qLogNEHVI's input constructor

Summary: This commit adds qLogNEHVI's input constructor, enabling integration with Ax in a follow up diff.

Differential Revision: D50345278

fbshipit-source-id: aa255309a47c29419ac60fd18b3f48057cb5fff9

botorch/acquisition/input_constructors.py

@@ -71,6 +71,9 @@
     qExpectedHypervolumeImprovement,
     qNoisyExpectedHypervolumeImprovement,
 )
+from botorch.acquisition.multi_objective.logei import (
+    qLogNoisyExpectedHypervolumeImprovement,
+)
 from botorch.acquisition.multi_objective.objective import (
     AnalyticMultiOutputObjective,
     IdentityAnalyticMultiOutputObjective,
@@ -688,8 +691,8 @@ def construct_inputs_qLogNEI(
         eta: Temperature parameter(s) governing the smoothness of the sigmoid
             approximation to the constraint indicators. For more details, on this
             parameter, see the docs of `compute_smoothed_feasibility_indicator`.
-        fat: Toggles the logarithmic / linear asymptotic behavior of the smooth
-            approximation to the ReLU.
+        fat: Toggles the use of the fat-tailed non-linearities to smoothly approximate
+            the constraints indicator function.
         tau_max: Temperature parameter controlling the sharpness of the smooth
             approximations to max.
         tau_relu: Temperature parameter controlling the sharpness of the smooth
@@ -961,6 +964,7 @@ def construct_inputs_qNEHVI(
     sampler: Optional[MCSampler] = None,
     X_pending: Optional[Tensor] = None,
     eta: float = 1e-3,
+    fat: bool = False,
     mc_samples: int = 128,
     qmc: bool = True,
     prune_baseline: bool = True,
@@ -969,7 +973,7 @@ def construct_inputs_qNEHVI(
     incremental_nehvi: bool = True,
     cache_root: bool = True,
 ) -> Dict[str, Any]:
-    r"""Construct kwargs for `qNoisyExpectedHypervolumeImprovement` constructor."""
+    r"""Construct kwargs for `qNoisyExpectedHypervolumeImprovement`'s constructor."""
     if X_baseline is None:
         X_baseline = _get_dataset_field(
             training_data,
@@ -1010,6 +1014,7 @@ def construct_inputs_qNEHVI(
         "constraints": constraints,
         "X_pending": X_pending,
         "eta": eta,
+        "fat": fat,
         "prune_baseline": prune_baseline,
         "alpha": alpha,
         "cache_pending": cache_pending,
@@ -1019,6 +1024,58 @@ def construct_inputs_qNEHVI(
     }
 
 
+@acqf_input_constructor(qLogNoisyExpectedHypervolumeImprovement)
+def construct_inputs_qLogNEHVI(
+    model: Model,
+    training_data: MaybeDict[SupervisedDataset],
+    objective_thresholds: Tensor,
+    objective: Optional[MCMultiOutputObjective] = None,
+    X_baseline: Optional[Tensor] = None,
+    constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
+    alpha: Optional[float] = None,
+    sampler: Optional[MCSampler] = None,
+    X_pending: Optional[Tensor] = None,
+    eta: float = 1e-3,
+    fat: bool = True,
+    mc_samples: int = 128,
+    qmc: bool = True,
+    prune_baseline: bool = True,
+    cache_pending: bool = True,
+    max_iep: int = 0,
+    incremental_nehvi: bool = True,
+    cache_root: bool = True,
+    tau_relu: float = TAU_RELU,
+    tau_max: float = TAU_MAX,
+) -> Dict[str, Any]:
+    """
+    Construct kwargs for `qLogNoisyExpectedHypervolumeImprovement`'s constructor."
+    """
+    return {
+        **construct_inputs_qNEHVI(
+            model=model,
+            training_data=training_data,
+            objective_thresholds=objective_thresholds,
+            objective=objective,
+            X_baseline=X_baseline,
+            constraints=constraints,
+            alpha=alpha,
+            sampler=sampler,
+            X_pending=X_pending,
+            eta=eta,
+            fat=fat,
+            mc_samples=mc_samples,
+            qmc=qmc,
+            prune_baseline=prune_baseline,
+            cache_pending=cache_pending,
+            max_iep=max_iep,
+            incremental_nehvi=incremental_nehvi,
+            cache_root=cache_root,
+        ),
+        "tau_relu": tau_relu,
+        "tau_max": tau_max,
+    }
+
+
 @acqf_input_constructor(qMaxValueEntropy)
 def construct_inputs_qMES(
     model: Model,

test/acquisition/test_input_constructors.py

@@ -61,6 +61,9 @@
     qExpectedHypervolumeImprovement,
     qNoisyExpectedHypervolumeImprovement,
 )
+from botorch.acquisition.multi_objective.logei import (
+    qLogNoisyExpectedHypervolumeImprovement,
+)
 from botorch.acquisition.multi_objective.multi_output_risk_measures import (
     MultiOutputExpectation,
 )
@@ -928,7 +931,13 @@ def test_construct_inputs_qEHVI(self) -> None:
         self.assertEqual(sampler.seed, 1234)
 
     def test_construct_inputs_qNEHVI(self) -> None:
-        c = get_acqf_input_constructor(qNoisyExpectedHypervolumeImprovement)
+        self._test_construct_inputs_qNEHVI(qNoisyExpectedHypervolumeImprovement)
+
+    def test_construct_inputs_qLogNEHVI(self) -> None:
+        self._test_construct_inputs_qNEHVI(qLogNoisyExpectedHypervolumeImprovement)
+
+    def _test_construct_inputs_qNEHVI(self, acqf_class: Type[AcquisitionFunction]):
+        c = get_acqf_input_constructor(acqf_class)
         objective_thresholds = torch.rand(2)
 
         # Test defaults
@@ -953,6 +962,15 @@ def test_construct_inputs_qNEHVI(self) -> None:
         self.assertTrue(kwargs["incremental_nehvi"])
         self.assertTrue(kwargs["cache_root"])
 
+        if acqf_class == qLogNoisyExpectedHypervolumeImprovement:
+            self.assertEqual(kwargs["tau_relu"], TAU_RELU)
+            self.assertEqual(kwargs["tau_max"], TAU_MAX)
+            self.assertEqual(kwargs["fat"], True)
+        else:
+            self.assertNotIn("tau_relu", kwargs)
+            self.assertNotIn("tau_max", kwargs)
+            self.assertEqual(kwargs["fat"], False)
+
         # Test check for block designs
         mock_model = mock.Mock()
         mock_model.num_outputs = 2