ktr code clean up (#638)

* ktr code clean up

* seasonality label clean up

* seasonality label clean up

orbit/template/ktr.py

@@ -296,44 +296,11 @@ def _set_model_param_names(self):
 
     def _set_default_args(self):
         """Set default attributes for None"""
+        # default checks for seasonality and seasonality_fs_order will be conducted
+        # in ktrlite model and we will extract them from ktrlite model directly later
         if self.coef_prior_list is not None:
             self._coef_prior_list = deepcopy(self.coef_prior_list)
 
-        # set default seasonality and related attributes
-        if self.seasonality is None:
-            self._seasonality = list()
-            self._seasonality_fs_order = list()
-        elif not isinstance(self._seasonality, list) and isinstance(self._seasonality, (int, float)):
-            self._seasonality = [self.seasonality]
-
-        # set some defaults for seasonality_fs_order
-        if self._seasonality and self._seasonality_fs_order is None:
-            self._seasonality_fs_order = [2] * len(self._seasonality)
-        elif not isinstance(self._seasonality_fs_order, list) and isinstance(self._seasonality_fs_order, (int, float)):
-            self._seasonality_fs_order = [self.seasonality_fs_order]
-
-        if len(self._seasonality_fs_order) != len(self._seasonality):
-            raise IllegalArgument('length of seasonality and fs_order not matching')
-
-        seasonality_labels = list()
-        for idx, order in enumerate(self._seasonality_fs_order):
-            if 2 * order > self._seasonality[idx] - 1:
-                raise IllegalArgument('reduce seasonality_fs_order to avoid over-fitting')
-            seasonality_labels.append('seasonality_{}'.format(self._seasonality[idx]))
-        self._seasonality_labels = seasonality_labels
-
-        # TODO: this is done by KTRLite; we may not need this for now
-        # if not isinstance(self.seasonal_initial_knot_scale, list) and \
-        #         isinstance(self.seasonal_initial_knot_scale * 1.0, float):
-        #     self._seasonal_initial_knot_scale = [self.seasonal_initial_knot_scale] * len(self._seasonality)
-        # else:
-        #     self._seasonal_initial_knot_scale = self.seasonal_initial_knot_scale
-        #
-        # if not isinstance(self.seasonal_knot_scale, list) and isinstance(self.seasonal_knot_scale * 1.0, float):
-        #     self._seasonal_knot_scale = [self.seasonal_knot_scale] * len(self._seasonality)
-        # else:
-        #     self._seasonal_knot_scale = self.seasonal_knot_scale
-
         # if no regressors, end here #
         if self.regressor_col is None:
             # regardless of what args are set for these, if regressor_col is None
@@ -687,7 +654,8 @@ def _generate_insample_tp(self, training_meta, date_array):
     #     coefs = np.squeeze(np.matmul(coef_knot, kernel_coef.transpose(1, 0)), axis=0).transpose(1, 0)
     #     return coefs
 
-    def _generate_seas(self, df, training_meta, coef_knot_dates, coef_knots, seasonality, seasonality_fs_order):
+    def _generate_seas(self, df, training_meta, coef_knot_dates, coef_knots,
+                        seasonality, seasonality_fs_order, seasonality_labels):
         """To calculate the seasonality term based on the _seasonal_knots_input.
         Parameters
         ----------
@@ -735,7 +703,7 @@ def _generate_seas(self, df, training_meta, coef_knot_dates, coef_knots, seasona
                 n=df.shape[0],
                 periods=seasonality,
                 orders=seasonality_fs_order,
-                labels=self._seasonality_labels,
+                labels=seasonality_labels,
                 shift=start,
             )
 
@@ -746,7 +714,7 @@ def _generate_seas(self, df, training_meta, coef_knot_dates, coef_knots, seasona
             # init of regression matrix depends on length of response vector
             total_seas_regression = np.zeros((1, df.shape[0]), dtype=np.double)
 
-            for k in self._seasonality_labels:
+            for k in seasonality_labels:
                 seas_regresor_matrix = seas_regressors[k]
                 coef_knot = coef_knots[k]
                 # time-step x coefficients
@@ -775,7 +743,7 @@ def _set_levs_and_seas(self, df, training_meta):
             level_knot_distance=self.level_knot_distance,
             seasonality=self.seasonality,
             seasonality_fs_order=self.seasonality_fs_order,
-            seasonal_initial_knot_scale=self.seasonal_knot_scale,
+            seasonal_initial_knot_scale=self.seasonal_initial_knot_scale,
             seasonal_knot_scale=self.seasonal_knot_scale,
             seasonality_segments=self.seasonality_segments,
             degree_of_freedom=self.degree_of_freedom,
@@ -787,6 +755,13 @@ def _set_levs_and_seas(self, df, training_meta):
         # self._ktrlite_model = ktrlite
         ktrlite_pt_posteriors = ktrlite.get_point_posteriors()
         ktrlite_obs_scale = ktrlite_pt_posteriors['map']['obs_scale']
+
+        # load _seasonality and _seasonality_fs_order
+        self._seasonality = ktrlite._model._seasonality
+        self._seasonality_fs_order = ktrlite._model._seasonality_fs_order
+        for seas in self._seasonality:
+            self._seasonality_labels.append('seasonality_{}'.format(seas))
+
         # if input None for upper bound of residuals scale, use data-driven input
         if self.residuals_scale_upper is None:
             # make it 5 times to have some buffer in case we over-fit in KTRLite
@@ -836,7 +811,8 @@ def _set_levs_and_seas(self, df, training_meta):
                 self._seasonality_coef_knot_dates,
                 self._seasonality_coef_knots,
                 self._seasonality,
-                self._seasonality_fs_order)
+                self._seasonality_fs_order,
+                self._seasonality_labels)
             # remove batch size as an input for models
             self._seas_term = np.squeeze(self._seas_term, 0)
 
@@ -862,12 +838,12 @@ def _filter_coef_prior(self, df):
 
     def set_dynamic_attributes(self, df, training_meta):
         """Overriding: func: `~orbit.models.BaseETS._set_dynamic_attributes"""
-        self._set_valid_response_attributes(training_meta)
         self._set_regressor_matrix(df, training_meta)
         self._set_coefficients_kernel_matrix(df, training_meta)
         self._set_knots_scale_matrix(df, training_meta)
         self._set_levs_and_seas(df, training_meta)
         self._filter_coef_prior(df)
+        self._set_valid_response_attributes(training_meta)
 
     @staticmethod
     def _concat_regression_coefs(pr_beta=None, rr_beta=None):
@@ -971,7 +947,8 @@ def predict(self, posterior_estimates, df, training_meta, prediction_meta,
                                                       self._seasonality_coef_knot_dates,
                                                       self._seasonality_coef_knots,
                                                       self._seasonality,
-                                                      self._seasonality_fs_order)
+                                                      self._seasonality_fs_order,
+                                                      self._seasonality_labels)
 
         #     # seas is 1-d array, add the batch size back
         #     seas = np.expand_dims(seas, 0)