Merge branch 'main' into skbase-dep

Author: fkiraly

.github/workflows/pypi_release.yml

@@ -5,14 +5,40 @@ on:
     types: [published]
 
 jobs:
+  check_tag:
+    name: Check tag
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - uses: actions/setup-python@v6
+        with:
+          python-version: '3.11'
+
+      - shell: bash
+        run: |
+          TAG="${{ github.event.release.tag_name }}"
+          GH_TAG_NAME="${TAG#v}"
+          PY_VERSION=$(python - <<'PY'
+          import pathlib, tomllib
+          data = tomllib.loads(pathlib.Path("pyproject.toml").read_text(encoding="utf-8"))
+          print(data.get("project").get("version"))
+          PY
+          )
+          if [ "${GH_TAG_NAME}" != "${PY_VERSION}" ]; then
+            echo "::error::Tag (${GH_TAG_NAME}) does not match pyproject.toml version (${PY_VERSION})."
+            exit 2
+          fi
+
   build_wheels:
     name: Build wheels
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: '3.11'
 
@@ -22,7 +48,7 @@ jobs:
           python -m build --wheel --sdist --outdir wheelhouse
 
       - name: Store wheels
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: wheels
           path: wheelhouse/*
@@ -35,13 +61,13 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -76,7 +102,7 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/download-artifact@v5
+      - uses: actions/download-artifact@v7
         with:
           name: wheels
           path: wheelhouse

.github/workflows/test.yml

@@ -19,18 +19,18 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: repository checkout step
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: python environment step
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.11"
 
       - name: install pre-commit
         run: python3 -m pip install pre-commit
 
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
@@ -57,11 +57,11 @@ jobs:
     needs: code-quality
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
-          python-version: 3.9
+          python-version: "3.11"
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -82,13 +82,13 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -122,13 +122,13 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 

CHANGELOG.md

@@ -1,5 +1,95 @@
 # Release Notes
 
+## v1.5.0
+Release focusing on:
+
+* python 3.9 end-of-life
+* changes to testing framework.
+* New estimators in `pytorch-forecasting` *v1* and *beta v2*.
+
+### Highlights
+#### `pytorch-forecasting` ***v1.5.0***
+* Kolmogorov Arnold Block for `NBeats` by @Sohaib-Ahmed21 in https://github.com/sktime/pytorch-forecasting/pull/1751
+* `xLSTMTime` implementation by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1709
+
+#### `pytorch-forecasting` ***Beta v2***
+* Implementing D2 data module, tests and `TimeXer` model from `tslib`  for PTF v2 by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1836
+* Add `DLinear` model from `tslib` for PTF v2 by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1874
+* Add `Samformer` model for  PTF v2 from DSIPTS by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1952
+* `Tide` model in PTF v2 interface from `dsipts` by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1889
+
+### Enhancements
+* [ENH] Test framework for `ptf-v2` by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1841
+* [ENH] Implementing D2 data module, tests and `TimeXer` model from `tslib`  for v2 by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1836
+* [ENH] `DLinear` model from `tslib` by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1874
+* [ENH] Enable `DeprecationWarning` , `PendingDeprecationWarning` and `FutureWarning` when running pytest by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1912
+* [ENH] Suppress `__array_wrap__` warning in `numpy 2` for `torch` and `pandas` by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1911
+* [ENH] Suppress PyTorch deprecation warning: UserWarning: `nn.init.constant` is now deprecated in favor of `nn.init.constant_` by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1915
+* [ENH] two-way linkage of model package classes and neural network classes by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1888
+* [ENH] Add a copy of `BaseFixtureGenerator` to `pytorch-forecasting/tests/_base` as a true base class by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1919
+* [ENH] Remove references to model from the `BaseFixtureGenerator` by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1923
+* [ENH] Improve test framework for v1 models by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1908
+* [ENH] `xLSTMTime` implementation by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1709
+* [ENH] Improve test framework for v1 metrics by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1907
+* [ENH] `Tide` model in `v2` interface by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1889
+* [ENH] docstring test suite for functions by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1955
+* [ENH] Add missing test for forward output of `TimeXer` as proposed in #1936 by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1951
+* [ENH] Add `Samformer` model for  PTF v2 from DSIPTS by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1952
+* [ENH] Kolmogorov Arnold Block for NBeats by @Sohaib-Ahmed21 in https://github.com/sktime/pytorch-forecasting/pull/1751
+* [ENH] Standardize output format for `tslib` v2 models by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1965
+* [ENH] Add `Metrics` support to `ptf-v2` by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1960
+* [ENH] `check_estimator` utility for checking new estimators against unified API contract by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1954
+* [ENH] Standardize testing of estimator outputs and skip tests for non-conformant estimators by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1971
+
+### Fixes
+* [BUG] Fix issue with `EncodeNormalizer(method='standard', center=False)` for scale value by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1902
+* [BUG] fixed memory leak in `TimeSeriesDataset` by using `@cached_property` and clean-up of index construction by @Vishnu-Rangiah in https://github.com/sktime/pytorch-forecasting/pull/1905
+* [BUG] Fix issue with `plot_prediction_actual_by_variable`  unsupported operand type(s) for *: 'numpy.ndarray' and 'Tensor' by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1903
+* [BUG] Correctly set lagged variables to known when lag >= horizon by @hubkrieb in https://github.com/sktime/pytorch-forecasting/pull/1910
+* [BUG] Updated base_model.py to account for importing error by @Himanshu-Verma-ds in https://github.com/sktime/pytorch-forecasting/pull/1488
+* [BUG][DOC] Fix documentation: pass loss argument to BaseModel in custom models tutorial example by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1931
+* [BUG] fix broken version inspection if package distribution has `None` name by @lohraspco in https://github.com/sktime/pytorch-forecasting/pull/1926
+* [BUG] fix sporadic `tkinter` failures in CI by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1937
+* [BUG] Device inconsistency in `MQF2DistributionLoss` raising: RuntimeError: Expected all tensors to be on the same device by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1916
+* [BUG] fixed memory leak in BaseModel by detach some tensor by @zju-ys in https://github.com/sktime/pytorch-forecasting/pull/1924
+* [BUG] Fix `TimeSeriesDataSet` wrong inferred `tensor` `dtype` when `time_idx` is included in features by @cngmid in https://github.com/sktime/pytorch-forecasting/pull/1950
+* [BUG] standardize output format of xLSTMTime estimator for point predictions by @sanskarmodi8 in https://github.com/sktime/pytorch-forecasting/pull/1978
+* [BUG] Standardize output format of NBeats and NBeatsKAN estimators by @sanskarmodi8 in https://github.com/sktime/pytorch-forecasting/pull/1977
+
+### Documentation
+* [DOC] Correct documentation for N-BEATS by @Pinaka07 in https://github.com/sktime/pytorch-forecasting/pull/1914
+* [DOC] 1.1.0 changelog - missing entries by @jdb78 in https://github.com/sktime/pytorch-forecasting/pull/1512
+* [DOC] fix minor typo in changelog by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1917
+* [DOC] Missing parenthesis in docstring of MASE by @caph1993 in https://github.com/sktime/pytorch-forecasting/pull/1944
+
+### Maintenance
+* [MNT] remove import conditionals for `python 3.6` by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1928
+* [MNT] [Dependabot](deps): bump actions/download-artifact from 4 to 5 by @dependabot[bot] in https://github.com/sktime/pytorch-forecasting/pull/1939
+* [MNT] [Dependabot](deps): Bump actions/checkout from 4 to 5 by @dependabot[bot] in https://github.com/sktime/pytorch-forecasting/pull/1942
+* [MNT] Check versions in wheels workflow by @szepeviktor in https://github.com/sktime/pytorch-forecasting/pull/1948
+* [MNT] [Dependabot](deps): Bump actions/setup-python from 5 to 6 by @dependabot[bot] in https://github.com/sktime/pytorch-forecasting/pull/1963
+* [MNT] Update CODEOWNERS with current core dev state by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1972
+* [MNT] python 3.9 end-of-life by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1980
+
+### All Contributors
+@agobbifbk,
+@caph1993,
+@cngmid,
+@fkiraly,
+@fnhirwa,
+@Himanshu-Verma-ds,
+@hubkrieb,
+@jdb78,
+@lohraspco,
+@phoeenniixx,
+@Pinaka07,
+@PranavBhatP,
+@sanskarmodi8,
+@Sohaib-Ahmed21,
+@szepeviktor
+@Vishnu-Rangiah,
+@zju-ys
+
 ## v1.4.0
 
 Feature and maintenance update.
@@ -291,7 +381,7 @@ Maintenance update widening compatibility ranges and consolidating dependencies:
 
 ### Changed
 
-- Dropping Python 3.6 suppport, adding 3.10 support (#479)
+- Dropping Python 3.6 support, adding 3.10 support (#479)
 - Refactored dataloader sampling - moved samplers to pytorch_forecasting.data.samplers module (#479)
 - Changed transformation format for Encoders to dict from tuple (#949)
 
@@ -318,7 +408,7 @@ Maintenance update widening compatibility ranges and consolidating dependencies:
 - Allow using [torchmetrics](https://torchmetrics.readthedocs.io/) as loss metrics (#776)
 - Enable fitting `EncoderNormalizer()` with limited data history using `max_length` argument (#782)
 - More flexible `MultiEmbedding()` with convenience `output_size` and `input_size` properties (#829)
-- Fix concatentation of attention (#902)
+- Fix concatenation of attention (#902)
 
 ### Fixed
 
@@ -340,7 +430,7 @@ Maintenance update widening compatibility ranges and consolidating dependencies:
 ### Fixed
 
 - Fix inattention mutation to `x_cont` (#732).
-- Compatability with pytorch-lightning 1.5 (#758)
+- Compatibility with pytorch-lightning 1.5 (#758)
 
 ### Contributors
 
@@ -427,7 +517,7 @@ Maintenance update widening compatibility ranges and consolidating dependencies:
 
 ### Added
 
-- Adding a filter functionality to the timeseries datasset (#329)
+- Adding a filter functionality to the timeseries dataset (#329)
 - Add simple models such as LSTM, GRU and a MLP on the decoder (#380)
 - Allow usage of any torch optimizer such as SGD (#380)
 
@@ -496,7 +586,7 @@ Maintenance update widening compatibility ranges and consolidating dependencies:
 ### Added
 
 - Adding support for multiple targets in the TimeSeriesDataSet (#199) and amended tutorials.
-- Temporal fusion transformer and DeepAR with support for multiple tagets (#199)
+- Temporal fusion transformer and DeepAR with support for multiple targets (#199)
 - Check for non-finite values in TimeSeriesDataSet and better validate scaler argument (#220)
 - LSTM and GRU implementations that can handle zero-length sequences (#235)
 - Helpers for implementing auto-regressive models (#236)

CODEOWNERS

@@ -1,3 +1,3 @@
 # The file specifies framework level core developers for automated review requests
 
-* @benheid @fkiraly @fnhirwa @geetu040 @jdb78 @pranavvp16 @XinyuWuu @yarnabrina
+* @benheid @fkiraly @fnhirwa @jdb78 @phoeenniixx @pranavbhatp @yarnabrina

CODE_OF_CONDUCT.md

@@ -0,0 +1,4 @@
+# Code of Conduct
+
+All contributors, maintainers, and participants are expected to follow the
+`sktime` Code of Conduct found on our [website](https://www.sktime.net/en/latest/get_involved/code_of_conduct.html).

GOVERNANCE.md

@@ -0,0 +1,5 @@
+# Governance
+
+`pytorch-forecasting` is governed by the `sktime` community.
+
+You can find our governance guidelines on our [website](https://www.sktime.net/en/latest/get_involved/governance.html).

README.md

@@ -75,7 +75,7 @@ To implement new models or other custom components, see the [How to implement ne
 
 # Usage example
 
-Networks can be trained with the [PyTorch Lighning Trainer](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) on [pandas Dataframes](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe) which are first converted to a [TimeSeriesDataSet](https://pytorch-forecasting.readthedocs.io/en/latest/data.html).
+Networks can be trained with the [PyTorch Lightning Trainer](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) on [pandas Dataframes](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe) which are first converted to a [TimeSeriesDataSet](https://pytorch-forecasting.readthedocs.io/en/latest/data.html).
 
 ```python
 # imports for training
@@ -122,7 +122,7 @@ batch_size = 128
 train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2)
 val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2)
 
-# create PyTorch Lighning Trainer with early stopping
+# create PyTorch Lightning Trainer with early stopping
 early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")
 lr_logger = LearningRateMonitor()
 trainer = pl.Trainer(

docs/source/faq.rst

@@ -27,7 +27,7 @@ Creating datasets
   200 for the decoder length. Consider that longer lengths increase the time it takes
   for your model to train.
 
-  The ratio of decoder and encoder length depends on the used alogrithm.
+  The ratio of decoder and encoder length depends on the used algorithm.
   Look at :ref:`documentation <models>` to get clues.
 
 * **It takes very long to create the dataset. Why is that?**
@@ -61,7 +61,7 @@ Training models
 
 * **Why does the learning rate finder not finish?**
 
-  First, ensure that the trainer does not have the keword ``fast_dev_run=True`` and
+  First, ensure that the trainer does not have the keyword ``fast_dev_run=True`` and
   ``limit_train_batches=...`` set. Second, use a target normalizer in your training dataset.
   Third, increase the ``early_stop_threshold`` argument
   of the ``lr_find`` method to a large number.

docs/source/index.rst

@@ -52,7 +52,7 @@ To use the MQF2 loss (multivariate quantile loss), also execute
 
    pip install pytorch-forecasting[mqf2]
 
-Vist :ref:`Getting started <getting-started>` to learn more about the package and detailled installation instruction.
+Visit :ref:`Getting started <getting-started>` to learn more about the package and detailed installation instruction.
 The :ref:`Tutorials <tutorials>` section provides guidance on how to use models and implement new ones.
 
 .. toctree::

docs/source/installation.rst

@@ -3,7 +3,7 @@ Installation
 
 ``pytorch-forecasting`` currently supports:
 
-* Python versions 3.8, 3.9, 3.10, 3.11, and 3.12.
+* Python versions 3.10, 3.11, 3.12 and 3.13.
 * Operating systems : Linux, macOS, and Windows
 
 Installing pytorch-forecasting
@@ -81,7 +81,7 @@ Contributing to ``pytorch-forecasting``
 Contributions to PyTorch Forecasting are very welcome! You do not have to be an expert in deep learning
 to contribute. If you find a bug - fix it! If you miss a feature - propose it!
 
-To obtain an editible version ``pytorch-forecasting`` for development or contributions,
+To obtain an editable version ``pytorch-forecasting`` for development or contributions,
 you will need to set up:
 
 * a local clone of the ``pytorch-forecasting`` repository.
@@ -128,7 +128,7 @@ Creating a fork and cloning the repository
       > upstream  https://github.com/sktime/pytorch-forecasting.git (fetch)
       > upstream  https://github.com/sktime/pytorch-forecasting.git (push)
 
-Setting up an editible virtual environment
+Setting up an editable virtual environment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 1. Set up a new virtual environment. Our instructions will go through the commands to set up a ``conda`` environment which is recommended for ``pytorch-forecasting`` development.
@@ -183,13 +183,13 @@ Technical Design Principles
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 When writing code for your new feature, it is recommended to follow these
-technical design principles to ensure compatability between the feature and the library.
+technical design principles to ensure compatibility between the feature and the library.
 
 * Backward compatible API if possible to prevent breaking code.
 * Powerful abstractions to enable quick experimentation. At the same time, the abstractions should
   allow the user to still take full control.
 * Intuitive default values that do not need changing in most cases.
-* Focus on forecasting time-related data - specificially timeseries regression and classificiation.
+* Focus on forecasting time-related data - specifically timeseries regression and classification.
   Contributions not directly related to this topic might not be merged. We want to keep the library as
   crisp as possible.
 * Install ``pre-commit`` and have it run on every commit that you make on your feature branches.