Comments (5)
If these other parameters are fixed for all trials, can they be encoded directly in the evaluation_function itself rather than passed as arguments? You could even write a wrapper function which takes the fixed params as inputs and outputs an evaluation_function with them encoded inside.
If by argparse you mean an easy to use CLI, that is something we've discussed supporting and would be interested to know how you're using it with CLI.
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Thanks, the wrapper function worked.
And yes this was for CLI. I'm trying to do Bayesian optimization on a pre-existing library which uses CLI to define the model and I was trying to integrate ax with minimal changes to the existing code.
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That kind of helps but not fully.
I can partially use that to convert an argparse object to a list of fixed parameters, however, there are some types that can't be converted, for example, there isn't a ParameterType for list (which is not being used as a range).
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Ah so not invoking Ax via CLI. Glad this solution worked for you.
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Related Issues (20)
- Ax pulling numpy 2.0 with breaking changes HOT 3
- How to set the beta coefficient of generation strategy? HOT 6
- Numpy 2.0 Compatibility Issue HOT 1
- save the state HOT 9
- Defining a Model class for ModelListGP HOT 2
- Error : Try again with more data HOT 26
- Using nonlinear constraints with boolean masks HOT 5
- input data is not standardized (mean = tensor([0.] HOT 9
- nonlinear constarined in evaluate paramter instead add to the experiment HOT 2
- Ax 0.4.0 Causing Segmentation Fault When Calling `.get_next_trial()` HOT 2
- Defining Metric in Ax Service HOT 2
- Hierarchical Search Spaces with Multiple Independent Search Spaces HOT 3
- Services API :×1-1.5*×2>=0 HOT 4
- primary_objective and secondary_objective HOT 1
- Comparison of multi-objective acquistion functions HOT 14
- plot_pareto_frontier HOT 6
- attach trials HOT 8
- Multi-task BO with Service API HOT 2
- constrains HOT 17
- Question : Generation 12 trials HOT 8
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