.. REP documentation master file
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to REP's documentation!
===============================
REP (Reproducible Experiment Platform) library provides functionality for all basic needs to deal with machine learning.
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It includes:
* :doc:`estimators` is sklearn-like wrappers for variety of machine learning libraries:
* TMVA
* Sklearn
* XGBoost
* Pybrain
* Neurolab
* Theanets.
* MatrixNet service (**available to CERN**)
These can be used as base estimators in sklearn.
* :doc:`metaml` contains factory (the set of estimators), grid search, folding algorithm. Also parallel execution on a cluster is supported.
* :doc:`metrics` implement some basis for metrics used in reports and during grid search.
* :doc:`report` contains helpful classes to get model result information on any dataset.
* :doc:`plotting` is a wrapper for different plotting libraries including interactive plots.
* matplotlib
* bokeh
* tmva
* :doc:`parallel` describes REP way to parallelize tasks.
* :doc:`data` defines LabeledDataStorage - a custom way to store training data in a single object.
* :doc:`utils` contains additional functions.
* :doc:`reproducibility` is a recipe to make research reliable.
* `Howto notebooks `_ contains examples.
Main repository: http://github.com/yandex/rep
Installation
============
REP provides several installation ways.
Please find instructions at the `repository `_ main page.
Documentation index:
====================
.. toctree::
:maxdepth: 2
estimators
metaml
report
metrics
plotting
parallel
data
utils
reproducibility
Howto notebooks