Metadata-Version: 1.1
Name: raredecay
Version: 2.0.2
Summary: A package for analysis of rare particle decays with machine-learning algorithms
Home-page: https://github.com/mayou36/raredecay
Author: Jonas Eschle
Author-email: mayou36@jonas.eschle.com
License: Apache-2.0 License
Description: |Code Health| |Build Status| |PyPI version| |Dependency Status|
        
        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:
        
        .. code:: python
        
            import raredecay as rd  
        
            mc_data = rd.data.HEPDataStorage(df, weights=*pd.Series weights*, target=0)  
            real_data = rd.data.HEPDataStorage(df, weights=*pd.Series weights*, target=1)  
        
            score = rd.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.github.io/raredecay/>`__
        
        Setup and installation
        ----------------------
        
        Anaconda
        ~~~~~~~~
        
        Easiest way: use conda to install everything (except of the rep, which
        has to be upgraded with pip for some functionalities)
        
        ::
        
            conda install raredecay -c mayou36
        
        PyPI
        ~~~~
        
        The package with all extras 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.
        
        If you want to install all the extra, first install the very newest
        version of REP (may also needed with conda install) (the -U can be
        omitted, but is recommended to have the newest dependencies):
        
        ::
        
            pip install -U https://github.com/yandex/rep/archive/stratifiedkfold.zip
        
        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*
        
        or, instead of root/additionally (comma separated) ``reweight`` or
        ``reweight`` for the specific functionalities.
        
        In order to have all functionalities, use
        
        ::
        
            pip install raredecay[all]
        
        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.
        
        .. |Code Health| image:: https://landscape.io/github/mayou36/raredecay/master/landscape.svg?style=flat
           :target: https://landscape.io/github/mayou36/raredecay/master
        .. |Build Status| image:: https://travis-ci.org/mayou36/raredecay.svg?branch=master
           :target: https://travis-ci.org/mayou36/raredecay
        .. |PyPI version| image:: https://badge.fury.io/py/raredecay.svg
           :target: https://badge.fury.io/py/raredecay
        .. |Dependency Status| image:: https://www.versioneye.com/user/projects/58273f1df09d22004f5914f9/badge.svg?style=flat-square
           :target: https://www.versioneye.com/user/projects/58273f1df09d22004f5914f9
        
Keywords: particle physics,analysis,machine learning,reweight,high energy physics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
