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

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)