changed raise to warning, moved warning to low level clone_graph, added doc example, updated pytest

Author: Dekermanjian

pymc/model/core.py

@@ -1574,31 +1574,57 @@ def __contains__(self, key):
     def __copy__(self):
         """
         Clone a pymc model by overiding the python copy method using the clone_model method from fgraph.
-        if guassian process variables are detected then an exception will be raised.
+        Constants are not cloned and if guassian process variables are detected then a warning will be triggered.
+
+        Examples
+        --------
+        .. code-block:: python
+
+            import pymc as pm
+            import copy
+
+            with pm.Model() as m:
+                p = pm.Beta("p", 1, 1)
+                x = pm.Bernoulli("x", p=p, shape=(3,))
+
+            clone_m = copy.copy(m)
+
+            # Access cloned variables by name
+            clone_x = clone_m["x"]
+
+            # z will be part of clone_m but not m
+            z = pm.Deterministic("z", clone_x + 1)
         """
         from pymc.model.fgraph import clone_model
 
-        check_for_gp_vars = [
-            k for x in ["_rotated_", "_hsgp_coeffs_"] for k in self.named_vars.keys() if x in k
-        ]
-        if len(check_for_gp_vars) > 0:
-            raise Exception("Unable to clone Gaussian Process Variables")
-
         return clone_model(self)
 
     def __deepcopy__(self, _):
         """
         Clone a pymc model by overiding the python copy method using the clone_model method from fgraph.
-        if guassian process variables are detected then an exception will be raised.
+        Constants are not cloned and if guassian process variables are detected then a warning will be triggered.
+
+        Examples
+        --------
+        .. code-block:: python
+
+            import pymc as pm
+            import copy
+
+            with pm.Model() as m:
+                p = pm.Beta("p", 1, 1)
+                x = pm.Bernoulli("x", p=p, shape=(3,))
+
+            clone_m = copy.deepcopy(m)
+
+            # Access cloned variables by name
+            clone_x = clone_m["x"]
+
+            # z will be part of clone_m but not m
+            z = pm.Deterministic("z", clone_x + 1)
         """
         from pymc.model.fgraph import clone_model
 
-        check_for_gp_vars = [
-            k for x in ["_rotated_", "_hsgp_coeffs_"] for k in self.named_vars.keys() if x in k
-        ]
-        if len(check_for_gp_vars) > 0:
-            raise Exception("Unable to clone Gaussian Process Variables")
-
         return clone_model(self)
 
     def replace_rvs_by_values(

pymc/model/fgraph.py

@@ -11,6 +11,8 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+import warnings
+
 from copy import copy, deepcopy
 
 import pytensor
@@ -369,7 +371,7 @@ def clone_model(model: Model) -> Model:
 
     Recreates a PyMC model with clones of the original variables.
     Shared variables will point to the same container but be otherwise different objects.
-    Constants are not cloned.
+    Constants are not cloned and if guassian process variables are detected then a warning will be triggered.
 
 
     Examples
@@ -391,6 +393,11 @@ def clone_model(model: Model) -> Model:
             z = pm.Deterministic("z", clone_x + 1)
 
     """
+    check_for_gp_vars = [
+        k for x in ["_rotated_", "_hsgp_coeffs_"] for k in model.named_vars.keys() if x in k
+    ]
+    if len(check_for_gp_vars) > 0:
+        warnings.warn("Unable to clone Gaussian Process Variables", UserWarning)
     return model_from_fgraph(fgraph_from_model(model)[0], mutate_fgraph=True)
 
 

tests/model/test_core.py

@@ -1764,15 +1764,15 @@ def test_graphviz_call_function(self, var_names, filenames) -> None:
             )
 
 
-class TestModelCopy(unittest.TestCase):
+class TestModelCopy:
     @staticmethod
     def simple_model() -> pm.Model:
         with pm.Model() as simple_model:
             error = pm.HalfNormal("error", 0.5)
             alpha = pm.Normal("alpha", 0, 1)
             pm.Normal("y", alpha, error)
         return simple_model
-    
+
     @staticmethod
     def gp_model() -> pm.Model:
         with pm.Model() as gp_model:
@@ -1782,7 +1782,7 @@ def gp_model() -> pm.Model:
             f = gp.prior("f", X=np.arange(10)[:, None])
             pm.Normal("y", f * 2)
         return gp_model
-    
+
     def test_copy_model(self) -> None:
         simple_model = self.simple_model()
         copy_simple_model = copy.copy(simple_model)
@@ -1797,17 +1797,27 @@ def test_copy_model(self) -> None:
         with deepcopy_simple_model:
             deepcopy_simple_model_prior_predictive = pm.sample_prior_predictive(random_seed=42)
 
-        simple_model_prior_predictive_mean = simple_model_prior_predictive['prior']['y'].mean(('chain', 'draw'))
-        copy_simple_model_prior_predictive_mean = copy_simple_model_prior_predictive['prior']['y'].mean(('chain', 'draw'))
-        deepcopy_simple_model_prior_predictive_mean = deepcopy_simple_model_prior_predictive['prior']['y'].mean(('chain', 'draw'))
+        simple_model_prior_predictive_mean = simple_model_prior_predictive["prior"]["y"].mean(
+            ("chain", "draw")
+        )
+        copy_simple_model_prior_predictive_mean = copy_simple_model_prior_predictive["prior"][
+            "y"
+        ].mean(("chain", "draw"))
+        deepcopy_simple_model_prior_predictive_mean = deepcopy_simple_model_prior_predictive[
+            "prior"
+        ]["y"].mean(("chain", "draw"))
 
-        assert np.isclose(simple_model_prior_predictive_mean, copy_simple_model_prior_predictive_mean)
-        assert np.isclose(simple_model_prior_predictive_mean, deepcopy_simple_model_prior_predictive_mean)
+        assert np.isclose(
+            simple_model_prior_predictive_mean, copy_simple_model_prior_predictive_mean
+        )
+        assert np.isclose(
+            simple_model_prior_predictive_mean, deepcopy_simple_model_prior_predictive_mean
+        )
 
     def test_guassian_process_copy_failure(self) -> None:
         gaussian_process_model = self.gp_model()
-        with pytest.raises(Exception) as e:
+        with pytest.warns(UserWarning):
             copy.copy(gaussian_process_model)
-        
-        with pytest.raises(Exception) as e:
-            copy.deepcopy(gaussian_process_model)
+
+        with pytest.warns(UserWarning):
+            copy.deepcopy(gaussian_process_model)