Support numpy arrays in model_config

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -362,12 +362,17 @@ def channel_contributions_forward_pass(
 
     @property
     def _serializable_model_config(self) -> Dict[str, Any]:
+        def ndarray_to_list(d: Dict) -> Dict:
+            new_d = d.copy()  # Copy the dictionary to avoid mutating the original one
+            for key, value in new_d.items():
+                if isinstance(value, np.ndarray):
+                    new_d[key] = value.tolist()
+                elif isinstance(value, dict):
+                    new_d[key] = ndarray_to_list(value)
+            return new_d
+
         serializable_config = self.model_config.copy()
-        if type(serializable_config["beta_channel"]["sigma"]) == np.ndarray:
-            serializable_config["beta_channel"]["sigma"] = serializable_config[
-                "beta_channel"
-            ]["sigma"].tolist()
-        return serializable_config
+        return ndarray_to_list(serializable_config)
 
     @classmethod
     def load(cls, fname: str):
@@ -481,6 +486,28 @@ def _data_setter(
         with self.model:
             pm.set_data(data)
 
+    @classmethod
+    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.
+        """
+
+        def format_nested_dict(d: Dict) -> Dict:
+            for key, value in d.items():
+                if isinstance(value, dict):
+                    d[key] = format_nested_dict(value)
+                elif isinstance(value, list):
+                    # Check if the key is "dims" to convert it to tuple
+                    if key == "dims":
+                        d[key] = tuple(value)
+                    # Convert all other lists to numpy arrays
+                    else:
+                        d[key] = np.array(value)
+            return d
+
+        return format_nested_dict(model_config.copy())
+
 
 class DelayedSaturatedMMM(
     MaxAbsScaleTarget,

tests/mmm/test_delayed_saturated_mmm.py

@@ -38,6 +38,33 @@ def toy_X() -> pd.DataFrame:
     )
 
 
+@pytest.fixture(scope="class")
+def model_config_requiring_serialization() -> dict:
+    model_config = {
+        "intercept": {"mu": 0, "sigma": 2},
+        "beta_channel": {
+            "sigma": np.array([0.4533017, 0.25488063]),
+            "dims": ("channel",),
+        },
+        "alpha": {
+            "alpha": np.array([3, 3]),
+            "beta": np.array([3.55001301, 2.87092431]),
+            "dims": ("channel",),
+        },
+        "lam": {
+            "alpha": np.array([3, 3]),
+            "beta": np.array([4.12231653, 5.02896872]),
+            "dims": ("channel",),
+        },
+        "sigma": {"sigma": 2},
+        "gamma_control": {"mu": 0, "sigma": 2, "dims": ("control",)},
+        "mu": {"dims": ("date",)},
+        "likelihood": {"dims": ("date",)},
+        "gamma_fourier": {"mu": 0, "b": 1, "dims": "fourier_mode"},
+    }
+    return model_config
+
+
 @pytest.fixture(scope="class")
 def toy_y(toy_X: pd.DataFrame) -> pd.Series:
     return pd.Series(data=rng.integers(low=0, high=100, size=toy_X.shape[0]))
@@ -62,6 +89,46 @@ def mmm_fitted(
 
 
 class TestDelayedSaturatedMMM:
+    def test_save_load_with_not_serializable_model_config(
+        self, model_config_requiring_serialization, toy_X, toy_y
+    ):
+        def deep_equal(dict1, dict2):
+            for key, value in dict1.items():
+                if key not in dict2:
+                    return False
+                if isinstance(value, dict):
+                    if not deep_equal(value, dict2[key]):
+                        return False
+                elif isinstance(value, np.ndarray):
+                    if not np.array_equal(value, dict2[key]):
+                        return False
+                else:
+                    if value != dict2[key]:
+                        return False
+            return True
+
+        model = DelayedSaturatedMMM(
+            date_column="date",
+            channel_columns=["channel_1", "channel_2"],
+            adstock_max_lag=4,
+            model_config=model_config_requiring_serialization,
+        )
+        model.fit(
+            toy_X, toy_y, target_accept=0.81, draws=100, chains=2, random_seed=rng
+        )
+        model.save("test_save_load")
+        model2 = DelayedSaturatedMMM.load("test_save_load")
+        assert model.date_column == model2.date_column
+        assert model.control_columns == model2.control_columns
+        assert model.channel_columns == model2.channel_columns
+        assert model.adstock_max_lag == model2.adstock_max_lag
+        assert model.validate_data == model2.validate_data
+        assert model.yearly_seasonality == model2.yearly_seasonality
+        assert deep_equal(model.model_config, model2.model_config)
+
+        assert model.sampler_config == model2.sampler_config
+        os.remove("test_save_load")
+
     @pytest.mark.parametrize(
         argnames="adstock_max_lag",
         argvalues=[1, 4],