Public Repository

Last pushed: 3 years ago
Short Description
Machine Learning Toolbox for Humans --
Full Description

Reproducible Experiment Platform (REP)

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

Main REP features include:

  • unified classifiers wrapper for variety of implementations
    • TMVA
    • Sklearn
    • XGBoost
    • uBoost
    • Theanets
    • Pybrain
    • Neurolab
  • parallel training of classifiers on cluster
  • classification/regression reports with plots
  • support for interactive plots
  • grid-search algorithms with parallelized execution
  • versioning of research using git
  • pluggable quality metrics for classification
  • meta-algorithm design (aka 'rep-lego')

Installation with Docker

We provide the docker image with REP and all it's dependencies

Installation with bare hands

However, if you want to install REP and all of its dependencies on your machine yourself, follow this manual:

First steps

To get started with the framework, look at the notebooks in /howto/
Notebooks in repository can be viewed (not executed) online at nbviewer:
There are basic introductory notebooks (about python, IPython) and more advanced ones (about the REP itself)

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