Modifies qMFKG.evaluate() to work with project, expand and cost aware…

… utility (#594)

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## Motivation

Modifies `qMFKG.evaluate()` to work with `project`, `expand` and `cost_aware_utility`. Partially fixes #587.

- Introduces a `ProjectedValueFunction` that wraps the `value_function` and applies the `project` operator on the `forward` call.
- Changes `evaluate()` signature to use `X` instead of `X_actual`. Current implementation raises an exception with the decorators when called with `evaluate(X_actual=...)`.

Note: The treatment of `cost_aware_utility` assumes that it is monotone non-decreasing in `deltas`. Otherwise, optimizing the inner problem and passing through `cost_aware_utility` may not produce the correct output.

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/master/CONTRIBUTING.md#pull-requests)?

Yes

Pull Request resolved: #594

Test Plan: Added mock unit tests. Verified the expected behavior in additional offline tests.

Reviewed By: qingfeng10

Differential Revision: D25173613

Pulled By: Balandat

fbshipit-source-id: 3ba0f196a622a84c951fdc3526a53cb6905e85d2

botorch/acquisition/__init__.py

@@ -23,7 +23,10 @@
     InverseCostWeightedUtility,
 )
 from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
-from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
+from botorch.acquisition.knowledge_gradient import (
+    qKnowledgeGradient,
+    qMultiFidelityKnowledgeGradient,
+)
 from botorch.acquisition.max_value_entropy_search import (
     qMaxValueEntropy,
     qMultiFidelityMaxValueEntropy,
@@ -62,6 +65,7 @@
     "UpperConfidenceBound",
     "qExpectedImprovement",
     "qKnowledgeGradient",
+    "qMultiFidelityKnowledgeGradient",
     "qMaxValueEntropy",
     "qMultiFidelityMaxValueEntropy",
     "qNoisyExpectedImprovement",

botorch/acquisition/knowledge_gradient.py

@@ -189,12 +189,12 @@ def forward(self, X: Tensor) -> Tensor:
 
     @concatenate_pending_points
     @t_batch_mode_transform()
-    def evaluate(self, X_actual: Tensor, bounds: Tensor, **kwargs: Any) -> Tensor:
+    def evaluate(self, X: Tensor, bounds: Tensor, **kwargs: Any) -> Tensor:
         r"""Evaluate qKnowledgeGradient on the candidate set `X_actual` by
         solving the inner optimization problem.
 
         Args:
-            X_actual: A `b x q x d` Tensor with `b` t-batches of `q` design points
+            X: A `b x q x d` Tensor with `b` t-batches of `q` design points
                 each. Unlike `forward()`, this does not include solutions of the
                 inner optimization problem.
             bounds: A `2 x d` tensor of lower and upper bounds for each column of
@@ -206,18 +206,24 @@ def evaluate(self, X_actual: Tensor, bounds: Tensor, **kwargs: Any) -> Tensor:
 
         Returns:
             A Tensor of shape `b`. For t-batch b, the q-KG value of the design
-                `X_actual[b]` is averaged across the fantasy models.
+                `X[b]` is averaged across the fantasy models.
                 NOTE: If `current_value` is not provided, then this is not the
-                true KG value of `X_actual[b]`.
+                true KG value of `X[b]`.
         """
+        if hasattr(self, "expand"):
+            X = self.expand(X)
+
         # construct the fantasy model of shape `num_fantasies x b`
         fantasy_model = self.model.fantasize(
-            X=X_actual, sampler=self.sampler, observation_noise=True
+            X=X, sampler=self.sampler, observation_noise=True
         )
 
         # get the value function
         value_function = _get_value_function(
-            model=fantasy_model, objective=self.objective, sampler=self.inner_sampler
+            model=fantasy_model,
+            objective=self.objective,
+            sampler=self.inner_sampler,
+            project=getattr(self, "project", None),
         )
 
         from botorch.generation.gen import gen_candidates_scipy
@@ -246,6 +252,10 @@ def evaluate(self, X_actual: Tensor, bounds: Tensor, **kwargs: Any) -> Tensor:
         if self.current_value is not None:
             values = values - self.current_value
 
+        if hasattr(self, "cost_aware_utility"):
+            values = self.cost_aware_utility(
+                X=X, deltas=values, sampler=self.cost_sampler
+            )
         # return average over the fantasy samples
         return values.mean(dim=0)
 
@@ -409,13 +419,16 @@ def forward(self, X: Tensor) -> Tensor:
 
         # get the value function
         value_function = _get_value_function(
-            model=fantasy_model, objective=self.objective, sampler=self.inner_sampler
+            model=fantasy_model,
+            objective=self.objective,
+            sampler=self.inner_sampler,
+            project=self.project,
         )
 
         # make sure to propagate gradients to the fantasy model train inputs
         # project the fantasy points
         with settings.propagate_grads(True):
-            values = value_function(X=self.project(X_fantasies))  # num_fantasies x b
+            values = value_function(X=X_fantasies)  # num_fantasies x b
 
         if self.current_value is not None:
             values = values - self.current_value
@@ -429,16 +442,47 @@ def forward(self, X: Tensor) -> Tensor:
         return values.mean(dim=0)
 
 
+class ProjectedAcquisitionFunction(AcquisitionFunction):
+    r"""
+    Defines a wrapper around  an `AcquisitionFunction` that incorporates the project
+    operator. Typically used to handle value functions in look-ahead methods.
+    """
+
+    def __init__(
+        self,
+        base_value_function: AcquisitionFunction,
+        project: Callable[[Tensor], Tensor],
+    ) -> None:
+        super().__init__(base_value_function.model)
+        self.base_value_function = base_value_function
+        self.project = project
+        self.objective = base_value_function.objective
+        self.sampler = getattr(base_value_function, "sampler", None)
+
+    def forward(self, X: Tensor) -> Tensor:
+        return self.base_value_function(self.project(X))
+
+
 def _get_value_function(
     model: Model,
     objective: Optional[Union[MCAcquisitionObjective, ScalarizedObjective]] = None,
     sampler: Optional[MCSampler] = None,
+    project: Optional[Callable[[Tensor], Tensor]] = None,
 ) -> AcquisitionFunction:
     r"""Construct value function (i.e. inner acquisition function)."""
     if isinstance(objective, MCAcquisitionObjective):
-        return qSimpleRegret(model=model, sampler=sampler, objective=objective)
+        base_value_function = qSimpleRegret(
+            model=model, sampler=sampler, objective=objective
+        )
     else:
-        return PosteriorMean(model=model, objective=objective)
+        base_value_function = PosteriorMean(model=model, objective=objective)
+    if project is None:
+        return base_value_function
+    else:
+        return ProjectedAcquisitionFunction(
+            base_value_function=base_value_function,
+            project=project,
+        )
 
 
 def _split_fantasy_points(X: Tensor, n_f: int) -> Tuple[Tensor, Tensor]:

botorch/optim/initializers.py

@@ -16,7 +16,6 @@
     _get_value_function,
     qKnowledgeGradient,
 )
-from botorch.acquisition.monte_carlo import MCAcquisitionFunction
 from botorch.acquisition.utils import is_nonnegative
 from botorch.exceptions.warnings import BadInitialCandidatesWarning, SamplingWarning
 from botorch.models.model import Model
@@ -210,6 +209,7 @@ def gen_one_shot_kg_initial_conditions(
         model=acq_function.model,
         objective=acq_function.objective,
         sampler=acq_function.inner_sampler,
+        project=getattr(acq_function, "project", None),
     )
     from botorch.optim.optimize import optimize_acqf
 
@@ -304,9 +304,8 @@ def gen_value_function_initial_conditions(
     value_function = _get_value_function(
         model=current_model,
         objective=acq_function.objective,
-        sampler=acq_function.sampler
-        if isinstance(acq_function, MCAcquisitionFunction)
-        else None,
+        sampler=getattr(acq_function, "sampler", None),
+        project=getattr(acq_function, "project", None),
     )
     from botorch.optim.optimize import optimize_acqf
 

test/acquisition/test_knowledge_gradient.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from contextlib import ExitStack
 from unittest import mock
 
 import torch
@@ -14,6 +15,7 @@
     _split_fantasy_points,
     qKnowledgeGradient,
     qMultiFidelityKnowledgeGradient,
+    ProjectedAcquisitionFunction,
 )
 from botorch.acquisition.monte_carlo import qSimpleRegret
 from botorch.acquisition.objective import GenericMCObjective, ScalarizedObjective
@@ -399,6 +401,72 @@ def test_evaluate_q_multi_fidelity_knowledge_gradient(self):
             self.assertTrue(torch.allclose(val, val_exp, atol=1e-4))
             self.assertTrue(torch.equal(qMFKG.extract_candidates(X), X[..., :-n_f, :]))
 
+    def test_evaluate_qMFKG(self):
+        # mock test qMFKG.evaluate() with expand, project & cost aware utility
+        for dtype in (torch.float, torch.double):
+            mean = torch.zeros(1, 1, 1, device=self.device, dtype=dtype)
+            mm = MockModel(MockPosterior(mean=mean))
+            mm._input_batch_shape = torch.Size([1])
+            cau = GenericCostAwareUtility(mock_util)
+            n_f = 4
+            mean = torch.rand(n_f, 2, 1, 1, device=self.device, dtype=dtype)
+            variance = torch.rand(n_f, 2, 1, 1, device=self.device, dtype=dtype)
+            mfm = MockModel(MockPosterior(mean=mean, variance=variance))
+            mfm._input_batch_shape = torch.Size([n_f, 2])
+            with ExitStack() as es:
+                patch_f = es.enter_context(
+                    mock.patch.object(MockModel, "fantasize", return_value=mfm)
+                )
+                mock_num_outputs = es.enter_context(
+                    mock.patch(NO, new_callable=mock.PropertyMock)
+                )
+                es.enter_context(
+                    mock.patch(
+                        "botorch.optim.optimize.optimize_acqf",
+                        return_value=(
+                            torch.ones(1, 1, 1, device=self.device, dtype=dtype),
+                            torch.ones(1, device=self.device, dtype=dtype),
+                        ),
+                    ),
+                )
+                es.enter_context(
+                    mock.patch(
+                        "botorch.generation.gen.gen_candidates_scipy",
+                        return_value=(
+                            torch.ones(1, 1, 1, device=self.device, dtype=dtype),
+                            torch.ones(1, device=self.device, dtype=dtype),
+                        ),
+                    ),
+                )
+
+                mock_num_outputs.return_value = 1
+                qMFKG = qMultiFidelityKnowledgeGradient(
+                    model=mm,
+                    num_fantasies=n_f,
+                    X_pending=torch.rand(1, 1, 1, device=self.device, dtype=dtype),
+                    current_value=torch.zeros(1, device=self.device, dtype=dtype),
+                    cost_aware_utility=cau,
+                    project=lambda X: torch.zeros_like(X),
+                    expand=lambda X: torch.ones_like(X),
+                )
+                val = qMFKG.evaluate(
+                    X=torch.zeros(1, 1, 1, device=self.device, dtype=dtype),
+                    bounds=torch.tensor([[0.0], [1.0]]),
+                    num_restarts=1,
+                    raw_samples=1,
+                )
+                patch_f.asset_called_once()
+                cargs, ckwargs = patch_f.call_args
+                self.assertTrue(
+                    torch.equal(
+                        ckwargs["X"],
+                        torch.ones(1, 2, 1, device=self.device, dtype=dtype),
+                    )
+                )
+            self.assertEqual(
+                val, cau(None, torch.ones(1, device=self.device, dtype=dtype))
+            )
+
 
 class TestKGUtils(BotorchTestCase):
     def test_get_value_function(self):
@@ -416,6 +484,29 @@ def test_get_value_function(self):
             self.assertIsInstance(vf, qSimpleRegret)
             self.assertEqual(vf.objective, obj)
             self.assertEqual(vf.sampler, sampler)
+            # test with project
+            mock_project = mock.Mock(
+                return_value=torch.ones(1, 1, 1, device=self.device)
+            )
+            vf = _get_value_function(
+                model=mm,
+                objective=obj,
+                sampler=sampler,
+                project=mock_project,
+            )
+            self.assertIsInstance(vf, ProjectedAcquisitionFunction)
+            self.assertEqual(vf.objective, obj)
+            self.assertEqual(vf.sampler, sampler)
+            self.assertEqual(vf.project, mock_project)
+            test_X = torch.rand(1, 1, 1, device=self.device)
+            with mock.patch.object(
+                vf, "base_value_function", __class__=torch.nn.Module, return_value=None
+            ) as patch_bvf:
+                vf(test_X)
+                mock_project.assert_called_once_with(test_X)
+                patch_bvf.assert_called_once_with(
+                    torch.ones(1, 1, 1, device=self.device)
+                )
 
     def test_split_fantasy_points(self):
         for dtype in (torch.float, torch.double):