Source code for ax.modelbridge.transforms.task_encode

#!/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 typing import Dict, List, Optional, TYPE_CHECKING

from ax.core.observation import Observation
from ax.core.parameter import ChoiceParameter, Parameter, ParameterType
from ax.core.search_space import SearchSpace
from ax.core.types import TParamValue
from ax.modelbridge.transforms.choice_encode import OrderedChoiceToIntegerRange
from ax.modelbridge.transforms.utils import construct_new_search_space
from ax.models.types import TConfig

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

[docs]class TaskEncode(OrderedChoiceToIntegerRange): """Convert task ChoiceParameters to integer-valued ChoiceParameters. Parameters will be transformed to an integer ChoiceParameter with property `is_task=True`, mapping values from the original choice domain to a contiguous range integers `0, 1, ..., n_choices-1`. In the inverse transform, parameters will be mapped back onto the original domain. 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 search_space is not None, "TaskEncode requires search space" # Identify parameters that should be transformed self.encoded_parameters: Dict[str, Dict[TParamValue, int]] = {} self.target_values: Dict[str, int] = {} for p in search_space.parameters.values(): if isinstance(p, ChoiceParameter) and p.is_task: if p.is_fidelity: raise ValueError( f"Task parameter {} cannot simultaneously be " "a fidelity parameter." ) self.encoded_parameters[] = { original_value: transformed_value for transformed_value, original_value in enumerate(p.values) } self.target_values[] = self.encoded_parameters[][ p.target_value ] self.encoded_parameters_inverse: Dict[str, Dict[int, TParamValue]] = { p_name: { transformed_value: original_value for original_value, transformed_value in transforms.items() } for p_name, transforms in self.encoded_parameters.items() } def _transform_search_space(self, search_space: SearchSpace) -> SearchSpace: transformed_parameters: Dict[str, Parameter] = {} for p_name, p in search_space.parameters.items(): if p_name in self.encoded_parameters and isinstance(p, ChoiceParameter): if p.is_fidelity: raise ValueError( f"Cannot choice-encode fidelity parameter {p_name}." ) # Choice(|K|) => Choice(0, K-1, is_task=True) transformed_parameters[p_name] = ChoiceParameter( name=p_name, parameter_type=ParameterType.INT, values=list(range(len(p.values))), # pyre-ignore [6] is_ordered=p.is_ordered, is_task=True, sort_values=True, target_value=self.target_values[p_name], ) else: transformed_parameters[] = p return construct_new_search_space( search_space=search_space, parameters=list(transformed_parameters.values()), parameter_constraints=[ pc.clone_with_transformed_parameters( transformed_parameters=transformed_parameters ) for pc in search_space.parameter_constraints ], )