Latest: v1.1.0
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.
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']
Partial Ranker requires Python>=3.6 and can be installed using the command:
pip install git+https://github.com/HPAC/PartialRankerDetails on the usage and application examples can be found here. For a hands-on experience, please follow the jupyter notebooks under the folder examples/.
More details on partial ranking, the methodologies and applications can be found in this paper.
Financial support from the Deutsche Forschungsgemeinschaft (German Research Foundation) through the grant IRTG 2379 is gratefully acknowledged.