Source code for botorch.utils.constraints

#!/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.

Helpers for handling input or outcome constraints.

from __future__ import annotations

from functools import partial
from typing import Callable, List, Optional, Tuple

import torch
from torch import Tensor

[docs] def get_outcome_constraint_transforms( outcome_constraints: Optional[Tuple[Tensor, Tensor]] ) -> Optional[List[Callable[[Tensor], Tensor]]]: r"""Create outcome constraint callables from outcome constraint tensors. Args: outcome_constraints: A tuple of `(A, b)`. For `k` outcome constraints and `m` outputs at `f(x)``, `A` is `k x m` and `b` is `k x 1` such that `A f(x) <= b`. Returns: A list of callables, each mapping a Tensor of size `b x q x m` to a tensor of size `b x q`, where `m` is the number of outputs of the model. Negative values imply feasibility. The callables support broadcasting (e.g. for calling on a tensor of shape `mc_samples x b x q x m`). Example: >>> # constrain `f(x)[0] <= 0` >>> A = torch.tensor([[1., 0.]]) >>> b = torch.tensor([[0.]]) >>> outcome_constraints = get_outcome_constraint_transforms((A, b)) """ if outcome_constraints is None: return None A, b = outcome_constraints def _oc(a: Tensor, rhs: Tensor, Y: Tensor) -> Tensor: r"""Evaluate constraints. Note: einsum multiples Y by a and sums over the `m`-dimension. Einsum is ~2x faster than using `(Y * a.view(1, 1, -1)).sum(dim-1)`. Args: a: `m`-dim tensor of weights for the outcomes rhs: Singleton tensor containing the outcome constraint value Y: `... x b x q x m` tensor of function values Returns: A `... x b x q`-dim tensor where negative values imply feasibility """ lhs = torch.einsum("...m, m", [Y, a]) return lhs - rhs return [partial(_oc, a, rhs) for a, rhs in zip(A, b)]
[docs] def get_monotonicity_constraints( d: int, descending: bool = False, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, ) -> Tuple[Tensor, Tensor]: """Returns a system of linear inequalities `(A, b)` that generically encodes order constraints on the elements of a `d`-dimsensional space, i.e. `A @ x < b` implies `x[i] < x[i + 1]` for a `d`-dimensional vector `x`. Idea: Could encode `A` as sparse matrix, if it is supported well. Args: d: Dimensionality of the constraint space, i.e. number of monotonic parameters. descending: If True, forces the elements of a vector to be monotonically de- creasing and be monotonically increasing otherwise. dtype: The dtype of the returned Tensors. device: The device of the returned Tensors. Returns: A tuple of Tensors `(A, b)` representing the monotonicity constraint as a system of linear inequalities `A @ x < b`. `A` is `(d - 1) x d`-dimensional and `b` is `(d - 1) x 1`-dimensional. """ A = torch.zeros(d - 1, d, dtype=dtype, device=device) idx = torch.arange(d - 1) A[idx, idx] = 1 A[idx, idx + 1] = -1 b = torch.zeros(d - 1, 1, dtype=dtype, device=device) if descending: A = -A return A, b