Release 1.3.0 (#1765)

Release PR for 1.3.0

* version number bump
* changelog
* scheduled deprecations

CHANGELOG.md

@@ -1,5 +1,62 @@
 # Release Notes
 
+## v1.3.0
+
+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
+
+
 ## v1.2.0
 
 Maintenance update, minor feature additions and bugfixes.

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "pytorch-forecasting"
 readme = "README.md"         # Markdown files are supported
-version = "1.2.0"            # is being replaced automatically
+version = "1.3.0"            # is being replaced automatically
 
 authors = [
   {name = "Jan Beitner"},

pytorch_forecasting/__init__.py

@@ -2,7 +2,7 @@
 PyTorch Forecasting package for timeseries forecasting with PyTorch.
 """
 
-__version__ = "1.2.0"
+__version__ = "1.3.0"
 
 from pytorch_forecasting.data import (
     EncoderNormalizer,

pytorch_forecasting/models/base_model.py

@@ -485,7 +485,7 @@ def __init__(
         optimizer_params: Dict[str, Any] = None,
         monotone_constraints: Dict[str, int] = {},
         output_transformer: Callable = None,
-        optimizer=None,
+        optimizer="adam",
     ):
         """
         BaseModel for timeseries forecasting from which to inherit from
@@ -518,9 +518,7 @@ def __init__(
             optimizer (str): Optimizer, "ranger", "sgd", "adam", "adamw" or class name of optimizer in ``torch.optim``
                 or ``pytorch_optimizer``.
                 Alternatively, a class or function can be passed which takes parameters as first argument and
-                a `lr` argument (optionally also `weight_decay`). Defaults to
-                `"ranger" <https://pytorch-optimizers.readthedocs.io/en/latest/optimizer_api.html#ranger21>`_,
-                if pytorch_optimizer is installed, otherwise "adam".
+                a `lr` argument (optionally also `weight_decay`). Defaults to "adam".
         """  # noqa: E501
         if monotone_constraints is None:
             monotone_constraints = {}
@@ -529,38 +527,6 @@ def __init__(
         frame = inspect.currentframe()
         init_args = get_init_args(frame)
 
-        # TODO 1.2.0: remove warnings and change default optimizer to "adam"
-        if init_args["optimizer"] is None:
-            ptopt_in_env = "pytorch_optimizer" in _get_installed_packages()
-            if ptopt_in_env:
-                init_args["optimizer"] = "ranger"
-                warnings.warn(
-                    "In pytorch-forecasting models, from version 1.2.0, "
-                    "the default optimizer will be 'adam', in order to "
-                    "minimize the number of dependencies in default"
-                    " parameter settings. Users who wish to ensure their"
-                    " code continues using 'ranger' as optimizer should ensure"
-                    " that pytorch_optimizer is installed, and set the optimizer "
-                    "parameter explicitly to 'ranger'.",
-                    stacklevel=2,
-                )
-            else:
-                init_args["optimizer"] = "adam"
-                warnings.warn(
-                    "In pytorch-forecasting models, on versions 1.1.X, "
-                    "the default optimizer defaults to 'adam', "
-                    "if pytorch_optimizer is not installed, "
-                    "otherwise it defaults to 'ranger' from pytorch_optimizer. "
-                    "From version 1.2.0, the default optimizer will be 'adam' "
-                    "regardless of whether pytorch_optimizer is installed, in order to "
-                    "minimize the number of dependencies in default parameter"
-                    " settings. Users who wish to ensure their code continues"
-                    " using 'ranger' as optimizer should ensure that pytorch_optimizer"
-                    " is installed, and set the optimizer "
-                    "parameter explicitly to 'ranger'.",
-                    stacklevel=2,
-                )
-
         self.save_hyperparameters(
             {
                 name: val