pyproject.toml

[build-system]
requires = ["uv_build>=0.12.5,<0.13"]
build-backend = "uv_build"

[project]
name = "optimal-classification-cutoffs"
version = "2.0.1"
description = "Utilities for computing optimal classification cutoffs for binary and multiclass classification"
readme = "README.md"
license = "MIT"
license-files = ["LICENSE"]
dependencies = [
    "numpy>=1.20.0",
    "scipy",
    "scikit-learn",
]
authors = [
    {name = "Gaurav Sood", email = "contact@gsood.com"}
]
requires-python = ">=3.12"
keywords = [
  "classification",
  "threshold",
  "cutoff",
  "f1-score",
  "cost-sensitive-learning",
  "machine-learning",
]
classifiers = [
  "Development Status :: 4 - Beta",
  "Intended Audience :: Science/Research",
  "Programming Language :: Python :: 3",
  "Programming Language :: Python :: 3.12",
  "Programming Language :: Python :: 3.13",
  "Programming Language :: Python :: 3.14",
  "Topic :: Scientific/Engineering :: Mathematics",
]

[project.urls]
Homepage = "https://github.com/finite-sample/optimal-classification-cutoffs"
Documentation = "https://finite-sample.github.io/optimal-classification-cutoffs/"
Repository = "https://github.com/finite-sample/optimal-classification-cutoffs"
Changelog = "https://github.com/finite-sample/optimal-classification-cutoffs/blob/master/CHANGELOG.md"

[tool.uv.build-backend]
module-name = "optimal_cutoffs"
module-root = "."

[tool.ruff]
line-length = 88
target-version = "py312"
exclude = [
    "docs/",
    "*.ipynb",  # Exclude Jupyter notebooks
]

[tool.ruff.lint]
external = ["DOC"]
select = [
    "E", "W", "F", "I", "B", "C4", "UP", "N", "D", "S", "SIM", "T20", "PT", "RUF",
    "PTH", "RET", "PIE", "FURB", "PERF", "DTZ", "LOG", "G", "TC", "FLY",
    "RSE", "SLOT", "FA", "A", "EXE", "ICN", "PGH", "PLE", "ARG", "SLF",
]
# D203/D213: the google convention the fleet standard picks. W191/D206/D300:
# ruff's own docs list these as always incompatible with `ruff format`, which
# the standard also runs.
ignore = ["D203", "D213", "W191", "D206", "D300"]

[tool.ruff.lint.pydocstyle]
convention = "google"

[tool.ruff.lint.per-file-ignores]
# Unused fixture arguments and private-member access are how tests are written.
# N802/N806: test names and locals carry the same maths notation as the code
# under test (D for the utility denominator, P for a probability matrix).
# T201: several suites print diagnostics; pytest captures them.
"tests/**" = ["S101", "D", "ARG", "SLF", "B007", "N802", "N803", "N806", "T201", "PT017"]
"docs/**" = ["D"]
# The single-letter names here are the notation the surrounding docstrings and
# error messages use: A/B/D for the Bayes utility differences, P for a
# probability matrix, P/N for total positive and negative weight.
"optimal_cutoffs/bayes_core.py" = ["N806"]
"optimal_cutoffs/expected.py" = ["N806"]
"optimal_cutoffs/metrics_core.py" = ["N806", "ARG001"]
# Every metric in the registry is called with the same (tp, tn, fp, fn)
# signature whether or not it reads all four; the numba stand-ins mirror
# numba's own decorator signature; y_true and n_samples are part of published
# signatures and documented as unused (see their docstrings).
"optimal_cutoffs/binary.py" = ["ARG001"]
"optimal_cutoffs/core.py" = ["ARG001"]
"optimal_cutoffs/numba_utils.py" = ["ARG001"]
"optimal_cutoffs/optimize.py" = ["ARG001"]

[tool.pytest.ini_options]
filterwarnings = [
    # Suppress specific expected warnings in tests
    "ignore:Multiclass probabilities don't sum to 1.0:UserWarning",
    "ignore:unique_scan with micro averaging uses independent:UserWarning",
    "ignore:invalid value encountered in divide:RuntimeWarning",
]

[tool.coverage.run]
source = ["optimal_cutoffs"]

[tool.pyright]
include = ["optimal_cutoffs"]
typeCheckingMode = "standard"

[dependency-groups]
# For examples and visualizations
examples = ["matplotlib", "pandas", "ipywidgets"]

# For performance optimization
performance = ["numba>=0.57.0"]

# For adaptive optimization methods
adaptive = ["scikit-optimize>=0.9.0"]

# For documentation generation
docs = [
    "sphinx>=8",
    "py-canon @ git+https://github.com/gojiplus/py-canon@v1",
    "furo",
    # myst-nb pulls myst-parser in and renders docs/examples/*.ipynb without
    # the pandoc binary nbsphinx required.
    "myst-nb>=1.1",
    "sphinx-copybutton",
    "matplotlib",
    "pandas",
    "ipywidgets",
]

dev = [
    "ruff>=0.14",
    "pyright>=1.1.390",
    "pre-commit>=4",
    "pydoclint>=0.5.0",
    { include-group = "test" },
]

# numba stays out: it is exercised through the optional 'performance' group.
test = [
    "pytest>=8",
    "pytest-cov>=6",
    "hypothesis>=6.0",
    "psutil>=5.0",
    "matplotlib>=3.10.7",
    "pandas>=2.3.3",
]

# All optional dependencies
all = [
    "matplotlib",
    "pandas",
    "numba>=0.57.0",
    "scikit-optimize>=0.9.0",
    "psutil>=5.0"
]

[tool.pydoclint]
style = "google"
arg-type-hints-in-docstring = false
check-return-types = false
check-class-attributes = false
allow-init-docstring = true
exclude = '\.venv|tests|docs'
check-arg-order = true
should-document-star-arguments = true
ignore-underscore-args = true
ignore-private-args = false
show-filenames-in-every-violation-message = true

[tool.deptry]
# Exclude specific paths from analysis
exclude = [".venv", "build", "dist", ".*\\.ipynb$"]
extend_exclude = ["tests", "examples", "docs"]
known_first_party = ["optimal_cutoffs"]

# Package name to module name mappings
[tool.deptry.package_module_name_map]
scikit-optimize = "skopt"
ipywidgets = "ipywidgets"
psutil = "psutil"

[tool.deptry.per_rule_ignores]
# DEP004: Allow optional performance dependency in specific file
DEP004 = ["numba"]

# DEP003: Allow transitive dependencies in examples/notebooks
DEP003 = ["IPython"]