nevergrad/optimization/recastlib.py [168:212]:
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    def __init__(
        self,
        parametrization: IntOrParameter,
        budget: tp.Optional[int] = None,
        num_workers: int = 1,
        *,
        algorithm: str,
    ) -> None:
        super().__init__(parametrization, budget=budget, num_workers=num_workers)
        # configuration
        self.algorithm = algorithm
        self._no_hypervolume = True
        self._initial_seed = -1

    def get_optimization_function(self) -> tp.Callable[[tp.Callable[..., tp.Any]], tp.Optional[tp.ArrayLike]]:
        if self._initial_seed == -1:
            self._initial_seed = self._rng.randint(2 ** 30)
        return functools.partial(self._optimization_function, weakref.proxy(self))
        # pylint:disable=useless-return

    @staticmethod
    def _optimization_function(
        weakself: tp.Any, objective_function: tp.Callable[[tp.ArrayLike], float]
    ) -> tp.Optional[tp.ArrayLike]:
        # pylint:disable=unused-argument, import-outside-toplevel
        from pymoo import optimize as pymoooptimize

        from pymoo.factory import get_algorithm as get_pymoo_algorithm

        # from pymoo.factory import get_reference_directions

        # reference direction code for when we want to use the other MOO optimizers in Pymoo
        # if self.algorithm in [
        #     "rnsga2",
        #     "nsga3",
        #     "unsga3",
        #     "rnsga3",
        #     "moead",
        #     "ctaea",
        # ]:  # algorithms that require reference points or reference directions
        #     the appropriate n_partitions must be looked into
        #     ref_dirs = get_reference_directions("das-dennis", self.num_objectives, n_partitions=12)
        #     algorithm = get_pymoo_algorithm(self.algorithm, ref_dirs)
        # else:
        algorithm = get_pymoo_algorithm(weakself.algorithm)
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nevergrad/optimization/recastlib.py [296:340]:
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    def __init__(
        self,
        parametrization: IntOrParameter,
        budget: tp.Optional[int] = None,
        num_workers: int = 1,
        *,
        algorithm: str,
    ) -> None:
        super().__init__(parametrization, budget=budget, num_workers=num_workers)
        # configuration
        self.algorithm = algorithm
        self._no_hypervolume = True
        self._initial_seed = -1

    def get_optimization_function(self) -> tp.Callable[[tp.Callable[..., tp.Any]], tp.Optional[tp.ArrayLike]]:
        if self._initial_seed == -1:
            self._initial_seed = self._rng.randint(2 ** 30)
        return functools.partial(self._optimization_function, weakref.proxy(self))
        # pylint:disable=useless-return

    @staticmethod
    def _optimization_function(
        weakself: tp.Any, objective_function: tp.Callable[[tp.ArrayLike], float]
    ) -> tp.Optional[tp.ArrayLike]:
        # pylint:disable=unused-argument, import-outside-toplevel
        from pymoo import optimize as pymoooptimize

        from pymoo.factory import get_algorithm as get_pymoo_algorithm

        # from pymoo.factory import get_reference_directions

        # reference direction code for when we want to use the other MOO optimizers in Pymoo
        # if self.algorithm in [
        #     "rnsga2",
        #     "nsga3",
        #     "unsga3",
        #     "rnsga3",
        #     "moead",
        #     "ctaea",
        # ]:  # algorithms that require reference points or reference directions
        #     the appropriate n_partitions must be looked into
        #     ref_dirs = get_reference_directions("das-dennis", self.num_objectives, n_partitions=12)
        #     algorithm = get_pymoo_algorithm(self.algorithm, ref_dirs)
        # else:
        algorithm = get_pymoo_algorithm(weakself.algorithm)
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