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How to use the nevergrad.optimization.optimizerlib.ParametrizedOnePlusOne.with_name function in nevergrad

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github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
if isinstance(noise_handling, str):
                assert noise_handling in ["random", "optimistic"], f"Unkwnown noise handling: '{noise_handling}'"
            else:
                assert isinstance(noise_handling, tuple), "noise_handling must be a string or  a tuple of type (strategy, factor)"
                assert noise_handling[1] > 0.0, "the factor must be a float greater than 0"
                assert noise_handling[0] in ["random", "optimistic"], f"Unkwnown noise handling: '{noise_handling}'"
        assert mutation in ["gaussian", "cauchy", "discrete", "fastga", "doublefastga", "portfolio"], f"Unkwnown mutation: '{mutation}'"
        self.noise_handling = noise_handling
        self.mutation = mutation
        self.crossover = crossover
        super().__init__()


OnePlusOne = ParametrizedOnePlusOne().with_name("OnePlusOne", register=True)
NoisyOnePlusOne = ParametrizedOnePlusOne(noise_handling="random").with_name("NoisyOnePlusOne", register=True)
OptimisticNoisyOnePlusOne = ParametrizedOnePlusOne(
    noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
OnePlusOne = ParametrizedOnePlusOne().with_name("OnePlusOne", register=True)
NoisyOnePlusOne = ParametrizedOnePlusOne(noise_handling="random").with_name("NoisyOnePlusOne", register=True)
OptimisticNoisyOnePlusOne = ParametrizedOnePlusOne(
    noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").with_name(
    "FastGANoisyDiscreteOnePlusOne", register=True
)
PortfolioDiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="portfolio").with_name("PortfolioDiscreteOnePlusOne", register=True)
PortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="portfolio").with_name(
    "PortfolioOptimisticNoisyDiscreteOnePlusOne", register=True
)
PortfolioNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="portfolio").with_name(
    "PortfolioNoisyDiscreteOnePlusOne", register=True
)
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
) -> None:
        if noise_handling is not None:
            if isinstance(noise_handling, str):
                assert noise_handling in ["random", "optimistic"], f"Unkwnown noise handling: '{noise_handling}'"
            else:
                assert isinstance(noise_handling, tuple), "noise_handling must be a string or  a tuple of type (strategy, factor)"
                assert noise_handling[1] > 0.0, "the factor must be a float greater than 0"
                assert noise_handling[0] in ["random", "optimistic"], f"Unkwnown noise handling: '{noise_handling}'"
        assert mutation in ["gaussian", "cauchy", "discrete", "fastga", "doublefastga", "portfolio"], f"Unkwnown mutation: '{mutation}'"
        self.noise_handling = noise_handling
        self.mutation = mutation
        self.crossover = crossover
        super().__init__()


OnePlusOne = ParametrizedOnePlusOne().with_name("OnePlusOne", register=True)
NoisyOnePlusOne = ParametrizedOnePlusOne(noise_handling="random").with_name("NoisyOnePlusOne", register=True)
OptimisticNoisyOnePlusOne = ParametrizedOnePlusOne(
    noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").with_name(
    "FastGANoisyDiscreteOnePlusOne", register=True
)
PortfolioDiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="portfolio").with_name("PortfolioDiscreteOnePlusOne", register=True)
PortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="portfolio").with_name(
    "PortfolioOptimisticNoisyDiscreteOnePlusOne", register=True
)
PortfolioNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="portfolio").with_name(
    "PortfolioNoisyDiscreteOnePlusOne", register=True
)
CauchyOnePlusOne = ParametrizedOnePlusOne(mutation="cauchy").with_name("CauchyOnePlusOne", register=True)
RecombiningOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
    crossover=True, mutation="discrete", noise_handling="optimistic"
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
"NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").with_name(
    "FastGANoisyDiscreteOnePlusOne", register=True
)
PortfolioDiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="portfolio").with_name("PortfolioDiscreteOnePlusOne", register=True)
PortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="portfolio").with_name(
    "PortfolioOptimisticNoisyDiscreteOnePlusOne", register=True
)
PortfolioNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="portfolio").with_name(
    "PortfolioNoisyDiscreteOnePlusOne", register=True
)
CauchyOnePlusOne = ParametrizedOnePlusOne(mutation="cauchy").with_name("CauchyOnePlusOne", register=True)
RecombiningOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
    crossover=True, mutation="discrete", noise_handling="optimistic"
).with_name("RecombiningOptimisticNoisyDiscreteOnePlusOne", register=True)
RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
    crossover=True, mutation="portfolio", noise_handling="optimistic"
).with_name("RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
OnePlusOne = ParametrizedOnePlusOne().with_name("OnePlusOne", register=True)
NoisyOnePlusOne = ParametrizedOnePlusOne(noise_handling="random").with_name("NoisyOnePlusOne", register=True)
OptimisticNoisyOnePlusOne = ParametrizedOnePlusOne(
    noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").with_name(
    "FastGANoisyDiscreteOnePlusOne", register=True
)
PortfolioDiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="portfolio").with_name("PortfolioDiscreteOnePlusOne", register=True)
PortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="portfolio").with_name(
    "PortfolioOptimisticNoisyDiscreteOnePlusOne", register=True
)
PortfolioNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="portfolio").with_name(
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").with_name(
    "FastGANoisyDiscreteOnePlusOne", register=True
)
PortfolioDiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="portfolio").with_name("PortfolioDiscreteOnePlusOne", register=True)
PortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="portfolio").with_name(
    "PortfolioOptimisticNoisyDiscreteOnePlusOne", register=True
)
PortfolioNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="portfolio").with_name(
    "PortfolioNoisyDiscreteOnePlusOne", register=True
)
CauchyOnePlusOne = ParametrizedOnePlusOne(mutation="cauchy").with_name("CauchyOnePlusOne", register=True)
RecombiningOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
    crossover=True, mutation="discrete", noise_handling="optimistic"
).with_name("RecombiningOptimisticNoisyDiscreteOnePlusOne", register=True)
RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
    crossover=True, mutation="portfolio", noise_handling="optimistic"
).with_name("RecombiningPortfolioOptimisticNoisyDiscreteOnePlusOne", register=True)


class _CMA(base.Optimizer):
    def __init__(self, instrumentation: Union[int, Instrumentation], budget: Optional[int] = None, num_workers: int = 1) -> None:
        super().__init__(instrumentation, budget=budget, num_workers=num_workers)
        self._parameters = ParametrizedCMA()
        self._es: Optional[cma.CMAEvolutionStrategy] = None
        # delay initialization to ease implementation of variants
        self.listx: List[ArrayLike] = []
        self.listy: List[float] = []
        self.to_be_asked: Deque[np.ndarray] = deque()
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
else:
                assert isinstance(noise_handling, tuple), "noise_handling must be a string or  a tuple of type (strategy, factor)"
                assert noise_handling[1] > 0.0, "the factor must be a float greater than 0"
                assert noise_handling[0] in ["random", "optimistic"], f"Unkwnown noise handling: '{noise_handling}'"
        assert mutation in ["gaussian", "cauchy", "discrete", "fastga", "doublefastga", "portfolio"], f"Unkwnown mutation: '{mutation}'"
        self.noise_handling = noise_handling
        self.mutation = mutation
        self.crossover = crossover
        super().__init__()


OnePlusOne = ParametrizedOnePlusOne().with_name("OnePlusOne", register=True)
NoisyOnePlusOne = ParametrizedOnePlusOne(noise_handling="random").with_name("NoisyOnePlusOne", register=True)
OptimisticNoisyOnePlusOne = ParametrizedOnePlusOne(
    noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
github facebookresearch / nevergrad / nevergrad / optimization / optimizerlib.py View on Github external
assert isinstance(noise_handling, tuple), "noise_handling must be a string or  a tuple of type (strategy, factor)"
                assert noise_handling[1] > 0.0, "the factor must be a float greater than 0"
                assert noise_handling[0] in ["random", "optimistic"], f"Unkwnown noise handling: '{noise_handling}'"
        assert mutation in ["gaussian", "cauchy", "discrete", "fastga", "doublefastga", "portfolio"], f"Unkwnown mutation: '{mutation}'"
        self.noise_handling = noise_handling
        self.mutation = mutation
        self.crossover = crossover
        super().__init__()


OnePlusOne = ParametrizedOnePlusOne().with_name("OnePlusOne", register=True)
NoisyOnePlusOne = ParametrizedOnePlusOne(noise_handling="random").with_name("NoisyOnePlusOne", register=True)
OptimisticNoisyOnePlusOne = ParametrizedOnePlusOne(
    noise_handling="optimistic").with_name("OptimisticNoisyOnePlusOne", register=True)
DiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="discrete").with_name("DiscreteOnePlusOne", register=True)
OptimisticDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="discrete").with_name(
    "OptimisticDiscreteOnePlusOne", register=True
)
NoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling=("random", 1.0), mutation="discrete").with_name(
    "NoisyDiscreteOnePlusOne", register=True
)
DoubleFastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="doublefastga").with_name("DoubleFastGADiscreteOnePlusOne", register=True)
FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(
    mutation="fastga").with_name("FastGADiscreteOnePlusOne", register=True)
DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="doublefastga").with_name(
    "DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="optimistic", mutation="fastga").with_name(
    "FastGAOptimisticNoisyDiscreteOnePlusOne", register=True
)
FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").with_name(

nevergrad

A Python toolbox for performing gradient-free optimization

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MIT
Latest version published 2 months ago

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