Welcome to REP’s documentation!¶
REP (Reproducible Experiment Platform) library provides functionality for all basic needs to deal with machine learning.
Estimators (classification and regression) is sklearn-like wrappers for variety of machine learning libraries:
- MatrixNet service (available to CERN)
These can be used as base estimators in sklearn.
Meta Machine Learning contains factory (the set of estimators), grid search, folding algorithm. Also parallel execution on a cluster is supported.
Metrics implement some basis for metrics used in reports and during grid search.
Report for models contains helpful classes to get model result information on any dataset.
Plotting is a wrapper for different plotting libraries including interactive plots.
Parallel computing describes REP way to parallelize tasks.
Data defines LabeledDataStorage - a custom way to store training data in a single object.
Utilities contains additional functions.
REProducibility is a recipe to make research reliable.
Howto notebooks contains examples.
Main repository: http://github.com/yandex/rep
REP provides several installation ways.
Please find instructions at the repository main page.
- Estimators (classification and regression)
- Estimators interfaces (for classification and regression)
- Sklearn classifier and regressor
- TMVA classifier and regressor
- XGBoost classifier and regressor
- Theanets classifier and regressor
- Neurolab classifier and regressor
- Pybrain classifier and regressor
- MatrixNet classifier and regressor
- Compatible libraries
- Meta Machine Learning
- Report for models
- Parallel computing
- Howto notebooks