fix twisted import structure in botorch.acquisition.utils

Summary:
X-link: facebookexternal/botorch_fb#14

See title. This fixes a twisted import structure where botorch.acquisition.monte_carlo imports from utils and utils imports botorch.acquisition.monte_carlo

Differential Revision: D48329866

fbshipit-source-id: dab419da350b05fa8c1d095b1f8e8822b8e295cf

botorch/acquisition/__init__.py

@@ -29,6 +29,7 @@
     InverseCostWeightedUtility,
 )
 from botorch.acquisition.decoupled import DecoupledAcquisitionFunction
+from botorch.acquisition.factory import get_acquisition_function
 from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
 from botorch.acquisition.input_constructors import get_acqf_input_constructor
 from botorch.acquisition.knowledge_gradient import (
@@ -72,7 +73,6 @@
 )
 from botorch.acquisition.prior_guided import PriorGuidedAcquisitionFunction
 from botorch.acquisition.proximal import ProximalAcquisitionFunction
-from botorch.acquisition.utils import get_acquisition_function
 
 __all__ = [
     "AcquisitionFunction",

botorch/acquisition/factory.py

@@ -0,0 +1,252 @@
+#!/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 acquisition functions.
+"""
+
+from __future__ import annotations
+
+from typing import Callable, List, Optional, Union
+
+import torch
+from botorch.acquisition import monte_carlo
+from botorch.acquisition.multi_objective import monte_carlo as moo_monte_carlo
+from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
+from botorch.acquisition.utils import compute_best_feasible_objective
+from botorch.models.model import Model
+from botorch.sampling.get_sampler import get_sampler
+from botorch.utils.multi_objective.box_decompositions.non_dominated import (
+    FastNondominatedPartitioning,
+    NondominatedPartitioning,
+)
+from torch import Tensor
+
+
+def get_acquisition_function(
+    acquisition_function_name: str,
+    model: Model,
+    objective: MCAcquisitionObjective,
+    X_observed: Tensor,
+    posterior_transform: Optional[PosteriorTransform] = None,
+    X_pending: Optional[Tensor] = None,
+    constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
+    eta: Optional[Union[Tensor, float]] = 1e-3,
+    mc_samples: int = 512,
+    seed: Optional[int] = None,
+    *,
+    # optional parameters that are only needed for certain acquisition functions
+    tau: float = 1e-3,
+    prune_baseline: bool = True,
+    marginalize_dim: Optional[int] = None,
+    cache_root: bool = True,
+    beta: Optional[float] = None,
+    ref_point: Union[None, List[float], Tensor] = None,
+    Y: Optional[Tensor] = None,
+    alpha: float = 0.0,
+) -> monte_carlo.MCAcquisitionFunction:
+    r"""Convenience function for initializing botorch acquisition functions.
+
+    Args:
+        acquisition_function_name: Name of the acquisition function.
+        model: A fitted model.
+        objective: A MCAcquisitionObjective.
+        X_observed: A `m1 x d`-dim Tensor of `m1` design points that have
+            already been observed.
+        posterior_transform: A PosteriorTransform (optional).
+        X_pending: A `m2 x d`-dim Tensor of `m2` design points whose evaluation
+            is pending.
+        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. Used for all acquisition functions except qSR and qUCB.
+        eta: The temperature parameter for the sigmoid function used for the
+            differentiable approximation of the constraints. In case of a float the
+            same eta is used for every constraint in constraints. In case of a
+            tensor the length of the tensor must match the number of provided
+            constraints. The i-th constraint is then estimated with the i-th
+            eta value. Used for all acquisition functions except qSR and qUCB.
+        mc_samples: The number of samples to use for (q)MC evaluation of the
+            acquisition function.
+        seed: If provided, perform deterministic optimization (i.e. the
+            function to optimize is fixed and not stochastic).
+
+    Returns:
+        The requested acquisition function.
+
+    Example:
+        >>> model = SingleTaskGP(train_X, train_Y)
+        >>> obj = LinearMCObjective(weights=torch.tensor([1.0, 2.0]))
+        >>> acqf = get_acquisition_function("qEI", model, obj, train_X)
+    """
+    # initialize the sampler
+    sampler = get_sampler(
+        posterior=model.posterior(X_observed[:1]),
+        sample_shape=torch.Size([mc_samples]),
+        seed=seed,
+    )
+    if posterior_transform is not None and acquisition_function_name in [
+        "qEHVI",
+        "qNEHVI",
+    ]:
+        raise NotImplementedError(
+            "PosteriorTransforms are not yet implemented for multi-objective "
+            "acquisition functions."
+        )
+    # instantiate and return the requested acquisition function
+    if acquisition_function_name in ("qEI", "qLogEI", "qPI"):
+        # Since these are the non-noisy variants, use the posterior mean at the observed
+        # inputs directly to compute the best feasible value without sampling.
+        Y = model.posterior(X_observed, posterior_transform=posterior_transform).mean
+        obj = objective(samples=Y, X=X_observed)
+        best_f = compute_best_feasible_objective(
+            samples=Y,
+            obj=obj,
+            constraints=constraints,
+            model=model,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_baseline=X_observed,
+        )
+    if acquisition_function_name == "qEI":
+        return monte_carlo.qExpectedImprovement(
+            model=model,
+            best_f=best_f,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+            constraints=constraints,
+            eta=eta,
+        )
+    if acquisition_function_name == "qLogEI":
+        # putting the import here to avoid circular imports
+        # ideally, the entire function should be moved out of this file,
+        # but since it is used for legacy code to be deprecated, we keep it here.
+        from botorch.acquisition.logei import qLogExpectedImprovement
+
+        return qLogExpectedImprovement(
+            model=model,
+            best_f=best_f,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+            constraints=constraints,
+            eta=eta,
+        )
+    elif acquisition_function_name == "qPI":
+        return monte_carlo.qProbabilityOfImprovement(
+            model=model,
+            best_f=best_f,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+            tau=tau,
+            constraints=constraints,
+            eta=eta,
+        )
+    elif acquisition_function_name == "qNEI":
+        return monte_carlo.qNoisyExpectedImprovement(
+            model=model,
+            X_baseline=X_observed,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+            prune_baseline=prune_baseline,
+            marginalize_dim=marginalize_dim,
+            cache_root=cache_root,
+            constraints=constraints,
+            eta=eta,
+        )
+    elif acquisition_function_name == "qLogNEI":
+        from botorch.acquisition.logei import qLogNoisyExpectedImprovement
+
+        return qLogNoisyExpectedImprovement(
+            model=model,
+            X_baseline=X_observed,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+            prune_baseline=prune_baseline,
+            marginalize_dim=marginalize_dim,
+            cache_root=cache_root,
+            constraints=constraints,
+            eta=eta,
+        )
+    elif acquisition_function_name == "qSR":
+        return monte_carlo.qSimpleRegret(
+            model=model,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+        )
+    elif acquisition_function_name == "qUCB":
+        if beta is None:
+            raise ValueError("`beta` must be not be None for qUCB.")
+        return monte_carlo.qUpperConfidenceBound(
+            model=model,
+            beta=beta,
+            sampler=sampler,
+            objective=objective,
+            posterior_transform=posterior_transform,
+            X_pending=X_pending,
+        )
+    elif acquisition_function_name == "qEHVI":
+        if Y is None:
+            raise ValueError("`Y` must not be None for qEHVI")
+        if ref_point is None:
+            raise ValueError("`ref_point` must not be None for qEHVI")
+        # get feasible points
+        if constraints is not None:
+            feas = torch.stack([c(Y) <= 0 for c in constraints], dim=-1).all(dim=-1)
+            Y = Y[feas]
+        obj = objective(Y)
+        if alpha > 0:
+            partitioning = NondominatedPartitioning(
+                ref_point=torch.as_tensor(ref_point, dtype=Y.dtype, device=Y.device),
+                Y=obj,
+                alpha=alpha,
+            )
+        else:
+            partitioning = FastNondominatedPartitioning(
+                ref_point=torch.as_tensor(ref_point, dtype=Y.dtype, device=Y.device),
+                Y=obj,
+            )
+        return moo_monte_carlo.qExpectedHypervolumeImprovement(
+            model=model,
+            ref_point=ref_point,
+            partitioning=partitioning,
+            sampler=sampler,
+            objective=objective,
+            constraints=constraints,
+            eta=eta,
+            X_pending=X_pending,
+        )
+    elif acquisition_function_name == "qNEHVI":
+        if ref_point is None:
+            raise ValueError("`ref_point` must not be None for qNEHVI")
+        return moo_monte_carlo.qNoisyExpectedHypervolumeImprovement(
+            model=model,
+            ref_point=ref_point,
+            X_baseline=X_observed,
+            sampler=sampler,
+            objective=objective,
+            constraints=constraints,
+            eta=eta,
+            prune_baseline=prune_baseline,
+            alpha=alpha,
+            X_pending=X_pending,
+            marginalize_dim=marginalize_dim,
+            cache_root=cache_root,
+        )
+    raise NotImplementedError(
+        f"Unknown acquisition function {acquisition_function_name}"
+    )