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Optuna currently supports only single-objective CMA-ES for sampling, it would be useful to have multi-objective CMA-ES as a lot of tasks need to consider multiple objectives to be optimized together.
This would also be helpful for other HPO/AutoML frameworks that depend on Optuna for their functionality. This issue is particularly motivated from the need to support MOCMAES in kubeflow/katib.
Description
Similar to other multi-objective sampling algorithms, MOCMAES will be able to work with multiple objectives.
# multi-objective function to be optimized.defmulti_objective(trial):
...
returnobjective_1, objective_2# use CMA-ES sampler with multi-ojective.sampler=optuna.samplers.CmaEsSampler()
# specify directions for each objective.study=optuna.create_study(sampler=sampler, directions=['maximize', 'minimize'])
# execute the optimization process with multi-objective.study.optimize(multi_objective, n_trials=1000, timeout=1000)
# visualize pareto-frontoptuna.visualization.plot_pareto_front(study, target_names=["objective_1", "objective_2"])
Alternatives (optional)
Currently, there are no decent solutions to MOCMAES, which are also maintained in the open-source domain.
Some relevant solutions are chocolate and pycomocma which were both last committed 4 years ago.
An option would be add pycomocma as an optuna-integration and maintain it further.
Additional context (optional)
No response
The text was updated successfully, but these errors were encountered:
hnanacc
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Mar 29, 2024
hnanacc
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Support multi-ojbective CMA-ES (MOCMAES) sampling algorithm
Support multi-objective CMA-ES (MOCMAES) sampling algorithm
Mar 29, 2024
@nomuramasahir0, the maintainer of cmaes (https://github.com/CyberAgentAILab/cmaes), is planning to work on implementing MOCMAES. However, we do not have a definite schedule for the completion of this task yet. Would it be possible for you (and Katib) to wait until our implementation is completed?
@nomuramasahir0, the maintainer of cmaes (https://github.com/CyberAgentAILab/cmaes), is planning to work on implementing MOCMAES. However, we do not have a definite schedule for the completion of this task yet. Would it be possible for you (and Katib) to wait until our implementation is completed?
@hnanacc@c-bata Thank you for pointing this out! Yes, we (katib) can wait for the implementation.
It might be better to create a feature request issue in the cmaes (https://github.com/CyberAgentAILab/cmaes) repository to ask cmaes maintainers to raise the priority of MOCMAES. WDYT?
Motivation
Optuna currently supports only single-objective CMA-ES for sampling, it would be useful to have multi-objective CMA-ES as a lot of tasks need to consider multiple objectives to be optimized together.
This would also be helpful for other HPO/AutoML frameworks that depend on Optuna for their functionality. This issue is particularly motivated from the need to support MOCMAES in kubeflow/katib.
Description
Similar to other multi-objective sampling algorithms, MOCMAES will be able to work with multiple objectives.
Alternatives (optional)
Currently, there are no decent solutions to MOCMAES, which are also maintained in the open-source domain.
Some relevant solutions are chocolate and pycomocma which were both last committed 4 years ago.
An option would be add pycomocma as an optuna-integration and maintain it further.
Additional context (optional)
No response
The text was updated successfully, but these errors were encountered: