ST_MTGP model for MTGP MOO (#1962)

Summary:
Pull Request resolved: #1962

- Changed the legacy Ax model ST_MTGP_NEHVI into the new modular botorch model ST_MTGP.
- Changed the number of objectives in MOO problems to not depend on the number of constraints but only on the number of objectives.

Reviewed By: saitcakmak

Differential Revision: D47739378

fbshipit-source-id: 9d0bab44d589c1e1d61c47f8be430c122539b884

Author: Jelena Markovic-Voronov

botorch/acquisition/input_constructors.py

@@ -986,7 +986,6 @@ def construct_inputs_qNEHVI(
             first_only=True,
             assert_shared=True,
         )
-
     # This selects the objectives (a subset of the outcomes) and set each
     # objective threhsold to have the proper optimization direction.
     if objective is None:
@@ -1014,6 +1013,8 @@ def construct_inputs_qNEHVI(
     else:
         ref_point = objective(objective_thresholds)
 
+    num_objectives = objective_thresholds[~torch.isnan(objective_thresholds)].shape[0]
+
     return {
         "model": model,
         "ref_point": ref_point,
@@ -1024,7 +1025,7 @@ def construct_inputs_qNEHVI(
         "X_pending": kwargs.get("X_pending"),
         "eta": kwargs.get("eta", 1e-3),
         "prune_baseline": kwargs.get("prune_baseline", True),
-        "alpha": kwargs.get("alpha", get_default_partitioning_alpha(model.num_outputs)),
+        "alpha": kwargs.get("alpha", get_default_partitioning_alpha(num_objectives)),
         "cache_pending": kwargs.get("cache_pending", True),
         "max_iep": kwargs.get("max_iep", 0),
         "incremental_nehvi": kwargs.get("incremental_nehvi", True),

test/acquisition/test_input_constructors.py

@@ -810,7 +810,7 @@ def test_construct_inputs_qNEHVI(self):
             X_pending=X_pending,
             eta=1e-2,
             prune_baseline=True,
-            alpha=0.1,
+            alpha=0.0,
             cache_pending=False,
             max_iep=1,
             incremental_nehvi=False,
@@ -831,7 +831,7 @@ def test_construct_inputs_qNEHVI(self):
         self.assertTrue(torch.equal(kwargs["X_pending"], X_pending))
         self.assertEqual(kwargs["eta"], 1e-2)
         self.assertTrue(kwargs["prune_baseline"])
-        self.assertEqual(kwargs["alpha"], 0.1)
+        self.assertEqual(kwargs["alpha"], 0.0)
         self.assertFalse(kwargs["cache_pending"])
         self.assertEqual(kwargs["max_iep"], 1)
         self.assertFalse(kwargs["incremental_nehvi"])
@@ -874,7 +874,7 @@ def test_construct_inputs_qNEHVI(self):
             training_data=self.blockX_blockY,
             objective_thresholds=objective_thresholds,
         )
-        self.assertEqual(kwargs["alpha"], 1e-3)
+        self.assertEqual(kwargs["alpha"], 0.0)
 
     def test_construct_inputs_kg(self):
         current_value = torch.tensor(1.23)