add almost every nevergrad optimizer
This commit is contained in:
parent
5118c93097
commit
3353b27884
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@ -1,5 +1,8 @@
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hey
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**note:** i have a personal bias against bayesian optimizers.
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these results may not represent them fairly (or at all).
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```
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python3 go_benchmark_it.py baseline -2 80
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python3 go_benchmark_it.py baseline -3 130
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381
nevergradcube2.py
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381
nevergradcube2.py
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from nevergrad.common.errors import InefficientSettingsWarning
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import logging
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import nevergrad as ng
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import nevergrad.optimization.experimentalvariants as ev
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import nevergrad.optimization.optimizerlib as ol
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import warnings
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logging.getLogger("nevergrad").setLevel(logging.WARNING)
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warnings.filterwarnings("ignore", category=InefficientSettingsWarning)
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optimizers = [
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ol.ASCMADEthird,
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ol.AdaptiveDiscreteOnePlusOne,
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ol.AlmostRotationInvariantDE,
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ol.AnisotropicAdaptiveDiscreteOnePlusOne,
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ol.AvgMetaRecenteringNoHull,
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# ol.BO, # slow
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# ol.BOSplit, # slow
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# ol.BayesOptimBO, # ModuleNotFoundError: No module named 'bayes_optim'
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ol.CMA,
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ol.CMAbounded,
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ol.CMApara,
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ol.CMAsmall,
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ol.CMAstd,
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ol.CMAtuning,
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ol.CauchyLHSSearch,
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ol.CauchyOnePlusOne,
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ol.CauchyScrHammersleySearch,
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ol.ChainCMAPowell,
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ol.ChainDiagonalCMAPowell,
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ol.ChainMetaModelPowell,
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ol.ChainMetaModelSQP,
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ol.ChainNaiveTBPSACMAPowell,
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ol.ChainNaiveTBPSAPowell,
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ol.CmaFmin2,
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ol.Cobyla,
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ol.DE,
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ol.DiagonalCMA,
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ol.DiscreteBSOOnePlusOne,
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ol.DiscreteDE,
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ol.DiscreteDoerrOnePlusOne,
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ol.DiscreteLenglerOnePlusOne,
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ol.DiscreteLenglerOnePlusOneT,
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ol.DiscreteOnePlusOne,
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ol.DiscreteOnePlusOneT,
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ol.DoubleFastGADiscreteOnePlusOne,
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ol.ES,
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ol.FCMA,
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ol.GeneticDE,
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ol.HaltonSearch,
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ol.HaltonSearchPlusMiddlePoint,
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ol.HammersleySearch,
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ol.HammersleySearchPlusMiddlePoint,
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ol.HullAvgMetaRecentering,
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ol.HullAvgMetaTuneRecentering,
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# ol.HyperOpt, # fairly slow with larger problems (NOTE: newly removed)
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ol.LHSSearch,
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ol.LargeHaltonSearch,
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ol.LhsDE,
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ol.MetaModel,
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ol.MetaModelOnePlusOne,
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ol.MetaRecentering,
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ol.MetaTuneRecentering,
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ol.MixES,
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ol.MultiCMA,
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ol.MultiScaleCMA,
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ol.MutDE,
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# ol.NEWUOA, # RuntimeError: Recast optimizer raised an error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
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# ol.NLOPT, # RuntimeError: Recast optimizer raised an error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
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ol.NaiveIsoEMNA,
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ol.NaiveTBPSA,
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ol.NelderMead,
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ol.NoisyDE,
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ol.NoisyDiscreteOnePlusOne,
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ol.NoisyOnePlusOne,
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ol.NonNSGAIIES,
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ol.ORandomSearch,
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ol.OScrHammersleySearch,
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ol.OldCMA,
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ol.OnePlusOne,
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ol.OptimisticDiscreteOnePlusOne,
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ol.OptimisticNoisyOnePlusOne,
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# ol.PCABO, # ModuleNotFoundError: No module named 'bayes_optim'
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ol.PSO,
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ol.ParaPortfolio,
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ol.PolyCMA,
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ol.PortfolioDiscreteOnePlusOne,
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ol.PortfolioDiscreteOnePlusOneT,
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ol.Powell,
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# ol.PymooBatchNSGA2, # NotImplementedError: This optimizer isn't supported by the way minimize works by default.
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# ol.PymooNSGA2, # RuntimeError: Recast optimizer raised an error: The method `get_algorithm` has been deprecated since 0.6.0 Please use the object-oriented interface.
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ol.QORandomSearch,
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ol.QOScrHammersleySearch,
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ol.QrDE,
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ol.RCobyla,
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ol.RLSOnePlusOne,
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ol.RPowell,
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# ol.RSLSQP,
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ol.RSQP,
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ol.RandomSearch,
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ol.RandomSearchPlusMiddlePoint,
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ol.RealSpacePSO,
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ol.RecES,
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ol.RecMixES,
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ol.RecMutDE,
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ol.RecombiningPortfolioDiscreteOnePlusOne,
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ol.RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne,
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ol.RescaledCMA,
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ol.RotatedTwoPointsDE,
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ol.RotationInvariantDE,
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ol.SADiscreteLenglerOnePlusOneExp09,
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ol.SADiscreteLenglerOnePlusOneExp099,
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ol.SADiscreteLenglerOnePlusOneExp09Auto,
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ol.SADiscreteLenglerOnePlusOneLin1,
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ol.SADiscreteLenglerOnePlusOneLin100,
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ol.SADiscreteLenglerOnePlusOneLinAuto,
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ol.SADiscreteOnePlusOneExp09,
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ol.SADiscreteOnePlusOneExp099,
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ol.SADiscreteOnePlusOneLin100,
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# ol.SLSQP,
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ol.SQP,
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ol.ScrHaltonSearch,
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ol.ScrHaltonSearchPlusMiddlePoint,
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ol.ScrHammersleySearch,
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ol.ScrHammersleySearchPlusMiddlePoint,
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ol.SparseDoubleFastGADiscreteOnePlusOne,
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ol.TBPSA,
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ol.TripleCMA,
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ol.TwoPointsDE,
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ol.discretememetic,
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]
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more_optimizers = [
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ev.AlmostRotationInvariantDEAndBigPop,
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ev.AnisoEMNA,
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ev.AnisoEMNATBPSA,
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ev.AvgHammersleySearch,
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ev.AvgHammersleySearchPlusMiddlePoint,
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ev.AvgRandomSearch,
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# ev.BO, # slow
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ev.BPRotationInvariantDE,
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ev.CMA,
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ev.CauchyRandomSearch,
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# ev.ChainBOwithLHS, # slow
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# ev.ChainBOwithLHS30, # slow
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# ev.ChainBOwithLHSdim, # slow
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# ev.ChainBOwithLHSsqrt, # slow
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# ev.ChainBOwithMetaRecentering, # slow
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# ev.ChainBOwithMetaRecentering30, # slow
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# ev.ChainBOwithMetaRecenteringdim, # slow
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# ev.ChainBOwithMetaRecenteringsqrt, # slow
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# ev.ChainBOwithMetaTuneRecentering, # slow
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# ev.ChainBOwithMetaTuneRecentering30, # slow
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# ev.ChainBOwithMetaTuneRecenteringdim, # slow
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# ev.ChainBOwithMetaTuneRecenteringsqrt, # slow
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# ev.ChainBOwithR, # slow
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# ev.ChainBOwithR30, # slow
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# ev.ChainBOwithRdim, # slow
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# ev.ChainBOwithRsqrt, # slow
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ev.ChainCMASQP,
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ev.ChainCMAwithLHS,
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ev.ChainCMAwithLHS30,
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ev.ChainCMAwithLHSdim,
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ev.ChainCMAwithLHSsqrt,
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ev.ChainCMAwithMetaRecentering,
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ev.ChainCMAwithMetaRecentering30,
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ev.ChainCMAwithMetaRecenteringdim,
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ev.ChainCMAwithMetaRecenteringsqrt,
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ev.ChainCMAwithR,
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ev.ChainCMAwithR30,
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ev.ChainCMAwithRdim,
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ev.ChainCMAwithRsqrt,
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ev.ChainDEwithLHS,
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ev.ChainDEwithLHS30,
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ev.ChainDEwithLHSdim,
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ev.ChainDEwithLHSsqrt,
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ev.ChainDEwithMetaRecentering,
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ev.ChainDEwithMetaRecentering30,
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ev.ChainDEwithMetaRecenteringdim,
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ev.ChainDEwithMetaRecenteringsqrt,
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ev.ChainDEwithMetaTuneRecentering,
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ev.ChainDEwithMetaTuneRecentering30,
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ev.ChainDEwithMetaTuneRecenteringdim,
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ev.ChainDEwithMetaTuneRecenteringsqrt,
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ev.ChainDEwithR,
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ev.ChainDEwithR30,
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ev.ChainDEwithRdim,
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ev.ChainDEwithRsqrt,
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ev.ChainPSOwithLHS,
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ev.ChainPSOwithLHS30,
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ev.ChainPSOwithLHSdim,
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ev.ChainPSOwithLHSsqrt,
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ev.ChainPSOwithMetaRecentering,
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ev.ChainPSOwithMetaRecentering30,
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ev.ChainPSOwithMetaRecenteringdim,
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ev.ChainPSOwithMetaRecenteringsqrt,
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ev.ChainPSOwithR,
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ev.ChainPSOwithR30,
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ev.ChainPSOwithRdim,
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ev.ChainPSOwithRsqrt,
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ev.CmaFmin2,
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ev.DE,
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ev.DiagonalCMA,
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ev.DiscreteNoisy13Splits,
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ev.DiscreteNoisyInfSplits,
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ev.DoubleFastGAOptimisticNoisyDiscreteOnePlusOne,
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ev.ECMA,
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ev.FCMAp13,
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ev.FCMAs03,
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ev.FastGADiscreteOnePlusOne,
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ev.FastGANoisyDiscreteOnePlusOne,
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ev.FastGAOptimisticNoisyDiscreteOnePlusOne,
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ev.GeneticDE,
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ev.HSCMA,
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ev.HSDE,
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ev.HSMetaModel,
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ev.HullCenterHullAvgCauchyLHSSearch,
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ev.HullCenterHullAvgCauchyScrHammersleySearch,
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ev.HullCenterHullAvgLHSSearch,
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ev.HullCenterHullAvgLargeHammersleySearch,
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ev.HullCenterHullAvgRandomSearch,
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ev.HullCenterHullAvgScrHaltonSearch,
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ev.HullCenterHullAvgScrHaltonSearchPlusMiddlePoint,
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ev.HullCenterHullAvgScrHammersleySearch,
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ev.HullCenterHullAvgScrHammersleySearchPlusMiddlePoint,
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ev.IsoEMNA,
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ev.IsoEMNATBPSA,
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# ev.LBO, # slow
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ev.LHSSearch,
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ev.LhsHSDE,
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ev.MetaCauchyRecentering,
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ev.MetaModelDiagonalCMA,
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ev.MetaModelFmin2,
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# ev.MetaNGOpt10, # hangs: Optimizing the schwefel function with the ngx_metangopt10 optimizer
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ev.MetaRecentering,
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ev.MetaTuneRecentering,
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ev.MicroCMA,
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# ev.MidQRBO, # slow
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ev.MilliCMA,
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ev.MiniDE,
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ev.MiniLhsDE,
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ev.MiniQrDE,
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ev.MixDeterministicRL, # InefficientSettingsWarning: DE algorithms are inefficient with budget < 60
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# ev.NGOptSingle16, # slow
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# ev.NGOptSingle25, # slow
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# ev.NGOptSingle9, # slow
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ev.NaiveAnisoEMNA,
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ev.NaiveAnisoEMNATBPSA,
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ev.NaiveIsoEMNATBPSA,
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ev.Noisy13Splits,
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ev.NoisyInfSplits,
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ev.NoisyOnePlusOne,
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ev.NoisyRL1, # note: inefficiency warnings
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ev.NoisyRL2, # note: inefficiency warnings
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ev.NoisyRL3, # note: inefficiency warnings
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ev.OnePointDE,
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ev.OptimisticNoisyOnePlusOne,
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# ev.PCABO80, # ModuleNotFoundError: No module named 'bayes_optim'
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# ev.PCABO95DoE20, # ModuleNotFoundError: No module named 'bayes_optim'
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ev.PSO,
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ev.ParametrizationDE,
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ev.PortfolioNoisyDiscreteOnePlusOne,
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ev.PortfolioOptimisticNoisyDiscreteOnePlusOne,
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# ev.QRBO, # slow
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# ev.RBO, # slow
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ev.RandomScaleRandomSearch,
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ev.RandomScaleRandomSearchPlusMiddlePoint,
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ev.RandomSearch,
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ev.RecombiningGA,
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ev.RecombiningOptimisticNoisyDiscreteOnePlusOne,
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ev.RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne,
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ev.RescaleScrHammersleySearch,
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ev.RotatedRecombiningGA,
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ev.SQP,
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ev.SmoothAdaptiveDiscreteOnePlusOne,
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ev.SmoothDiscreteLenglerOnePlusOne,
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ev.SmoothDiscreteOnePlusOne,
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ev.SmoothPortfolioDiscreteOnePlusOne,
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ev.SparseDiscreteOnePlusOne,
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ev.SpecialRL, # note: inefficiency warnings
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ev.StupidRandom,
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ev.TBPSA,
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ev.TEAvgCauchyLHSSearch,
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ev.TEAvgCauchyScrHammersleySearch,
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ev.TEAvgLHSSearch,
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ev.TEAvgRandomSearch,
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ev.TEAvgScrHammersleySearch,
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ev.TEAvgScrHammersleySearchPlusMiddlePoint,
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ev.Zero,
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]
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bayes_optimizers = [
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ol.BO,
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ev.RBO,
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ev.QRBO,
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ev.MidQRBO,
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ev.LBO,
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ol.ParametrizedBO(utility_kind="ei").set_name("BOEI"),
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ol.ParametrizedBO(utility_kind="poi").set_name("BOPOI"),
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]
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assert ol.RSLSQP is ol.RSQP, "weirdness is gone, please adjust accordingly"
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assert ol.SLSQP is ol.SQP, "weirdness is gone, please adjust accordingly"
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def nevergrad_cube_factory(optimizer, objective, n_trials, n_dim, with_count):
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instrument = ng.p.Array(lower=0, upper=1, shape=(n_dim,)) # better sigma
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# ev.RBO, ev.QRBO, ev.MidQRBO, and ev.LBO still complain, though:
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# /home/py/.local/lib/python3.10/site-packages/nevergrad/parametrization/_datalayers.py:107: NevergradRuntimeWarning: Bounds are 1.0 sigma away from each other at the closest, you should aim for at least 3 for better quality.
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if optimizer in (ev.RBO, ev.QRBO, ev.MidQRBO, ev.LBO):
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instrument.set_mutation(sigma=0.3)
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opt = optimizer
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assert opt is not None, optimizer
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optimizer = opt(parametrization=instrument, budget=n_trials, num_workers=1)
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feval_count = 0
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def cube_objective(us):
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nonlocal feval_count
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feval_count += 1
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return objective(us)
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recommendation = optimizer.minimize(cube_objective)
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best_x = recommendation.value
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best_val = cube_objective(best_x) # don't trust recommendation.loss
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return (best_val, best_x, feval_count) if with_count else (best_val, best_x)
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def named_optimizer(optimizer, experimental=False):
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def f(*args, **kwargs):
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return nevergrad_cube_factory(optimizer, *args, **kwargs)
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name = optimizer.name if hasattr(optimizer, "name") else optimizer.__name__
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# TODO: make these names less awful.
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new_name = ("ngx_" if experimental else "ng_") + name + "_cube"
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new_name = new_name.replace("OnePlusOne", "_1p1")
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new_name = new_name.replace("PlusMiddlePoint", "_pmp")
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new_name = new_name.replace("Search", "")
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while "__" in new_name:
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new_name = new_name.replace("__", "_")
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f.__name__ = new_name.lower()
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return f
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# some selections:
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ng_1p1_cube = named_optimizer(ol.OnePlusOne)
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ng_bo_cube = named_optimizer(ol.BO)
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ng_cauchy_1p1_cube = named_optimizer(ol.CauchyOnePlusOne)
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ng_chaincmapowell_cube = named_optimizer(ol.ChainCMAPowell)
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ng_chaindiagonalcmapowell_cube = named_optimizer(ol.ChainDiagonalCMAPowell)
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ng_chainmetamodelpowell_cube = named_optimizer(ol.ChainMetaModelPowell)
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ng_chainmetamodelsqp_cube = named_optimizer(ol.ChainMetaModelSQP)
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ng_chainnaivetbpsapowell_cube = named_optimizer(ol.ChainNaiveTBPSAPowell)
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ng_cma_cube = named_optimizer(ol.CMA)
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ng_cobyla_cube = named_optimizer(ol.Cobyla)
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ng_diagonalcma_cube = named_optimizer(ol.DiagonalCMA)
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ng_ecma_cube = named_optimizer(ev.ECMA)
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ng_fcma_cube = named_optimizer(ol.FCMA)
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ng_fcmap13_cube = named_optimizer(ev.FCMAp13)
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ng_fcmas03_cube = named_optimizer(ev.FCMAs03)
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ng_metamodel_1p1_cube = named_optimizer(ol.MetaModelOnePlusOne)
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ng_metamodel_cube = named_optimizer(ol.MetaModel)
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ng_neldermead_cube = named_optimizer(ol.NelderMead)
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ng_parametrizationde_cube = named_optimizer(ev.ParametrizationDE)
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ng_powell_cube = named_optimizer(ol.Powell)
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ng_rescaledcma_cube = named_optimizer(ol.RescaledCMA)
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ng_rpowell_cube = named_optimizer(ol.RPowell)
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ng_rsqp_cube = named_optimizer(ol.RSQP)
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ng_sqp_cube = named_optimizer(ol.SQP)
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ngx_chaincmasqp_cube = named_optimizer(ev.ChainCMASQP)
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ngx_chaincmawithmetarecenteringdim_cube = named_optimizer(ev.ChainCMAwithMetaRecenteringdim)
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NEVERGRAD2_OPTIMIZERS = list(map(named_optimizer, optimizers)) + list(
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map(lambda o: named_optimizer(o, experimental=True), more_optimizers)
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)
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BAYES_OPTIMIZERS = list(map(lambda o: named_optimizer(o, experimental=o != ol.BO), bayes_optimizers))
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@ -1,4 +1,5 @@
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from dlibcube2 import dlib_cube
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from nevergradcube2 import NEVERGRAD2_OPTIMIZERS
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from nloptcube2 import nlopt_neldermead_cube
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from notwacube2 import make_birect, make_mercury, make_soo
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from randomcube2 import another_random_cube, quasirandom_cube
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