Source code for optuna.integration.skopt

import numpy as np

import optuna
from optuna import distributions
from optuna import samplers
from optuna.samplers import BaseSampler
from import StudyDirection
from optuna.trial import TrialState
from optuna import type_checking

    import skopt
    from import space

    _available = True
except ImportError as e:
    _import_error = e
    # SkoptSampler is disabled because Scikit-Optimize is not available.
    _available = False

if type_checking.TYPE_CHECKING:
    from typing import Any  # NOQA
    from typing import Dict  # NOQA
    from typing import List  # NOQA
    from typing import Optional  # NOQA
    from typing import Tuple  # NOQA

    from optuna.distributions import BaseDistribution  # NOQA
    from import Study  # NOQA
    from optuna.trial import FrozenTrial  # NOQA

[docs]class SkoptSampler(BaseSampler): """Sampler using Scikit-Optimize as the backend. Example: Optimize a simple quadratic function by using :class:`~optuna.integration.SkoptSampler`. .. testcode:: import optuna def objective(trial): x = trial.suggest_uniform('x', -10, 10) y = trial.suggest_int('y', 0, 10) return x**2 + y sampler = optuna.integration.SkoptSampler() study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=10) Args: independent_sampler: A :class:`~optuna.samplers.BaseSampler` instance that is used for independent sampling. The parameters not contained in the relative search space are sampled by this sampler. The search space for :class:`~optuna.integration.SkoptSampler` is determined by :func:`~optuna.samplers.intersection_search_space()`. If :obj:`None` is specified, :class:`~optuna.samplers.RandomSampler` is used as the default. .. seealso:: :class:`optuna.samplers` module provides built-in independent samplers such as :class:`~optuna.samplers.RandomSampler` and :class:`~optuna.samplers.TPESampler`. warn_independent_sampling: If this is :obj:`True`, a warning message is emitted when the value of a parameter is sampled by using an independent sampler. Note that the parameters of the first trial in a study are always sampled via an independent sampler, so no warning messages are emitted in this case. skopt_kwargs: Keyword arguments passed to the constructor of `skopt.Optimizer <>`_ class. Note that ``dimensions`` argument in ``skopt_kwargs`` will be ignored because it is added by :class:`~optuna.integration.SkoptSampler` automatically. n_startup_trials: The independent sampling is used until the given number of trials finish in the same study. """ def __init__( self, independent_sampler=None, warn_independent_sampling=True, skopt_kwargs=None, n_startup_trials=1, ): # type: (Optional[BaseSampler], bool, Optional[Dict[str, Any]], int) -> None _check_skopt_availability() self._skopt_kwargs = skopt_kwargs or {} if "dimensions" in self._skopt_kwargs: del self._skopt_kwargs["dimensions"] self._independent_sampler = independent_sampler or samplers.RandomSampler() self._warn_independent_sampling = warn_independent_sampling self._n_startup_trials = n_startup_trials self._search_space = samplers.IntersectionSearchSpace()
[docs] def reseed_rng(self) -> None: self._independent_sampler.reseed_rng()
def infer_relative_search_space(self, study, trial): # type: (Study, FrozenTrial) -> Dict[str, BaseDistribution] search_space = {} for name, distribution in self._search_space.calculate(study).items(): if distribution.single(): if not isinstance(distribution, distributions.CategoricalDistribution): # `skopt` cannot handle non-categorical distributions that contain just # a single value, so we skip this distribution. # # Note that `Trial` takes care of this distribution during suggestion. continue search_space[name] = distribution return search_space def sample_relative(self, study, trial, search_space): # type: (Study, FrozenTrial, Dict[str, BaseDistribution]) -> Dict[str, Any] if len(search_space) == 0: return {} complete_trials = [t for t in study.trials if t.state == TrialState.COMPLETE] if len(complete_trials) < self._n_startup_trials: return {} optimizer = _Optimizer(search_space, self._skopt_kwargs) optimizer.tell(study, complete_trials) return optimizer.ask() def sample_independent(self, study, trial, param_name, param_distribution): # type: (Study, FrozenTrial, str, BaseDistribution) -> Any if self._warn_independent_sampling: complete_trials = [t for t in study.trials if t.state == TrialState.COMPLETE] if len(complete_trials) >= self._n_startup_trials: self._log_independent_sampling(trial, param_name) return self._independent_sampler.sample_independent( study, trial, param_name, param_distribution ) def _log_independent_sampling(self, trial, param_name): # type: (FrozenTrial, str) -> None logger = optuna.logging.get_logger(__name__) logger.warning( "The parameter '{}' in trial#{} is sampled independently " "by using `{}` instead of `SkoptSampler` " "(optimization performance may be degraded). " "You can suppress this warning by setting `warn_independent_sampling` " "to `False` in the constructor of `SkoptSampler`, " "if this independent sampling is intended behavior.".format( param_name, trial.number, self._independent_sampler.__class__.__name__ ) )
class _Optimizer(object): def __init__(self, search_space, skopt_kwargs): # type: (Dict[str, BaseDistribution], Dict[str, Any]) -> None self._search_space = search_space dimensions = [] for name, distribution in sorted(self._search_space.items()): # TODO(nzw0301) support IntLogUniform if isinstance(distribution, distributions.UniformDistribution): # Convert the upper bound from exclusive (optuna) to inclusive (skopt). high = np.nextafter(distribution.high, float("-inf")) dimension = space.Real(distribution.low, high) elif isinstance(distribution, distributions.LogUniformDistribution): # Convert the upper bound from exclusive (optuna) to inclusive (skopt). high = np.nextafter(distribution.high, float("-inf")) dimension = space.Real(distribution.low, high, prior="log-uniform") elif isinstance(distribution, distributions.IntUniformDistribution): count = (distribution.high - distribution.low) // distribution.step dimension = space.Integer(0, count) elif isinstance(distribution, distributions.DiscreteUniformDistribution): count = int((distribution.high - distribution.low) // distribution.q) dimension = space.Integer(0, count) elif isinstance(distribution, distributions.CategoricalDistribution): dimension = space.Categorical(distribution.choices) else: raise NotImplementedError( "The distribution {} is not implemented.".format(distribution) ) dimensions.append(dimension) self._optimizer = skopt.Optimizer(dimensions, **skopt_kwargs) def tell(self, study, complete_trials): # type: (Study, List[FrozenTrial]) -> None xs = [] ys = [] for trial in complete_trials: if not self._is_compatible(trial): continue x, y = self._complete_trial_to_skopt_observation(study, trial) xs.append(x) ys.append(y) self._optimizer.tell(xs, ys) def ask(self): # type: () -> Dict[str, Any] params = {} param_values = self._optimizer.ask() for (name, distribution), value in zip(sorted(self._search_space.items()), param_values): if isinstance(distribution, distributions.DiscreteUniformDistribution): value = value * distribution.q + distribution.low if isinstance(distribution, distributions.IntUniformDistribution): value = value * distribution.step + distribution.low params[name] = value return params def _is_compatible(self, trial): # type: (FrozenTrial) -> bool # Thanks to `intersection_search_space()` function, in sequential optimization, # the parameters of complete trials are always compatible with the search space. # # However, in distributed optimization, incompatible trials may complete on a worker # just after an intersection search space is calculated on another worker. for name, distribution in self._search_space.items(): if name not in trial.params: return False distributions.check_distribution_compatibility(distribution, trial.distributions[name]) param_value = trial.params[name] param_internal_value = distribution.to_internal_repr(param_value) if not distribution._contains(param_internal_value): return False return True def _complete_trial_to_skopt_observation(self, study, trial): # type: (Study, FrozenTrial) -> Tuple[List[Any], float] param_values = [] for name, distribution in sorted(self._search_space.items()): param_value = trial.params[name] if isinstance(distribution, distributions.DiscreteUniformDistribution): param_value = (param_value - distribution.low) // distribution.q if isinstance(distribution, distributions.IntUniformDistribution): param_value = (param_value - distribution.low) // distribution.step param_values.append(param_value) value = trial.value assert value is not None if study.direction == StudyDirection.MAXIMIZE: value = -value return param_values, value def _check_skopt_availability(): # type: () -> None if not _available: raise ImportError( "Scikit-Optimize is not available. Please install it to use this feature. " "Scikit-Optimize can be installed by executing `$ pip install scikit-optimize`. " "For further information, please refer to the installation guide of Scikit-Optimize. " "(The actual import error is as follows: " + str(_import_error) + ")" )