Bug fix in estimating feasible volume

Summary: Added a special case when outcome constraints are None.

Reviewed By: Balandat

Differential Revision: D21477628

fbshipit-source-id: 05b1de471cd3ef1beaa64e4a87446530d893f372

Author: jelena-markovic

botorch/utils/feasible_volume.py

@@ -47,7 +47,8 @@ def get_feasible_samples(
     feasible = torch.ones(nsamples, device=samples.device, dtype=torch.bool)
 
     for (indices, coefficients, rhs) in inequality_constraints:
-        feasible &= samples.index_select(1, indices) @ coefficients >= rhs
+        lhs = samples.index_select(1, indices) @ coefficients.to(dtype=samples.dtype)
+        feasible &= lhs >= rhs
 
     feasible_samples = samples[feasible]
 
@@ -82,6 +83,9 @@ def get_outcome_feasibility_probability(
         given outcome constraints with probability above or equal to
         the given threshold.
     """
+    if outcome_constraints is None:
+        return 1.0
+
     from botorch.sampling import SobolQMCNormalSampler
 
     seed = seed if seed is not None else torch.randint(0, 1000000, (1,)).item()

test/utils/test_feasible_volume.py

@@ -67,7 +67,19 @@ def test_estimate_feasible_volume(self):
                     outcome_constraints=outcome_constraints,
                     nsample_feature=2,
                     nsample_outcome=1,
+                    dtype=dtype,
                 )
 
                 self.assertEqual(p_linear, 1.0)
                 self.assertEqual(p_outcome, 1.0 - samples[0, 0].item())
+
+                p_linear, p_outcome = estimate_feasible_volume(
+                    bounds=bounds,
+                    model=mm,
+                    outcome_constraints=None,
+                    nsample_feature=2,
+                    nsample_outcome=1,
+                    dtype=dtype,
+                )
+                self.assertEqual(p_linear, 1.0)
+                self.assertEqual(p_outcome, 1.0)