fix: adding tests, fixing logic

CHANGELOG.md

@@ -12,7 +12,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 **Improved**
 
 - Improvements to `ForecastingModel`: Improved `start` handling for historical forecasts, backtest, residuals, and gridsearch. If `start` is not within the trainable / forecastable points, uses the closest valid start point that is a round multiple of `stride` ahead of start. Raises a ValueError, if no valid start point exists. This guarantees that all historical forecasts are `n * stride` points away from start, and will simplify many downstream tasks. [#2560](https://github.com/unit8co/darts/issues/2560) by [Dennis Bader](https://github.com/dennisbader).
-- Added `data_transformers` argument to `historical_forecasts`, `backtest` and `gridsearch` that allows scaling of the series without data-leakage. [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) and [Jan Fidor](https://github.com/JanFidor)
+- Added `data_transformers` argument to `historical_forecasts`, `backtest`, `residuals`, and `gridsearch` that allows scaling of the series without data-leakage. [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) and [Jan Fidor](https://github.com/JanFidor)
+- Added `idx_params` argument to `DataTransformer` that allows users to use only a subset of the transformers when `global_fit=False` and severals series are used. [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou)
 
 **Fixed**
 

darts/dataprocessing/pipeline.py

@@ -5,7 +5,7 @@
 
 from collections.abc import Iterator, Sequence
 from copy import deepcopy
-from typing import Union
+from typing import Optional, Union
 
 from darts import TimeSeries
 from darts.dataprocessing.transformers import (
@@ -158,7 +158,9 @@ def fit_transform(
         return data
 
     def transform(
-        self, data: Union[TimeSeries, Sequence[TimeSeries]]
+        self,
+        data: Union[TimeSeries, Sequence[TimeSeries]],
+        idx_params: Optional[Union[int, Sequence[int]]] = None,
     ) -> Union[TimeSeries, Sequence[TimeSeries]]:
         """
         For each data transformer in pipeline transform data. Then transformed data is passed to next transformer.
@@ -174,11 +176,14 @@ def transform(
             Transformed data.
         """
         for transformer in self._transformers:
-            data = transformer.transform(data)
+            data = transformer.transform(data, idx_params=idx_params)
         return data
 
     def inverse_transform(
-        self, data: Union[TimeSeries, Sequence[TimeSeries]], partial: bool = False
+        self,
+        data: Union[TimeSeries, Sequence[TimeSeries]],
+        partial: bool = False,
+        idx_params: Optional[Union[int, Sequence[int]]] = None,
     ) -> Union[TimeSeries, Sequence[TimeSeries]]:
         """
         For each data transformer in the pipeline, inverse-transform data. Then inverse transformed data is passed to
@@ -207,12 +212,12 @@ def inverse_transform(
             )
 
             for transformer in reversed(self._transformers):
-                data = transformer.inverse_transform(data)
+                data = transformer.inverse_transform(data, idx_params=idx_params)
             return data
         else:
             for transformer in reversed(self._transformers):
                 if isinstance(transformer, InvertibleDataTransformer):
-                    data = transformer.inverse_transform(data)
+                    data = transformer.inverse_transform(data, idx_params=idx_params)
             return data
 
     @property

darts/dataprocessing/transformers/base_data_transformer.py

@@ -363,7 +363,10 @@ def transform(
         # Take note of original input for unmasking purposes:
         if isinstance(series, TimeSeries):
             data = [series]
-            transformer_selector = [0]
+            if idx_params:
+                transformer_selector = self._check_idx_params(idx_params)
+            else:
+                transformer_selector = [0]
         else:
             data = series
             if idx_params:

darts/dataprocessing/transformers/fittable_data_transformer.py

@@ -271,7 +271,8 @@ def fit(
             transformer_selector = range(len(series))
 
         params_iterator = self._get_params(
-            transformer_selector=transformer_selector, calling_fit=True
+            transformer_selector=transformer_selector,
+            calling_fit=True,
         )
         fit_iterator = (
             zip(data, params_iterator)

darts/dataprocessing/transformers/invertible_data_transformer.py

@@ -327,7 +327,10 @@ def inverse_transform(
         called_with_sequence_series = False
         if isinstance(series, TimeSeries):
             data = [series]
-            transformer_selector = [0]
+            if idx_params:
+                transformer_selector = self._check_idx_params(idx_params)
+            else:
+                transformer_selector = [0]
             called_with_single_series = True
         elif isinstance(series[0], TimeSeries):  # Sequence[TimeSeries]
             data = series
@@ -346,7 +349,6 @@ def inverse_transform(
             for idx, series_list in iterator_:
                 data.extend(series_list)
                 transformer_selector += [idx] * len(series_list)
-
         input_iterator = _build_tqdm_iterator(
             zip(data, self._get_params(transformer_selector=transformer_selector)),
             verbose=self._verbose,

darts/models/forecasting/forecasting_model.py

@@ -911,8 +911,10 @@ def retrain_func(
             data_transformers=data_transformers, copy=True
         )
 
-        # data transformer must already be fitted and can be directly applied to all the series
+        using_prefitted_transformers = False
+        # data transformer already fitted and can be directly applied to all the series
         if data_transformers and not retrain:
+            using_prefitted_transformers = True
             series, past_covariates, future_covariates = _apply_data_transformers(
                 series=series,
                 past_covariates=past_covariates,
@@ -1097,6 +1099,7 @@ def retrain_func(
                     train_series = train_series[-train_length_:]
 
                 # when `retrain=True`, data transformers are also retrained between iterations to avoid data-leakage
+                # using a single series
                 if data_transformers and retrain:
                     train_series, past_covariates_, future_covariates_ = (
                         _apply_data_transformers(
@@ -1187,11 +1190,11 @@ def retrain_func(
                     **predict_kwargs,
                 )
 
-                # target transformer is either already fitted or fitted during the retraining
                 forecast = _apply_inverse_data_transformers(
                     series=train_series,
                     forecasts=forecast,
                     data_transformers=data_transformers,
+                    idx_transformer=idx if using_prefitted_transformers else None,
                 )
 
                 show_predict_warnings = False