[Minor] fix future regressor

neuralprophet/components/future_regressors/linear.py

@@ -1,7 +1,6 @@
 import torch
 import torch.nn as nn
 
-from neuralprophet import utils
 from neuralprophet.components.future_regressors import FutureRegressors
 from neuralprophet.utils_torch import init_parameter
 

neuralprophet/components/future_regressors/neural_nets.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 
 from neuralprophet.components.future_regressors import FutureRegressors
-from neuralprophet.utils_torch import init_parameter, interprete_model
+from neuralprophet.utils_torch import interprete_model
 
 # from neuralprophet.utils_torch import init_parameter
 

neuralprophet/components/future_regressors/shared_neural_nets.py

@@ -1,10 +1,9 @@
-from collections import Counter, OrderedDict
+from collections import Counter
 
-import torch
 import torch.nn as nn
 
 from neuralprophet.components.future_regressors import FutureRegressors
-from neuralprophet.utils_torch import init_parameter, interprete_model
+from neuralprophet.utils_torch import interprete_model
 
 # from neuralprophet.utils_torch import init_parameter
 

neuralprophet/components/future_regressors/shared_neural_nets_coef.py

@@ -1,10 +1,9 @@
-from collections import Counter, OrderedDict
+from collections import Counter
 
-import torch
 import torch.nn as nn
 
 from neuralprophet.components.future_regressors import FutureRegressors
-from neuralprophet.utils_torch import init_parameter, interprete_model
+from neuralprophet.utils_torch import interprete_model
 
 # from neuralprophet.utils_torch import init_parameter
 

neuralprophet/configure.py

@@ -305,17 +305,17 @@
             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 == True:
+        if self.trend_local_reg:
             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 self.trend_global_local == "global" and self.trend_local_reg != False:
+        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
 
@@ -355,13 +355,13 @@
             log.error("Invalid global_local mode '{}'. Set to 'global'".format(self.global_local))
             self.global_local = "global"
 
         self.periods = OrderedDict(
             {
                 "yearly": Season(
                     resolution=6,
                     period=365.25,
                     arg=self.yearly_arg,
                     global_local=(
                         self.yearly_global_local
                         if self.yearly_global_local in ["global", "local"]
                         else self.global_local
@@ -372,7 +372,7 @@
                     resolution=3,
                     period=7,
                     arg=self.weekly_arg,
                     global_local=(
                         self.weekly_global_local
                         if self.weekly_global_local in ["global", "local"]
                         else self.global_local
@@ -383,7 +383,7 @@
                     resolution=6,
                     period=1,
                     arg=self.daily_arg,
                     global_local=(
                         self.daily_global_local if self.daily_global_local in ["global", "local"] else self.global_local
                     ),
                     condition_name=None,
@@ -392,17 +392,17 @@
         )
 
         # 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 == True:
+        if self.seasonality_local_reg:
             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 self.global_local == "global" and self.seasonality_local_reg != False:
+        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)
             )
@@ -413,7 +413,7 @@
             resolution=resolution,
             period=period,
             arg=arg,
             global_local=global_local if global_local in ["global", "local"] else self.global_local,
             condition_name=condition_name,
         )
 
@@ -487,7 +487,7 @@
     regressors: OrderedDict = field(init=False)  # contains RegressorConfig objects
 
     def __post_init__(self):
         self.regressors = None
 
 
 @dataclass
@@ -507,11 +507,12 @@
     lower_window: int
     upper_window: int
     mode: str = "additive"
+    subdivision: Optional[Union[str, dict]] = (None,)
     reg_lambda: Optional[float] = None
     holiday_names: set = field(init=False)
 
     def init_holidays(self, df=None):
-        self.holiday_names = utils.get_holidays_from_country(self.country, df)
+        self.holiday_names = utils.get_holidays_from_country(self.country, self.subdivision, df)
 
 
 ConfigCountryHolidays = Holidays

neuralprophet/forecaster.py

@@ -749,6 +749,7 @@
     def add_country_holidays(
         self,
         country_name: Union[str, list],
+        subdivision_name: Optional[Union[str, dict]] = None,
         lower_window: int = 0,
         upper_window: int = 0,
         regularization: Optional[float] = None,
@@ -766,6 +767,9 @@
         ----------
             country_name : str, list
                 name or list of names of the country
+            subdivision_name : str, dict
+                name or list of names of the subdivisions (e.g., provinces or states) or
+                a dictionary where the key is the country name and the value is a list of subdivisions
             lower_window : int
                 the lower window for all the country holidays
             upper_window : int
@@ -789,6 +793,7 @@
                 regularization = None
         self.config_country_holidays = configure.Holidays(
             country=country_name,
+            subdivision=subdivision_name,
             lower_window=lower_window,
             upper_window=upper_window,
             reg_lambda=regularization,
@@ -1102,12 +1107,12 @@
             # Only display the plot if the session is interactive, eg. do not show in github actions since it
             # causes an error in the Windows and MacOS environment
             if matplotlib.is_interactive():
                 fig
 
         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=False):
         """Runs the model to make predictions.
 
         Expects all data needed to be present in dataframe.
@@ -1176,7 +1181,7 @@
                     quantiles=self.config_train.quantiles,
                     components=components,
                 )
-                if periods_added[df_name] > 0:
+                if not auto_extend and periods_added[df_name] > 0:
                     fcst = fcst[:-1]
             else:
                 fcst = _reshape_raw_predictions_to_forecst_df(
@@ -1191,9 +1196,10 @@
                     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 not auto_extend and periods_added[df_name] > 0:
+                    fcst = fcst[:-1]
             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

@@ -3,7 +3,9 @@
 import holidays
 
 
-def get_country_holidays(country: str, years: Optional[Union[int, Iterable[int]]] = None):
+def get_country_holidays(
+    country: str, years: Optional[Union[int, Iterable[int]]] = None, subdivision: Optional[str] = None
+):
     """
     Helper function to get holidays for a country.
 
@@ -13,6 +15,8 @@ def get_country_holidays(country: str, years: Optional[Union[int, Iterable[int]]
             Country name to retrieve country specific holidays
         years : int, list
             Year or list of years to retrieve holidays for
+        subdivision : str
+            Subdivision name to retrieve subdivision specific holidays
 
     Returns
     -------
@@ -27,5 +31,9 @@ def get_country_holidays(country: str, years: Optional[Union[int, Iterable[int]]
     country = substitutions.get(country, country)
     if not hasattr(holidays, country):
         raise AttributeError(f"Holidays in {country} are not currently supported!")
+    if subdivision:
+        holiday_obj = getattr(holidays, country)(years=years, subdiv=subdivision)
+    else:
+        holiday_obj = getattr(holidays, country)(years=years)
 
-    return getattr(holidays, country)(years=years)
+    return holiday_obj

neuralprophet/time_net.py

@@ -940,7 +940,7 @@ def _add_batch_regularizations(self, loss, epoch, progress):
         trend_glocal_loss = torch.zeros(1, dtype=torch.float, requires_grad=False)
         # Glocal Trend
         if self.config_trend is not None:
-            if self.config_trend.trend_global_local == "local" and self.config_trend.trend_local_reg != False:
+            if self.config_trend.trend_global_local == "local" and self.config_trend.trend_local_reg:
                 trend_glocal_loss = reg_func_trend_glocal(
                     self.trend.trend_k0, self.trend.trend_deltas, self.config_trend.trend_local_reg
                 )
@@ -949,7 +949,7 @@ def _add_batch_regularizations(self, loss, epoch, progress):
         if self.config_seasonality is not None:
             if (
                 self.config_seasonality.global_local in ["local", "glocal"]
-                and self.config_seasonality.seasonality_local_reg != False
+                and self.config_seasonality.seasonality_local_reg
             ):
                 seasonality_glocal_loss = reg_func_seasonality_glocal(
                     self.seasonality.season_params, self.config_seasonality.seasonality_local_reg

neuralprophet/utils.py

@@ -5,7 +5,7 @@
 import os
 import sys
 from collections import OrderedDict
-from typing import TYPE_CHECKING, Iterable, Optional, Union, BinaryIO, IO
+from typing import IO, TYPE_CHECKING, BinaryIO, Iterable, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -23,6 +23,7 @@
 
 FILE_LIKE = Union[str, os.PathLike, BinaryIO, IO[bytes]]
 
+
 def save(forecaster, path: FILE_LIKE):
     """Save a fitted Neural Prophet model to disk.
 
@@ -375,14 +376,19 @@ def config_seasonality_to_model_dims(config_seasonality: ConfigSeasonality):
     return seasonal_dims
 
 
-def get_holidays_from_country(country: Union[str, Iterable[str]], df=None):
+def get_holidays_from_country(
+    country: Union[str, Iterable[str]], subdivision: Optional[Union[str, dict]] = None, df=None
+):
     """
     Return all possible holiday names of given country
 
     Parameters
     ----------
         country : str, list
             List of country names to retrieve country specific holidays
+        subdivision : str, dict
+            a single subdivision (e.g., province or state) as a string or
+            a dictionary where the key is the country name and the value is a subdivision
         df : pd.Dataframe
             Dataframe from which datestamps will be retrieved from
 
@@ -399,10 +405,16 @@ def get_holidays_from_country(country: Union[str, Iterable[str]], df=None):
     # support multiple countries
     if isinstance(country, str):
         country = [country]
-
+    # support subdivisions
+    if subdivision is not None:
+        if isinstance(subdivision, str):
+            if isinstance(country, list):
+                raise ValueError("If country_name is a list, subdivisions must be a dictionary.")
+            subdivision = {country: subdivision}
     unique_holidays = {}
     for single_country in country:
-        holidays_country = get_country_holidays(single_country, years)
+        subdivision = subdivision.get(single_country) if subdivision else None
+        holidays_country = get_country_holidays(single_country, years, subdivision)
         for date, name in holidays_country.items():
             if date not in unique_holidays:
                 unique_holidays[date] = name