Ensure finite initial point in test_marginalized_transforms

tests/model/marginal/test_marginal_model.py

@@ -410,15 +410,15 @@ def test_not_supported_marginalized_deterministic_and_potential():
         (None, does_not_warn()),
         (UNSET, does_not_warn()),
         (transforms.log, does_not_warn()),
-        (transforms.Chain([transforms.log, transforms.logodds]), does_not_warn()),
+        (transforms.Chain([transforms.logodds, transforms.log]), does_not_warn()),
         (
-            transforms.Interval(0, 1),
+            transforms.Interval(0, 2),
             pytest.warns(
                 UserWarning, match="which depends on the marginalized idx may no longer work"
             ),
         ),
         (
-            transforms.Chain([transforms.log, transforms.Interval(0, 1)]),
+            transforms.Chain([transforms.log, transforms.Interval(-1, 1)]),
             pytest.warns(
                 UserWarning, match="which depends on the marginalized idx may no longer work"
             ),
@@ -428,7 +428,7 @@ def test_not_supported_marginalized_deterministic_and_potential():
 def test_marginalized_transforms(transform, expected_warning):
     w = [0.1, 0.3, 0.6]
     data = [0, 5, 10]
-    initval = 0.5  # Value that will be negative on the unconstrained space
+    initval = 0.7  # Value that will be negative on the unconstrained space
 
     with pm.Model() as m_ref:
         sigma = pm.Mixture(
@@ -467,7 +467,7 @@ def test_marginalized_transforms(transform, expected_warning):
             transform_name = "log"
         else:
             transform_name = transform.name
-        assert f"sigma_{transform_name}__" in ip
+        assert -np.inf < ip[f"sigma_{transform_name}__"] < 0.0
     np.testing.assert_allclose(m.compile_logp()(ip), m_ref.compile_logp()(ip))