poli.objective_repository.toy_continuous_problem.register.ToyContinuousBlackBox#
- class poli.objective_repository.toy_continuous_problem.register.ToyContinuousBlackBox(function_name: str, n_dimensions: int = 2, embed_in: Optional[int] = None, dimensions_to_embed_in: Optional[List[int]] = None, batch_size: Optional[int] = None, parallelize: bool = False, num_workers: Optional[int] = None, evaluation_budget: int = inf)#
A black box implementation for evaluating the Toy Continuous Problem.
- Parameters
function_name (str) – The name of the toy continuous function to evaluate, by default None.
n_dimensions (int) – The number of dimensions for the toy continuous function, by default 2.
embed_in (int, optional) – If not None, the continuous problem is randomly embedded in this dimension. By default, None.
dimensions_to_embed_in (List[int], optional) – The dimensions in which to embed the problem, by default None. Only has an effect if embed_in is not None.
batch_size (int, optional) – The batch size for parallel evaluation, by default None.
parallelize (bool, optional) – Whether to parallelize the evaluation, by default False.
num_workers (int, optional) – The number of workers for parallel evaluation, by default None.
evaluation_budget (int, optional) – The maximum number of evaluations, by default float(“inf”).
- function_name#
The name of the toy continuous function.
- Type
str
- n_dimensions#
The number of dimensions for the toy continuous function.
- Type
int
- embed_in#
The dimension in which to embed the problem.
- Type
int
- function#
The toy continuous problem instance.
- Type
- bounds#
The lower and upper bounds for the toy continuous problem.
- Type
Tuple[np.ndarray, np.ndarray]
- _black_box(x, context=None)#
Evaluates the toy continuous problem on a continuous input x.
- __init__(function_name: str, n_dimensions: int = 2, embed_in: Optional[int] = None, dimensions_to_embed_in: Optional[List[int]] = None, batch_size: Optional[int] = None, parallelize: bool = False, num_workers: Optional[int] = None, evaluation_budget: int = inf)#
Initialize the AbstractBlackBox object.
- Parameters
batch_size (int, optional) – The batch size for parallel execution, by default None.
parallelize (bool, optional) – Flag indicating whether to parallelize the execution, by default False.
num_workers (int, optional) – The number of workers for parallel execution, by default we use half the available CPUs.
evaluation_budget (int, optional) – The maximum number of evaluations allowed for the black box function, by default float(“inf”).
Methods
__init__(function_name[, n_dimensions, ...])Initialize the AbstractBlackBox object.
reset_evaluation_budget()Resets the evaluation budget by setting the number of evaluations made to 0.
set_observer(observer)Set the observer object for recording observations during evaluation.
terminate()Terminate the black box optimization problem.