Source code for ax.modelbridge.transforms.centered_unit_x
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional, Tuple
from ax.core.observation import ObservationData, ObservationFeatures
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.core.types import TConfig
from ax.modelbridge.transforms.base import Transform
from ax.utils.common.docutils import copy_doc
[docs]class CenteredUnitX(Transform):
"""Map X to [-1, 1]^d for RangeParameter of type float and not log scale.
Currently does not support linear constraints, but could in the future be
adjusted to transform them too, since this is a linear operation.
Transform is done in-place.
"""
def __init__(
self,
search_space: SearchSpace,
observation_features: List[ObservationFeatures],
observation_data: List[ObservationData],
config: Optional[TConfig] = None,
) -> None:
# Identify parameters that should be transformed
self.bounds: Dict[str, Tuple[float, float]] = {}
for p_name, p in search_space.parameters.items():
if (
isinstance(p, RangeParameter)
and p.parameter_type == ParameterType.FLOAT
and not p.log_scale
):
self.bounds[p_name] = (p.lower, p.upper)
[docs] @copy_doc(Transform.transform_observation_features)
def transform_observation_features(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationFeatures]:
for obsf in observation_features:
for p_name, (l, u) in self.bounds.items():
if p_name in obsf.parameters:
# pyre: param is declared to have type `float` but is used
# pyre-fixme[9]: as type `Optional[typing.Union[bool, float, str]]`.
param: float = obsf.parameters[p_name]
obsf.parameters[p_name] = -1 + 2 * (param - l) / (u - l)
return observation_features
[docs] @copy_doc(Transform.transform_search_space)
def transform_search_space(self, search_space: SearchSpace) -> SearchSpace:
for p_name, p in search_space.parameters.items():
if p_name in self.bounds and isinstance(p, RangeParameter):
p.update_range(lower=-1.0, upper=1.0)
if p.target_value is not None:
l, u = self.bounds[p_name]
new_tval = -1 + 2 * (p.target_value - l) / (u - l) # pyre-ignore [16]
p._target_value = new_tval
for c in search_space.parameter_constraints:
for p_name in c.constraint_dict:
if p_name in self.bounds:
raise ValueError("Does not support parameter constraints")
return search_space
[docs] @copy_doc(Transform.untransform_observation_features)
def untransform_observation_features(
self, observation_features: List[ObservationFeatures]
) -> List[ObservationFeatures]:
for obsf in observation_features:
for p_name, (l, u) in self.bounds.items():
# pyre: param is declared to have type `float` but is used as
# pyre-fixme[9]: type `Optional[typing.Union[bool, float, str]]`.
param: float = obsf.parameters[p_name]
obsf.parameters[p_name] = ((param + 1) / 2) * (u - l) + l
return observation_features