Source code for botorch.utils.multi_objective.box_decompositions.non_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 non-dominated space into rectangles.


.. [Couckuyt2012]
    I. Couckuyt, D. Deschrijver and T. Dhaene, "Towards Efficient
    Multiobjective Optimization: Multiobjective statistical criterions,"
    2012 IEEE Congress on Evolutionary Computation, Brisbane, QLD, 2012,
    pp. 1-8.


from __future__ import annotations

from typing import Optional

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 NondominatedPartitioning(BoxDecomposition): r"""A class for partitioning the non-dominated space into hyper-cells. Note: this assumes maximization. Internally, it multiplies outcomes by -1 and performs the decomposition under minimization. TODO: use maximization internally as well. Note: it is only feasible to use this algorithm to compute an exact decomposition of the non-dominated space for `m<5` objectives (alpha=0.0). The alpha parameter can be increased to obtain an approximate partitioning faster. The `alpha` is a fraction of the total hypervolume encapsuling the entire Pareto set. When a hypercell's volume divided by the total hypervolume is less than `alpha`, we discard the hypercell. See Figure 2 in [Couckuyt2012]_ for a visual representation. This PyTorch implementation of the binary partitioning algorithm ([Couckuyt2012]_) is adapted from numpy/tensorflow implementation at: TODO: replace this with a more efficient decomposition. E.g. """ def __init__( self, ref_point: Tensor, Y: Optional[Tensor] = None, alpha: float = 0.0, ) -> None: """Initialize NondominatedPartitioning. Args: ref_point: A `m`-dim tensor containing the reference point. Y: A `(batch_shape) x n x m`-dim tensor. alpha: A thresold fraction of total volume used in an approximate decomposition. Example: >>> bd = NondominatedPartitioning(ref_point, Y=Y1) """ self.alpha = alpha super().__init__(ref_point=ref_point, sort=True, Y=Y) def _partition_space(self) -> None: r"""Partition the non-dominated space into disjoint hypercells. This method supports an arbitrary number of outcomes, but is less efficient than `partition_space_2d` for the 2-outcome case. """ # The binary parititoning algorithm uses indices the augmented Pareto front. # n_pareto + 2 x m aug_pareto_Y_idcs = self._get_augmented_pareto_front_indices() # Initialize one cell over entire pareto front cell = torch.zeros( 2, self.num_outcomes, dtype=torch.long, device=self._neg_Y.device ) cell[1] = aug_pareto_Y_idcs.shape[0] - 1 stack = [cell] # hypercells contains the indices of the (augmented) Pareto front # that specify that bounds of the each hypercell. # It is a `2 x num_cells x m`-dim tensor self.hypercells = torch.empty( 2, 0, self.num_outcomes, dtype=torch.long, device=self._neg_Y.device ) outcome_idxr = torch.arange( self.num_outcomes, dtype=torch.long, device=self._neg_Y.device ) # edge case: empty pareto set # use a single cell if self._neg_pareto_Y.shape[-2] == 0: # 2 x m cell_bounds_pareto_idcs = aug_pareto_Y_idcs[cell, outcome_idxr] self.hypercells = [self.hypercells, cell_bounds_pareto_idcs.unsqueeze(1)], dim=1 ) else: # Extend Pareto front with the ideal and anti-ideal point ideal_point = self._neg_pareto_Y.min(dim=0, keepdim=True).values - 1 anti_ideal_point = self._neg_pareto_Y.max(dim=0, keepdim=True).values + 1 # `n_pareto + 2 x m` aug_pareto_Y = [ideal_point, self._neg_pareto_Y, anti_ideal_point], dim=0 ) total_volume = (anti_ideal_point - ideal_point).prod() # Use binary partitioning while len(stack) > 0: # The following 3 tensors are all `2 x m` cell = stack.pop() cell_bounds_pareto_idcs = aug_pareto_Y_idcs[cell, outcome_idxr] cell_bounds_pareto_values = aug_pareto_Y[ cell_bounds_pareto_idcs, outcome_idxr ] # Check cell bounds # - if cell upper bound is better than Pareto front on all outcomes: # - accept the cell # - elif cell lower bound is better than Pareto front on all outcomes: # - this means the cell overlaps the Pareto front. Divide the cell # along its longest edge. if ( (cell_bounds_pareto_values[1] <= self._neg_pareto_Y) .any(dim=1) .all() ): # Cell is entirely non-dominated self.hypercells = [self.hypercells, cell_bounds_pareto_idcs.unsqueeze(1)], dim=1 ) elif ( (cell_bounds_pareto_values[0] <= self._neg_pareto_Y) .any(dim=1) .all() ): # The cell overlaps the pareto front # compute the distance (in integer indices) # This has shape `m` idx_dist = cell[1] - cell[0] any_not_adjacent = (idx_dist > 1).any() cell_volume = ( (cell_bounds_pareto_values[1] - cell_bounds_pareto_values[0]) .prod(dim=-1) .item() ) # Only divide a cell when it is not composed of adjacent indices # and the fraction of total volume is above the approximation # threshold fraction if ( any_not_adjacent and ((cell_volume / total_volume) > self.alpha).all() ): # Divide the test cell over its largest dimension # largest (by index length) length, longest_dim = torch.max(idx_dist, dim=0) length = length.item() longest_dim = longest_dim.item() new_length1 = int(round(length / 2.0)) new_length2 = length - new_length1 # Store divided cells # cell 1: subtract new_length1 from the upper bound of the cell # cell 2: add new_length2 to the lower bound of the cell for bound_idx, length_delta in ( (1, -new_length1), (0, new_length2), ): new_cell = cell.clone() new_cell[bound_idx, longest_dim] += length_delta stack.append(new_cell) def _partition_space_2d(self) -> None: r"""Partition the non-dominated space into disjoint hypercells. This direct method works for `m=2` outcomes. """ pf_ext_idx = self._get_augmented_pareto_front_indices() n_pf_plus_1 = self._neg_pareto_Y.shape[-2] + 1 view_shape = torch.Size([1] * len(self.batch_shape) + [n_pf_plus_1]) expand_shape = self.batch_shape + torch.Size([n_pf_plus_1]) range_pf_plus1 = torch.arange( n_pf_plus_1, dtype=torch.long, device=self._neg_pareto_Y.device ) range_pf_plus1_expanded = range_pf_plus1.view(view_shape).expand(expand_shape) lower = torch.stack( [range_pf_plus1_expanded, torch.zeros_like(range_pf_plus1_expanded)], dim=-1 ) upper = torch.stack( [1 + range_pf_plus1_expanded, pf_ext_idx[..., -range_pf_plus1 - 1, -1]], dim=-1, ) # 2 x batch_shape x n_cells x 2 self.hypercells = torch.stack([lower, upper], dim=0) def _get_augmented_pareto_front_indices(self) -> Tensor: r"""Get indices of augmented Pareto front.""" pf_idx = torch.argsort(self._neg_pareto_Y, dim=-2) return [ torch.zeros( *self.batch_shape, 1, self.num_outcomes, dtype=torch.long, device=self._neg_Y.device, ), # Add 1 because index zero is used for the ideal point pf_idx + 1, torch.full( torch.Size( [ *self.batch_shape, 1, self.num_outcomes, ] ), self._neg_pareto_Y.shape[-2] + 1, dtype=torch.long, device=self._neg_Y.device, ), ], dim=-2, )
[docs] def get_hypercell_bounds(self) -> Tensor: r"""Get the bounds of each hypercell in the decomposition. Args: ref_point: A `(batch_shape) x m`-dim tensor containing the reference point. Returns: A `2 x num_cells x m`-dim tensor containing the lower and upper vertices bounding each hypercell. """ ref_point = _expand_ref_point( ref_point=self.ref_point, batch_shape=self.batch_shape ) aug_pareto_Y = [ # -inf is the lower bound of the non-dominated space torch.full( torch.Size( [ *self.batch_shape, 1, self.num_outcomes, ] ), float("-inf"), dtype=self._neg_pareto_Y.dtype, device=self._neg_pareto_Y.device, ), self._neg_pareto_Y, # note: internally, this class minimizes, so use negative here -(ref_point.unsqueeze(-2)), ], dim=-2, ) minimization_cell_bounds = self._get_hypercell_bounds(aug_pareto_Y=aug_pareto_Y) # swap upper and lower bounds and multiply by -1 return -minimization_cell_bounds.flip(0)
def _get_hypercell_bounds(self, aug_pareto_Y: Tensor) -> Tensor: r"""Get the bounds of each hypercell in the decomposition. Args: aug_pareto_Y: A `n_pareto + 2 x m`-dim tensor containing the augmented Pareto front. Returns: A `2 x (batch_shape) x num_cells x m`-dim tensor containing the lower and upper vertices bounding each hypercell. """ num_cells = self.hypercells.shape[-2] cells_times_outcomes = num_cells * self.num_outcomes outcome_idxr = ( torch.arange(self.num_outcomes, dtype=torch.long, device=self._neg_Y.device) .repeat(num_cells) .view( *(1 for _ in self.hypercells.shape[:-2]), cells_times_outcomes, ) .expand(*self.hypercells.shape[:-2], cells_times_outcomes) ) # this tensor is 2 x (num_cells * m) x 2 # the batch dim corresponds to lower/upper bound cell_bounds_idxr = torch.stack( [ self.hypercells.view(*self.hypercells.shape[:-2], -1), outcome_idxr, ], dim=-1, ).view(2, -1, 2) if len(self.batch_shape) > 0: # TODO: support multiple batch dimensions here batch_idxr = ( torch.arange( self.batch_shape[0], dtype=torch.long, device=self._neg_Y.device ) .unsqueeze(1) .expand(-1, cells_times_outcomes) .reshape(1, -1, 1) .expand(2, -1, 1) ) cell_bounds_idxr =[batch_idxr, cell_bounds_idxr], dim=-1) cell_bounds_values = aug_pareto_Y[ cell_bounds_idxr.chunk(cell_bounds_idxr.shape[-1], dim=-1) ] view_shape = (2, *self.batch_shape, num_cells, self.num_outcomes) return cell_bounds_values.view(view_shape) def _compute_hypervolume_if_y_has_data(self) -> Tensor: ref_point = _expand_ref_point( ref_point=self.ref_point, batch_shape=self.batch_shape ) # internally we minimize ref_point = -ref_point.unsqueeze(-2) ideal_point = self._neg_pareto_Y.min(dim=-2, keepdim=True).values aug_pareto_Y =[ideal_point, self._neg_pareto_Y, ref_point], dim=-2) cell_bounds_values = self._get_hypercell_bounds(aug_pareto_Y=aug_pareto_Y) total_volume = (ref_point - ideal_point).squeeze(-2).prod(dim=-1) non_dom_volume = ( (cell_bounds_values[1] - cell_bounds_values[0]).prod(dim=-1).sum(dim=-1) ) return total_volume - non_dom_volume
[docs] class FastNondominatedPartitioning(FastPartitioning): r"""A class for partitioning the non-dominated space into hyper-cells. Note: this assumes maximization. Internally, it multiplies by -1 and performs the decomposition under minimization. This class is far more efficient than NondominatedPartitioning for exact box partitionings This class uses the two-step approach similar to that in [Yang2019]_, where: a) first, Alg 1 from [Lacour17]_ is used to find the local lower bounds for the maximization problem b) second, the local lower bounds are used as the Pareto frontier for the minimization problem, and [Lacour17]_ is applied again to partition the space dominated by that Pareto frontier. """ def __init__( self, ref_point: Tensor, Y: Optional[Tensor] = None, ) -> None: """Initialize FastNondominatedPartitioning. Args: ref_point: A `m`-dim tensor containing the reference point. Y: A `(batch_shape) x n x m`-dim tensor. Example: >>> bd = FastNondominatedPartitioning(ref_point, Y=Y1) """ super().__init__(ref_point=ref_point, Y=Y) def _get_single_cell(self) -> None: r"""Set the partitioning to be a single cell in the case of no Pareto points.""" cell_bounds = torch.full( (2, *self._neg_pareto_Y.shape[:-2], 1, self.num_outcomes), float("inf"), dtype=self._neg_pareto_Y.dtype, device=self._neg_pareto_Y.device, ) cell_bounds[0] = self.ref_point self.hypercell_bounds = cell_bounds def _get_partitioning(self) -> None: r"""Compute non-dominated partitioning. Given local upper bounds for the minimization problem (self._U), this computes the non-dominated partitioning for the maximization problem. Note that -self.U contains the local lower bounds for the maximization problem. Following [Yang2019]_, this treats -self.U as a *new* pareto frontier for a minimization problem with a reference point of [infinity]^m and computes a dominated partitioning for this minimization problem. """ new_ref_point = torch.full( torch.Size([1]) + self._neg_ref_point.shape, float("inf"), dtype=self._neg_ref_point.dtype, device=self._neg_ref_point.device, ) # initialize local upper bounds for the second minimization problem self._U2 = new_ref_point # initialize defining points for the second minimization problem # use ref point for maximization as the ideal point for minimization. self._Z2 = self.ref_point.expand( 1, self.num_outcomes, self.num_outcomes ).clone() for j in range(self._neg_ref_point.shape[-1]): self._Z2[0, j, j] = self._U2[0, j] # incrementally update local upper bounds and defining points # for each new Pareto point self._U2, self._Z2 = update_local_upper_bounds_incremental( new_pareto_Y=-self._U, U=self._U2, Z=self._Z2, ) cell_bounds = get_partition_bounds( Z=self._Z2, U=self._U2, ref_point=new_ref_point.view(-1) ) self.hypercell_bounds = cell_bounds 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_non_dominated_hypercell_bounds_2d( pareto_Y_sorted=self.pareto_Y.flip(-2), ref_point=self.ref_point, ) self.hypercell_bounds = cell_bounds def _compute_hypervolume_if_y_has_data(self) -> Tensor: ideal_point = self.pareto_Y.max(dim=-2, keepdim=True).values total_volume = ( (ideal_point.squeeze(-2) - self.ref_point).clamp_min(0.0).prod(dim=-1) ) finite_cell_bounds = torch.min(self.hypercell_bounds, ideal_point) non_dom_volume = ( (finite_cell_bounds[1] - finite_cell_bounds[0]) .clamp_min(0.0) .prod(dim=-1) .sum(dim=-1) ) return total_volume - non_dom_volume