Source code for ax.modelbridge.transforms.log

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math
from typing import TYPE_CHECKING, List, Optional, Set

from ax.core.observation import ObservationData, ObservationFeatures
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.modelbridge.transforms.base import Transform
from ax.models.types import TConfig

if TYPE_CHECKING:
    # import as module to make sphinx-autodoc-typehints happy
    from ax import modelbridge as modelbridge_module  # noqa F401  # pragma: no cover


[docs]class Log(Transform): """Apply log base 10 to a float RangeParameter domain. Transform is done in-place. """ def __init__( self, search_space: SearchSpace, observation_features: List[ObservationFeatures], observation_data: List[ObservationData], modelbridge: Optional["modelbridge_module.base.ModelBridge"] = None, config: Optional[TConfig] = None, ) -> None: # Identify parameters that should be transformed self.transform_parameters: Set[str] = { p_name for p_name, p in search_space.parameters.items() if isinstance(p, RangeParameter) and p.parameter_type == ParameterType.FLOAT and p.log_scale is True }
[docs] def transform_observation_features( self, observation_features: List[ObservationFeatures] ) -> List[ObservationFeatures]: for obsf in observation_features: for p_name in self.transform_parameters: 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] = math.log10(param) return observation_features
[docs] def transform_search_space(self, search_space: SearchSpace) -> SearchSpace: for p_name, p in search_space.parameters.items(): if p_name in self.transform_parameters and isinstance(p, RangeParameter): p.set_log_scale(False).update_range( lower=math.log10(p.lower), upper=math.log10(p.upper) ) if p.target_value is not None: p._target_value = math.log10(p.target_value) # pyre-ignore [6] return search_space
[docs] def untransform_observation_features( self, observation_features: List[ObservationFeatures] ) -> List[ObservationFeatures]: for obsf in observation_features: for p_name in self.transform_parameters: 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] = math.pow(10, param) return observation_features