[Minor] fix future regressor (#1585)

* add io buffer support

* added tests

* remove typealias

* add subdivions to country

* removed  perods_added logic

* added auto_extend

* fix linting issues

* reversed auto_extended

* removed subdisions

* fix isTrue

* update lock

* remove subdivision

* uncomment test_glocal lines 180 onward

---------

Co-authored-by: Maisa Ben Salah <maisabensalah@AminsMBP131.attlocal.net>
Co-authored-by: Oskar Triebe <ourownstory@users.noreply.github.com>

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

@@ -305,18 +305,16 @@ 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 == 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 self.trend_global_local == "global" and self.trend_local_reg != False:
+        # 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
 
@@ -392,18 +390,16 @@ 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 == 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 self.global_local == "global" and self.seasonality_local_reg != False:
+        # 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

@@ -1107,7 +1107,7 @@ 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.
@@ -1176,7 +1176,7 @@ 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(
@@ -1191,9 +1191,10 @@ 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/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

@@ -375,7 +375,7 @@ 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

tests/test_model_performance.py

@@ -11,19 +11,20 @@
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
+from neuralprophet import NeuralProphet, set_random_seed
+
 log = logging.getLogger("NP.test")
 log.setLevel("DEBUG")
 log.parent.setLevel("WARNING")
 
 try:
-    from plotly_resampler import register_plotly_resampler, unregister_plotly_resampler
+    from plotly_resampler import unregister_plotly_resampler
 
     plotly_resampler_installed = True
 except ImportError:
     plotly_resampler_installed = False
     log.error("Importing plotly failed. Interactive plots will not work.")
 
-from neuralprophet import NeuralProphet, set_random_seed
 
 DIR = pathlib.Path(__file__).parent.parent.absolute()
 DATA_DIR = os.path.join(DIR, "tests", "test-data")