{"id":22954952,"url":"https://github.com/hpac/partialranker","last_synced_at":"2025-04-02T00:27:08.840Z","repository":{"id":46604797,"uuid":"515488334","full_name":"HPAC/PartialRanker","owner":"HPAC","description":"Partial Ranker is a python library that implements methodologies for ranking a given set of objects that have a strict partial order relation. 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The full documentation can be found [here](https://hpac.github.io/PartialRanker/).\n\n**Input**:\n\nAt the moment, we support only vector objects as input. An example for a set of vector objects would be:\n\n```\nobjects = {\n    't0' : [0.1, 0.12, 0.11, 0.13 ],\n    't1' : [0.10, 0.13, 0.10 ],\n    't2' : [0.32, 0.31, 0.38, 0.32, 0.37, 0.32 ],\n    ...\n}\n```\nThe *better-than relation* between a pair of objects with which the partial order is formed is implemented in the library. At the moment, we support better-than relation based on comparisons of the Inter-Quantile-Intervals of the objects.\n\n**Output**:\n\nThe output is an *ordered set partition* of the objects into ranks. For example:\n\n```\nRank 0: ['t0', 't1'],\nRank 1: ['t2']\n```\n\n## Installation\n\nPartial Ranker requires Python\u003e=3.6 and can be installed using the command:\n\n```bash\npip install git+https://github.com/HPAC/PartialRanker\n```\n## Examples\n\nDetails on the usage and application examples can be found [here](https://hpac.github.io/PartialRanker/notebooks-usage/01U_Usage.html). For a hands-on experience, please follow the jupyter notebooks under the folder ``examples/``.\n\n## Cite\n\nMore details on partial ranking, the methodologies and applications can be found in [this paper](https://arxiv.org/abs/2405.18259). If you are using this library, please cite:\n\n```\n@article{sankaran2024ranking,\n  title={Ranking with Ties based on Noisy Performance Data},\n  author={Sankaran, Aravind and Karlsson, Lars and Bientinesi, Paolo},\n  journal={arXiv preprint arXiv:2405.18259},\n  year={2024}\n}\n```\n\n## Acknowledgement\n\nFinancial support from the Deutsche Forschungsgemeinschaft (German Research Foundation) through the grant IRTG 2379 is gratefully acknowledged.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhpac%2Fpartialranker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhpac%2Fpartialranker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhpac%2Fpartialranker/lists"}