'round_ints'
- USE_DATABASE_OPTION = 'use_database'
- USE_ONE_LINE_PROGRESS_BAR: ClassVar[bool] = False
Whether to use a one line progress bar.
- 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.
- problem: Any | None
The problem to be solved.