Metadata-Version: 2.1
Name: raredecay
Version: 2.1.0
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|
        
        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
        ----------------------
        
        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.
        
        Recommended installation (requires ROOT):
        
        
        ::
        
            pip install raredecay[all] --process-dependency-links
        
        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
        
        
        
        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.
        
        
        .. |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
Requires-Python: >=2.7
Provides-Extra: all
