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Study's best_value (or best_xxx) will output a value that does not consider the constraint, even if they exist.
I want to output the best value that takes into account the constraints.
Description
For example, best_trials gets COMPLETE trials and creates pareto front trials, but by making it trials that is COMPLETE and feasible, the best_trials including the constraint condition is obtained.
Thanks for the suggestion. I think it is an important feature, but there are a couple of points that need to be discussed.
I have two concerns: first, the loss of backward compatibility with this improvement, and second, the difficulty of efficient implementation at the storage layer.
It is debatable, but since constrained optimization is an experimental feature, backward compatibility may not be too much of a concern. Also, efficiency can be ignored, at least in the current situation.
In my opinion, I am in favor of implementing this feature in the Study's methods with UserWarning.
Motivation
Study's best_value (or best_xxx) will output a value that does not consider the constraint, even if they exist.
I want to output the best value that takes into account the constraints.
Description
For example, best_trials gets COMPLETE trials and creates pareto front trials, but by making it trials that is COMPLETE and feasible, the best_trials including the constraint condition is obtained.
optuna/optuna/study/_multi_objective.py
Line 14 in b8ff10b
However, since there are multiple codes related to Study.best_xxx, is it possible to implement them?
Let me know if you have any concerns.
Alternatives (optional)
No response
Additional context (optional)
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