Source code for botorch.optim.utils.acquisition_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.

r"""Utilities for maximizing acquisition functions."""

from __future__ import annotations

from typing import Dict, Optional, Union
from warnings import warn

import torch
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.exceptions.errors import BotorchError
from botorch.exceptions.warnings import BotorchWarning
from botorch.models.gpytorch import ModelListGPyTorchModel
from torch import Tensor

[docs]def columnwise_clamp( X: Tensor, lower: Optional[Union[float, Tensor]] = None, upper: Optional[Union[float, Tensor]] = None, raise_on_violation: bool = False, ) -> Tensor: r"""Clamp values of a Tensor in column-wise fashion (with support for t-batches). This function is useful in conjunction with optimizers from the torch.optim package, which don't natively handle constraints. If you apply this after a gradient step you can be fancy and call it "projected gradient descent". This funtion is also useful for post-processing candidates generated by the scipy optimizer that satisfy bounds only up to numerical accuracy. Args: X: The `b x n x d` input tensor. If 2-dimensional, `b` is assumed to be 1. lower: The column-wise lower bounds. If scalar, apply bound to all columns. upper: The column-wise upper bounds. If scalar, apply bound to all columns. raise_on_violation: If `True`, raise an exception when the elments in `X` are out of the specified bounds (up to numerical accuracy). This is useful for post-processing candidates generated by optimizers that satisfy imposed bounds only up to numerical accuracy. Returns: The clamped tensor. """ if lower is None and upper is None: return X if lower is not None: lower = torch.as_tensor(lower).expand_as(X).to(X) if upper is not None: upper = torch.as_tensor(upper).expand_as(X).to(X) if lower is not None and (lower > upper).any(): raise ValueError("Lower bounds cannot exceed upper bounds.") out = X.clamp(lower, upper) if raise_on_violation and not X.allclose(out): raise BotorchError("Original value(s) are out of bounds.") return out
[docs]def fix_features( X: Tensor, fixed_features: Optional[Dict[int, Optional[float]]] = None ) -> Tensor: r"""Fix feature values in a Tensor. The fixed features will have zero gradient in downstream calculations. Args: X: input Tensor with shape `... x p`, where `p` is the number of features fixed_features: A dictionary with keys as column indices and values equal to what the feature should be set to in `X`. If the value is None, that column is just considered fixed. Keys should be in the range `[0, p - 1]`. Returns: The tensor X with fixed features. """ if fixed_features is None: return X columns = list(X.unbind(dim=-1)) for index, value in fixed_features.items(): if value is None: columns[index] = columns[index].detach() else: columns[index] = torch.full_like(columns[index], value) return torch.stack(columns, dim=-1)
[docs]def get_X_baseline(acq_function: AcquisitionFunction) -> Optional[Tensor]: r"""Extract X_baseline from an acquisition function. This tries to find the baseline set of points. First, this checks if the acquisition function has an `X_baseline` attribute. If it does not, then this method attempts to use the model's `train_inputs` as `X_baseline`. Args: acq_function: The acquisition function. Returns An optional `n x d`-dim tensor of baseline points. This is None if no baseline points are found. """ try: X = acq_function.X_baseline # if there are no baseline points, use training points if X.shape[0] == 0: raise BotorchError except (BotorchError, AttributeError): try: # for entropy MOO methods model = acq_function.mo_model except AttributeError: try: # some acquisition functions do not have a model attribute # e.g. FixedFeatureAcquisitionFunction model = acq_function.model except AttributeError: warn("Failed to extract X_baseline.", BotorchWarning) return try: # Make sure we get the original train inputs. m = model.models[0] if isinstance(model, ModelListGPyTorchModel) else model if m._has_transformed_inputs: X = m._original_train_inputs else: X = m.train_inputs[0] except (BotorchError, AttributeError): warn("Failed to extract X_baseline.", BotorchWarning) return # just use one batch while X.ndim > 2: X = X[0] return X