Add informative error when attempting imputation on non-pure RandomVariables

Author: ricardoV94

pymc/model.py

@@ -44,6 +44,7 @@
 from aesara.graph.fg import FunctionGraph
 from aesara.scalar import Cast
 from aesara.tensor.elemwise import Elemwise
+from aesara.tensor.random.op import RandomVariable
 from aesara.tensor.random.rewriting import local_subtensor_rv_lift
 from aesara.tensor.sharedvar import ScalarSharedVariable
 from aesara.tensor.var import TensorConstant, TensorVariable
@@ -1405,9 +1406,16 @@ def make_obs_var(
             )
             warnings.warn(impute_message, ImputationWarning)
 
+            if not isinstance(rv_var.owner.op, RandomVariable):
+                raise NotImplementedError(
+                    "Automatic inputation is only supported for univariate RandomVariables."
+                    f" {rv_var} of type {type(rv_var.owner.op)} is not supported."
+                )
+
             if rv_var.owner.op.ndim_supp > 0:
                 raise NotImplementedError(
-                    f"Automatic inputation is only supported for univariate RandomVariables, but {rv_var} is multivariate"
+                    f"Automatic inputation is only supported for univariate "
+                    f"RandomVariables, but {rv_var} is multivariate"
                 )
 
             # We can get a random variable comprised of only the unobserved

pymc/tests/test_model.py

@@ -1377,6 +1377,19 @@ def test_dims(self):
                 x = pm.Normal("x", observed=data, dims=("observed",))
         assert model.RV_dims == {"x": ("observed",)}
 
+    def test_error_non_random_variable(self):
+        data = np.array([np.nan] * 3 + [0] * 7)
+        with pm.Model() as model:
+            msg = "x of type <class 'pymc.distributions.censored.CensoredRV'> is not supported"
+            with pytest.raises(NotImplementedError, match=msg):
+                x = pm.Censored(
+                    "x",
+                    pm.Normal.dist(),
+                    lower=0,
+                    upper=10,
+                    observed=data,
+                )
+
 
 class TestShared(SeededTest):
     def test_deterministic(self):