"""Contains the implementations for the optimisation methods
Implements the supported optimisation methods.
"""
from collections.abc import Callable
from pydantic import Field
from ..base import CalibrationMethodBase, CalibrationWorkflowBase
from ..data_model import CalibrationModel
TASK = "optimisation"
BASE_IMPLEMENTATIONS: dict[str, str] = dict(
optuna=f"calisim.{TASK}.optuna_wrapper:OptunaOptimisation",
emukit=f"calisim.{TASK}.emukit_wrapper:EmukitOptimisation",
openturns=f"calisim.{TASK}.openturns_wrapper:OpenTurnsOptimisation",
botorch=f"calisim.{TASK}.botorch_wrapper:BoTorchOptimisation",
)
[docs]
def get_implementations() -> dict[str, str]:
"""Get the calibration implementations for optimisation.
Returns:
Dict[str, type[CalibrationWorkflowBase]]:
The dictionary of calibration implementations for optimisation.
"""
implementations = dict(BASE_IMPLEMENTATIONS)
implementations.update(CalibrationMethodBase.load_external_implementations(TASK))
return implementations
[docs]
class OptimisationMethodModel(CalibrationModel):
"""The optimisation method data model.
Args:
BaseModel (CalibrationModel): The calibration base model class.
"""
directions: list[str] | None = Field(
description="The list of objective directions", default=["minimize"]
)
acquisition_func: str | None = Field(
description="The acquisition function for Bayesian optimisation", default="ei"
)
use_saasbo: bool = Field(
description="Enable Sparse Axis-Aligned Subspace Bayesian Optimization",
default=False,
)
[docs]
class OptimisationMethod(CalibrationMethodBase):
"""The optimisation method class."""
def __init__(
self,
calibration_func: Callable,
specification: OptimisationMethodModel,
engine: str = "optuna",
implementation: type[CalibrationWorkflowBase]
| CalibrationWorkflowBase
| None = None,
) -> None:
"""OptimisationMethod constructor.
Args:
calibration_func (Callable): The calibration function.
For example, a simulation function or objective function.
specification (OptimisationMethodModel): The calibration
specification.
engine (str, optional): The optimisation backend.
Defaults to "optuna".
implementation (type[CalibrationWorkflowBase] | CalibrationWorkflowBase
| None): The calibration workflow implementation.
"""
super().__init__(
calibration_func,
specification,
TASK,
engine,
get_implementations(),
implementation,
)