pyproject.toml

[project]
name = "loadcast"
version = "0.1.0"
description = "Day-ahead electricity load forecasting with XGBoost, LSTM and a from-scratch Transformer, benchmarked against the TSO forecast."
readme = "README.md"
license = "MIT"
requires-python = ">=3.11,<3.14"
dependencies = [
    "entsoe-py>=0.7",
    "holidays>=0.50",
    "matplotlib>=3.8",
    "numpy>=1.26",
    "pandas>=2.2",
    "pyarrow>=15",
    "python-dotenv>=1.0",
    "pyyaml>=6.0",
    "requests>=2.31",
    "scikit-learn>=1.4",  # required by the xgboost scikit-learn API
    "scipy>=1.12",
    "torch>=2.2",
    "xgboost>=2.0",
]

[project.scripts]
loadcast = "loadcast.cli:main"

[dependency-groups]
dev = ["pytest>=8", "ruff>=0.5", "pre-commit>=3.7"]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.ruff]
line-length = 100
target-version = "py311"

[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B", "SIM", "N"]
ignore = [
    "N803", "N806",  # X, y style names in ML code
    "N812",          # `import torch.nn.functional as F` is the PyTorch convention
    "B027",          # Forecaster.fit is an intentional no-op default
]

[tool.pytest.ini_options]
testpaths = ["tests"]

# CPU-only torch on Linux (GitHub Actions): avoids ~2.5 GB of CUDA wheels per job.
[tool.uv.sources]
torch = [{ index = "pytorch-cpu", marker = "sys_platform == 'linux'" }]

[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true