Simplify configurable optimizer API #518
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jrapin
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Feb 17, 2020
| budget: Optional[int] = None, | ||
| num_workers: int = 1, | ||
| scale: float = 1.0, | ||
| diagonal: bool = False |
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Specify extra configuration parameters with defaults here (as you usually do)
| @@ -239,8 +247,8 @@ def _internal_provide_recommendation(self) -> ArrayLike: | |||
| return self.es.result.xbest # type: ignore | |||
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| class ParametrizedCMA(base.ParametrizedFamily): | |||
| """TODO | |||
| class ParametrizedCMA(base.ConfiguredOptimizer): | |||
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Then just create a Wrapper class
| MilliCMA = ParametrizedCMA(scale=1e-3).with_name("MilliCMA", register=True) | ||
| MicroCMA = ParametrizedCMA(scale=1e-6).with_name("MicroCMA", register=True) | ||
| CMA = ParametrizedCMA().set_name("CMA", register=True) | ||
| DiagonalCMA = ParametrizedCMA(diagonal=True).set_name("DiagonalCMA", register=True) |
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And decline the different values of the extra parameters
jrapin
commented
Feb 17, 2020
| ) -> None: | ||
| super().__init__(parametrization, budget=budget, num_workers=num_workers) | ||
| if num_vars: | ||
| if num_optims: | ||
| if num_vars is not None: |
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@teytaud make if num_vars can lead to unexpected behavior if what you wanted is to test agains None, so always use if num_vars is not None
| num_vars: Optional[List[Any]] = None | ||
| ) -> None: | ||
| super().__init__(parametrization, budget=budget, num_workers=num_workers, num_optims=num_optims, num_vars=num_vars) | ||
| SplitOptimizer3 = ConfSplitOptimizer(num_optims=3).set_name("SplitOptimizer3", register=True) |
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This is all it takes now to have all the variants
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Agreed by @teytaud , merging and following with other PRs to propagage |
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