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)
- Evaluating custom candidates HOT 2
- Input Feature Selection - Does the relevant code exist? HOT 6
- [Feature Request] support constraints on `ChoiceParameters` HOT 4
- Extending Models.THOMPSON with an extra parameter HOT 1
- There are some questions when i use the Ax HOT 7
- Space characters in the objective name AND specifying a threshold leads to an error message: "AssertionError: Outcome constraint should be of form `metric_name >= x" HOT 2
- Pandas deprecation warning when deserializing AxClient JSON HOT 2
- AX seems to get stuck with Ray
- `StandardizeY` transform requires non-empty data." when using SAASBO
- Plotting outside of a notebook HOT 1
- Setting search space step size in Ax Service API HOT 10
- Problem when Sobol falls back to HitAndRunPolytopeSampler HOT 5
- Arms from previous batch keep appearing in new batches HOT 5
- EHVI & NEHVI break with more than 7 objectives HOT 4
- Multi-objective experiments generate duplicated data HOT 5
- Question: Transforming objective when passing `best_f` to `ProbabilityOfImprovement`, etc. HOT 3
- [Question] Multiobjective optimization where one target has no optimization direction (but required range)
- How to expose the default acquisition function being used by AxClient() HOT 1
- Fix core docs related to linear constraints HOT 3
- Improve string normalization for constraint string parsing
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