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rank-aggregation

Important files:

  • election.py -- code for HAC, "Election" class which outputs clusters and cluster centers
  • util.py -- different distance metrics used in election and CPS model, as well as some code for reading from the sushi dataset
  • learncps.py -- code for learning CPS model, including gradient ascent / MLE of theta paramaters
  • inference.py -- code for testing sequential / community inference for outputting best ranking
  • comparison.py -- prints comparison of different metrics
  • requirements.txt -- please run 'pip install -r requirements.txt' before attempting to run any code
  • results -- folder of results for learned CPS models, files are named by the target ranking and the number of cluster, each file is a text file that contains the parameters for the CPS models as well as the losses incurred by learning the optimal theta

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