aryandeshwal / bodi Goto Github PK

Hi Aryan,
I am working on a research project dealing with a categorical optimization. I came across your paper and it is very interesting. I want to run it on some of problems with different number of possible categorical values per dimension.However, I noticed in your code on pest control branch, all categorical variable only takes ncatgs values. I am trying to generalize this so that categorical variables can potentially come from sets with different cardinalities (tau_i !=tau_j). Looking deeper into your code, I realized that I need to change the function generate_random_basis_vector(), where you only have one parameter ncatgs unlike Algorithm 5 in your paper wherein you consider different {tau_1…,tau_N}. Moreover, the python code for the function deviates from the pseudo code of Algorithm 5 quite a lot. I don’t see any obvious way to change the code to enable desired functionality. If you could help me understand how the code of the function works, I can make the desired change. Can you please help me figure this out?
Thanks
Muddassar

Hello,

I am currently dealing with a black-box problem that involves numbers of binary variables and categorical variables (5 categories). I believe that the proposed BODi has great potential for application to this high-dimensional problem. However, the experiments and code only cover three scenarios: problems with only binary variables, only categorical variables, and both binary and continuous variables. How can this method be extended to problems that involve both binary and categorical variables?

Additionally, I noticed that when solving for $z_{j} = arg max_{z∈Z} \alpha (\mathcal{M} (\phi_{ A} (z)))$ in the discrete space of $Z$, optimize.py provides two functions, i.e., optimize_acqf_binary_local_search and optimize_acqf_categorical_local_search, for binary variables and categorical variables, respectively. If I want to apply it to a problem that involves mixed binary and categorical variables, can I directly replace these with the botorch.optim.optimize.optimize_acqf_discrete_local_search from the BOTORCH package since this function uses discrete_choices parameter for each dimension?

Thanks

Hello,

I have some question regarding categorical variables. Your paper for BODi shows some experiment results of the pest control probelm, but processing of non-binary categorical variables is marked as "TODO" in bodi/run_experiment.py. How did you run experiment on pest control problem with BODi? Did you use binary encoding or other encoding methods for categorical variables?