#!/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 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
if TYPE_CHECKING:
# import as module to make sphinx-autodoc-typehints happy
from ax import modelbridge as modelbridge_module # noqa F401
TRIAL_PARAM = "TRIAL_PARAM"
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]
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 TaskChoiceToIntTaskChoice
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
