fix tests

neuralprophet/configure.py

@@ -511,8 +511,8 @@ class ConfigLaggedRegressors:
     # List of hidden layers for shared NN across LaggedReg. The default value is ``[]``, which initializes no hidden layers.
     regressors: OrderedDict[LaggedRegressor] = field(init=False)
 
-    # def __post_init__(self):
-    #     self.regressors = None
+    def __post_init__(self):
+        self.regressors = None
 
 
 @dataclass

neuralprophet/data/process.py

@@ -101,7 +101,7 @@ def _reshape_raw_predictions_to_forecst_df(
     lagged_components = [
         "ar",
     ]
-    if config_lagged_regressors.regressors is not None:
+    if config_lagged_regressors is not None and config_lagged_regressors.regressors is not None:
         for name in config_lagged_regressors.regressors.keys():
             lagged_components.append(f"lagged_regressor_{name}")
     for comp in lagged_components:
@@ -362,7 +362,7 @@ def _validate_column_name(
     if seasons and config_seasonality is not None:
         if name in config_seasonality.periods:
             raise ValueError(f"Name {name!r} already used for a seasonality.")
-    if covariates and config_lagged_regressors.regressors is not None:
+    if covariates and config_lagged_regressors is not None and config_lagged_regressors.regressors is not None:
         if name in config_lagged_regressors.regressors.keys():
             raise ValueError(f"Name {name!r} already used for an added covariate.")
     if regressors and config_regressors.regressors is not None:
@@ -423,7 +423,7 @@ def _check_dataframe(
             model.config_regressors.regressors.pop(reg)
         if model.config_regressors.regressors is not None and len(model.config_regressors.regressors) == 0:
             model.config_regressors.regressors = None
-    if model.config_lagged_regressors.regressors is not None:
+    if model.config_lagged_regressors is not None and model.config_lagged_regressors.regressors is not None:
         for reg in lag_regressors_to_remove:
             log.warning(f"Removing lagged regressor {reg} because it is not present in the data.")
             model.config_lagged_regressors.regressors.pop(reg)
@@ -528,7 +528,7 @@ def _handle_missing_data(
         data_columns = []
         if n_lags > 0:
             data_columns.append("y")
-        if config_lagged_regressors.regressors is not None:
+        if config_lagged_regressors is not None and config_lagged_regressors.regressors is not None:
             data_columns.extend(config_lagged_regressors.regressors.keys())
         if config_regressors is not None and config_regressors.regressors is not None:
             data_columns.extend(config_regressors.regressors.keys())

neuralprophet/df_utils.py

@@ -103,7 +103,7 @@ def get_max_num_lags(n_lags: int, config_lagged_regressors: Optional[ConfigLagge
         int
             Maximum number of lags between the autoregression lags and the covariates lags.
     """
-    if config_lagged_regressors.regressors is not None:
+    if config_lagged_regressors is not None and config_lagged_regressors.regressors is not None:
         # log.debug("config_lagged_regressors exists")
         return max([n_lags] + [val.n_lags for key, val in config_lagged_regressors.regressors.items()])
     else:

neuralprophet/forecaster.py

@@ -640,6 +640,8 @@ def add_lagged_regressor(
                 config_lagged_regressors=self.config_lagged_regressors,
                 config_regressors=self.config_regressors,
             )
+            if self.config_lagged_regressors.regressors is None:
+                self.config_lagged_regressors.regressors = OrderedDict()
             self.config_lagged_regressors.regressors[name] = configure.LaggedRegressor(
                 reg_lambda=regularization,
                 normalize=normalize,

neuralprophet/plot_utils.py

@@ -190,9 +190,10 @@ def check_if_configured(m, components, error_flag=False):  # move to utils
     if "autoregression" in components and not m.config_ar.n_lags > 0:
         components.remove("autoregression")
         invalid_components.append("autoregression")
-    if "lagged_regressors" in components and m.config_lagged_regressors is None:
-        components.remove("lagged_regressors")
-        invalid_components.append("lagged_regressors")
+    if "lagged_regressors" in components:
+        if m.config_lagged_regressors is None or m.config_lagged_regressors.regressors is None:
+            components.remove("lagged_regressors")
+            invalid_components.append("lagged_regressors")
     if "events" in components and (m.config_events is None and m.config_country_holidays is None):
         components.remove("events")
         invalid_components.append("events")
@@ -209,7 +210,7 @@ def check_if_configured(m, components, error_flag=False):  # move to utils
     return components
 
 
-def get_valid_configuration(  # move to utils
+def get_valid_configuration(
     m, components=None, df_name=None, valid_set=None, validator=None, forecast_in_focus=None, quantile=0.5
 ):
     """Validate and adapt the selected components to be plotted.
@@ -382,7 +383,7 @@ def get_valid_configuration(  # move to utils
     if "lagged_regressors" in components:
         if validator == "plot_components":
             if forecast_in_focus is None:
-                for name in m.config_lagged_regressors.keys():
+                for name in m.config_lagged_regressors.regressors.keys():
                     plot_components.append(
                         {
                             "plot_name": f'Lagged Regressor "{name}"',
@@ -392,16 +393,16 @@ def get_valid_configuration(  # move to utils
                         }
                     )
             else:
-                for name in m.config_lagged_regressors.keys():
+                for name in m.config_lagged_regressors.regressors.keys():
                     plot_components.append(
                         {
                             "plot_name": f'Lagged Regressor "{name}" ({forecast_in_focus})-ahead',
                             "comp_name": f"lagged_regressor_{name}{forecast_in_focus}",
                         }
                     )
         elif validator == "plot_parameters":
-            for name in m.config_lagged_regressors.keys():
-                if m.config_lagged_regressors[name].as_scalar:
+            for name in m.config_lagged_regressors.regressors.keys():
+                if m.config_lagged_regressors.regressors[name].as_scalar:
                     lagged_scalar_regressors.append((name, m.model.get_covar_weights()[name].detach().numpy()))
                 else:
                     plot_components.append(

neuralprophet/time_dataset.py

@@ -389,7 +389,9 @@ def tabularize_univariate_datetime_single_index(
             inputs["lags"] = lags
 
         # COVARIATES / LAGGED REGRESSORS: Lagged regressor inputs: analogous to LAGS
-        if config_lagged_regressors.regressors is not None:  # and max_lags > 0:
+        if (
+            config_lagged_regressors is not None and config_lagged_regressors.regressors is not None
+        ):  # and max_lags > 0:
             inputs["covariates"] = self.get_sample_lagged_regressors(
                 df_tensors=df_tensors, origin_index=origin_index, config_lagged_regressors=config_lagged_regressors
             )
@@ -651,7 +653,9 @@ def create_nan_mask(
             valid_origins &= y_lags_valid
 
         # LAGGED REGRESSORS
-        if config_lagged_regressors.regressors is not None:  # and max_lags > 0:
+        if (
+            config_lagged_regressors is not None and config_lagged_regressors.regressors is not None
+        ):  # and max_lags > 0:
             reg_lags_valid = torch.ones(tensor_length, dtype=torch.bool)
             for name, lagged_regressor in config_lagged_regressors.regressors.items():
                 n_reg_lags = lagged_regressor.n_lags

neuralprophet/time_net.py

@@ -318,7 +318,7 @@ def get_covar_weights(self, covar_input=None) -> torch.Tensor:
         if self.config_lagged_regressors is not None and self.config_lagged_regressors.regressors is not None:
             # Accumulate the lags of the covariates
             covar_splits = np.add.accumulate(
-                [covar.n_lags for _, covar in self.config_lagged_regressors.items()][:-1]
+                [covar.n_lags for _, covar in self.config_lagged_regressors.regressors.items()][:-1]
             ).tolist()
             # If actual covariates are provided, use them to compute the attributions
             if covar_input is not None:

tests/test_regularization.py

@@ -169,7 +169,7 @@ def test_regularization_lagged_regressor():
     lagged_regressors_config = dict(lagged_regressors)
 
     weights = m.model.get_covar_weights()
-    for name in m.config_lagged_regressors.keys():
+    for name in m.config_lagged_regressors.regressors.keys():
         weight_average = np.average(weights[name].detach().numpy())
 
         lagged_regressor_weight = lagged_regressors_config[name]