Source code for botorch.utils.multi_objective.box_decompositions.dominated

#!/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"""Algorithms for partitioning the dominated space into hyperrectangles."""

from __future__ import annotations

import torch
from botorch.utils.multi_objective.box_decompositions.box_decomposition import (
from botorch.utils.multi_objective.box_decompositions.utils import (
from torch import Tensor

[docs]class DominatedPartitioning(FastPartitioning): r"""Partition dominated space into axis-aligned hyperrectangles. This uses the Algorithm 1 from [Lacour17]_. Example: >>> bd = DominatedPartitioning(ref_point, Y) """ def _partition_space_2d(self) -> None: r"""Partition the non-dominated space into disjoint hypercells. This direct method works for `m=2` outcomes. """ cell_bounds = compute_dominated_hypercell_bounds_2d( # flip self.pareto_Y because it is sorted in decreasing order (since # self._pareto_Y was sorted in increasing order and we multiplied by -1) pareto_Y_sorted=self.pareto_Y.flip(-2), ref_point=self.ref_point, ) self.register_buffer("hypercell_bounds", cell_bounds) def _get_partitioning(self) -> None: r"""Get the bounds of each hypercell in the decomposition.""" minimization_cell_bounds = get_partition_bounds( Z=self._Z, U=self._U, ref_point=self._neg_ref_point.view(-1) ) cell_bounds = -minimization_cell_bounds.flip(0) self.register_buffer("hypercell_bounds", cell_bounds)
[docs] def compute_hypervolume(self) -> Tensor: r"""Compute hypervolume that is dominated by the Pareto Frontier. Returns: A `(batch_shape)`-dim tensor containing the hypervolume dominated by each Pareto frontier. """ if self._neg_pareto_Y.shape[-2] == 0: return torch.zeros( self._neg_pareto_Y.shape[:-2], dtype=self._neg_pareto_Y.dtype, device=self._neg_pareto_Y.device, ) return ( (self.hypercell_bounds[1] - self.hypercell_bounds[0]) .prod(dim=-1) .sum(dim=-1) )
def _get_single_cell(self) -> None: r"""Set the partitioning to be a single cell in the case of no Pareto points.""" # Set lower and upper bounds to be the reference point to define an empty cell cell_bounds = self.ref_point.expand( 2, *self._neg_pareto_Y.shape[:-2], 1, self.num_outcomes ).clone() self.register_buffer("hypercell_bounds", cell_bounds)