Source code for optuna.distributions
import abc
import copy
import decimal
import json
from typing import Any
from typing import Dict
from typing import Sequence
from typing import Union
import warnings
CategoricalChoiceType = Union[None, bool, int, float, str]
class BaseDistribution(object, metaclass=abc.ABCMeta):
"""Base class for distributions.
Note that distribution classes are not supposed to be called by library users.
They are used by :class:`~optuna.trial.Trial` and :class:`~optuna.samplers` internally.
"""
def to_external_repr(self, param_value_in_internal_repr: float) -> Any:
"""Convert internal representation of a parameter value into external representation.
Args:
param_value_in_internal_repr:
Optuna's internal representation of a parameter value.
Returns:
Optuna's external representation of a parameter value.
"""
return param_value_in_internal_repr
def to_internal_repr(self, param_value_in_external_repr: Any) -> float:
"""Convert external representation of a parameter value into internal representation.
Args:
param_value_in_external_repr:
Optuna's external representation of a parameter value.
Returns:
Optuna's internal representation of a parameter value.
"""
return param_value_in_external_repr
@abc.abstractmethod
def single(self) -> bool:
"""Test whether the range of this distribution contains just a single value.
When this method returns :obj:`True`, :mod:`~optuna.samplers` always sample
the same value from the distribution.
Returns:
:obj:`True` if the range of this distribution contains just a single value,
otherwise :obj:`False`.
"""
raise NotImplementedError
@abc.abstractmethod
def _contains(self, param_value_in_internal_repr: float) -> bool:
"""Test if a parameter value is contained in the range of this distribution.
Args:
param_value_in_internal_repr:
Optuna's internal representation of a parameter value.
Returns:
:obj:`True` if the parameter value is contained in the range of this distribution,
otherwise :obj:`False`.
"""
raise NotImplementedError
def _asdict(self) -> Dict:
return self.__dict__
def __eq__(self, other: Any) -> bool:
if not isinstance(other, BaseDistribution):
return NotImplemented
if not type(self) is type(other):
return False
return self.__dict__ == other.__dict__
def __hash__(self) -> int:
return hash((self.__class__,) + tuple(sorted(self.__dict__.items())))
def __repr__(self) -> str:
kwargs = ", ".join("{}={}".format(k, v) for k, v in sorted(self.__dict__.items()))
return "{}({})".format(self.__class__.__name__, kwargs)
[docs]class UniformDistribution(BaseDistribution):
"""A uniform distribution in the linear domain.
This object is instantiated by :func:`~optuna.trial.Trial.suggest_uniform`, and passed to
:mod:`~optuna.samplers` in general.
Attributes:
low:
Lower endpoint of the range of the distribution. ``low`` is included in the range.
high:
Upper endpoint of the range of the distribution. ``high`` is excluded from the range.
"""
[docs] def __init__(self, low: float, high: float) -> None:
if low > high:
raise ValueError(
"The `low` value must be smaller than or equal to the `high` value "
"(low={}, high={}).".format(low, high)
)
self.low = low
self.high = high
def _contains(self, param_value_in_internal_repr: float) -> bool:
value = param_value_in_internal_repr
if self.low == self.high:
return value == self.low
else:
return self.low <= value < self.high
[docs]class LogUniformDistribution(BaseDistribution):
"""A uniform distribution in the log domain.
This object is instantiated by :func:`~optuna.trial.Trial.suggest_loguniform`, and passed to
:mod:`~optuna.samplers` in general.
Attributes:
low:
Lower endpoint of the range of the distribution. ``low`` is included in the range.
high:
Upper endpoint of the range of the distribution. ``high`` is excluded from the range.
"""
[docs] def __init__(self, low: float, high: float) -> None:
if low > high:
raise ValueError(
"The `low` value must be smaller than or equal to the `high` value "
"(low={}, high={}).".format(low, high)
)
if low <= 0.0:
raise ValueError(
"The `low` value must be larger than 0 for a log distribution "
"(low={}, high={}).".format(low, high)
)
self.low = low
self.high = high
def _contains(self, param_value_in_internal_repr: float) -> bool:
value = param_value_in_internal_repr
if self.low == self.high:
return value == self.low
else:
return self.low <= value < self.high
[docs]class DiscreteUniformDistribution(BaseDistribution):
"""A discretized uniform distribution in the linear domain.
This object is instantiated by :func:`~optuna.trial.Trial.suggest_discrete_uniform`, and passed
to :mod:`~optuna.samplers` in general.
.. note::
If the range :math:`[\\mathsf{low}, \\mathsf{high}]` is not divisible by :math:`q`,
:math:`\\mathsf{high}` will be replaced with the maximum of :math:`k q + \\mathsf{low}
\\lt \\mathsf{high}`, where :math:`k` is an integer.
Attributes:
low:
Lower endpoint of the range of the distribution. ``low`` is included in the range.
high:
Upper endpoint of the range of the distribution. ``high`` is included in the range.
q:
A discretization step.
"""
[docs] def __init__(self, low: float, high: float, q: float) -> None:
if low > high:
raise ValueError(
"The `low` value must be smaller than or equal to the `high` value "
"(low={}, high={}, q={}).".format(low, high, q)
)
high = _adjust_discrete_uniform_high(low, high, q)
self.low = low
self.high = high
self.q = q
[docs] def single(self) -> bool:
if self.low == self.high:
return True
high = decimal.Decimal(str(self.high))
low = decimal.Decimal(str(self.low))
q = decimal.Decimal(str(self.q))
if (high - low) < q:
return True
return False
def _contains(self, param_value_in_internal_repr: float) -> bool:
value = param_value_in_internal_repr
return self.low <= value <= self.high
[docs]class IntUniformDistribution(BaseDistribution):
"""A uniform distribution on integers.
This object is instantiated by :func:`~optuna.trial.Trial.suggest_int`, and passed to
:mod:`~optuna.samplers` in general.
.. note::
If the range :math:`[\\mathsf{low}, \\mathsf{high}]` is not divisible by
:math:`\\mathsf{step}`, :math:`\\mathsf{high}` will be replaced with the maximum of
:math:`k \\times \\mathsf{step} + \\mathsf{low} \\lt \\mathsf{high}`, where :math:`k` is
an integer.
Attributes:
low:
Lower endpoint of the range of the distribution. ``low`` is included in the range.
high:
Upper endpoint of the range of the distribution. ``high`` is included in the range.
step:
A step for spacing between values.
"""
[docs] def __init__(self, low: int, high: int, step: int = 1) -> None:
if low > high:
raise ValueError(
"The `low` value must be smaller than or equal to the `high` value "
"(low={}, high={}).".format(low, high)
)
if step <= 0:
raise ValueError(
"The `step` value must be non-zero positive value, but step={}.".format(step)
)
high = _adjust_int_uniform_high(low, high, step)
self.low = low
self.high = high
self.step = step
[docs] def to_external_repr(self, param_value_in_internal_repr: float) -> int:
return int(param_value_in_internal_repr)
[docs] def to_internal_repr(self, param_value_in_external_repr: int) -> float:
return float(param_value_in_external_repr)
[docs] def single(self) -> bool:
if self.low == self.high:
return True
return (self.high - self.low) < self.step
def _contains(self, param_value_in_internal_repr: float) -> bool:
value = param_value_in_internal_repr
return self.low <= value <= self.high
[docs]class IntLogUniformDistribution(BaseDistribution):
"""A uniform distribution on integers in the log domain.
This object is instantiated by :func:`~optuna.trial.Trial.suggest_int`, and passed to
:mod:`~optuna.samplers` in general.
Attributes:
low:
Lower endpoint of the range of the distribution. ``low`` is included in the range.
high:
Upper endpoint of the range of the distribution. ``high`` is included in the range.
step:
A step for spacing between values.
.. warning::
Deprecated in v2.0.0. ``step`` argument will be removed in the future.
The removal of this feature is currently scheduled for v4.0.0,
but this schedule is subject to change.
Samplers and other components in Optuna relying on this distribution will ignore
this value and assume that ``step`` is always 1.
User-defined samplers may continue to use other values besides 1 during the
deprecation.
"""
[docs] def __init__(self, low: int, high: int, step: int = 1) -> None:
if low > high:
raise ValueError(
"The `low` value must be smaller than or equal to the `high` value "
"(low={}, high={}).".format(low, high)
)
if low < 1.0:
raise ValueError(
"The `low` value must be equal to or greater than 1 for a log distribution "
"(low={}, high={}).".format(low, high)
)
if step != 1:
self._warn_step()
self.low = low
self.high = high
self._step = step
def __repr__(self) -> str:
# TODO(hvy): `BaseDistribution.__repr__` could rely on `_asdict` instead of `__dict__`.
# `IntLogUniformDistribution` would not have to override `__repr__`.
kwargs = ", ".join("{}={}".format(k, v) for k, v in sorted(self._asdict().items()))
return "{}({})".format(self.__class__.__name__, kwargs)
def _asdict(self) -> Dict:
d = copy.copy(self.__dict__)
d["step"] = d.pop("_step")
return d
def _warn_step(self) -> None:
warnings.warn(
"Samplers and other components in Optuna will assume that `step` is 1. "
"`step` argument is deprecated and will be removed in the future. "
"The removal of this feature is currently scheduled for v4.0.0, "
"but this schedule is subject to change.",
FutureWarning,
)
[docs] def to_external_repr(self, param_value_in_internal_repr: float) -> int:
return int(param_value_in_internal_repr)
[docs] def to_internal_repr(self, param_value_in_external_repr: int) -> float:
return float(param_value_in_external_repr)
def _contains(self, param_value_in_internal_repr: float) -> bool:
value = param_value_in_internal_repr
return self.low <= value <= self.high
@property
def step(self) -> int:
self._warn_step()
return self._step
@step.setter
def step(self, value: int) -> None:
self._warn_step()
self._step = value
[docs]class CategoricalDistribution(BaseDistribution):
"""A categorical distribution.
This object is instantiated by :func:`~optuna.trial.Trial.suggest_categorical`, and
passed to :mod:`~optuna.samplers` in general.
Args:
choices:
Parameter value candidates.
.. note::
Not all types are guaranteed to be compatible with all storages. It is recommended to
restrict the types of the choices to :obj:`None`, :class:`bool`, :class:`int`,
:class:`float` and :class:`str`.
Attributes:
choices:
Parameter value candidates.
"""
[docs] def __init__(self, choices: Sequence[CategoricalChoiceType]) -> None:
if len(choices) == 0:
raise ValueError("The `choices` must contains one or more elements.")
for choice in choices:
if choice is not None and not isinstance(choice, (bool, int, float, str)):
message = (
"Choices for a categorical distribution should be a tuple of None, bool, "
"int, float and str for persistent storage but contains {} which is of type "
"{}.".format(choice, type(choice).__name__)
)
warnings.warn(message)
self.choices = tuple(choices)
[docs] def to_external_repr(self, param_value_in_internal_repr: float) -> CategoricalChoiceType:
return self.choices[int(param_value_in_internal_repr)]
[docs] def to_internal_repr(self, param_value_in_external_repr: CategoricalChoiceType) -> float:
try:
return self.choices.index(param_value_in_external_repr)
except ValueError as e:
raise ValueError(
"'{}' not in {}.".format(param_value_in_external_repr, self.choices)
) from e
def _contains(self, param_value_in_internal_repr: float) -> bool:
index = int(param_value_in_internal_repr)
return 0 <= index < len(self.choices)
DISTRIBUTION_CLASSES = (
UniformDistribution,
LogUniformDistribution,
DiscreteUniformDistribution,
IntUniformDistribution,
IntLogUniformDistribution,
CategoricalDistribution,
)
[docs]def json_to_distribution(json_str: str) -> BaseDistribution:
"""Deserialize a distribution in JSON format.
Args:
json_str: A JSON-serialized distribution.
Returns:
A deserialized distribution.
"""
json_dict = json.loads(json_str)
if json_dict["name"] == CategoricalDistribution.__name__:
json_dict["attributes"]["choices"] = tuple(json_dict["attributes"]["choices"])
for cls in DISTRIBUTION_CLASSES:
if json_dict["name"] == cls.__name__:
return cls(**json_dict["attributes"])
raise ValueError("Unknown distribution class: {}".format(json_dict["name"]))
[docs]def distribution_to_json(dist: BaseDistribution) -> str:
"""Serialize a distribution to JSON format.
Args:
dist: A distribution to be serialized.
Returns:
A JSON string of a given distribution.
"""
return json.dumps({"name": dist.__class__.__name__, "attributes": dist._asdict()})
[docs]def check_distribution_compatibility(
dist_old: BaseDistribution, dist_new: BaseDistribution
) -> None:
"""A function to check compatibility of two distributions.
Note that this method is not supposed to be called by library users.
Args:
dist_old: A distribution previously recorded in storage.
dist_new: A distribution newly added to storage.
Returns:
True denotes given distributions are compatible. Otherwise, they are not.
"""
if dist_old.__class__ != dist_new.__class__:
raise ValueError("Cannot set different distribution kind to the same parameter name.")
if not isinstance(dist_old, CategoricalDistribution):
return
if not isinstance(dist_new, CategoricalDistribution):
return
if dist_old.choices != dist_new.choices:
raise ValueError(
CategoricalDistribution.__name__ + " does not support dynamic value space."
)
def _adjust_discrete_uniform_high(low: float, high: float, q: float) -> float:
d_high = decimal.Decimal(str(high))
d_low = decimal.Decimal(str(low))
d_q = decimal.Decimal(str(q))
d_r = d_high - d_low
if d_r % d_q != decimal.Decimal("0"):
old_high = high
high = float((d_r // d_q) * d_q + d_low)
warnings.warn(
"The distribution is specified by [{low}, {old_high}] and q={step}, but the range "
"is not divisible by `q`. It will be replaced by [{low}, {high}].".format(
low=low, old_high=old_high, high=high, step=q
)
)
return high
def _adjust_int_uniform_high(low: int, high: int, step: int) -> int:
r = high - low
if r % step != 0:
old_high = high
high = r // step * step + low
warnings.warn(
"The distribution is specified by [{low}, {old_high}] and step={step}, but the range "
"is not divisible by `step`. It will be replaced by [{low}, {high}].".format(
low=low, old_high=old_high, high=high, step=step
)
)
return high
def _get_single_value(distribution: BaseDistribution) -> Union[int, float, CategoricalChoiceType]:
assert distribution.single()
if isinstance(
distribution,
(
UniformDistribution,
LogUniformDistribution,
DiscreteUniformDistribution,
IntUniformDistribution,
IntLogUniformDistribution,
),
):
return distribution.low
elif isinstance(distribution, CategoricalDistribution):
return distribution.choices[0]
assert False