Source code for botorch.utils.multi_objective.pareto

#!/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.

from __future__ import annotations

import torch
from torch import Tensor

# maximum tensor size for simple pareto computation

[docs]def is_non_dominated(Y: Tensor, deduplicate: bool = True) -> Tensor: r"""Computes the non-dominated front. Note: this assumes maximization. For small `n`, this method uses a highly parallel methodology that compares all pairs of points in Y. However, this is memory intensive and slow for large `n`. For large `n` (or if Y is larger than 5MB), this method will dispatch to a loop-based approach that is faster and has a lower memory footprint. Args: Y: A `(batch_shape) x n x m`-dim tensor of outcomes. deduplicate: A boolean indicating whether to only return unique points on the pareto frontier. Returns: A `(batch_shape) x n`-dim boolean tensor indicating whether each point is non-dominated. """ n = Y.shape[-2] if n == 0: return torch.zeros(Y.shape[:-1], dtype=torch.bool, device=Y.device) el_size = 64 if Y.dtype == torch.double else 32 if n > 1000 or n**2 * Y.shape[:-2].numel() * el_size / 8 > MAX_BYTES: return _is_non_dominated_loop(Y) Y1 = Y.unsqueeze(-3) Y2 = Y.unsqueeze(-2) dominates = (Y1 >= Y2).all(dim=-1) & (Y1 > Y2).any(dim=-1) nd_mask = ~(dominates.any(dim=-1)) if deduplicate: # remove duplicates # find index of first occurrence of each unique element indices = (Y1 == Y2).all(dim=-1).long().argmax(dim=-1) keep = torch.zeros_like(nd_mask) keep.scatter_(dim=-1, index=indices, value=1.0) return nd_mask & keep return nd_mask
def _is_non_dominated_loop(Y: Tensor, maximize: bool = True) -> Tensor: r"""Determine which points are non-dominated. Compared to `is_non_dominated`, this method is significantly faster for large `n` on a CPU and will significant reduce memory overhead. However, `is_non_dominated` is faster for smaller problems. Args: Y: A `(batch_shape) x n x m` Tensor of outcomes. maximize: A boolean indicating if the goal is maximization. Returns: A `(batch_shape) x n`-dim Tensor of booleans indicating whether each point is non-dominated. """ is_efficient = torch.ones(*Y.shape[:-1], dtype=bool, device=Y.device) for i in range(Y.shape[-2]): i_is_efficient = is_efficient[..., i] if i_is_efficient.any(): vals = Y[..., i : i + 1, :] if maximize: update = (Y > vals).any(dim=-1) else: update = (Y < vals).any(dim=-1) # If an element in Y[..., i, :] is efficient, mark it as efficient update[..., i] = i_is_efficient.clone() # Only include batches where Y[..., i, :] is efficient # Create a copy is_efficient2 = is_efficient.clone() if Y.ndim > 2: # Set all elements in all batches where Y[..., i, :] is not # efficient to False is_efficient2[~i_is_efficient] = False # Only include elements from in_efficient from the batches # where Y[..., i, :] is efficient is_efficient[is_efficient2] = update[is_efficient2] return is_efficient