def __init__(
self,
*,
consider_prior: bool = True,
prior_weight: float = 1.0,
consider_magic_clip: bool = True,
consider_endpoints: bool = True,
n_startup_trials: int = 10,
n_ehvi_candidates: int = 24,
gamma: Callable[[int], int] = default_gamma,
weights_above: Callable[[int], np.ndarray] = _default_weights_above,
seed: Optional[int] = None,
) -> None:
super().__init__(
consider_prior=consider_prior,
prior_weight=prior_weight,
consider_magic_clip=consider_magic_clip,
consider_endpoints=consider_endpoints,
n_startup_trials=n_startup_trials,
n_ei_candidates=n_ehvi_candidates,
gamma=gamma,
weights=weights_above,
seed=seed,
)
self._n_ehvi_candidates = n_ehvi_candidates
self._mo_random_sampler = RandomSampler(seed=seed)
def __init__(
self,
*,
consider_prior: bool = True,
prior_weight: float = 1.0,
consider_magic_clip: bool = True,
consider_endpoints: bool = True,
n_startup_trials: int = 10,
n_ehvi_candidates: int = 24,
gamma: Callable[[int], int] = default_gamma,
weights_above: Callable[[int], np.ndarray] = _default_weights_above,
seed: Optional[int] = None,
) -> None:
self._parzen_estimator_parameters = _ParzenEstimatorParameters(
consider_prior, prior_weight, consider_magic_clip,
consider_endpoints, weights_above)
self._prior_weight = prior_weight
self._n_startup_trials = n_startup_trials
self._n_ehvi_candidates = n_ehvi_candidates
self._gamma = gamma
self._weights = weights_above
self._warn_independent_sampling = True
self._rng = np.random.RandomState(seed)
self._mo_random_sampler = RandomSampler(seed=seed)
self._search_space = IntersectionSearchSpace(include_pruned=True)
self._split_cache: Dict[int, Any] = {}
self._weights_below: Dict[int, Any] = {}
def __init__(
self,
consider_prior: bool = True,
prior_weight: float = 1.0,
consider_magic_clip: bool = True,
consider_endpoints: bool = False,
n_startup_trials: int = 10,
n_ei_candidates: int = 24,
gamma: Callable[[int], int] = default_gamma,
weights: Callable[[int], np.ndarray] = default_weights,
seed: Optional[int] = None,
*,
multivariate: bool = False,
group: bool = False,
warn_independent_sampling: bool = True,
) -> None:
self._parzen_estimator_parameters = _ParzenEstimatorParameters(
consider_prior, prior_weight, consider_magic_clip,
consider_endpoints, weights)
self._prior_weight = prior_weight
self._n_startup_trials = n_startup_trials
self._n_ei_candidates = n_ei_candidates
self._gamma = gamma
self._weights = weights
self._warn_independent_sampling = warn_independent_sampling
self._rng = np.random.RandomState(seed)
self._random_sampler = RandomSampler(seed=seed)
self._multivariate = multivariate
self._group = group
self._group_decomposed_search_space: Optional[
_GroupDecomposedSearchSpace] = None
self._search_space_group: Optional[_SearchSpaceGroup] = None
self._search_space = IntersectionSearchSpace(include_pruned=True)
if multivariate:
warnings.warn(
"``multivariate`` option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
if group:
if not multivariate:
raise ValueError(
"``group`` option can only be enabled when ``multivariate`` is enabled."
)
warnings.warn(
"``group`` option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
self._group_decomposed_search_space = _GroupDecomposedSearchSpace(
True)
def __init__(
self,
*,
population_size: int = 50,
mutation_prob: Optional[float] = None,
crossover_prob: float = 0.9,
swapping_prob: float = 0.5,
seed: Optional[int] = None,
constraints_func: Optional[Callable[[FrozenTrial],
Sequence[float]]] = None,
) -> None:
# TODO(ohta): Reconsider the default value of each parameter.
if not isinstance(population_size, int):
raise TypeError("`population_size` must be an integer value.")
if population_size < 2:
raise ValueError(
"`population_size` must be greater than or equal to 2.")
if not (mutation_prob is None or 0.0 <= mutation_prob <= 1.0):
raise ValueError(
"`mutation_prob` must be None or a float value within the range [0.0, 1.0]."
)
if not (0.0 <= crossover_prob <= 1.0):
raise ValueError(
"`crossover_prob` must be a float value within the range [0.0, 1.0]."
)
if not (0.0 <= swapping_prob <= 1.0):
raise ValueError(
"`swapping_prob` must be a float value within the range [0.0, 1.0]."
)
if constraints_func is not None:
warnings.warn(
"The constraints_func option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
self._population_size = population_size
self._mutation_prob = mutation_prob
self._crossover_prob = crossover_prob
self._swapping_prob = swapping_prob
self._random_sampler = RandomSampler(seed=seed)
self._rng = np.random.RandomState(seed)
self._constraints_func = constraints_func
def __init__(
self,
*,
population_size: int = 50,
mutation_prob: Optional[float] = None,
crossover_prob: float = 0.9,
swapping_prob: float = 0.5,
seed: Optional[int] = None,
) -> None:
# TODO(ohta): Reconsider the default value of each parameter.
if not isinstance(population_size, int):
raise TypeError("`population_size` must be an integer value.")
if population_size < 2:
raise ValueError(
"`population_size` must be greater than or equal to 2.")
if not (mutation_prob is None or 0.0 <= mutation_prob <= 1.0):
raise ValueError(
"`mutation_prob` must be None or a float value within the range [0.0, 1.0]."
)
if not (0.0 <= crossover_prob <= 1.0):
raise ValueError(
"`crossover_prob` must be a float value within the range [0.0, 1.0]."
)
if not (0.0 <= swapping_prob <= 1.0):
raise ValueError(
"`swapping_prob` must be a float value within the range [0.0, 1.0]."
)
self._population_size = population_size
self._mutation_prob = mutation_prob
self._crossover_prob = crossover_prob
self._swapping_prob = swapping_prob
self._random_sampler = RandomSampler(seed=seed)
self._rng = np.random.RandomState(seed)
def __init__(
self,
consider_prior: bool = True,
prior_weight: float = 1.0,
consider_magic_clip: bool = True,
consider_endpoints: bool = False,
n_startup_trials: int = 10,
n_ei_candidates: int = 24,
gamma: Callable[[int], int] = default_gamma,
weights: Callable[[int], np.ndarray] = default_weights,
seed: Optional[int] = None,
*,
multivariate: bool = False,
warn_independent_sampling: bool = True,
) -> None:
self._parzen_estimator_parameters = _ParzenEstimatorParameters(
consider_prior, prior_weight, consider_magic_clip,
consider_endpoints, weights)
self._prior_weight = prior_weight
self._n_startup_trials = n_startup_trials
self._n_ei_candidates = n_ei_candidates
self._gamma = gamma
self._weights = weights
self._warn_independent_sampling = warn_independent_sampling
self._rng = np.random.RandomState(seed)
self._random_sampler = RandomSampler(seed=seed)
self._multivariate = multivariate
self._search_space = IntersectionSearchSpace()
if multivariate:
warnings.warn(
"``multivariate`` option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
def __init__(
self,
*,
population_size: int = 50,
mutation_prob: Optional[float] = None,
crossover: Optional[BaseCrossover] = None,
crossover_prob: float = 0.9,
swapping_prob: float = 0.5,
seed: Optional[int] = None,
constraints_func: Optional[Callable[[FrozenTrial],
Sequence[float]]] = None,
) -> None:
# TODO(ohta): Reconsider the default value of each parameter.
if not isinstance(population_size, int):
raise TypeError("`population_size` must be an integer value.")
if population_size < 2:
raise ValueError(
"`population_size` must be greater than or equal to 2.")
if not (mutation_prob is None or 0.0 <= mutation_prob <= 1.0):
raise ValueError(
"`mutation_prob` must be None or a float value within the range [0.0, 1.0]."
)
if not (0.0 <= crossover_prob <= 1.0):
raise ValueError(
"`crossover_prob` must be a float value within the range [0.0, 1.0]."
)
if not (0.0 <= swapping_prob <= 1.0):
raise ValueError(
"`swapping_prob` must be a float value within the range [0.0, 1.0]."
)
if constraints_func is not None:
warnings.warn(
"The constraints_func option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
if crossover is None:
crossover = UniformCrossover(swapping_prob)
if not isinstance(crossover, BaseCrossover):
raise ValueError(
f"'{crossover}' is not a valid crossover."
" For valid crossovers see"
" https://optuna.readthedocs.io/en/stable/reference/samplers.html."
)
if population_size < crossover.n_parents:
raise ValueError(
f"Using {crossover},"
f" the population size should be greater than or equal to {crossover.n_parents}."
f" The specified `population_size` is {population_size}.")
self._population_size = population_size
self._mutation_prob = mutation_prob
self._crossover = crossover
self._crossover_prob = crossover_prob
self._swapping_prob = swapping_prob
self._random_sampler = RandomSampler(seed=seed)
self._rng = np.random.RandomState(seed)
self._constraints_func = constraints_func
self._search_space = IntersectionSearchSpace()
def __init__(
self,
*,
population_size: int = 50,
mutation_prob: Optional[float] = None,
crossover: str = "uniform",
crossover_prob: float = 0.9,
swapping_prob: float = 0.5,
seed: Optional[int] = None,
constraints_func: Optional[Callable[[FrozenTrial],
Sequence[float]]] = None,
) -> None:
# TODO(ohta): Reconsider the default value of each parameter.
if not isinstance(population_size, int):
raise TypeError("`population_size` must be an integer value.")
if population_size < 2:
raise ValueError(
"`population_size` must be greater than or equal to 2.")
if not (mutation_prob is None or 0.0 <= mutation_prob <= 1.0):
raise ValueError(
"`mutation_prob` must be None or a float value within the range [0.0, 1.0]."
)
if not (0.0 <= crossover_prob <= 1.0):
raise ValueError(
"`crossover_prob` must be a float value within the range [0.0, 1.0]."
)
if not (0.0 <= swapping_prob <= 1.0):
raise ValueError(
"`swapping_prob` must be a float value within the range [0.0, 1.0]."
)
if constraints_func is not None:
warnings.warn(
"The constraints_func option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
if crossover not in [
"uniform", "blxalpha", "sbx", "vsbx", "undx", "spx"
]:
raise ValueError(
f"'{crossover}' is not a valid crossover name."
" The available crossovers are"
" `uniform` (default), `blxalpha`, `sbx`, `vsbx`, `undx`, and `spx`."
)
if crossover != "uniform":
warnings.warn(
"``crossover`` option is an experimental feature."
" The interface can change in the future.",
ExperimentalWarning,
)
n_parents = get_n_parents(crossover)
if population_size < n_parents:
raise ValueError(
f"Using {crossover},"
f" the population size should be greater than or equal to {n_parents}."
f" The specified `population_size` is {population_size}.")
self._population_size = population_size
self._mutation_prob = mutation_prob
self._crossover = crossover
self._crossover_prob = crossover_prob
self._swapping_prob = swapping_prob
self._random_sampler = RandomSampler(seed=seed)
self._rng = np.random.RandomState(seed)
self._constraints_func = constraints_func
self._search_space = IntersectionSearchSpace()