Source code for ax.modelbridge.transforms.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 TYPE_CHECKING, Dict, List, Optional, Tuple

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

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 UnitX(Transform): """Map X to [0, 1]^d for RangeParameter of type float and not log scale. Uses bounds l <= x <= u, sets x_tilde_i = (x_i - l_i) / (u_i - l_i). Constraints wTx <= b are converted to gTx_tilde <= h, where g_i = w_i (u_i - l_i) and h = b - wTl. 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.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] 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] = normalize_value(param, (l, u)) 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.bounds and isinstance(p, RangeParameter): p.update_range( lower=normalize_value(p.lower, self.bounds[p_name]), upper=normalize_value(p.upper, self.bounds[p_name]), ) if p.target_value is not None: p._target_value = normalize_value( p.target_value, self.bounds[p_name] # pyre-ignore[6] ) new_constraints: List[ParameterConstraint] = [] for c in search_space.parameter_constraints: constraint_dict: Dict[str, float] = {} bound = float(c.bound) for p_name, w in c.constraint_dict.items(): # p is RangeParameter, but may not be transformed (Int or log) if p_name in self.bounds: l, u = self.bounds[p_name] constraint_dict[p_name] = w * (u - l) bound -= w * l else: constraint_dict[p_name] = w new_constraints.append( ParameterConstraint(constraint_dict=constraint_dict, bound=bound) ) search_space.set_parameter_constraints(new_constraints) return search_space
[docs] 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 * (u - l) + l return observation_features
[docs]def normalize_value(value: float, bounds: Tuple[float, float]) -> float: """Transform bounds to [0,1], and apply the same transform to the value. Note: if the value is outside of the bounds, then the value will be mapped outside of [0,1]. """ lower, upper = bounds return (value - lower) / (upper - lower)