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Add BoTorch_Modular to config, giving custom GP and acq func ability
Now users can customize their GP (kernel, MLL, GP class) as well as the acq func in the config without writing any code. Giving users a lot more control and power from any language with little setup
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tests/scripts/other_langs/r_package_streamlined/config_modular_botorch.yaml
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objective: | ||
metrics: | ||
- name: metric | ||
scheduler: | ||
n_trials: 15 | ||
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parameters: | ||
x0: | ||
'bounds': [ 0, 1 ] | ||
'type': 'range' | ||
'value_type': 'float' | ||
x1: | ||
'bounds': [ 0, 1] | ||
'type': 'range' | ||
'value_type': 'float' | ||
x2: | ||
'bounds': [ 0, 1 ] | ||
'type': 'range' | ||
'value_type': 'float' | ||
x3: | ||
'bounds': [ 0, 1] | ||
'type': 'range' | ||
'value_type': 'float' | ||
x4: | ||
'bounds': [ 0, 1 ] | ||
'type': 'range' | ||
'value_type': 'float' | ||
x5: | ||
'bounds': [ 0, 1] | ||
'type': 'range' | ||
'value_type': 'float' | ||
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script_options: | ||
# notice here that this is a shell command | ||
# this is what BOA will do to launch your script | ||
# it will also pass as a command line argument the current trial directory | ||
# that is being parameterized | ||
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# This can either be a relative path or absolute path | ||
# (by default when BOA launches from a config file | ||
# it uses the config file directory as your working directory) | ||
# here config.yaml and run_model.R are in the same directory | ||
run_model: Rscript run_model.R | ||
exp_name: "r_streamlined_botorch_modular" | ||
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generation_strategy: | ||
steps: | ||
- model: SOBOL | ||
num_trials: 5 | ||
- model: BOTORCH_MODULAR | ||
num_trials: -1 # No limitation on how many trials should be produced from this step | ||
model_kwargs: | ||
surrogate: | ||
botorch_model_class: SingleTaskGP # BoTorch model class name | ||
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covar_module_class: RBFKernel # GPyTorch kernel class name | ||
mll_class: LeaveOneOutPseudoLikelihood | ||
botorch_acqf_class: qUpperConfidenceBound # BoTorch acquisition function class name | ||
acquisition_options: | ||
beta: 0.5 |
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