From 7da9806f1dee22dd414abe7701ed0f26acb5fafb Mon Sep 17 00:00:00 2001 From: Markus Bilz Date: Sun, 24 Nov 2024 17:17:13 +0100 Subject: [PATCH] =?UTF-8?q?ci:=20migrate=20to=20uv=20+=20tox=20based=20set?= =?UTF-8?q?up=F0=9F=94=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 2 +- docs/index.md | 2 +- pyproject.toml | 4 +- src/tclf/classical_classifier.py | 4 + uv.lock | 251 +++++++++++++++++++++++++++++++ 5 files changed, 260 insertions(+), 3 deletions(-) create mode 100644 uv.lock diff --git a/README.md b/README.md index 94f9ffd..6e4e115 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ pip install tclf **[uv⚡](https://github.com/astral-sh/uv)** ```console -uv pip install tclf +uv add tclf ``` ## Supported Algorithms diff --git a/docs/index.md b/docs/index.md index 94f9ffd..6e4e115 100644 --- a/docs/index.md +++ b/docs/index.md @@ -27,7 +27,7 @@ pip install tclf **[uv⚡](https://github.com/astral-sh/uv)** ```console -uv pip install tclf +uv add tclf ``` ## Supported Algorithms diff --git a/pyproject.toml b/pyproject.toml index 5220de9..2536327 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,8 @@ classifiers = [ dependencies = [ "numpy", "pandas", - "scikit-learn" + "ruff>=0.8.0", + "scikit-learn", ] dynamic = ["version"] @@ -99,6 +100,7 @@ include = ["*.py", "*.pyi", "**/pyproject.toml", "*.ipynb"] select = [ "C", # flake8-comprehensions "D", # pydocstyle + "DOC", # pydoclint "E", # pycodestyle errors "F", # pyflakes "FURB", # refurb diff --git a/src/tclf/classical_classifier.py b/src/tclf/classical_classifier.py index f0a090c..0613c5b 100644 --- a/src/tclf/classical_classifier.py +++ b/src/tclf/classical_classifier.py @@ -106,6 +106,9 @@ def _more_tags(self) -> dict[str, bool | dict[str, str]]: """Set tags for sklearn. See: https://scikit-learn.org/stable/developers/develop.html#estimator-tags + + Returns: + dict[str, bool | dict[str, str]]: dict with tags """ return { "allow_nan": True, @@ -213,6 +216,7 @@ def _mid(self, subset: str) -> npt.NDArray: Args: subset (str): subset i.e., 'ex' or 'best' + Returns: npt.NDArray: midpoints. Can be np.NaN. 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