https://github.com/hpac/partialranker
Partial Ranker is a python library that implements methodologies for ranking a given set of objects that have a strict partial order relation.
https://github.com/hpac/partialranker
Last synced: over 1 year ago
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Partial Ranker is a python library that implements methodologies for ranking a given set of objects that have a strict partial order relation.
- Host: GitHub
- URL: https://github.com/hpac/partialranker
- Owner: HPAC
- License: gpl-3.0
- Created: 2022-07-19T07:53:04.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-05T17:30:51.000Z (almost 2 years ago)
- Last Synced: 2025-02-07T15:45:21.770Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage: https://hpac.github.io/PartialRanker/
- Size: 8.54 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Partial Ranker
**Latest: v1.0.0**
[](https://doi.org/10.5281/zenodo.12082779)
Partial Ranker is a library that implements methodologies for ranking a given set of objects that have a *strict partial order* relation. The full documentation can be found [here](https://hpac.github.io/PartialRanker/).
**Input**:
At the moment, we support only vector objects as input. An example for a set of vector objects would be:
```
objects = {
't0' : [0.1, 0.12, 0.11, 0.13 ],
't1' : [0.10, 0.13, 0.10 ],
't2' : [0.32, 0.31, 0.38, 0.32, 0.37, 0.32 ],
...
}
```
The *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.
**Output**:
The output is an *ordered set partition* of the objects into ranks. For example:
```
Rank 0: ['t0', 't1'],
Rank 1: ['t2']
```
## Installation
Partial Ranker requires Python>=3.6 and can be installed using the command:
```bash
pip install git+https://github.com/HPAC/PartialRanker
```
## Examples
Details 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/``.
## Cite
More 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:
```
@article{sankaran2024ranking,
title={Ranking with Ties based on Noisy Performance Data},
author={Sankaran, Aravind and Karlsson, Lars and Bientinesi, Paolo},
journal={arXiv preprint arXiv:2405.18259},
year={2024}
}
```
## Acknowledgement
Financial support from the Deutsche Forschungsgemeinschaft (German Research Foundation) through the grant IRTG 2379 is gratefully acknowledged.