Source code for botorch.utils.multi_objective.scalarization

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

Helper utilities for constructing scalarizations.


.. [Knowles2005]
    J. Knowles, "ParEGO: a hybrid algorithm with on-line landscape approximation
    for expensive multiobjective optimization problems," in IEEE Transactions
    on Evolutionary Computation, vol. 10, no. 1, pp. 50-66, Feb. 2006.
from __future__ import annotations

from typing import Callable, Optional

import torch
from botorch.exceptions.errors import BotorchTensorDimensionError
from botorch.utils.transforms import normalize
from torch import Tensor

[docs]def get_chebyshev_scalarization( weights: Tensor, Y: Tensor, alpha: float = 0.05 ) -> Callable[[Tensor, Optional[Tensor]], Tensor]: r"""Construct an augmented Chebyshev scalarization. Outcomes are first normalized to [0,1] and then an augmented Chebyshev scalarization is applied. Augmented Chebyshev scalarization: objective(y) = min(w * y) + alpha * sum(w * y) Note: this assumes maximization. See [Knowles2005]_ for details. This scalarization can be used with qExpectedImprovement to implement q-ParEGO as proposed in [Daulton2020qehvi]_. Args: weights: A `m`-dim tensor of weights. Y: A `n x m`-dim tensor of observed outcomes, which are used for scaling the outcomes to [0,1]. alpha: Parameter governing the influence of the weighted sum term. The default value comes from [Knowles2005]_. Returns: Transform function using the objective weights. Example: >>> weights = torch.tensor([0.75, 0.25]) >>> transform = get_aug_chebyshev_scalarization(weights, Y) """ if weights.shape != Y.shape[-1:]: raise BotorchTensorDimensionError( "weights must be an `m`-dim tensor where Y is `... x m`." f"Got shapes {weights.shape} and {Y.shape}." ) elif Y.ndim > 2: raise NotImplementedError("Batched Y is not currently supported.") Y_bounds = torch.stack([Y.min(dim=-2).values, Y.max(dim=-2).values]) def obj(Y: Tensor, X: Optional[Tensor] = None) -> Tensor: # scale to [0,1] Y_normalized = normalize(Y, bounds=Y_bounds) product = weights * Y_normalized return product.min(dim=-1).values + alpha * product.sum(dim=-1) return obj