https://github.com/arogozhnikov/rep

Machine Learning toolbox for Humans

https://github.com/arogozhnikov/rep

Repository

Machine Learning toolbox for Humans

https://github.com/arogozhnikov/rep/blob/master/

# Reproducible Experiment Platform (REP)

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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
  * 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.

### Howto examples

To get started, look at the notebooks in [/howto/](https://github.com/yandex/rep/tree/master/howto)

Notebooks 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)

Examples code is written in python 2, but library is python 2 and python 3 compatible.

### Installation with Docker

We provide the [docker image](https://registry.hub.docker.com/u/yandex/rep/) with `REP` and all it's dependencies 
* [install with Docker on Linux](https://github.com/yandex/rep/wiki/Install-REP-with-Docker-(Linux))
* [install with Docker on Mac and Windows](https://github.com/yandex/rep/wiki/Install-REP-with-Docker-(Mac-OS-X,-Windows))


### Installation with bare hands

However, if you want to install `REP` and all of its dependencies on your machine yourself, follow this manual: 
[installing manually](https://github.com/yandex/rep/wiki/Installing-manually) and 
[running manually](https://github.com/yandex/rep/wiki/Running-manually).


### Links

* [documentation](http://yandex.github.io/rep/)
* [howto](http://nbviewer.ipython.org/github/yandex/rep/tree/master/howto/)
* [bugtracker](https://github.com/yandex/rep/issues)
* [gitter chat, troubleshooting](https://gitter.im/yandex/rep)
* [API, contributing new estimator](https://github.com/yandex/rep/wiki/Contributing-new-estimator)
* [API, contributing new metric](https://github.com/yandex/rep/wiki/Contributing-new-metrics)
* [Tutorial](https://github.com/yandexdataschool/REP_tutorial) based on [Flavour of physics challenge](https://www.kaggle.com/c/flavours-of-physics)
* If you use REP in research, please consider [citing](http://arxiv.org/abs/1510.00624)

### License
Apache 2.0, library is open-source.

### Minimal examples

__REP__ wrappers are sklearn compatible:

```python
from rep.estimators import XGBoostClassifier, SklearnClassifier, TheanetsClassifier
clf = XGBoostClassifier(n_estimators=300, eta=0.1).fit(trainX, trainY)
probabilities = clf.predict_proba(testX)
```

Beloved trick of kagglers is to run bagging over complex algorithms. This is how it is done in __REP__:

```python
from sklearn.ensemble import BaggingClassifier
clf = BaggingClassifier(base_estimator=XGBoostClassifier(), n_estimators=10)
# wrapping sklearn to REP wrapper
clf = SklearnClassifier(clf)
```

Another useful trick is to use folding instead of splitting data into train/test. 
This is specially useful when you're using some kind of complex stacking

```python
from rep.metaml import FoldingClassifier
clf = FoldingClassifier(TheanetsClassifier(), n_folds=3)
probabilities = clf.fit(X, y).predict_proba(X)
```
In example above all data are splitted into 3 folds, 
and each fold is predicted by classifier which was trained on other 2 folds.  

Also __REP__ classifiers provide report:

```python
report = clf.test_on(testX, testY)
report.roc().plot() # plot ROC curve
from rep.report.metrics import RocAuc
# learning curves are useful when training GBDT!
report.learning_curve(RocAuc(), steps=10)  
```

You can read about other __REP__ tools (like smart distributed grid search, folding and factory) 
in documentation and howto examples.

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