Feat/encoder improvement

darts/explainability/explainability.py

@@ -99,8 +99,7 @@ def __init__(
                 (
                     background_past_covariates,
                     background_future_covariates,
-                ) = self.model.generate_predict_encodings(
-                    n=len(background_series) - self.model.min_train_series_length,
+                ) = self.model.generate_fit_encodings(
                     series=background_series,
                     past_covariates=background_past_covariates,
                     future_covariates=background_future_covariates,

darts/explainability/shap_explainer.py

@@ -201,22 +201,12 @@ def explain(
             foreground_past_covariates = series2seq(foreground_past_covariates)
             foreground_future_covariates = series2seq(foreground_future_covariates)
 
-            # For encoding inference, in case we don't have past and future covariates,
-            # We need to provide the minimum elements for the foreground_series. If the foreground_series
-            # is a list of TimeSeries, we need to provide the minimum elements for each TimeSeries.
-            foreground_series_start_encoder = []
-            for i in range(len(foreground_series)):
-                foreground_series_start_encoder.append(
-                    foreground_series[i][: self.model.min_train_series_length]
-                )
-
             if self.model.encoders.encoding_available:
                 (
                     foreground_past_covariates,
                     foreground_future_covariates,
-                ) = self.model.generate_predict_encodings(
-                    n=len(foreground_series) - self.model.min_train_series_length,
-                    series=foreground_series_start_encoder,
+                ) = self.model.generate_fit_encodings(
+                    series=foreground_series,
                     past_covariates=foreground_past_covariates,
                     future_covariates=foreground_future_covariates,
                 )
@@ -377,8 +367,7 @@ def force_plot_from_ts(
             (
                 foreground_past_covariates,
                 foreground_future_covariates,
-            ) = self.model.generate_predict_encodings(
-                n=len(foreground_series) - self.model.min_train_series_length,
+            ) = self.model.generate_fit_encodings(
                 series=foreground_series,
                 past_covariates=foreground_past_covariates,
                 future_covariates=foreground_future_covariates,

darts/models/filtering/filtering_model.py

@@ -20,6 +20,8 @@ class FilteringModel(ABC):
 
     @abstractmethod
     def __init__(self):
+        self._expect_past_covariates = False
+        self._expect_future_covariates = False
         pass
 
     @abstractmethod

darts/models/filtering/kalman_filter.py

@@ -55,7 +55,6 @@ def __init__(self, dim_x: int = 1, kf: Optional[Kalman] = None):
         # TODO: Add support for x_init. Needs reimplementation of NFourSID.
 
         super().__init__()
-        self._expect_covariates = False
 
         if kf is None:
             self.kf = None
@@ -68,7 +67,7 @@ def __init__(self, dim_x: int = 1, kf: Optional[Kalman] = None):
             self.dim_y = kf.state_space.y_dim
             self._kf_provided = True
             if self.dim_u > 0:
-                self._expect_covariates = True
+                self._expect_future_covariates = True
 
     def __str__(self):
         return f"KalmanFilter(dim_x={self.dim_x})"
@@ -101,7 +100,7 @@ def fit(
             Fitted Kalman filter.
         """
         if covariates is not None:
-            self._expect_covariates = True
+            self._expect_future_covariates = True
             covariates = covariates.slice_intersect(series)
             raise_if_not(
                 series.has_same_time_as(covariates),
@@ -181,11 +180,11 @@ def filter(
         )
 
         raise_if(
-            covariates is not None and not self._expect_covariates,
+            covariates is not None and not self._expect_future_covariates,
             "Covariates were provided, but the Kalman filter was not fitted with covariates.",
         )
 
-        if self._expect_covariates:
+        if self._expect_future_covariates:
             raise_if(
                 covariates is None,
                 "The Kalman filter was fitted with covariates, but these were not provided.",
@@ -205,7 +204,7 @@ def filter(
         kf = deepcopy(self.kf)
 
         y_values = series.values(copy=False)
-        if self._expect_covariates:
+        if self._expect_future_covariates:
             u_values = covariates.values(copy=False)
 
             # set control signal to 0 if it contains NaNs:

darts/models/forecasting/arima.py

@@ -33,6 +33,7 @@ def __init__(
         seasonal_order: Tuple[int, int, int, int] = (0, 0, 0, 0),
         trend: Optional[str] = None,
         random_state: int = 0,
+        add_encoders: Optional[dict] = None,
     ):
         """ARIMA
         ARIMA-type models extensible with exogenous variables (future covariates)
@@ -54,8 +55,28 @@ def __init__(
             Parameter controlling the deterministic trend. 'n' indicates no trend,
             'c' a constant term, 't' linear trend in time, and 'ct' includes both.
             Default is 'c' for models without integration, and no trend for models with integration.
+        add_encoders
+            A large number of future covariates can be automatically generated with `add_encoders`.
+            This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that
+            will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to
+            transform the generated covariates. This happens all under one hood and only needs to be specified at
+            model creation.
+            Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about
+            ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features:
+
+            .. highlight:: python
+            .. code-block:: python
+
+                add_encoders={
+                    'cyclic': {'future': ['month']},
+                    'datetime_attribute': {'future': ['hour', 'dayofweek']},
+                    'position': {'future': ['relative']},
+                    'custom': {'future': [lambda idx: (idx.year - 1950) / 50]},
+                    'transformer': Scaler()
+                }
+            ..
         """
-        super().__init__()
+        super().__init__(add_encoders=add_encoders)
         self.order = p, d, q
         self.seasonal_order = seasonal_order
         self.trend = trend

darts/models/forecasting/auto_arima.py

@@ -17,7 +17,9 @@
 
 
 class AutoARIMA(FutureCovariatesLocalForecastingModel):
-    def __init__(self, *autoarima_args, **autoarima_kwargs):
+    def __init__(
+        self, *autoarima_args, add_encoders: Optional[dict] = None, **autoarima_kwargs
+    ):
         """Auto-ARIMA
 
         This implementation is a thin wrapper around `pmdarima AutoARIMA model
@@ -40,8 +42,28 @@ def __init__(self, *autoarima_args, **autoarima_kwargs):
             Positional arguments for the pmdarima.AutoARIMA model
         autoarima_kwargs
             Keyword arguments for the pmdarima.AutoARIMA model
+        add_encoders
+            A large number of future covariates can be automatically generated with `add_encoders`.
+            This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that
+            will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to
+            transform the generated covariates. This happens all under one hood and only needs to be specified at
+            model creation.
+            Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about
+            ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features:
+
+            .. highlight:: python
+            .. code-block:: python
+
+                add_encoders={
+                    'cyclic': {'future': ['month']},
+                    'datetime_attribute': {'future': ['hour', 'dayofweek']},
+                    'position': {'future': ['relative']},
+                    'custom': {'future': [lambda idx: (idx.year - 1950) / 50]},
+                    'transformer': Scaler()
+                }
+            ..
         """
-        super().__init__()
+        super().__init__(add_encoders=add_encoders)
         self.model = PmdAutoARIMA(*autoarima_args, **autoarima_kwargs)
         self.trend = self.model.trend
 

darts/models/forecasting/croston.py

@@ -17,7 +17,11 @@
 
 class Croston(FutureCovariatesLocalForecastingModel):
     def __init__(
-        self, version: str = "classic", alpha_d: float = None, alpha_p: float = None
+        self,
+        version: str = "classic",
+        alpha_d: float = None,
+        alpha_p: float = None,
+        add_encoders: Optional[dict] = None,
     ):
         """An implementation of the `Croston method
         <https://otexts.com/fpp3/counts.html>`_ for intermittent
@@ -42,6 +46,26 @@ def __init__(
             For the "tsb" version, the alpha smoothing parameter to apply on demand.
         alpha_p
             For the "tsb" version, the alpha smoothing parameter to apply on probability.
+        add_encoders
+            A large number of future covariates can be automatically generated with `add_encoders`.
+            This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that
+            will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to
+            transform the generated covariates. This happens all under one hood and only needs to be specified at
+            model creation.
+            Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about
+            ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features:
+
+            .. highlight:: python
+            .. code-block:: python
+
+                add_encoders={
+                    'cyclic': {'future': ['month']},
+                    'datetime_attribute': {'future': ['hour', 'dayofweek']},
+                    'position': {'future': ['relative']},
+                    'custom': {'future': [lambda idx: (idx.year - 1950) / 50]},
+                    'transformer': Scaler()
+                }
+            ..
 
         References
         ----------
@@ -51,7 +75,7 @@ def __init__(
                Intermittent demand: Linking forecasting to inventory obsolescence.
                European Journal of Operational Research, 214(3):606 – 615, 2011.
         """
-        super().__init__()
+        super().__init__(add_encoders=add_encoders)
         raise_if_not(
             version.lower() in ["classic", "optimized", "sba", "tsb"],
             'The provided "version" parameter must be set to "classic", "optimized", "sba" or "tsb".',