Merge branch 'master' into feature/pl_auto_batch_size

Author: dennisbader

CHANGELOG.md

@@ -87,13 +87,16 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - Moved functions `retain_period_common_to_all()`, `series2seq()`, `seq2series()`, `get_single_series()` from `darts.utils.utils` to `darts.utils.ts_utils`.
 - Improvements to `ForecastingModel`: [#2269](https://github.com/unit8co/darts/pull/2269) by [Felix Divo](https://github.com/felixdivo).
   - Renamed the private `_is_probabilistic` property to a public `supports_probabilistic_prediction`.
+- Improvements to `RegressionModel`: [#2320](https://github.com/unit8co/darts/pull/2320) by [Felix Divo](https://github.com/felixdivo).
+  - Added a progress bar when performing optimized historical forecasts (`retrain=False` and no autoregression) to display the series-level progress.
 - Improvements to `DataTransformer`: [#2267](https://github.com/unit8co/darts/pull/2267) by [Alicja Krzeminska-Sciga](https://github.com/alicjakrzeminska).
   - `InvertibleDataTransformer` now supports parallelized inverse transformation for `series` being a list of lists of `TimeSeries` (`Sequence[Sequence[TimeSeries]]`). This `series` type represents for example the output from `historical_forecasts()` when using multiple series. 
 - New method `TorchForecastingModel.scale_batch_size()` that helps to find batch size automatically. [#2318](https://github.com/unit8co/darts/pull/2318) by [Bohdan Bilonoh](https://github.com/BohdanBilonoh)
 
 **Fixed**
 - Fixed a bug in `quantile_loss`, where the loss was computed on all samples rather than only on the predicted quantiles. [#2284](https://github.com/unit8co/darts/pull/2284) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed type hint warning "Unexpected argument" when calling `historical_forecasts()` caused by the `_with_sanity_checks` decorator. The type hinting is now properly configured to expect any input arguments and return the output type of the method for which the sanity checks are performed for. [#2286](https://github.com/unit8co/darts/pull/2286) by [Dennis Bader](https://github.com/dennisbader).
+- Fixed the order of the features when using component-wise lags so that they are grouped by values, then by components (before, were grouped by components, then by values). [#2272](https://github.com/unit8co/darts/pull/2272) by [Antoine Madrona](https://github.com/madtoinou).
 - Fixed a segmentation fault that some users were facing when importing a `LightGBMModel`. [#2304](https://github.com/unit8co/darts/pull/2304) by [Dennis Bader](https://github.com/dennisbader).
 - Fixed a bug when using a dropout with a `TorchForecasting` and pytorch lightning versions >= 2.2.0, where the dropout was not properly activated during training. [#2312](https://github.com/unit8co/darts/pull/2312) by [Dennis Bader](https://github.com/dennisbader).
 

darts/models/forecasting/regression_model.py

@@ -43,7 +43,7 @@
 from darts.models.forecasting.forecasting_model import GlobalForecastingModel
 from darts.timeseries import TimeSeries
 from darts.utils.data.tabularization import (
-    add_static_covariates_to_lagged_data,
+    _create_lagged_data_autoregression,
     create_lagged_component_names,
     create_lagged_training_data,
 )
@@ -1019,83 +1019,25 @@ def predict(
                 last_step_shift = t_pred - (n - step)
                 t_pred = n - step
 
-            np_X = []
-            # retrieve target lags
-            if "target" in self.lags:
-                if predictions:
-                    series_matrix = np.concatenate(
-                        [series_matrix, predictions[-1]], axis=1
-                    )
-                # component-wise lags
-                if "target" in self.component_lags:
-                    tmp_X = [
-                        series_matrix[
-                            :,
-                            [lag - (shift + last_step_shift) for lag in comp_lags],
-                            comp_i,
-                        ]
-                        for comp_i, (comp, comp_lags) in enumerate(
-                            self.component_lags["target"].items()
-                        )
-                    ]
-                    # values are grouped by component
-                    np_X.append(
-                        np.concatenate(tmp_X, axis=1).reshape(
-                            len(series) * num_samples, -1
-                        )
-                    )
-                else:
-                    # values are grouped by lags
-                    np_X.append(
-                        series_matrix[
-                            :,
-                            [
-                                lag - (shift + last_step_shift)
-                                for lag in self.lags["target"]
-                            ],
-                        ].reshape(len(series) * num_samples, -1)
-                    )
-            # retrieve covariate lags, enforce order (dict only preserves insertion order for python 3.6+)
-            for cov_type in ["past", "future"]:
-                if cov_type in covariate_matrices:
-                    # component-wise lags
-                    if cov_type in self.component_lags:
-                        tmp_X = [
-                            covariate_matrices[cov_type][
-                                :,
-                                np.array(comp_lags) - self.lags[cov_type][0] + t_pred,
-                                comp_i,
-                            ]
-                            for comp_i, (comp, comp_lags) in enumerate(
-                                self.component_lags[cov_type].items()
-                            )
-                        ]
-                        np_X.append(
-                            np.concatenate(tmp_X, axis=1).reshape(
-                                len(series) * num_samples, -1
-                            )
-                        )
-                    else:
-                        np_X.append(
-                            covariate_matrices[cov_type][
-                                :, relative_cov_lags[cov_type] + t_pred
-                            ].reshape(len(series) * num_samples, -1)
-                        )
-
-            # concatenate retrieved lags
-            X = np.concatenate(np_X, axis=1)
-            # Need to split up `X` into three equally-sized sub-blocks
-            # corresponding to each timeseries in `series`, so that
-            # static covariates can be added to each block; valid since
-            # each block contains same number of observations:
-            X_blocks = np.split(X, len(series), axis=0)
-            X_blocks, _ = add_static_covariates_to_lagged_data(
-                X_blocks,
-                series,
+            # concatenate previous iteration forecasts
+            if "target" in self.lags and predictions:
+                series_matrix = np.concatenate([series_matrix, predictions[-1]], axis=1)
+
+            # extract and concatenate lags from target and covariates series
+            X = _create_lagged_data_autoregression(
+                target_series=series,
+                t_pred=t_pred,
+                shift=shift,
+                last_step_shift=last_step_shift,
+                series_matrix=series_matrix,
+                covariate_matrices=covariate_matrices,
+                lags=self.lags,
+                component_lags=self.component_lags,
+                relative_cov_lags=relative_cov_lags,
+                num_samples=num_samples,
                 uses_static_covariates=self.uses_static_covariates,
-                last_shape=self._static_covariates_shape,
+                last_static_covariates_shape=self._static_covariates_shape,
             )
-            X = np.concatenate(X_blocks, axis=0)
 
             # X has shape (n_series * n_samples, n_regression_features)
             prediction = self._predict_and_sample(
@@ -1257,6 +1199,7 @@ def _optimized_historical_forecasts(
                 stride=stride,
                 overlap_end=overlap_end,
                 show_warnings=show_warnings,
+                verbose=verbose,
                 predict_likelihood_parameters=predict_likelihood_parameters,
                 **kwargs,
             )
@@ -1273,6 +1216,7 @@ def _optimized_historical_forecasts(
                 stride=stride,
                 overlap_end=overlap_end,
                 show_warnings=show_warnings,
+                verbose=verbose,
                 predict_likelihood_parameters=predict_likelihood_parameters,
                 **kwargs,
             )