remove abstract method of data_setter

pymc_marketing/clv/models/basic.py

@@ -276,7 +276,3 @@ def output_var(self):
     def _generate_and_preprocess_model_data(self, *args, **kwargs):
         """Generate and preprocess model data."""
         pass
-
-    def _data_setter(self):
-        """Set the data for the model."""
-        pass

pymc_marketing/model_builder.py

@@ -204,7 +204,6 @@
         else:
             return check_array(X, accept_sparse=False)
 
-    @abstractmethod
     def _data_setter(
         self,
         X: np.ndarray | pd.DataFrame,
@@ -219,21 +218,25 @@
         y : array, shape (n_obs,)
             The target values (real numbers).
 
-        Returns
-        -------
-        None
-
         Examples
         --------
-        >>> def _data_setter(self, data : pd.DataFrame):
-        >>>     with self.model:
-        >>>         pm.set_data({'x': X['x'].values})
-        >>>         try: # if y values in new data
-        >>>             pm.set_data({'y_data': y.values})
-        >>>         except: # dummies otherwise
-        >>>             pm.set_data({'y_data': np.zeros(len(data))})
+        Example logic of data_setter method
+
+        .. code-block:: python
+
+            def _data_setter(self, X, y=None):
+
+                data = {"X": X}
+                if y is None:
+                    y = np.zeros(len(X))
+                data["y"] = y
+
+                with self.model:
+                    pm.set_data(data)
 
         """
+        msg = "This model doesn't support setting new data, posterior_predictive, or out of sample methods."
+        raise NotImplementedError(msg)
 
     @property
     @abstractmethod

tests/test_model_builder.py

@@ -417,9 +417,6 @@ def __init__(
         super().__init__(model_config=model_config, sampler_config=sampler_config)
         self.new_parameter = new_parameter
 
-    def _data_setter(self, X: pd.DataFrame, y: pd.Series = None) -> None:
-        pass
-
     def build_model(self, X: pd.DataFrame, y: pd.Series, model_config=None) -> None:
         with pm.Model() as self.model:
             intercept = pm.Normal("intercept")