Source code for ax.core.parameter_distribution

# 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 __future__ import annotations

from copy import deepcopy
from importlib import import_module
from typing import Any, Dict, List, Optional, TYPE_CHECKING

from ax.exceptions.core import UserInputError
from ax.utils.common.base import SortableBase

    from ax.core.search_space import RobustSearchSpace
    from scipy.stats.distributions import rv_frozen  # pyre-ignore [21]

TDistribution = str
TParamName = str

[docs]class ParameterDistribution(SortableBase): """A class for defining parameter distributions. Intended for robust optimization use cases. This could be used to specify the distribution of an environmental variable or the distribution of the input noise. """ def __init__( self, parameters: List[TParamName], distribution_class: TDistribution, distribution_parameters: Optional[Dict[str, Any]], multiplicative: bool = False, ) -> None: """Initialize a parameter distribution. Args: parameters: A list of parameters, which the distribution belongs to. If this represents the joint input noise distribution of the parameters `x1` and `x2`, pass in `parameters = ["x1", "x2"]`, etc. distribution_class: The name of the scipy distribution class. This must be importable as `from scipy.stats import <distribution_class>`. distribution_parameters: A dictionary of keyword arguments for initializing the distribution class. The distribution will be initialized as `distribution = distribution_class(**distribution_parameters)`. multiplicative: A boolean denoting whether the distribution will be used as a multiplicative input perturbation. Should be `False` for the distributions of environmental variables. """ super().__init__() self.parameters = parameters self._distribution_class = distribution_class self._distribution_parameters: Dict[str, Any] = distribution_parameters or {} self.multiplicative = multiplicative self._distribution: Optional[rv_frozen] = None # pyre-ignore [11] @property def distribution_class(self) -> TDistribution: r"""The name of the scipy distribution class.""" return self._distribution_class @distribution_class.setter def distribution_class(self, new_class: TDistribution) -> None: r"""Update the distribution class and delete the cached distribution object.""" self._distribution = None self._distribution_class = new_class @property def distribution_parameters(self) -> Dict[str, Any]: r"""The parameters of the distribution.""" return self._distribution_parameters @distribution_parameters.setter def distribution_parameters(self, new_parameters: Dict[str, Any]) -> None: r"""Update the distribution parameters and delete the cached distribution object. """ self._distribution = None self._distribution_parameters = new_parameters def _construct_distribution_object(self) -> None: r"""Constructs the scipy distribution object.""" stats = import_module("scipy.stats") try: dist_class = getattr(stats, self.distribution_class) except AttributeError: raise UserInputError( "Got an error while importing the distribution " f"{self.distribution_class}. Make sure that the " "`distribution_class` is importable from `scipy.stats`." ) self._distribution = dist_class(**self.distribution_parameters) @property def distribution(self) -> rv_frozen: """Get the distribution object.""" if self._distribution is None: self._construct_distribution_object() return self._distribution
[docs] def is_environmental(self, search_space: RobustSearchSpace) -> bool: r"""Check if the parameters are environmental variables of the given search space. Args: search_space: The search space to check. Returns: A boolean denoting whether the parameters are environmental variables. """ return any(search_space.is_environmental_variable(p) for p in self.parameters)
[docs] def clone(self) -> ParameterDistribution: """Clone.""" return ParameterDistribution( parameters=self.parameters.copy(), distribution_class=self.distribution_class, distribution_parameters=deepcopy(self.distribution_parameters), multiplicative=self.multiplicative, )
def __hash__(self) -> int: """Make the class hashable to support the use of `lru_cache` above. NOTE: The hash of two `ParameterDistribution`s with identical attributes will be the same. This is compatible with the use in `lru_cache` above, since the resulting distributions will be the same. """ return hash(repr(self)) def __repr__(self) -> str: return ( f"{self.__class__.__name__}(" "parameters=" + repr(self.parameters) + ", " "distribution_class=" + self.distribution_class + ", " "distribution_parameters=" + repr(self.distribution_parameters) + ", " "multiplicative=" + repr(self.multiplicative) + ")" ) @property def _unique_id(self) -> str: return str(self)