Add isinstance_af

Summary: Creates a new helper method for checking both if a given AF is an instance of a class or if the given AF wraps a base AF that is an instance of a class

Differential Revision: D43127722

fbshipit-source-id: 0ec0131cf1c7512c10ab14e5d0a0a20cf3025688

botorch/acquisition/utils.py

@@ -11,7 +11,7 @@
 from __future__ import annotations
 
 import math
-from typing import Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from botorch.acquisition import analytic, monte_carlo, multi_objective  # noqa F401
@@ -22,6 +22,7 @@
     MCAcquisitionObjective,
     PosteriorTransform,
 )
+from botorch.acquisition.wrapper import AbstractAcquisitionFunctionWrapper
 from botorch.exceptions.errors import UnsupportedError
 from botorch.models.fully_bayesian import MCMC_DIM
 from botorch.models.model import Model
@@ -253,6 +254,18 @@ def objective(Y: Tensor, X: Optional[Tensor] = None):
     return -(lb.clamp_max(0.0))
 
 
+def isinstance_af(
+    __obj: object,
+    __class_or_tuple: Union[type, tuple[Union[type, tuple[Any, ...]], ...]],
+) -> bool:
+    r"""A variant of isinstance first checks for the acq_func attribute on wrapped acquisition functions."""
+    if isinstance(__obj, AbstractAcquisitionFunctionWrapper):
+        isinstance_base_af = isinstance(__obj.acq_func, __class_or_tuple)
+    else:
+        isinstance_base_af = False
+    return isinstance_base_af or isinstance(__obj, __class_or_tuple)
+
+
 def is_nonnegative(acq_function: AcquisitionFunction) -> bool:
     r"""Determine whether a given acquisition function is non-negative.
 
@@ -267,7 +280,7 @@ def is_nonnegative(acq_function: AcquisitionFunction) -> bool:
         >>> qEI = qExpectedImprovement(model, best_f=0.1)
         >>> is_nonnegative(qEI)  # returns True
     """
-    return isinstance(
+    return isinstance_af(
         acq_function,
         (
             analytic.ExpectedImprovement,

test/acquisition/test_utils.py

@@ -8,7 +8,8 @@
 from unittest import mock
 
 import torch
-from botorch.acquisition import monte_carlo
+from botorch.acquisition import analytic, monte_carlo, multi_objective
+from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
 from botorch.acquisition.multi_objective import (
     MCMultiOutputObjective,
     monte_carlo as moo_monte_carlo,
@@ -18,10 +19,13 @@
     MCAcquisitionObjective,
     ScalarizedPosteriorTransform,
 )
+from botorch.acquisition.proximal import ProximalAcquisitionFunction
 from botorch.acquisition.utils import (
     expand_trace_observations,
     get_acquisition_function,
     get_infeasible_cost,
+    is_nonnegative,
+    isinstance_af,
     project_to_sample_points,
     project_to_target_fidelity,
     prune_inferior_points,
@@ -606,6 +610,61 @@ def test_get_infeasible_cost(self):
             self.assertAllClose(M4, torch.tensor([1.0], **tkwargs))
 
 
+class TestIsNonnegative(BotorchTestCase):
+    def test_is_nonnegative(self):
+        nonneg_afs = (
+            analytic.ExpectedImprovement,
+            analytic.ConstrainedExpectedImprovement,
+            analytic.ProbabilityOfImprovement,
+            analytic.NoisyExpectedImprovement,
+            monte_carlo.qExpectedImprovement,
+            monte_carlo.qNoisyExpectedImprovement,
+            monte_carlo.qProbabilityOfImprovement,
+            multi_objective.analytic.ExpectedHypervolumeImprovement,
+            multi_objective.monte_carlo.qExpectedHypervolumeImprovement,
+            multi_objective.monte_carlo.qNoisyExpectedHypervolumeImprovement,
+        )
+        mm = MockModel(
+            MockPosterior(
+                mean=torch.rand(1, 1, device=self.device),
+                variance=torch.ones(1, 1, device=self.device),
+            )
+        )
+        acq_func = analytic.ExpectedImprovement(model=mm, best_f=-1.0)
+        with mock.patch(
+            "botorch.acquisition.utils.isinstance_af", return_value=True
+        ) as mock_isinstance_af:
+            self.assertTrue(is_nonnegative(acq_function=acq_func))
+            mock_isinstance_af.assert_called_once()
+            cargs, _ = mock_isinstance_af.call_args
+            self.assertIs(cargs[0], acq_func)
+            self.assertEqual(cargs[1], nonneg_afs)
+        acq_func = analytic.UpperConfidenceBound(model=mm, beta=2.0)
+        self.assertFalse(is_nonnegative(acq_function=acq_func))
+
+
+class TestIsinstanceAf(BotorchTestCase):
+    def test_isinstance_af(self):
+        mm = MockModel(
+            MockPosterior(
+                mean=torch.rand(1, 1, device=self.device),
+                variance=torch.ones(1, 1, device=self.device),
+            )
+        )
+        acq_func = analytic.ExpectedImprovement(model=mm, best_f=-1.0)
+        self.assertTrue(isinstance_af(acq_func, analytic.ExpectedImprovement))
+        self.assertFalse(isinstance_af(acq_func, analytic.UpperConfidenceBound))
+        wrapped_af = FixedFeatureAcquisitionFunction(
+            acq_function=acq_func, d=2, columns=[1], values=[0.0]
+        )
+        # test base af class
+        self.assertTrue(isinstance_af(wrapped_af, analytic.ExpectedImprovement))
+        self.assertFalse(isinstance_af(wrapped_af, analytic.UpperConfidenceBound))
+        # test wrapper class
+        self.assertTrue(isinstance_af(wrapped_af, FixedFeatureAcquisitionFunction))
+        self.assertFalse(isinstance_af(wrapped_af, ProximalAcquisitionFunction))
+
+
 class TestPruneInferiorPoints(BotorchTestCase):
     def test_prune_inferior_points(self):
         for dtype in (torch.float, torch.double):