Source code for ax.modelbridge.transforms.trial_as_task

#!/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.

# pyre-strict

from logging import Logger
from typing import Dict, List, Optional, TYPE_CHECKING, Union

from ax.core.observation import Observation, ObservationFeatures
from ax.core.parameter import ChoiceParameter, ParameterType
from ax.core.search_space import RobustSearchSpace, SearchSpace
from ax.exceptions.core import UnsupportedError
from ax.modelbridge.transforms.base import Transform
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger

    # import as module to make sphinx-autodoc-typehints happy
    from ax import modelbridge as modelbridge_module  # noqa F401

logger: Logger = get_logger(__name__)

[docs]class TrialAsTask(Transform): """Convert trial to one or more task parameters. How trial is mapped to parameter is specified with a map like {parameter_name: {trial_index: level name}}. For example, {"trial_param1": {0: "level1", 1: "level1", 2: "level2"},} will create choice parameters "trial_param1" with is_task=True. Observations with trial 0 or 1 will have "trial_param1" set to "level1", and those with trial 2 will have "trial_param1" set to "level2". Multiple parameter names and mappings can be specified in this dict. The trial level mapping can be specified in config["trial_level_map"]. If not specified, defaults to a parameter with a level for every trial index. For the reverse transform, if there are multiple mappings in the transform the trial will not be set. The created parameter will be given a target value that will default to the lowest trial index in the mapping, or can be provided in config["target_trial"]. Will raise if trial not specified for every point in the training data. Transform is done in-place. """ def __init__( self, search_space: Optional[SearchSpace] = None, observations: Optional[List[Observation]] = None, modelbridge: Optional["modelbridge_module.base.ModelBridge"] = None, config: Optional[TConfig] = None, ) -> None: assert observations is not None, "TrialAsTask requires observations" # Identify values of trial. trials = {obs.features.trial_index for obs in observations} if isinstance(search_space, RobustSearchSpace): raise UnsupportedError( "TrialAsTask transform is not supported for RobustSearchSpace." ) if None in trials: raise ValueError( "Unable to use trial as task since not all observations have " "trial specified." ) # Get trial level map if config is not None and "trial_level_map" in config: # pyre-ignore [9] trial_level_map: Dict[str, Dict[Union[int, str], Union[int, str]]] = config[ "trial_level_map" ] # Validate self.trial_level_map: Dict[str, Dict[int, Union[int, str]]] = {} for _p_name, level_dict in trial_level_map.items(): # cast trial index as an integer int_keyed_level_dict = { int(trial_index): v for trial_index, v in level_dict.items() } self.trial_level_map[_p_name] = int_keyed_level_dict # Check that trials match those in data level_map = set(int_keyed_level_dict.keys()) if not trials.issubset(level_map): raise ValueError( f"Not all trials in data ({trials}) contained " f"in trial level map for {_p_name} ({level_map})" ) else: # Set TRIAL_PARAM for each trial to the corresponding trial_index. # pyre-fixme[6]: Expected `Union[bytes, str, typing.SupportsInt]` for # 1st param but got `Optional[np.int64]`. self.trial_level_map = {TRIAL_PARAM: {int(b): str(b) for b in trials}} if len(self.trial_level_map) == 1: level_dict = next(iter(self.trial_level_map.values())) self.inverse_map: Optional[Dict[Union[int, str], int]] = { v: k for k, v in level_dict.items() } else: self.inverse_map = None # Compute target values self.target_values: Dict[str, Union[int, str]] = {} for p_name, trial_map in self.trial_level_map.items(): if config is not None and "target_trial" in config: target_trial = int(config["target_trial"]) # pyre-ignore [6] else: target_trial = min(trial_map.keys()) logger.debug(f"Setting target value for {p_name} to {target_trial}") self.target_values[p_name] = trial_map[target_trial]
[docs] def transform_observation_features( self, observation_features: List[ObservationFeatures] ) -> List[ObservationFeatures]: for obsf in observation_features: if obsf.trial_index is not None: for p_name, level_dict in self.trial_level_map.items(): # pyre-fixme[6]: Expected `Union[bytes, str, # typing.SupportsInt]` for 1st param but got `Optional[np.int64]`. obsf.parameters[p_name] = level_dict[int(obsf.trial_index)] obsf.trial_index = None return observation_features
def _transform_search_space(self, search_space: SearchSpace) -> SearchSpace: for p_name, level_dict in self.trial_level_map.items(): level_values = sorted(set(level_dict.values())) if len(level_values) < 2: details = ( f"only 1 found: {level_values}" if level_values else "none found" ) raise ValueError( f"TrialAsTask transform expects 2+ task params, {details}" ) is_int = all(isinstance(val, int) for val in level_values) trial_param = ChoiceParameter( name=p_name, parameter_type=ParameterType.INT if is_int else ParameterType.STRING, values=level_values, # pyre-fixme [6] # if all values are integers, retain the original order # they are encoded in TaskEncode is_ordered=is_int, is_task=True, sort_values=True, target_value=self.target_values[p_name], ) search_space.add_parameter(trial_param) 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.trial_level_map: pval = obsf.parameters.pop(p_name) if self.inverse_map is not None: # pyre-fixme[61]: `pval` may not be initialized here. obsf.trial_index = self.inverse_map[pval] return observation_features