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')
We provide the docker container with REP
and all it's dependencies
https://github.com/yandex/rep/wiki/Running-REP-using-Docker/
However, if you want to install REP
on your machine, follow this manual:
https://github.com/yandex/rep/wiki/Installing-manually
and https://github.com/yandex/rep/wiki/Running-manually
To get started with the framework, look at the notebooks in /howto/
Notebooks in repository can be viewed (not executed) online at nbviewer: http://nbviewer.ipython.org/github/yandex/rep/tree/master/howto/
There are basic introductory notebooks (about python, IPython) and more advanced ones (about the REP itself)