def get_GPKG(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
cost_intercept: float = 0.01,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
transforms: List[Type[Transform]] = Cont_X_trans + Y_trans,
transform_configs: Optional[Dict[str, TConfig]] = None,
**kwargs: Any,
) -> TorchModelBridge:
"""Instantiates a GP model that generates points with KG."""
if search_space is None:
search_space = experiment.search_space
if data.df.empty: # pragma: no cover
raise ValueError("GP+KG BotorchModel requires non-empty data.")
inputs = {
"search_space": search_space,
"experiment": experiment,
"data": data,
"cost_intercept": cost_intercept,
"torch_dtype": dtype,
"torch_device": device,
"transforms": transforms,
"transform_configs": transform_configs,
}
if any(p.is_fidelity for k, p in experiment.parameters.items()):
inputs["linear_truncated"] = kwargs.get("linear_truncated", True)
return checked_cast(TorchModelBridge, Models.GPKG(**inputs)) # pyre-ignore: [16]
def get_GPKG(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
cost_intercept: float = 0.01,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
transforms: List[Type[Transform]] = Cont_X_trans + Y_trans,
winsorization_limits: Optional[Tuple[Optional[float],
Optional[float]]] = None,
**kwargs: Any,
) -> TorchModelBridge:
"""Instantiates a GP model that generates points with KG."""
if search_space is None:
search_space = experiment.search_space
if data.df.empty: # pragma: no cover
raise ValueError("GP+KG BotorchModel requires non-empty data.")
transform_configs = {}
if winsorization_limits is not None:
transform_configs["Winsorize"] = {
"winsorization_lower": winsorization_limits[0] or 0.0,
"winsorization_upper": winsorization_limits[1] or 0.0,
}
return checked_cast(
TorchModelBridge,
Models.GPKG(
search_space=search_space,
experiment=experiment,
data=data,
cost_intercept=cost_intercept,
linear_truncated=kwargs.get("linear_truncated", True),
torch_dtype=dtype,
torch_device=device,
transforms=transforms,
transform_configs=transform_configs,
),
)
