#!/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.
r"""
Base class for test functions for optimization benchmarks.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Optional, Union
import torch
from botorch.exceptions.errors import InputDataError
from torch import Tensor
from torch.nn import Module
[docs]
class BaseTestProblem(Module, ABC):
r"""Base class for test functions."""
dim: int
_bounds: list[tuple[float, float]]
_check_grad_at_opt: bool = True
def __init__(
self,
noise_std: Union[None, float, list[float]] = None,
negate: bool = False,
) -> None:
r"""Base constructor for test functions.
Args:
noise_std: Standard deviation of the observation noise. If a list is
provided, specifies separate noise standard deviations for each
objective in a multiobjective problem.
negate: If True, negate the function.
"""
super().__init__()
self.noise_std = noise_std
self.negate = negate
if len(self._bounds) != self.dim:
raise InputDataError(
"Expected the bounds to match the dimensionality of the domain. "
f"Got {self.dim=} and {len(self._bounds)=}."
)
self.register_buffer(
"bounds", torch.tensor(self._bounds, dtype=torch.double).transpose(-1, -2)
)
[docs]
def forward(self, X: Tensor, noise: bool = True) -> Tensor:
r"""Evaluate the function on a set of points.
Args:
X: A `(batch_shape) x d`-dim tensor of point(s) at which to evaluate
the function.
noise: If `True`, add observation noise as specified by `noise_std`.
Returns:
A `batch_shape`-dim tensor ouf function evaluations.
"""
f = self.evaluate_true(X=X)
if noise and self.noise_std is not None:
_noise = torch.tensor(self.noise_std, device=X.device, dtype=X.dtype)
f += _noise * torch.randn_like(f)
if self.negate:
f = -f
return f
[docs]
@abstractmethod
def evaluate_true(self, X: Tensor) -> Tensor:
r"""
Evaluate the function (w/o observation noise) on a set of points.
Args:
X: A `(batch_shape) x d`-dim tensor of point(s) at which to
evaluate.
Returns:
A `batch_shape`-dim tensor.
"""
pass # pragma: no cover
[docs]
class ConstrainedBaseTestProblem(BaseTestProblem, ABC):
r"""Base class for test functions with constraints.
In addition to one or more objectives, a problem may have a number of outcome
constraints of the form `c_i(x) >= 0` for `i=1, ..., n_c`.
This base class provides common functionality for such problems.
"""
num_constraints: int
_check_grad_at_opt: bool = False
constraint_noise_std: Union[None, float, list[float]] = None
[docs]
def evaluate_slack(self, X: Tensor, noise: bool = True) -> Tensor:
r"""Evaluate the constraint slack on a set of points.
Constraints `i` is assumed to be feasible at `x` if the associated slack
`c_i(x)` is positive. Zero slack means that the constraint is active. Negative
slack means that the constraint is violated.
Args:
X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
constraint slacks: `c_1(X), ...., c_{n_c}(X)`.
noise: If `True`, add observation noise to the slack as specified by
`noise_std`.
Returns:
A `batch_shape x n_c`-dim tensor of constraint slack (where positive slack
corresponds to the constraint being feasible).
"""
cons = self.evaluate_slack_true(X=X)
if noise and self.constraint_noise_std is not None:
_constraint_noise = torch.tensor(
self.constraint_noise_std, device=X.device, dtype=X.dtype
)
cons += _constraint_noise * torch.randn_like(cons)
return cons
[docs]
def is_feasible(self, X: Tensor, noise: bool = True) -> Tensor:
r"""Evaluate whether the constraints are feasible on a set of points.
Args:
X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
constraints.
noise: If `True`, add observation noise as specified by `noise_std`.
Returns:
A `batch_shape`-dim boolean tensor that is `True` iff all constraint
slacks (potentially including observation noise) are positive.
"""
return (self.evaluate_slack(X=X, noise=noise) >= 0.0).all(dim=-1)
[docs]
@abstractmethod
def evaluate_slack_true(self, X: Tensor) -> Tensor:
r"""Evaluate the constraint slack (w/o observation noise) on a set of points.
Args:
X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
constraint slacks: `c_1(X), ...., c_{n_c}(X)`.
Returns:
A `batch_shape x n_c`-dim tensor of constraint slack (where positive slack
corresponds to the constraint being feasible).
"""
pass # pragma: no cover
[docs]
class MultiObjectiveTestProblem(BaseTestProblem, ABC):
r"""Base class for multi-objective test functions.
TODO: add a pareto distance function that returns the distance
between a provided point and the closest point on the true pareto front.
"""
num_objectives: int
_ref_point: list[float]
_max_hv: Optional[float] = None
def __init__(
self,
noise_std: Union[None, float, list[float]] = None,
negate: bool = False,
) -> None:
r"""Base constructor for multi-objective test functions.
Args:
noise_std: Standard deviation of the observation noise. If a list is
provided, specifies separate noise standard deviations for each
objective.
negate: If True, negate the objectives.
"""
if isinstance(noise_std, list) and len(noise_std) != len(self._ref_point):
raise InputDataError(
f"If specified as a list, length of noise_std ({len(noise_std)}) "
f"must match the number of objectives ({len(self._ref_point)})"
)
super().__init__(noise_std=noise_std, negate=negate)
ref_point = torch.tensor(self._ref_point, dtype=torch.get_default_dtype())
if negate:
ref_point *= -1
self.register_buffer("ref_point", ref_point)
@property
def max_hv(self) -> float:
if self._max_hv is not None:
return self._max_hv
else:
raise NotImplementedError(
f"Problem {self.__class__.__name__} does not specify maximal "
"hypervolume."
)
[docs]
def gen_pareto_front(self, n: int) -> Tensor:
r"""Generate `n` pareto optimal points."""
raise NotImplementedError
