fixed covariates

neuralprophet/forecaster.py

@@ -1070,7 +1070,7 @@ def fit(
             or any(value != 1 for value in self.num_seasonalities_modelled_dict.values())
         )
 
-        ##### Data Setup, and Training Setup  #####
+        # Data Setup, and Training Setup
         # Train Configuration: overwrite self.config_train with user provided values
         if learning_rate is not None:
             self.config_train.learning_rate = learning_rate
@@ -1213,8 +1213,8 @@ def fit(
         if self.config_train.learning_rate is None:
             assert not self.fitted, "Learning rate must be provided for re-training a fitted model."
 
-            ## Init a separate Model, Loader and Trainer copy for LR finder (optional, done for safety)
-            ## Note Leads to a CUDA issue. Needs to be fixed before enabling this feature.
+            # Init a separate Model, Loader and Trainer copy for LR finder (optional, done for safety)
+            # Note Leads to a CUDA issue. Needs to be fixed before enabling this feature.
             # model_lr_finder = self._init_model()
             # loader_lr_finder = DataLoader(
             #     dataset,

neuralprophet/time_dataset.py

@@ -599,6 +599,7 @@ def sort_regressor_names(self, config):
                     multiplicative_regressors_names.append(reg)
         return additive_regressors_names, multiplicative_regressors_names
 
+
 class GlobalTimeDataset(TimeDataset):
     def __init__(
         self,

neuralprophet/time_net.py

@@ -609,7 +609,7 @@ def forward(
 
         # Unpack and process covariates
         covariates_input = None
-        if self.config_lagged_regressors:
+        if self.config_lagged_regressors and self.config_lagged_regressors.regressors is not None:
             covariates_input = self.features_extractor.extract_component(component_name="lagged_regressors")
             covariates = self.forward_covar_net(covariates=covariates_input)
             additive_components += covariates

neuralprophet/utils_time_dataset.py

@@ -95,8 +95,8 @@ def extract_lags(self):
 
     def extract_lagged_regressors(self):
         lagged_regressors = OrderedDict()
-        if self.lagged_regressor_config:
-            for name, lagged_regressor in self.lagged_regressor_config.items():
+        if self.lagged_regressor_config is not None and self.lagged_regressor_config.regressors is not None:
+            for name, lagged_regressor in self.lagged_regressor_config.regressors.items():
                 lagged_regressor_key = f"lagged_regressor_{name}"
                 if lagged_regressor_key in self.feature_indices:
                     lagged_regressor_start_idx, _ = self.feature_indices[lagged_regressor_key]
@@ -217,12 +217,14 @@ def pack_lagged_regerssors_component(df_tensors, feature_list, feature_indices,
     """
     Stack the lagged regressor features.
     """
-    if config_lagged_regressors:
-        lagged_regressor_tensors = [df_tensors[name].unsqueeze(-1) for name in config_lagged_regressors.keys()]
+    if config_lagged_regressors is not None and config_lagged_regressors.regressors is not None:
+        lagged_regressor_tensors = [
+            df_tensors[name].unsqueeze(-1) for name in config_lagged_regressors.regressors.keys()
+        ]
         stacked_lagged_regressor_tensor = torch.cat(lagged_regressor_tensors, dim=-1)
         feature_list.append(stacked_lagged_regressor_tensor)
         num_features = stacked_lagged_regressor_tensor.size(-1)
-        for i, name in enumerate(config_lagged_regressors.keys()):
+        for i, name in enumerate(config_lagged_regressors.regressors.keys()):
             feature_indices[f"lagged_regressor_{name}"] = (
                 current_idx + i,
                 current_idx + i + 1,