Refactor DynamicDML to remove incompatible method signatures

Author: kbattocchi

econml/panel/dml/_dml.py

@@ -547,32 +547,29 @@ def _gen_model_t(self):
     def _gen_model_final(self):
         return StatsModelsLinearRegression(fit_intercept=False)
 
-    def _gen_ortho_learner_model_nuisance(self, n_periods):
+    def _gen_ortho_learner_model_nuisance(self):
         return _DynamicModelNuisance(
             model_t=self._gen_model_t(),
             model_y=self._gen_model_y(),
-            n_periods=n_periods)
+            n_periods=self._n_periods)
 
-    def _gen_ortho_learner_model_final(self, n_periods):
+    def _gen_ortho_learner_model_final(self):
         wrapped_final_model = _DynamicFinalWrapper(
             StatsModelsLinearRegression(fit_intercept=False),
             fit_cate_intercept=self.fit_cate_intercept,
             featurizer=self.featurizer,
             use_weight_trick=False)
-        return _LinearDynamicModelFinal(wrapped_final_model, n_periods=n_periods)
+        return _LinearDynamicModelFinal(wrapped_final_model, n_periods=self._n_periods)
 
     def _prefit(self, Y, T, *args, groups=None, only_final=False, **kwargs):
+        # we need to set the number of periods before calling super()._prefit, since that will generate the
+        # final and nuisance models, which need to have self._n_periods set
         u_periods = np.unique(np.unique(groups, return_counts=True)[1])
         if len(u_periods) > 1:
             raise AttributeError(
                 "Imbalanced panel. Method currently expects only panels with equal number of periods. Pad your data")
         self._n_periods = u_periods[0]
-        # generate an instance of the final model
-        self._ortho_learner_model_final = self._gen_ortho_learner_model_final(self._n_periods)
-        if not only_final:
-            # generate an instance of the nuisance model
-            self._ortho_learner_model_nuisance = self._gen_ortho_learner_model_nuisance(self._n_periods)
-        TreatmentExpansionMixin._prefit(self, Y, T, *args, **kwargs)
+        super()._prefit(self, Y, T, *args, **kwargs)
 
     def _postfit(self, Y, T, *args, **kwargs):
         super()._postfit(Y, T, *args, **kwargs)