def __init__(self, domain: Domain, seed: int = None):
"""Initialise the :class:`RandomSearch` search space.
Args:
domain: :class:`Domain`. The domain of the objective function.
It will be sampled uniformly using the :py:meth:`sample()` method of the :class:`Domain`.
seed: (optional) :obj:`int`. The seed for the domain sampling.
"""
if seed is not None:
domain = Domain(domain.as_dict(), seed=seed)
super(RandomSearch, self).__init__(domain)
def __init__(self, domain, seed=None):
"""Initialise the optimiser's domain.
Args:
domain: :class:`Domain`. The domain of the objective function.
seed: (optional) :obj:`int`. The seed of the optimiser. Used for reproducibility purposes.
"""
np.random.seed(seed)
domain = Domain(domain.as_dict(), seed=seed)
super(BayesianOptimisation, self).__init__(domain)
converted_and_mappers = self._convert_to_gpyopt_domain(self.domain)
self.gpyopt_domain, self._categorical_value_mapper, self._discrete_type_mapper = converted_and_mappers
self._inv_categorical_value_mapper = {
name: {v: k
for k, v in mapping.items()}
for name, mapping in self._categorical_value_mapper.items()
}
self._data_x = np.array([[]])
self._data_fx = np.array([[]])
self.__is_empty_data = True
def test_as_dict():
dict_domain = {"a": {"b": [2, 3]}, "c": [0, 0.1]}
domain = Domain(dict_domain)
assert domain.as_dict() == dict_domain
