ipython-based environment for conducting data-driven research in a consistent and reproducible way

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REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way.

Main features:

  • unified python wrapper for different ML libraries (wrappers follow extended scikit-learn interface)
    • Sklearn
    • TMVA
    • XGBoost
    • uBoost
    • Theanets
    • Pybrain
    • Neurolab
    • MatrixNet service(available to CERN)
  • parallel training of classifiers on cluster
  • classification/regression reports with plots
  • interactive plots supported
  • smart grid-search algorithms with parallel execution
  • research versioning using git
  • pluggable quality metrics for classification
  • meta-algorithm design (aka ‘rep-lego’)

REP is not trying to substitute scikit-learn, but extends it and provides better user experience.

 

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