[Minor] Improve Season glocal reg invalid parameter handling (#1601)

* reduce warning

* assertionError for wrong values

neuralprophet/configure.py

@@ -390,12 +390,10 @@ def __post_init__(self):
             }
         )
 
-        if self.seasonality_local_reg < 0:
-            log.error("Invalid  negative seasonality_local_reg '{}'. Set to False".format(self.seasonality_local_reg))
-            self.seasonality_local_reg = False
+        assert self.seasonality_local_reg >= 0, "Invalid seasonality_local_reg '{}'.".format(self.seasonality_local_reg)
 
         if self.seasonality_local_reg is True:
-            log.error("seasonality_local_reg = True. Default seasonality_local_reg value set to 1")
+            log.warning("seasonality_local_reg = True. Default seasonality_local_reg value set to 1")
             self.seasonality_local_reg = 1
 
         # If Season modelling is global but local regularization is set.

tests/test_glocal.py

@@ -5,6 +5,7 @@
 import pathlib
 
 import pandas as pd
+import pytest
 
 from neuralprophet import NeuralProphet
 
@@ -341,14 +342,16 @@ def test_glocal_seasonality_reg():
     df2_0["ID"] = "df2"
     df3_0 = df.iloc[256:384, :].copy(deep=True)
     df3_0["ID"] = "df3"
-    for _ in [-30, 0, False, True]:
+    for coef_i in [0, 1.5, False, True]:
+
         m = NeuralProphet(
             n_forecasts=1,
             epochs=EPOCHS,
             batch_size=BATCH_SIZE,
             learning_rate=LR,
             season_global_local="local",
             yearly_seasonality_glocal_mode="global",
+            seasonality_local_reg=coef_i,
         )
 
         m.add_seasonality(period=30, fourier_order=8, name="monthly", global_local="global")
@@ -359,6 +362,24 @@ def test_glocal_seasonality_reg():
         metrics = m.test(test_df)
         log.info(f"forecast = {forecast}, metrics = {metrics}")
 
+    with pytest.raises(AssertionError, match="Invalid seasonality_local_reg"):
+        m = NeuralProphet(
+            n_forecasts=1,
+            epochs=EPOCHS,
+            batch_size=BATCH_SIZE,
+            learning_rate=LR,
+            season_global_local="local",
+            yearly_seasonality_glocal_mode="global",
+            seasonality_local_reg=-324,
+        )
+
+        m.add_seasonality(period=30, fourier_order=8, name="monthly", global_local="global")
+        train_df, test_df = m.split_df(pd.concat((df1_0, df2_0, df3_0)), valid_p=0.33, local_split=True)
+        m.fit(train_df)
+        future = m.make_future_dataframe(test_df, n_historic_predictions=True)
+        forecast = m.predict(future)
+        metrics = m.test(test_df)
+
 
 def test_trend_local_reg_if_global():
     # SEASONALITY GLOBAL LOCAL MODELLING - NO EXOGENOUS VARIABLES