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goshimmer

Choquet Rank

This repository contains the source code that was used in the experiments of the following paper : Vernerey et al. - Preference Learning to Rank Based on Choquet Integral: Application to Association Rules.

Experiments requirements

Before running the experiments, you need to follow these instructions:

Experiments running

To run active learning experiments, you need first to launch the kappalab server in a terminal: Rscript scripts/kappalab_server.R.

The following experiments are available:

  • make exp_passive: Passive learning experiments
  • make exp_passive_ranklib: Passive learning experiments with RankLib
  • make exp_active: Active learning experiments
  • make_exp_active_errors: Active learning with errors experiments
  • make exp_eisen: Eisen dataset experiments
  • make exp_time: Time experiments

You can find the results of one experiment in the folder called results/{NAME_OF_EXPERIMENT}.

Results of passive experiments are JSON files of the following format:

{"timeToLearn":0.619,"timeOut":false,"metricValues":{"kendall":0.9803213513121126,"rec@1%":0.9041095890410958,"AP@10%":0.995576406167752,"rec@10%":0.9673469387755103,"spearman":0.9606427026242251,"AP@1%":0.9843593247910968},"foldSize":10,"foldIdx":0,"oracle":"lexmin","learningAlgorithm":"KappalabRankLearn","dataset":"mushroom"}
  • timeToLearn: time running of the algorithm (seconds)
  • timeOut: false if the algorithm finished before time out
  • metricValues: values of the ranking metrics on the test set
  • foldSize: size of the training fold
  • foldIdx: index of the fold (for example, if k=5 then we have index from 0 to 4)
  • oracle: the oracle used in this experiment
  • learningAlgorithm: algorithm used to learn the ranking function (Kappalab = ChoquetRank)
  • dataset: the dataset from which the rules were extracted

Results of active experiments are similar, the only difference is that we have multiple values for one metric, for instance:

{"AP@10%":[0.9837964849788225,0.7745151667067416,0.9960244529964387,0.9982934829818166]}

means that the value of AP@10% was 0.9837964849788225 after one iteration on the test set, 0.7745151667067416 after two iterations, 0.9960244529964387 after three iterations, etc...