names

neuralprophet/utils_time_dataset.py

@@ -241,19 +241,13 @@ def stack_lagged_regressors(self, df_tensors, feature_list, current_idx, config)
             return current_idx + num_features
         return current_idx
 
-    def stack_additive_events(
-        self,
-        df_tensors,
-        feature_list,
-        current_idx,
-        additive_event_and_holiday_names,
-    ):
+    def stack_additive_events(self, df_tensors, feature_list, current_idx, names):
         """
         Stack the additive event and holiday features.
         """
-        if additive_event_and_holiday_names:
+        if names:
             additive_events_tensor = torch.cat(
-                [df_tensors[name].unsqueeze(-1) for name in additive_event_and_holiday_names],
+                [df_tensors[name].unsqueeze(-1) for name in names],
                 dim=1,
             )
             feature_list.append(additive_events_tensor)
@@ -264,16 +258,12 @@ def stack_additive_events(
             return current_idx + additive_events_tensor.size(1)
         return current_idx
 
-    def stack_multiplicative_events(
-        self, df_tensors, feature_list, current_idx, multiplicative_event_and_holiday_names
-    ):
+    def stack_multiplicative_events(self, df_tensors, feature_list, current_idx, names):
         """
         Stack the multiplicative event and holiday features.
         """
-        if multiplicative_event_and_holiday_names:
-            multiplicative_events_tensor = torch.cat(
-                [df_tensors[name].unsqueeze(-1) for name in multiplicative_event_and_holiday_names], dim=1
-            )
+        if names:
+            multiplicative_events_tensor = torch.cat([df_tensors[name].unsqueeze(-1) for name in names], dim=1)
             feature_list.append(multiplicative_events_tensor)
             self.feature_indices["multiplicative_events"] = (
                 current_idx,
@@ -282,14 +272,12 @@ def stack_multiplicative_events(
             return current_idx + multiplicative_events_tensor.size(1)
         return current_idx
 
-    def stack_additive_regressors(self, df_tensors, feature_list, current_idx, additive_regressors_names):
+    def stack_additive_regressors(self, df_tensors, feature_list, current_idx, names):
         """
         Stack the additive regressor features.
         """
-        if additive_regressors_names:
-            additive_regressors_tensor = torch.cat(
-                [df_tensors[name].unsqueeze(-1) for name in additive_regressors_names], dim=1
-            )
+        if names:
+            additive_regressors_tensor = torch.cat([df_tensors[name].unsqueeze(-1) for name in names], dim=1)
             feature_list.append(additive_regressors_tensor)
             self.feature_indices["additive_regressors"] = (
                 current_idx,
@@ -298,20 +286,20 @@ def stack_additive_regressors(self, df_tensors, feature_list, current_idx, addit
             return current_idx + additive_regressors_tensor.size(1)
         return current_idx
 
-    def stack_multiplicative_regressors(self, df_tensors, feature_list, current_idx, multiplicative_regressors_names):
+    def stack_multiplicative_regressors(self, df_tensors, feature_list, current_idx, names):
         """
         Stack the multiplicative regressor features.
         """
-        if multiplicative_regressors_names:
+        if names:
             multiplicative_regressors_tensor = torch.cat(
-                [df_tensors[name].unsqueeze(-1) for name in multiplicative_regressors_names], dim=1
+                [df_tensors[name].unsqueeze(-1) for name in names], dim=1
             )  # Shape: [batch_size, num_multiplicative_regressors, 1]
             feature_list.append(multiplicative_regressors_tensor)
             self.feature_indices["multiplicative_regressors"] = (
                 current_idx,
-                current_idx + len(multiplicative_regressors_names) - 1,
+                current_idx + len(names) - 1,
             )
-            return current_idx + len(multiplicative_regressors_names)
+            return current_idx + len(names)
         return current_idx
 
     def stack_seasonalities(self, df_tensors, feature_list, current_idx, config, seasonalities):