Evaluating custom candidates

[git627]

After creating my generation strategy and experiment, I'm able to receive the next recommended parameter values via get_next_trial. I've been trying to understand how candidates are being generated under the hood, and from my search it appears to use optimize_acqf from BoTorch (although correct me if I'm wrong). My question is: let's say I have some experimental design with predetermined candidates, and I want to evaluate only those candidates for potential selection. In BoTorch this is as simple as building an acquisition function over a trained model or models, and then evaluating the acquisition function over all the candidates. However, I'm having trouble finding a way to do this in the Ax framework. Is this currently supported? Thanks for your help.

[git627]

After seeing another issue posted here, I realized that the Torch Model Bridge has an evaluate_acquisition_function method, so my question is answered now. Thanks again

[git627]

Issue closed