Source code for ax.modelbridge.transforms.log

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

import math
from typing import 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.core.types import TConfig
from ax.modelbridge.transforms.base import Transform


[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], 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: # pyre: p_cast is declared to have type `RangeParameter` but # pyre-fixme[9]: is used as type `ax.core.parameter.Parameter`. p_cast: RangeParameter = p p_cast.set_log_scale(False).update_range( lower=math.log10(p_cast.lower), upper=math.log10(p_cast.upper) ) 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