implementing abstract methods

pymc_marketing/clv/models/basic.py

@@ -2,9 +2,11 @@
 import types
 import warnings
 from pathlib import Path
-from typing import Dict, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple, Union
 
 import arviz as az
+import numpy as np
+import pandas as pd
 import pymc as pm
 from pymc import str_for_dist
 from pymc.backends import NDArray
@@ -286,3 +288,17 @@ def fit_summary(self, **kwargs):
             return res["mean"].rename("value")
         else:
             return az.summary(self.fit_result, **kwargs)
+
+    @property
+    def output_var(self):
+        pass
+
+    def generate_and_preprocess_model_data(
+        self,
+        X: Union[pd.DataFrame, pd.Series],
+        y: Union[pd.Series, np.ndarray[Any, Any]],
+    ) -> None:
+        pass
+
+    def _data_setter(self):
+        pass

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -83,7 +83,7 @@ def default_sampler_config(self) -> Dict:
     def output_var(self):
         return "y"
 
-    def generate_and_preprocess_model_data(
+    def generate_and_preprocess_model_data(  # type: ignore
         self, X: Union[pd.DataFrame, pd.Series], y: pd.Series
     ) -> None:
         """

pymc_marketing/model_builder.py

@@ -196,7 +196,9 @@ def default_sampler_config(self) -> Dict:
 
     @abstractmethod
     def generate_and_preprocess_model_data(
-        self, X: Union[pd.DataFrame, pd.Series], y: pd.Series
+        self,
+        X: Union[pd.DataFrame, pd.Series],
+        y: Union[pd.Series, np.ndarray[Any, Any]],
     ) -> None:
         """
         Applies preprocessing to the data before fitting the model.
@@ -505,9 +507,11 @@ def fit(
             predictor_names = []
         if y is None:
             y = np.zeros(X.shape[0])
-        y = pd.Series({self.output_var: y})
-        self.generate_and_preprocess_model_data(X, y)
-        self.build_model(self.X, self.y)  # type: ignore
+        y_df = pd.DataFrame({self.output_var: y})
+        self.generate_and_preprocess_model_data(X, y_df.values.flatten())
+        if self.X is None or self.y is None:
+            raise ValueError("X and y must be set before calling build_model!")
+        self.build_model(self.X, self.y)
 
         sampler_config = self.sampler_config.copy()
         sampler_config["progressbar"] = progressbar
@@ -516,7 +520,7 @@ def fit(
         self.idata = self.sample_model(**sampler_config)
 
         X_df = pd.DataFrame(X, columns=X.columns)
-        combined_data = pd.concat([X_df, y], axis=1)
+        combined_data = pd.concat([X_df, y_df], axis=1)
         assert all(combined_data.columns), "All columns must have non-empty names"
         with warnings.catch_warnings():
             warnings.filterwarnings(

tests/mmm/test_base.py

@@ -74,6 +74,10 @@ def default_model_config(self):
         def default_sampler_config(self):
             pass
 
+        @property
+        def output_var(self):
+            pass
+
         def _data_setter(self, X, y=None):
             pass