scikit-lr
scikit-lr is a Python package for label ranking and partial label ranking, built on top of scikit-learn and distributed under the MIT license.
In label ranking, each sample is associated with a ranking of a fixed set of labels instead of a single class, and the goal is to learn a model that predicts that ranking for new samples. Partial label ranking extends the problem to rankings with ties. scikit-lr provides nearest neighbors and decision tree rankers for both problems, dummy rankers to compare them with, and metrics, all following the scikit-learn API, so they can be used with tools such as pipelines, cross-validation and hyperparameter search.
Website: https://alfaro96.github.io/scikit-lr
Installation
Dependencies
scikit-lr requires:
- Python (>= 3.13)
- joblib (>= 1.4.0)
- NumPy (>= 2.2)
- SciPy (>= 1.15)
- scikit-learn (1.9.x)
User installation
The easiest way to install scikit-lr is using pip:
pip install -U scikit-lr
The documentation includes more detailed installation instructions, with the versions of scikit-learn that each release of scikit-lr works with.
Documentation
Changelog
See the release history for a history of notable changes to scikit-lr.
Development
Contributions are welcome. The developer's guide has detailed information about contributing code, documentation and tests, and about the Cython extensions. All communications should respect our code of conduct.
Important links
- Source code repository: https://github.com/alfaro96/scikit-lr
- Download releases: https://pypi.org/project/scikit-lr
- Issue tracker: https://github.com/alfaro96/scikit-lr/issues
Source code
You can check the latest sources with the command:
git clone https://github.com/alfaro96/scikit-lr.git
Contributing
To learn more about making a contribution to scikit-lr, see our contributing guide.
Testing
After installation, you can launch the test suite with pytest:
pytest --pyargs sklr
See testing and improving test coverage for more information.
Project history
The project was started in 2019 as the Ph.D. thesis of Juan Carlos Alfaro Jiménez, whose advisors are Juan Ángel Aledo Sánchez and José Antonio Gámez Martín.
License
scikit-lr is distributed under the MIT license. See COPYING.
1 maintainer
Metadata
1 maintainer
Release files for scikit-lr 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distributions (wheels)
Total release size: 15.6 MB
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| Download URL | scikit_lr-0.3.0-cp313-cp313-macosx_10_13_x86_64.whl |
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| Size | 922.2 kB |
| Tags | CPython 3.13 macOS 10.13+ x86-64 |
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ec62a09e9bed8fdc6bf26125d211814a8218944339319d4d362c50f20b896cff
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twine/7.0.0 CPython/3.13.14
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