Merge branch 'main' into upgrade-lightning-2

neuralprophet/configure.py

@@ -305,17 +305,15 @@ def __post_init__(self):
             log.error("Invalid growth for global_local mode '{}'. Set to 'global'".format(self.trend_global_local))
             self.trend_global_local = "global"
 
-        # If trend_local_reg < 0
         if self.trend_local_reg < 0:
             log.error("Invalid  negative trend_local_reg '{}'. Set to False".format(self.trend_local_reg))
             self.trend_local_reg = False
 
-        # If trend_local_reg = True
         if self.trend_local_reg is True:
             log.error("trend_local_reg = True. Default trend_local_reg value set to 1")
             self.trend_local_reg = 1
 
-        # If Trend modelling is global.
+        # If Trend modelling is global but local regularization is set.
         if self.trend_global_local == "global" and self.trend_local_reg:
             log.error("Trend modeling is '{}'. Setting the trend_local_reg to False".format(self.trend_global_local))
             self.trend_local_reg = False
@@ -392,17 +390,15 @@ def __post_init__(self):
             }
         )
 
-        # If seasonality_local_reg < 0
         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
 
-        # If seasonality_local_reg = True
         if self.seasonality_local_reg is True:
             log.error("seasonality_local_reg = True. Default seasonality_local_reg value set to 1")
             self.seasonality_local_reg = 1
 
-        # If Season modelling is global.
+        # If Season modelling is global but local regularization is set.
         if self.global_local == "global" and self.seasonality_local_reg:
             log.error(
                 "Seasonality modeling is '{}'. Setting the seasonality_local_reg to False".format(self.global_local)

neuralprophet/forecaster.py

@@ -1108,7 +1108,7 @@ def fit(
         self.fitted = True
         return metrics_df
 
-    def predict(self, df: pd.DataFrame, decompose: bool = True, raw: bool = False):
+    def predict(self, df: pd.DataFrame, decompose: bool = True, raw: bool = False, auto_extend=True):
         """Runs the model to make predictions.
 
         Expects all data needed to be present in dataframe.
@@ -1177,7 +1177,7 @@ def predict(self, df: pd.DataFrame, decompose: bool = True, raw: bool = False):
                     quantiles=self.config_train.quantiles,
                     components=components,
                 )
-                if periods_added[df_name] > 0:
+                if auto_extend and periods_added[df_name] > 0:
                     fcst = fcst[:-1]
             else:
                 fcst = _reshape_raw_predictions_to_forecst_df(
@@ -1192,9 +1192,10 @@ def predict(self, df: pd.DataFrame, decompose: bool = True, raw: bool = False):
                     quantiles=self.config_train.quantiles,
                     config_lagged_regressors=self.config_lagged_regressors,
                 )
-                if periods_added[df_name] > 0:
-                    fcst = fcst[: -periods_added[df_name]]
+                if auto_extend and periods_added[df_name] > 0:
+                    fcst = fcst[:-periods_added[df_name]]
             forecast = pd.concat((forecast, fcst), ignore_index=True)
+
         df = df_utils.return_df_in_original_format(forecast, received_ID_col, received_single_time_series)
         self.predict_steps = self.n_forecasts
         return df

neuralprophet/hdays_utils.py

@@ -41,7 +41,6 @@ def get_country_holidays(
 
     return holiday_obj
 
-
 def get_holidays_from_country(country: Union[str, Iterable[str], dict], df=None):
     """
     Return all possible holiday names of given countries

neuralprophet/utils.py

@@ -374,7 +374,7 @@ def config_seasonality_to_model_dims(config_seasonality: ConfigSeasonality):
         seasonal_dims[name] = resolution
     return seasonal_dims
 
-
+  
 def config_events_to_model_dims(config_events: Optional[ConfigEvents], config_country_holidays):
     """
     Convert user specified events configurations along with country specific