fixing second loading issue (#220)

* fixing second loading issue

* adding ignores for warnings from numpy and arviz

Author: michaelraczycki

pymc_experimental/model_builder.py

@@ -381,14 +381,21 @@ def save(self, fname: str) -> None:
             raise RuntimeError("The model hasn't been fit yet, call .fit() first")
 
     @classmethod
-    def _convert_dims_to_tuple(cls, model_config: Dict) -> Dict:
+    def _model_config_formatting(cls, model_config: Dict) -> Dict:
+        """
+        Because of json serialization, model_config values that were originally tuples or numpy are being encoded as lists.
+        This function converts them back to tuples and numpy arrays to ensure correct id encoding.
+        """
         for key in model_config:
-            if (
-                isinstance(model_config[key], dict)
-                and "dims" in model_config[key]
-                and isinstance(model_config[key]["dims"], list)
-            ):
-                model_config[key]["dims"] = tuple(model_config[key]["dims"])
+            if isinstance(model_config[key], dict):
+                for sub_key in model_config[key]:
+                    if isinstance(model_config[key][sub_key], list):
+                        # Check if "dims" key to convert it to tuple
+                        if sub_key == "dims":
+                            model_config[key][sub_key] = tuple(model_config[key][sub_key])
+                        # Convert all other lists to numpy arrays
+                        else:
+                            model_config[key][sub_key] = np.array(model_config[key][sub_key])
         return model_config
 
     @classmethod
@@ -420,7 +427,7 @@ def load(cls, fname: str):
         filepath = Path(str(fname))
         idata = az.from_netcdf(filepath)
         # needs to be converted, because json.loads was changing tuple to list
-        model_config = cls._convert_dims_to_tuple(json.loads(idata.attrs["model_config"]))
+        model_config = cls._model_config_formatting(json.loads(idata.attrs["model_config"]))
         model = cls(
             model_config=model_config,
             sampler_config=json.loads(idata.attrs["sampler_config"]),

pymc_experimental/tests/__init__.py

@@ -11,7 +11,3 @@
 #   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.
-
-from numpy.testing import Tester
-
-test = Tester().test

pymc_experimental/tests/test_model_builder.py

@@ -160,7 +160,7 @@ def test_convert_dims_to_tuple(fitted_model_instance):
             ],
         },
     }
-    converted_model_config = fitted_model_instance._convert_dims_to_tuple(model_config)
+    converted_model_config = fitted_model_instance._model_config_formatting(model_config)
     assert converted_model_config["a"]["dims"] == ("x",)
 
 

setup.cfg

@@ -1,5 +1,10 @@
 [tool:pytest]
 testpaths = tests
+filterwarnings =
+    error
+    ignore::DeprecationWarning:numpy.core.fromnumeric
+    ignore:::arviz.*
+    ignore:DeprecationWarning
 
 [isort]
 lines_between_types = 1