From c262ce8c48a4d84fd50fdf638a83a1edf098304e Mon Sep 17 00:00:00 2001 From: AdrianSosic Date: Tue, 13 Aug 2024 09:14:14 +0200 Subject: [PATCH] Import botorch.acquisition as bo_acqf --- baybe/acquisition/base.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/baybe/acquisition/base.py b/baybe/acquisition/base.py index df07bc985..664061f70 100644 --- a/baybe/acquisition/base.py +++ b/baybe/acquisition/base.py @@ -57,7 +57,7 @@ def to_botorch( The required structure of `measurements` is specified in :meth:`baybe.recommenders.base.RecommenderProtocol.recommend`. """ - import botorch.acquisition as bacqf + import botorch.acquisition as bo_acqf import torch from botorch.acquisition.objective import LinearMCObjective @@ -66,7 +66,7 @@ def to_botorch( train_y = objective.transform(measurements) # Retrieve corresponding botorch class - acqf_cls = getattr(bacqf, self.__class__.__name__) + acqf_cls = getattr(bo_acqf, self.__class__.__name__) # Match relevant attributes params_dict = match_attributes( @@ -91,9 +91,9 @@ def to_botorch( if "best_f" in signature_params: additional_params["best_f"] = train_y.min().item() - if issubclass(acqf_cls, bacqf.AnalyticAcquisitionFunction): + if issubclass(acqf_cls, bo_acqf.AnalyticAcquisitionFunction): additional_params["maximize"] = False - elif issubclass(acqf_cls, bacqf.MCAcquisitionFunction): + elif issubclass(acqf_cls, bo_acqf.MCAcquisitionFunction): additional_params["objective"] = LinearMCObjective( torch.tensor([-1.0]) )