Source code for ax.modelbridge.transforms.stratified_standardize_y

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

from collections import defaultdict
from typing import TYPE_CHECKING, DefaultDict, List, Optional, Tuple

import numpy as np
from ax.core.observation import ObservationData, ObservationFeatures
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import ChoiceParameter
from ax.core.search_space import SearchSpace
from ax.core.types import TConfig, TParamValue
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.standardize_y import compute_standardization_parameters
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  # pragma: no cover

logger = get_logger("StratifiedStandardizeY")


[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: SearchSpace,
        observation_features: List[ObservationFeatures],
        observation_data: List[ObservationData],
        config: Optional[TConfig] = None,
    ) -> None:
        # Get parameter name for standardization.
        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")
        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]
        # Compute means and SDs
        Ys: DefaultDict[Tuple[str, TParamValue], List[float]] = defaultdict(list)
        for j, obsd in enumerate(observation_data):
            v = observation_features[j].parameters[self.p_name]
            for i, m in enumerate(obsd.metric_names):
                Ys[(m, v)].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...
        self.Ymean, self.Ystd = compute_standardization_parameters(Ys)

[docs]    def transform_observation_data(
        self,
        observation_data: List[ObservationData],
        observation_features: List[ObservationFeatures],
    ) -> List[ObservationData]:
        # Transform observation data
        for j, obsd in enumerate(observation_data):
            v = observation_features[j].parameters[self.p_name]
            means = np.array([self.Ymean[(m, v)] for m in obsd.metric_names])
            stds = np.array([self.Ystd[(m, v)] for m in obsd.metric_names])
            obsd.means = (obsd.means - means) / stds
            obsd.covariance /= np.dot(stds[:, None], stds[:, None].transpose())
        return observation_data

[docs]    def transform_optimization_config(
        self,
        optimization_config: OptimizationConfig,
        modelbridge: Optional["modelbridge_module.base.ModelBridge"],
        fixed_features: ObservationFeatures,
    ) -> OptimizationConfig:
        if len(optimization_config.outcome_constraints) == 0:
            return optimization_config
        if self.p_name not in fixed_features.parameters:
            raise ValueError(
                f"StratifiedStandardizeY transform requires {self.p_name} to be fixed "
                "during generation."
            )
        v = fixed_features.parameters[self.p_name]
        for c in optimization_config.outcome_constraints:
            if c.relative:
                raise ValueError(
                    f"StratifiedStandardizeY transform does not support relative "
                    "constraint {c}"
                )
            c.bound = (c.bound - self.Ymean[(c.metric.name, v)]) / self.Ystd[
                (c.metric.name, v)
            ]
        return optimization_config

[docs]    def untransform_observation_data(
        self,
        observation_data: List[ObservationData],
        observation_features: List[ObservationFeatures],
    ) -> List[ObservationData]:
        for j, obsd in enumerate(observation_data):
            v = observation_features[j].parameters[self.p_name]
            means = np.array([self.Ymean[(m, v)] for m in obsd.metric_names])
            stds = np.array([self.Ystd[(m, v)] for m in obsd.metric_names])
            obsd.means = obsd.means * stds + means
            obsd.covariance *= np.dot(stds[:, None], stds[:, None].transpose())
        return observation_data