"""Contains the implementations for the surrogate modelling methods
Implements the supported surrogate modelling methods.
"""
from collections.abc import Callable
from pydantic import Field
from ..base import CalibrationMethodBase, CalibrationWorkflowBase
from ..data_model import CalibrationModel
from .gpytorch_wrapper import GPyTorchSurrogateModel
from .sklearn_wrapper import SklearnSurrogateModel
TASK = "surrogate_modelling"
IMPLEMENTATIONS: dict[str, type[CalibrationWorkflowBase]] = dict(
gpytorch=GPyTorchSurrogateModel, sklearn=SklearnSurrogateModel
)
[docs]
def get_implementations() -> dict[str, type[CalibrationWorkflowBase]]:
"""Get the calibration implementations for surrogate modelling.
Returns:
Dict[str, type[CalibrationWorkflowBase]]: The dictionary of
calibration implementations for surrogate modelling.
"""
return IMPLEMENTATIONS
[docs]
class SurrogateModelMethodModel(CalibrationModel):
"""The surrogate modelling method data model.
Args:
BaseModel (CalibrationModel): The calibration base model class.
"""
batch_size: int = Field(
description="The batch size when training the surrogate model", default=1000
)
flatten_Y: bool = Field(description="Flatten the simulation outputs", default=False)
[docs]
class SurrogateModelMethod(CalibrationMethodBase):
"""The surrogate modelling method class."""
def __init__(
self,
calibration_func: Callable,
specification: SurrogateModelMethodModel,
engine: str = "sklearn",
implementation: CalibrationWorkflowBase | None = None,
) -> None:
"""SurrogateModelMethod constructor.
Args:
calibration_func (Callable): The calibration function.
For example, a simulation function or objective function.
specification (SurrogateModelMethodModel): The calibration
specification.
engine (str, optional): The surrogate modelling backend.
Defaults to "sklearn".
implementation (CalibrationWorkflowBase | None): The
calibration workflow implementation.
"""
super().__init__(
calibration_func,
specification,
TASK,
engine,
IMPLEMENTATIONS,
implementation,
)