Comments (5)
You'd want to construct a subclass of qLogEI that takes in, for example, a list of high_prob_constraints
that would be treated differently than in addition to typical constraints. This AF would have to handle the high_prob_constraints
in a reasonable way (weighting the AF value according to whether the constraints are satisfied). Since we wouldn't use Scipy's non-linear constraint functionality here, all constraints would be of the form g(x) <0. Then you'd need to define a new input constructor for this new acquisition function. This input constructor would need to separate constraints that should be treated by weighting by the probability of feasibility (standard) and constraints that need to be handled in the new way.
passing this to model_kwargs via the botorch_acqf_class keyword in the GenerationStep of a GenerationStrategy
Yes that's right.
- In addition, if i pass my nonlinear constraints here, do I need to pass them again to model_gen_kwargs in the GenerationStep?
No
Yes, but you'd need to specify which outcomes to use for the probabilistic constraints. model
will be the model for all outcomes, so when computing the constraint, you'd need to use the outcome relevant to that constraint
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Hi @Abrikosoff,
I am guessing that @Balandat intended to the constraint model to be pretrained outside of Ax. If you wanted to use non-linear constraints with scipy, you could implement a custom Acquisition that has a different optimize function that constructs the right non-linear constraint from the fitted model.
An alternative would be to create a new acquisition function that constructs and uses the probabilistic constraint. e.g. EI weighted by the probability that the probabilistic constraint is satisfied. One way to do this would be to make a subclass of (Log)EI that creates the necessary constraint within construct inputs similar to this.
You could then use this acquisition function in a GenerationStrategy that uses it. Parts 3b and 5 of this tutorial show how to do this.
from ax.
Hi @Abrikosoff,
I am guessing that @Balandat intended to the constraint model to be pretrained outside of Ax. If you wanted to use non-linear constraints with scipy, you could implement a custom Acquisition that has a different optimize function that constructs the right non-linear constraint from the fitted model.
An alternative would be to create a new acquisition function that constructs and uses the probabilistic constraint. e.g. EI weighted by the probability that the probabilistic constraint is satisfied. One way to do this would be to make a subclass of (Log)EI that creates the necessary constraint within construct inputs similar to this.
You could then use this acquisition function in a GenerationStrategy that uses it. Parts 3b and 5 of this tutorial show how to do this.
Hi Sam, thanks a lot for the reply! Actually currently what I'm doing is defining nonlinear constraints and passing them to a GenerationStrategy, something like the following:
local_nchoosek_strategy = GenerationStrategy(
steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=num_sobol_trials_for_nchoosek, # https://github.com/facebook/Ax/issues/922
min_trials_observed=min_trials_observed,
max_parallelism=max_parallelism,
model_kwargs=model_kwargs,
),
GenerationStep(
model=Models.BOTORCH_MODULAR,
num_trials=-1,
model_gen_kwargs={
"model_gen_options": {
"optimizer_kwargs": {
"nonlinear_inequality_constraints": [_ineq_constraint],
"batch_initial_conditions": batch_initial_conditions,
}
}
},
),
]
)
which I can then pass to my AxClient. My initial idea was to pass estimate_probabilities_of_satisfaction
along with _ineq_constraint
, which will enable me to do this relatively simply in the Service API. I guess what you mean is that there is no good way to do this if the trained model is required as one of the inputs (as in this case)?
from ax.
Yes that's right. If you need a trained model from Ax, using data collected during the experiment, I would recommend going with one of the two approaches that I mentioned, since then you would have access to the trained model.
from ax.
Hi @Abrikosoff,
I am guessing that @Balandat intended to the constraint model to be pretrained outside of Ax. If you wanted to use non-linear constraints with scipy, you could implement a custom Acquisition that has a different optimize function that constructs the right non-linear constraint from the fitted model.
An alternative would be to create a new acquisition function that constructs and uses the probabilistic constraint. e.g. EI weighted by the probability that the probabilistic constraint is satisfied. One way to do this would be to make a subclass of (Log)EI that creates the necessary constraint within construct inputs similar to this.
You could then use this acquisition function in a GenerationStrategy that uses it. Parts 3b and 5 of this tutorial show how to do this.
Hi Sam @sdaulton , once again thanks for your reply! I'm preparing to try your alternative suggestion (subclassing LogEI), and I have a few related questions regarding this:
-
the complete procedure I think is to define a input constructor that subclasses (inherits from?) qLogEI, which means defining a function akin to
construct_inputs_qLogEISpecialConstraints
(for want of a better name), with the necessary inputs, and passing this tomodel_kwargs
via thebotorch_acqf_class
keyword in theGenerationStep
of aGenerationStrategy
? Is that more or less correct? -
If the above is correct, and looking at your linked code snippet, I see there's a kwarg entry called
constraints
; should i pass my nonlinear constraints here? Am confused because from the docstring it seems like the constraints here assumeg(x) < 0
, which is bit different from the usualnonlinear_inequality_constraint
kwargs (which assumesg(x) > 0
) which one passes tomodel_gen_kwargs
. In addition, if i pass my nonlinear constraints here, do I need to pass them again tomodel_gen_kwargs
in theGenerationStep
? -
And if both the above questions are clarified, is the model from the
Model
keyword in the input constructor the model which i can use in the probability constraint?
Once again, thanks a lot for taking time out to help!
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Related Issues (20)
- MOO not respecting nonlinear constraints HOT 1
- Problem with Fixed parameters if nonlinear_inequality_constraint is imposed HOT 1
- The same point is evaluated multiple times during Integer Optimization with BO. HOT 5
- ax_client.generation_strategy.trials_as_df HOT 9
- Managing Objective Function Evaluation Failures in Ax for MOO HOT 6
- [GENERAL SUPPORT]: Managing Objective Function Evaluation Failures in Ax for MOO HOT 3
- [GENERAL SUPPORT]: Using qNegIntegratedPosteriorVariance HOT 3
- [GENERAL SUPPORT]: Reference point for multi-objective bayesian optimization HOT 4
- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
- [Bug]: Custom metric issue HOT 4
- [GENERAL SUPPORT]: CI_Level Paretofrontier
- [Bug]: Large sample time increase in ax-platform >= version 0.3.5 HOT 6
- [GENERAL SUPPORT]: Reference Point for Multi-Objective Bayesian Optimization HOT 5
- [GENERAL SUPPORT]: Plotting Pareto fronts / Posterior mean model HOT 5
- [GENERAL SUPPORT]: Getting best predicted point of a botorch model HOT 8
- [GENERAL SUPPORT]: Logical-or in outcome constraints HOT 4
- [GENERAL SUPPORT]: Parallelism and arbitrary parameter type support. HOT 5
- [GENERAL SUPPORT]: How to Standardize and Normalize Data for Service API HOT 6
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