Source code for botorch.acquisition.multi_objective.utils

#!/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.

Utilities for multi-objective acquisition functions.

from __future__ import annotations

import math
import warnings
from typing import Callable, List, Optional

import torch
from botorch import settings
from botorch.acquisition import monte_carlo  # noqa F401
from botorch.acquisition.multi_objective.objective import (
from botorch.exceptions.errors import UnsupportedError
from botorch.exceptions.warnings import BotorchWarning, SamplingWarning
from botorch.models.model import Model
from botorch.sampling.samplers import IIDNormalSampler, SobolQMCNormalSampler
from botorch.utils.multi_objective.pareto import is_non_dominated
from botorch.utils.transforms import normalize_indices
from torch import Tensor
from torch.quasirandom import SobolEngine

[docs]def get_default_partitioning_alpha(num_objectives: int) -> float: r"""Determines an approximation level based on the number of objectives. If `alpha` is 0, FastNondominatedPartitioning should be used. Otherwise, an approximate NondominatedPartitioning should be used with approximation level `alpha`. Args: num_objectives: the number of objectives. Returns: The approximation level `alpha`. """ if num_objectives <= 4: return 0.0 elif num_objectives > 6: warnings.warn("EHVI works best for less than 7 objectives.", BotorchWarning) return 10 ** (-8 + num_objectives)
[docs]def prune_inferior_points_multi_objective( model: Model, X: Tensor, ref_point: Tensor, objective: Optional[MCMultiOutputObjective] = None, constraints: Optional[List[Callable[[Tensor], Tensor]]] = None, num_samples: int = 2048, max_frac: float = 1.0, marginalize_dim: Optional[int] = None, ) -> Tensor: r"""Prune points from an input tensor that are unlikely to be pareto optimal. Given a model, an objective, and an input tensor `X`, this function returns the subset of points in `X` that have some probability of being pareto optimal, better than the reference point, and feasible. This function uses sampling to estimate the probabilities, the higher the number of points `n` in `X` the higher the number of samples `num_samples` should be to obtain accurate estimates. Args: model: A fitted model. Batched models are currently not supported. X: An input tensor of shape `n x d`. Batched inputs are currently not supported. ref_point: The reference point. objective: The objective under which to evaluate the posterior. constraints: A list of callables, each mapping a Tensor of dimension `sample_shape x batch-shape x q x m` to a Tensor of dimension `sample_shape x batch-shape x q`, where negative values imply feasibility. num_samples: The number of samples used to compute empirical probabilities of being the best point. max_frac: The maximum fraction of points to retain. Must satisfy `0 < max_frac <= 1`. Ensures that the number of elements in the returned tensor does not exceed `ceil(max_frac * n)`. marginalize_dim: A batch dimension that should be marginalized. For example, this is useful when using a batched fully Bayesian model. Returns: A `n' x d` with subset of points in `X`, where n' = min(N_nz, ceil(max_frac * n)) with `N_nz` the number of points in `X` that have non-zero (empirical, under `num_samples` samples) probability of being pareto optimal. """ if X.ndim > 2: # TODO: support batched inputs (req. dealing with ragged tensors) raise UnsupportedError( "Batched inputs `X` are currently unsupported by " "prune_inferior_points_multi_objective" ) max_points = math.ceil(max_frac * X.size(-2)) if max_points < 1 or max_points > X.size(-2): raise ValueError(f"max_frac must take values in (0, 1], is {max_frac}") with torch.no_grad(): posterior = model.posterior(X=X) if posterior.event_shape.numel() > SobolEngine.MAXDIM: if settings.debug.on(): warnings.warn( f"Sample dimension q*m={posterior.event_shape.numel()} exceeding Sobol " f"max dimension ({SobolEngine.MAXDIM}). Using iid samples instead.", SamplingWarning, ) sampler = IIDNormalSampler(num_samples=num_samples) else: sampler = SobolQMCNormalSampler(num_samples=num_samples) samples = sampler(posterior) if objective is None: objective = IdentityMCMultiOutputObjective() obj_vals = objective(samples, X=X) if obj_vals.ndim > 3: if obj_vals.ndim == 4 and marginalize_dim is not None: obj_vals = obj_vals.mean(dim=marginalize_dim) else: # TODO: support batched inputs (req. dealing with ragged tensors) raise UnsupportedError( "Models with multiple batch dims are currently unsupported by" " prune_inferior_points_multi_objective." ) if constraints is not None: infeas = torch.stack([c(samples) > 0 for c in constraints], dim=0).any(dim=0) if infeas.ndim == 3 and marginalize_dim is not None: # make sure marginalize_dim is not negative if marginalize_dim < 0: # add 1 to the normalize marginalize_dim since we have already # removed the output dim marginalize_dim = ( 1 + normalize_indices([marginalize_dim], d=infeas.ndim)[0] ) infeas = infeas.float().mean(dim=marginalize_dim).round().bool() # set infeasible points to be the ref point obj_vals[infeas] = ref_point pareto_mask = is_non_dominated(obj_vals, deduplicate=False) & ( obj_vals > ref_point ).all(dim=-1) probs = idcs = probs.nonzero().view(-1) if idcs.shape[0] > max_points: counts, order_idcs = torch.sort(probs, descending=True) idcs = order_idcs[:max_points] effective_n_w = obj_vals.shape[-2] // X.shape[-2] idcs = (idcs / effective_n_w).long().unique() return X[idcs]