#!/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.
from collections import defaultdict
from logging import Logger
from typing import DefaultDict, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
import numpy as np
from ax.core.observation import Observation, ObservationFeatures, separate_observations
from ax.core.optimization_config import OptimizationConfig
from ax.core.outcome_constraint import OutcomeConstraint
from ax.core.parameter import ChoiceParameter
from ax.core.search_space import SearchSpace
from ax.core.types import TParamValue
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.standardize_y import compute_standardization_parameters
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast, not_none
if TYPE_CHECKING:
# 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 StratifiedStandardizeY(Transform):
"""Standardize Y, separately for each metric and for each value of a
ChoiceParameter.
The name of the parameter by which to stratify the standardization can be
specified in config["parameter_name"]. If not specified, will use a task
parameter if search space contains exactly 1 task parameter, and will raise
an exception otherwise.
The stratification parameter must be fixed during generation if there are
outcome constraints, in order to apply the standardization to the
constraints.
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:
"""Initialize StratifiedStandardizeY.
Args:
search_space: The experiment search space.
observations: Observations from the experiment for all previous trials.
modelbridge: The modelbridge.
config: A that may containing a "parameter_name" key specifying the name of
the parameter to use for stratification and a "strata_mapping" key
that corresponds to a dictionary that maps parameter values to strata
for standardization. The strata can be of type bool, int, str, or
float.
"""
assert search_space is not None, "StratifiedStandardizeY requires search space"
assert observations is not None, "StratifiedStandardizeY requires observations"
# Get parameter name for standardization.
self.strata_mapping = None # pyre-ignore [8]
if config is not None and "parameter_name" in config:
# pyre: Attribute `p_name` declared in class `ax.modelbridge.
# pyre: transforms.stratified_standardize_y.
# pyre: StratifiedStandardizeY` has type `str` but is used as type
# pyre-fixme[8]: `typing.Union[float, int, str]`.
self.p_name: str = config["parameter_name"]
strat_p = search_space.parameters[self.p_name]
if not isinstance(strat_p, ChoiceParameter):
raise ValueError(f"{self.p_name} not a ChoiceParameter")
if "strata_mapping" in config:
# pyre-ignore [8]
self.strata_mapping: Dict[
Union[bool, float, int, str], Union[bool, float, int, str]
] = config["strata_mapping"]
if set(strat_p.values) != set(self.strata_mapping.keys()):
raise ValueError(
f"{self.p_name} values {strat_p.values} do not match "
f"strata_mapping keys {self.strata_mapping.keys()}."
)
else:
# See if there is a task parameter
task_parameters = [
p.name
for p in search_space.parameters.values()
if isinstance(p, ChoiceParameter) and p.is_task
]
if len(task_parameters) == 0:
raise ValueError(
"Must specify parameter for stratified standardization"
)
elif len(task_parameters) != 1:
raise ValueError(
"Must specify which task parameter to use for stratified "
"standardization"
)
self.p_name = task_parameters[0]
if self.strata_mapping is None:
strat_p = checked_cast(
ChoiceParameter, search_space.parameters[self.p_name]
)
# pyre-ignore [8]
self.strata_mapping = {v: v for v in strat_p.values}
# Compute means and SDs
observation_features, observation_data = separate_observations(observations)
Ys: DefaultDict[Tuple[str, TParamValue], List[float]] = defaultdict(list)
for j, obsd in enumerate(observation_data):
v = not_none(observation_features[j].parameters[self.p_name])
strata = self.strata_mapping[v]
for i, m in enumerate(obsd.metric_names):
Ys[(m, strata)].append(obsd.means[i])
# Expected `DefaultDict[typing.Union[str, typing.Tuple[str,
# Optional[typing.Union[bool, float, str]]]], List[float]]` for 1st anonymous
# parameter to call
# `ax.modelbridge.transforms.standardize_y.compute_standardization_parameters`
# but got `DefaultDict[typing.Tuple[str, Optional[typing.Union[bool, float,
# str]]], List[float]]`.
# pyre-fixme[6]: Expected `DefaultDict[Union[str, Tuple[str, Optional[Union[b...
# pyre-fixme[4]: Attribute must be annotated.
self.Ymean, self.Ystd = compute_standardization_parameters(Ys)
