baybe.recommenders.pure.bayesian.botorch.hybrid.recommend_hybrid_without_subsets

baybe.recommenders.pure.bayesian.botorch.hybrid.recommend_hybrid_without_subsets(recommender: BotorchRecommender, searchspace: SearchSpace, candidates_exp: DataFrame, batch_size: int)[source]

Recommend points using the optimize_acqf_mixed function of BoTorch.

This functions samples points from the discrete subspace, performs optimization in the continuous subspace with these points being fixed and returns the best found solution.

Important: This performs a brute-force calculation by fixing every possible assignment of discrete variables and optimizing the continuous subspace for each of them. It is thus computationally expensive.

Note: This function implicitly assumes that discrete search space parts in the respective data frame come first and continuous parts come second.

Parameters:

  • recommender (BotorchRecommender) – The recommender instance.

  • searchspace (SearchSpace) – The search space in which the recommendations should be made.

  • candidates_exp (DataFrame) – The experimental representation of the candidates of the discrete subspace.

  • batch_size (int) – The size of the calculated batch.

Raises:

IncompatibleAcquisitionFunctionError – If a non-Monte Carlo acquisition function is used with a batch size > 1.

Return type:

DataFrame

Returns:

The recommended points.