Pytorch-forecasting

Feature and maintenance update.

Highlights

* `python 3.13` support
* `tide` model
* bugfixes for TFT

Enhancements

* [ENH] Tide model. by Sohaib-Ahmed21 in https://github.com/sktime/pytorch-forecasting/pull/1734
* [ENH] refactor `__init__` modules to no longer contain classes - preparatory commit by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1739
* [ENH] refactor `__init__` modules to no longer contain classes by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1738
* [ENH] extend package author attribution requirement in license to present by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1737
* [ENH] linting tide model by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1742
* [ENH] move tide model - part 1 by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1743
* [ENH] move tide model - part 2 by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1744
* [ENH] clean-up refactor of `TimeSeriesDataSet` by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1746

Fixes

* [BUG] Bugfix when no exogenous variable is passed to TFT by XinyuWuu in https://github.com/sktime/pytorch-forecasting/pull/1667
* [BUG] Fix issue when training TFT model on mac M1 mps device. element 0 of tensors does not require grad and does not have a grad_fn by fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1725

Documentation

* [DOC] Fix the spelling error of holding by xiaokongkong in https://github.com/sktime/pytorch-forecasting/pull/1719
* [DOC] Updated documentation on `TimeSeriesDataSet.predict_mode` by madprogramer in https://github.com/sktime/pytorch-forecasting/pull/1720
* [DOC] General PR to improve docs by julian-fong in https://github.com/sktime/pytorch-forecasting/pull/1705
* [DOC] Correct argument for optimizer `ranger` in `Temporal Fusion Transformer` tutorial by fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1724
* [DOC] Fixed typo "monotone_constaints" by Luke-Chesley in https://github.com/sktime/pytorch-forecasting/pull/1516
* [DOC] minor fixes in documentation by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1763
* [DOC] improve and add `tide` model to docs by PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1762

Maintenance

* [MNT] update linting: limit line length to 88, add `isort` by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1740
* [MNT] update nbeats/sub_modules.py to remove overhead in tensor creation by d-schmitt in https://github.com/sktime/pytorch-forecasting/pull/1580
* [MNT] Temporary fix for lint errors to conform to the recent changes in linting rules see 1749 by fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1748
* [MNT] python 3.13 support by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1691

All Contributors

d-schmitt,
fkiraly,
fnhirwa,
julian-fong,
Luke-Chesley,
madprogramer,
PranavBhatP,
Sohaib-Ahmed21,
xiaokongkong,
XinyuWuu

Maintenance update, minor feature additions and bugfixes.

* support for `numpy 2.X`
* end of life for `python 3.8`
* fixed documentation build
* bugfixes

Dependency changes

* `pytorch-forecasting` is now compatible with `numpy 2.X` (core dependency)
* `optuna` (tuning soft dependency) bounds have been update to `>=3.1.0,<5.0.0`

Fixes

* [BUG] fix `AttributeError: 'ExperimentWriter' object has no attribute 'add_figure'` by ewth in https://github.com/sktime/pytorch-forecasting/pull/1694

Documentation

* [DOC] typo fixes in changelog by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1660
* [DOC] update URLs to `sktime` org by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1674

Maintenance

* [MNT] handle `mps backend` for lower versions of pytorch and fix `mps` failure on `macOS-latest` runner by fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1648
* [MNT] updates the actions in the doc build CI by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1673
* [MNT] fixes to `readthedocs.yml` by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1676
* [MNT] updates references in CI and doc locations to `main` by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1677
* [MNT] `show_versions` utility by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1688
* [MNT] Relax `numpy` bound to `numpy<3.0.0` by XinyuWuu in https://github.com/sktime/pytorch-forecasting/pull/1624
* [MNT] fix `pre-commit` failures on `main` by ewth in https://github.com/sktime/pytorch-forecasting/pull/1696
* [MNT] Move linting to ruff by airookie17 in https://github.com/sktime/pytorch-forecasting/pull/1692
1693
* [MNT] `ruff` linting - allow use of assert (S101) by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1701
* [MNT] `ruff` - fix list related linting failures C416 and C419 by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1702
* [MNT] Delete poetry.lock by benHeid in https://github.com/sktime/pytorch-forecasting/pull/1704
* [MNT] fix `black` doesn't have `extras` dependency by fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1697
* [MNT] Remove mutable objects from defaults by eugenio-mercuriali in https://github.com/sktime/pytorch-forecasting/pull/1699
* [MNT] remove docs build in ci for all pr by yarnabrina in https://github.com/sktime/pytorch-forecasting/pull/1712
* [MNT] EOL for python 3.8 by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1661
* [MNT] remove `poetry.lock` by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1651
* [MNT] update `pre-commit` requirement from `<4.0.0,>=3.2.0` to `>=3.2.0,<5.0.0` by dependabot in https://github.com/sktime/pytorch-forecasting/pull/
* [MNT] update optuna requirement from `<4.0.0,>=3.1.0` to `>=3.1.0,<5.0.0` by dependabot in https://github.com/sktime/pytorch-forecasting/pull/1715
* [MNT] CODEOWNERS file by fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1710

All Contributors

airookie17,
benHeid,
eugenio-mercuriali,
ewth,
fkiraly,
fnhirwa,
XinyuWuu,
yarnabrina

Hotfix for accidental package name change in `pyproject.toml`.

The package name is now corrected to `pytorch-forecasting`.

Maintenance update widening compatibility ranges and consolidating dependencies:

* support for python 3.11 and 3.12, added CI testing
* support for MacOS, added CI testing
* core dependencies have been minimized to `numpy`, `torch`, `lightning`, `scipy`, `pandas`, and `scikit-learn`.
* soft dependencies are available in soft dependency sets: `all_extras` for all soft dependencies, and `tuning` for `optuna` based optimization.

Dependency changes

* the following are no longer core dependencies and have been changed to optional dependencies : `optuna`, `statsmodels`, `pytorch-optimize`, `matplotlib`. Environments relying on functionality requiring these dependencies need to be updated to install these explicitly.
* `optuna` bounds have been updated to `optuna >=3.1.0,<4.0.0`
* `optuna-integrate` is now an additional soft dependency, in case of `optuna >=3.3.0`

Deprecations and removals

* from 1.2.0, the default optimizer will be changed from `"ranger"` to `"adam"` to avoid non-`torch` dependencies in defaults. `pytorch-optimize` optimizers can still be used. Users should set the optimizer explicitly to continue using `"ranger"`.
* from 1.1.0, the loggers do not log figures if soft dependency `matplotlib` is not present, but will raise no exceptions in this case. To log figures, ensure that `matplotlib` is installed.

Breaking Changes

- Upgraded to pytorch 2.0 and lightning 2.0. This brings a couple of changes, such as configuration of trainers. See the [lightning upgrade guide](https://lightning.ai/docs/pytorch/latest/upgrade/migration_guide.html). For PyTorch Forecasting, this particularly means if you are developing own models, the class method `epoch_end` has been renamed to `on_epoch_end` and replacing `model.summarize()` with `ModelSummary(model, max_depth=-1)` and `Tuner(trainer)` is its own class, so `trainer.tuner` needs replacing. (#1280)
- Changed the `predict()` interface returning named tuple - see tutorials.

Changes

- The predict method is now using the lightning predict functionality and allows writing results to disk (1280).

Fixed

- Fixed robust scaler when quantiles are 0.0, and 1.0, i.e. minimum and maximum (1142)

Fixed

- Removed pandoc from dependencies as issue with poetry install (1126)
- Added metric attributes for torchmetric resulting in better multi-GPU performance (1126)

Added

- "robust" encoder method can be customized by setting "center", "lower" and "upper" quantiles (1126)