Ranking Python Library
Project description
Rankability Toolbox
This repo contains various implementations that provide insights into the rankability of data and the linear ordering problem.
Install instructions
Prerequisites
graphviz headers must be installed:
apt-get install -y libgraphviz-dev
Recommended package installation
pip install pyrankability
Post package installation: Gurobi License
This library relies on Gurobi optimizer. Gurobi provides free academic licenses and more information on obtaining and installing your license can be found here: https://support.gurobi.com/hc/en-us/articles/360040541251.
Verify installation
import pyrankability
n=8
D=np.zeros((n,n))
D[np.triu_indices(n,1)]=1
D[[5,3,7]] = 1-D[[5,3,7]]
D=pd.DataFrame(D)
k_hillside,details_hillside = pyrankability.rank.solve(D,method='hillside')
k_lop,details_lop = pyrankability.rank.solve(D,method='lop')
assert k_hillside == 54 and k_lop == 12.0
Development notes
Running tests
cd ranking_toolbox
python3 -m venv ../env
source ../env/bin/activate
cd tests
pytest tests.py
Authors
Paul Anderson, Ph.D.
Department of Computer Science
Director, Data Science Program
College of Charleston
Amy Langville, Ph.D.
Department of Mathematics
College of Charleston
Tim Chartier, Ph.D.
Department of Mathematics
Davidson College
Acknowledgements
We would like to thank the entire IGARDS team for their invaluable insight and encouragement.
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