make qNIPV not an AnalyticAcquisitionFunction; optimize_acqf support clarity (#2286)

Summary:
Pull Request resolved: #2286

I put a `LogExpectedImprovement` instance into `optimize_acqf`, and when I got an error about it not having an attribute `X_pending`, I was not sure if this was a bug or if I did something known to be unsupported.

- Make `qNegIntegratedPosteriorVariance` inherit from `AcquisitionFunction` rather than `AnalyticAcquisitionFunction`, because the functionality it was inheriting from `AnalyticAcquisitionFunction` was not relevant.
- `qNegIntegratedPosteriorVariance` loses an error message about not supporting multi-output with a `PosteriorTransform` that is not scalarized and gains a unit test showing that it does.

Reviewed By: Balandat

Differential Revision: D55843171

fbshipit-source-id: 87cbc84783c61e09f5f8dce935422b429ad8699d

Author: esantorella

botorch/acquisition/active_learning.py

@@ -27,7 +27,7 @@
 
 import torch
 from botorch import settings
-from botorch.acquisition.analytic import AnalyticAcquisitionFunction
+from botorch.acquisition.acquisition import AcquisitionFunction
 from botorch.acquisition.monte_carlo import MCAcquisitionFunction
 from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
 from botorch.models.model import Model
@@ -37,7 +37,7 @@
 from torch import Tensor
 
 
-class qNegIntegratedPosteriorVariance(AnalyticAcquisitionFunction):
+class qNegIntegratedPosteriorVariance(AcquisitionFunction):
     r"""Batch Integrated Negative Posterior Variance for Active Learning.
 
     This acquisition function quantifies the (negative) integrated posterior variance
@@ -75,7 +75,8 @@ def __init__(
                 points that have been submitted for function evaluation but
                 have not yet been evaluated.
         """
-        super().__init__(model=model, posterior_transform=posterior_transform)
+        super().__init__(model=model)
+        self.posterior_transform = posterior_transform
         if sampler is None:
             # If no sampler is provided, we use the following dummy sampler for the
             # fantasize() method in forward. IMPORTANT: This assumes that the posterior

botorch/optim/optimize.py

@@ -153,6 +153,7 @@ def _raise_deprecation_warning_if_kwargs(fn_name: str, kwargs: Dict[str, Any]) -
             f"`{fn_name}` does not support arguments {list(kwargs.keys())}. In "
             "the future, this will become an error.",
             DeprecationWarning,
+            stacklevel=2,
         )
 
 
@@ -366,7 +367,7 @@ def _optimize_batch_candidates() -> Tuple[Tensor, Tensor, List[Warning]]:
             f"warning(s):\n{[w.message for w in ws]}\nTrying again with a new "
             "set of initial conditions."
         )
-        warnings.warn(first_warn_msg, RuntimeWarning)
+        warnings.warn(first_warn_msg, RuntimeWarning, stacklevel=2)
 
         if not initial_conditions_provided:
             batch_initial_conditions = opt_inputs.get_ic_generator()(
@@ -392,6 +393,7 @@ def _optimize_batch_candidates() -> Tuple[Tensor, Tensor, List[Warning]]:
                     "Optimization failed on the second try, after generating a "
                     "new set of initial conditions.",
                     RuntimeWarning,
+                    stacklevel=2,
                 )
 
     if opt_inputs.post_processing_func is not None:

test/acquisition/test_active_learning.py

@@ -77,31 +77,37 @@ def test_q_neg_int_post_variance(self):
                     val = qNIPV(X)
                     val_exp = -variance.mean(dim=-2).squeeze(-1)
                     self.assertAllClose(val, val_exp, atol=1e-4)
+
             # multi-output model
             mean = torch.zeros(4, 2, device=self.device, dtype=dtype)
             variance = torch.rand(4, 2, device=self.device, dtype=dtype)
             cov = torch.diag_embed(variance.view(-1))
             f_posterior = GPyTorchPosterior(MultitaskMultivariateNormal(mean, cov))
             mc_points = torch.rand(10, 1, device=self.device, dtype=dtype)
             mfm = MockModel(f_posterior)
-            with mock.patch.object(MockModel, "fantasize", return_value=mfm):
-                with mock.patch(no, new_callable=mock.PropertyMock) as mock_num_outputs:
-                    mock_num_outputs.return_value = 2
-                    mm = MockModel(None)
+            with mock.patch.object(
+                MockModel, "fantasize", return_value=mfm
+            ), mock.patch(no, new_callable=mock.PropertyMock) as mock_num_outputs:
+                mock_num_outputs.return_value = 2
+                mm = MockModel(None)
+
+                weights = torch.tensor([0.5, 0.5], device=self.device, dtype=dtype)
+                qNIPV = qNegIntegratedPosteriorVariance(
+                    model=mm,
+                    mc_points=mc_points,
+                    posterior_transform=ScalarizedPosteriorTransform(weights=weights),
+                )
+                X = torch.empty(1, 1, device=self.device, dtype=dtype)  # dummy
+                val = qNIPV(X)
+                self.assertAllClose(val, -0.5 * variance.mean(), atol=1e-4)
+                # without posterior_transform
+                qNIPV = qNegIntegratedPosteriorVariance(
+                    model=mm,
+                    mc_points=mc_points,
+                )
+                val = qNIPV(X)
+                self.assertAllClose(val, -variance.mean(0), atol=1e-4)
 
-                    weights = torch.tensor([0.5, 0.5], device=self.device, dtype=dtype)
-                    qNIPV = qNegIntegratedPosteriorVariance(
-                        model=mm,
-                        mc_points=mc_points,
-                        posterior_transform=ScalarizedPosteriorTransform(
-                            weights=weights
-                        ),
-                    )
-                    X = torch.empty(1, 1, device=self.device, dtype=dtype)  # dummy
-                    val = qNIPV(X)
-                    self.assertTrue(
-                        torch.allclose(val, -0.5 * variance.mean(), atol=1e-4)
-                    )
             # batched multi-output model
             mean = torch.zeros(4, 3, 1, 2, device=self.device, dtype=dtype)
             variance = torch.rand(4, 3, 1, 2, device=self.device, dtype=dtype)