raredecay 1.3.0

[](https://landscape.io/github/mayou36/raredecay/master)
[](https://travis-ci.org/mayou36/raredecay)
[](https://badge.fury.io/py/raredecay)
[](https://www.versioneye.com/user/projects/58273f1df09d22004f5914f9)




# raredecay #

This package consists of several tools for the event selection of particle decays, mostly built on machine learning techniques.
It contains:

- a **data-container** holding data, weights, labels and more and implemented root-to-python data conversion as well as plots and KFold-data splitting
- **reweighting** tools from the hep_ml-repository wrapped in a KFolding structure and with metrics to evaluate the reweighting quality
- **classifier optimization** tools for hyper-parameters as well as feature selection involving a backward-elimination
- an **output handler** which makes it easy to add text as well as figures into your code and automatically save them to a file
- ... and more

## HowTo examples ##

To get an idea of the package, have a look at the howto notebooks:
[HTML version](https://mayou36.bitbucket.io/raredecay/howto/) or the
[IPython Notebooks](https://github.com/mayou36/raredecay/tree/master/howto)

## Minimal example ##
Want to test whether your reweighting did overfit? Use train_similar:

```python
from raredecay.tools.data_storage import HEPDataStorage
from raredecay.tools.metrics import train_similar

mc_data = HEPDataStorage(df, weights=*pd.Series weights*, target=0)
real_data = HEPDataStorage(df, weights=*pd.Series weights*, target=1)

score = train_similar(mc_data, real_data, old_mc_weights=1 *or whatever weights the mc had before*)
```


## Getting started right now ##

If you want it the easy, fast way, have a look at the
[Ready-to-use scripts](https://github.com/mayou36/raredecay/tree/master/scripts_readyToUse).
All you need to do is to have a look at every "TODO" task and probably change them. Then you can run the script without the need of coding at all.

## Documentation and API ##

The API as well as the documentation:
[Documentation](https://mayou36.bitbucket.io/raredecay/docs/)

## Setup and installation ##

The package, in its current state, requires root_numpy as well as rootpy (and therefore a ROOT installation with python-bindings) to be installed on your system. If that is not the case, some functions won't work and you should install it with the --no-dependencies flag and install the other requirements by hand.

First install the very newest version of REP
(the -U can be omitted, but is recommended to have the newest dependencies, on the other hand may crashes REPs reproducibility):
```
pip install -U https://github.com/yandex/rep/archive/stratifiedkfold.zip
```
To install the newest version of hep_ml containing the loss-regularization:
```
pip install -U https://github.com/arogozhnikov/hep_ml.git
```

Then, install the raredecay package (without ROOT-support) via

```
pip install raredecay
```

To make sure you can convert ROOT-NTuples, use

```
pip install raredecay[root] # *use raredecay\[root\] in a zsh-console*
```
As it is a young package still under developement, it may receive regular updates and improvements and it is probably a good idea to regularly download the newest package.


[pandas.DataFrame]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
[LabeledDataStorage]: http://yandex.github.io/rep/data.html#module-rep.data.storage
[numpy.array]: http://docs.scipy.org/doc/numpy-1.10.1/user/basics.rec.html
[rootTree]: https://root.cern.ch/doc/v606/classTTree.html