Feat/fit predict encodings

CHANGELOG.md

@@ -17,6 +17,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - Added model property `ForecastingModel.supports_multivariate` to indicate whether the model supports multivariate forecasting. [#1848](https://github.com/unit8co/darts/pull/1848) by [Felix Divo](https://github.com/felixdivo).
   - `Prophet` now supports conditional seasonalities, and properly handles all parameters passed to `Prophet.add_seasonality()` and model creation parameter `add_seasonalities` [#1829](https://github.com/unit8co/darts/pull/#1829) by [Idan Shilon](https://github.com/id5h).
   - Added support for direct prediction of the likelihood parameters to probabilistic models using a likelihood (regression and torch models). Set `predict_likelihood_parameters=True` when calling `predict()`. [#1811](https://github.com/unit8co/darts/pull/1811) by [Antoine Madrona](https://github.com/madtoinou).
+  - Added method `generate_fit_predict_encodings()` to generate the encodings (from `add_encoders` at model creation) required for training and prediction. [#1925](https://github.com/unit8co/darts/pull/1925) by [Dennis Bader](https://github.com/dennisbader).
 - Improvements to `EnsembleModel`:
   - Model creation parameter `forecasting_models` now supports a mix of `LocalForecastingModel` and `GlobalForecastingModel` (single `TimeSeries` training/inference only, due to the local models). [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou).
   - Future and past covariates can now be used even if `forecasting_models` have different covariates support. The covariates passed to `fit()`/`predict()` are used only by models that support it. [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou).

darts/dataprocessing/encoders/encoder_base.py

@@ -14,11 +14,36 @@
 from darts.logging import get_logger, raise_if, raise_log
 from darts.utils.timeseries_generation import generate_index
 
+try:
+    from typing import Literal
+except ImportError:
+    from typing_extensions import Literal
+
 SupportedIndex = Union[pd.DatetimeIndex, pd.RangeIndex]
 EncoderOutputType = Optional[Union[Sequence[TimeSeries], List[TimeSeries]]]
 logger = get_logger(__name__)
 
 
+class _EncoderMethod:
+    """Connects the encoder stage to the corresponding methods"""
+
+    def __init__(self, stage: Literal["train", "inference", "train_inference"]):
+        self.method = None
+        if stage == "train":
+            self.method = "encode_train"
+        elif stage == "inference":
+            self.method = "encode_inference"
+        elif stage == "train_inference":
+            self.method = "encode_train_inference"
+        else:
+            raise_log(
+                ValueError(
+                    f"Unknown encoder `stage={stage}`. Must be on of `('train', 'inference', 'train_inference')`"
+                ),
+                logger,
+            )
+
+
 class CovariatesIndexGenerator(ABC):
     def __init__(
         self,
@@ -128,6 +153,33 @@ def generate_inference_idx(
         """
         pass
 
+    def generate_train_inference_idx(
+        self, n: int, target: TimeSeries, covariates: Optional[TimeSeries] = None
+    ) -> Tuple[SupportedIndex, pd.Timestamp]:
+        """
+        Generates/extracts time index (or integer index) for covariates for training and inference / prediction.
+
+        Parameters
+        ----------
+        n
+            The forecasting horizon.
+        target
+            The target TimeSeries used for training and inference / prediction as `series`.
+        covariates
+            Optionally, the covariates used for training and inference / prediction.
+            If given, the returned time index is equal to the `covariates` time index. Else, the returned time index
+            covers the minimum required covariate time spans for performing training and inference / prediction with a
+            specific forecasting model. These requirements are derived from parameters set at
+            :class:`CovariatesIndexGenerator` creation.
+        """
+        train_idx, target_end = self.generate_train_idx(
+            target=target, covariates=covariates
+        )
+        inference_idx, _ = self.generate_inference_idx(
+            n=n, target=target, covariates=covariates
+        )
+        return train_idx.__class__.union(train_idx, inference_idx), target_end
+
     @property
     @abstractmethod
     def base_component_name(self) -> str:
@@ -444,7 +496,7 @@ def encode_train(
         covariates
             Optionally, the past or future covariates used for training.
         merge_covariates
-            Whether or not to merge the encoded TimeSeries with `covariates`.
+            Whether to merge the encoded TimeSeries with `covariates`.
         """
         pass
 
@@ -468,8 +520,32 @@ def encode_inference(
         covariates
             Optionally, the past or future covariates used for prediction.
         merge_covariates
-            Whether or not to merge the encoded TimeSeries with `covariates`.
+            Whether to merge the encoded TimeSeries with `covariates`.
+
+        """
+        pass
+
+    @abstractmethod
+    def encode_train_inference(
+        self,
+        n: int,
+        target: TimeSeries,
+        covariates: Optional[TimeSeries] = None,
+        merge_covariates: bool = True,
+        **kwargs,
+    ) -> TimeSeries:
+        """Each subclass must implement a method to encode the covariates index for training and prediction.
 
+        Parameters
+        ----------
+        n
+            The forecast horizon
+        target
+            The target TimeSeries used during training and prediction.
+        covariates
+            Optionally, the past or future covariates used for training and prediction.
+        merge_covariates
+            Whether to merge the encoded TimeSeries with `covariates`.
         """
         pass
 
@@ -589,7 +665,7 @@ def encode_train(
             `PastCovariatesIndexGenerator`, future covariates if `self.index_generator` is a
             `FutureCovariatesIndexGenerator`
         merge_covariates
-            Whether or not to merge the encoded TimeSeries with `covariates`.
+            Whether to merge the encoded TimeSeries with `covariates`.
         """
         # exclude encoded components from covariates to add the newly encoded components later
         covariates = self._drop_encoded_components(covariates, self.components)
@@ -636,7 +712,7 @@ def encode_inference(
             `PastCovariatesIndexGenerator`, future covariates if `self.index_generator` is a
             `FutureCovariatesIndexGenerator`
         merge_covariates
-            Whether or not to merge the encoded TimeSeries with `covariates`.
+            Whether to merge the encoded TimeSeries with `covariates`.
         """
         # some encoders must be fit before `encode_inference()`
         raise_if(
@@ -671,6 +747,55 @@ def encode_inference(
 
         return encoded
 
+    def encode_train_inference(
+        self,
+        n: int,
+        target: TimeSeries,
+        covariates: Optional[TimeSeries] = None,
+        merge_covariates: bool = True,
+        **kwargs,
+    ) -> TimeSeries:
+        """Returns encoded index for inference/prediction.
+
+        Parameters
+        ----------
+        n
+            The forecast horizon
+        target
+            The target TimeSeries used during training and prediction.
+        covariates
+            Optionally, the covariates used for training and prediction: past covariates if `self.index_generator` is a
+            `PastCovariatesIndexGenerator`, future covariates if `self.index_generator` is a
+            `FutureCovariatesIndexGenerator`
+        merge_covariates
+            Whether to merge the encoded TimeSeries with `covariates`.
+        """
+        # exclude encoded components from covariates to add the newly encoded components later
+        covariates = self._drop_encoded_components(covariates, self.components)
+
+        # generate index and encodings
+        index, target_end = self.index_generator.generate_train_inference_idx(
+            n, target, covariates
+        )
+        encoded = self._encode(index, target_end, target.dtype)
+
+        # optionally, merge encodings with original `covariates` series
+        encoded = (
+            self._merge_covariates(encoded, covariates=covariates)
+            if merge_covariates
+            else encoded
+        )
+
+        # save encoded component names
+        if self.components.empty:
+            components = encoded.components
+            if covariates is not None:
+                components = components[~components.isin(covariates.components)]
+            self._components = components
+
+        self._fit_called = True
+        return encoded
+
     @property
     @abstractmethod
     def accept_transformer(self) -> List[bool]:
@@ -782,7 +907,7 @@ def _update_mask(self, covariates: List[TimeSeries]) -> None:
 
     @property
     def fit_called(self) -> bool:
-        """Return whether or not the transformer has been fitted."""
+        """Return whether the transformer has been fitted."""
         return self._fit_called