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pyproject.toml
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pyproject.toml
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[tool.black]
line-length = 120
include = '\.pyi?$'
exclude = '''
(
/(
\.eggs # exclude a few common directories in the
| \.git # root of the project
| \.hg
| \.mypy_cache
| \.tox
| \.venv
| _build
| buck-out
| build
| dist
)/
| docs/build/
| node_modules/
| venve/
| .venv/
)
'''
[tool.nbqa.mutate]
isort = 1
black = 1
[project]
name = "pytorch_forecasting"
readme = "README.md" # Markdown files are supported
version = "1.0.0" # is being replaced automatically
authors = [
{name = "Jan Beitner"},
]
requires-python = ">=3.8,<3.13"
classifiers = [
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Mathematics",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development",
"Topic :: Software Development :: Libraries",
"Topic :: Software Development :: Libraries :: Python Modules",
"License :: OSI Approved :: MIT License",
]
description = "Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models"
dependencies = [
"numpy<2.0.0",
"torch >=2.0.0,!=2.0.1,<3.0.0",
"lightning >=2.0.0,<3.0.0",
"scipy >=1.8,<2.0",
"pandas >=1.3.0,<3.0.0",
"scikit-learn >=1.2,<2.0",
]
[project.optional-dependencies]
# there are the following dependency sets:
# - all_extras - all soft dependencies
# - granular dependency sets:
# - tuning - dependencies for tuning hyperparameters via optuna
# - mqf2 - dependencies for multivariate quantile loss
# - graph - dependencies for graph based forecasting
# - dev - the developer dependency set, for contributors to pytorch-forecasting
# - CI related: e.g., dev, github-actions. Not for users of sktime.
#
# soft dependencies are not required for the core functionality of sktime
# but are required by popular estimators, e.g., prophet, tbats, etc.
# all soft dependencies
#
# users can install via "pip install pytorch-forecasting[all_extras]"
#
all_extras = [
"cpflows",
"matplotlib",
"optuna >=3.1.0,<4.0.0",
"optuna-integration",
"pytorch_optimizer >=2.5.1,<4.0.0",
"statsmodels",
]
tuning = [
"optuna >=3.1.0,<4.0.0",
"optuna-integration",
"statsmodels",
]
mqf2 = ["cpflows"]
# the graph set is not currently used within pytorch-forecasting
# but is kept for future development, as it has already been released
graph = ["networkx"]
# dev - the developer dependency set, for contributors to pytorch-forecasting
dev = [
"pydocstyle >=6.1.1,<7.0.0",
# checks and make tools
"pre-commit >=3.2.0,<4.0.0",
"invoke",
"flake8",
"mypy",
"pylint",
"isort",
# pytest
"pytest",
"pytest-xdist",
"pytest-cov",
"pytest-sugar",
"coverage",
"pyarrow",
# jupyter notebook
"ipykernel",
"nbconvert",
"black[extras]",
# documentatation
"sphinx",
"pydata-sphinx-theme",
"nbsphinx",
"recommonmark",
"ipywidgets>=8.0.1,<9.0.0",
"pytest-dotenv>=0.5.2,<1.0.0",
"tensorboard>=2.12.1,<3.0.0",
"pandoc>=2.3,<3.0.0",
]
github-actions = ["pytest-github-actions-annotate-failures"]
[tool.setuptools.packages.find]
exclude = ["build_tools"]
[build-system]
build-backend = "setuptools.build_meta"
requires = [
"setuptools>=70.0.0",
]