Active_Fedference/pyproject.toml at main · ActiveInferenceInstitute/Active_Fedference · GitHub

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# Active Fedference Configuration
# Robust federated active inference (FedGVI x Friston et al. 2024)
# Inherits workspace settings from root pyproject.toml

[build-system]
requires = ["setuptools==81.0.0"]
build-backend = "_fedference_build_backend"
backend-path = ["."]

[project]
name = "active_fedference"
version = "1.1.0"
description = "Robust federated active inference: FedGVI applied to Friston et al. (2024) belief sharing"
readme = {file = "README.md", content-type = "text/markdown"}
license = "MIT"
license-files = ["LICENSE"]
authors = [
    {name = "Daniel Ari Friedman", email = "daniel@activeinference.institute"}
]
keywords = [
    "active inference",
    "federated learning",
    "generalised variational inference",
    "belief sharing",
    "robustness",
    "FedGVI",
]
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Science/Research",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3 :: Only",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]
requires-python = ">=3.10"
dependencies = [
    "numpy>=1.24.0",
    "scipy>=1.10.0",
    "matplotlib>=3.7.0",
    "pyyaml>=6.0",
    "tomli>=2.0; python_version < '3.11'",
    "typing-extensions>=4.9.0",
]

[project.scripts]
fedference = "fedference_cli:main"

[project.urls]
DOI = "https://doi.org/10.5281/zenodo.22149133"
Homepage = "https://github.com/ActiveInferenceInstitute/Active_Fedference"
Repository = "https://github.com/ActiveInferenceInstitute/Active_Fedference"
Documentation = "https://github.com/ActiveInferenceInstitute/Active_Fedference/tree/main/docs"

[project.optional-dependencies]
dev = [
    "mypy>=1.17.1",
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "ruff>=0.15.21",
    "torch>=2.0.0",
    "hypothesis>=6.0",
]
torch = [
    "torch>=2.0.0",
]
bnn = [
    "torch>=2.0.0",
]

[tool.pytest.ini_options]
# RASP Strict Assertion: Tests must be clearly separated and structurally identifiable
pythonpath = [".", "src"]
testpaths = ["tests"]
python_files = ["test_*.py"]
python_functions = ["test_*"]
# `--strict-markers` prevents typos in marker definitions
addopts = "-v --tb=short --strict-markers"
markers = [
    "requires_torch: tests that require PyTorch installed",
    "slow: computationally expensive seeded experiments or publication checks",
    "integration: multi-module or transport integration coverage",
    "publication: tests that exercise generated manuscript or release artifacts",
]

[tool.coverage.run]
# Core Template Rule: We only calculate coverage for primary source code (`src/`),
# ensuring test code itself does not artificially inflate metric scores.
source = ["src"]
branch = true
omit = [
    "tests/*",
    "*/__init__.py",
    "*/test_*.py",
]

[tool.coverage.report]
# ≥90% on src/ is the enforced gate; the live percentage is recorded in the
# source-bound output/data/test_coverage_receipt.json (do not hardcode it here).
fail_under = 90
show_missing = true
precision = 2
exclude_lines = [
    "pragma: no cover",
    "if __name__ == .__main__.:",
    "raise NotImplementedError",
]

[tool.mypy]
mypy_path = "src"
ignore_missing_imports = true
namespace_packages = true
explicit_package_bases = true
disable_error_code = ["import-untyped"]

[tool.uv]
# Inherits workspace settings from root pyproject.toml

[tool.setuptools.package-data]
fedference = [
    "config/*.yaml",
    "data/*.csv",
    "py.typed",
]

[tool.ruff]
line-length = 110
target-version = "py310"
exclude = [".venv", "output", "**/__pycache__"]

[tool.ruff.lint]
# E=pycodestyle errors, F=pyflakes, W=pycodestyle warnings, I=import sort
# N (naming) and D (docstring) excluded: the project uses uppercase A/B/C for
# likelihood/transition matrices (Active Inference convention) and Google-style
# docstrings with relaxed markup.
select = ["E", "F", "W", "I"]