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"info": {
"author": "Jonas Eschle",
"author_email": "mayou36@jonas.eschle.com",
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"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
"Operating System :: MacOS",
"Operating System :: MacOS :: MacOS X",
"Operating System :: POSIX :: Linux",
"Operating System :: Unix",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: Implementation :: CPython",
"Topic :: Scientific/Engineering :: Information Analysis",
"Topic :: Scientific/Engineering :: Physics"
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"description": "|Code Health| |Build Status| |PyPI version|\n\nraredecay\n=========\n\nThis package consists of several tools for the event selection of\nparticle decays, mostly built on machine learning techniques. It\ncontains:\n\n- a **data-container** holding data, weights, labels and more and\n implemented root-to-python data conversion as well as plots and\n KFold-data splitting\n- **reweighting** tools from the hep\\_ml-repository wrapped in a\n KFolding structure and with metrics to evaluate the reweighting\n quality\n- **classifier optimization** tools for hyper-parameters as well as\n feature selection involving a backward-elimination\n- an **output handler** which makes it easy to add text as well as\n figures into your code and automatically save them to a file\n- ... and more\n\nHowTo examples\n--------------\n\nTo get an idea of the package, have a look at the howto notebooks: `HTML\nversion `__ or the\n`IPython\nNotebooks `__\n\nMinimal example\n---------------\n\nWant to test whether your reweighting did overfit? Use train\\_similar:\n\n.. code:: python\n\n import raredecay as rd\n\n mc_data = rd.data.HEPDataStorage(df, weights=*pd.Series weights*, target=0)\n real_data = rd.data.HEPDataStorage(df, weights=*pd.Series weights*, target=1)\n\n score = rd.score.train_similar(mc_data, real_data, old_mc_weights=1 *or whatever weights the mc had before*)\n\nGetting started right now\n-------------------------\n\nIf you want it the easy, fast way, have a look at the `Ready-to-use\nscripts `__.\nAll you need to do is to have a look at every \"TODO\" task and probably\nchange them. Then you can run the script without the need of coding at\nall.\n\nDocumentation and API\n---------------------\n\nThe API as well as the documentation:\n`Documentation `__\n\nSetup and installation\n----------------------\n\nPyPI\n~~~~\n\nThe package with all extras requires root\\_numpy as well as rootpy (and\ntherefore a ROOT installation with python-bindings) to be installed on\nyour system. If that is not the case, some functions won't work.\n\nRecommended installation (requires ROOT):\n\n\n::\n\n pip install raredecay[all] --process-dependency-links\n\nAnaconda\n~~~~~~~~\n\nEasiest way: use conda to install everything (except of the rep, which\nhas to be upgraded with pip for some functionalities)\n\n::\n\n conda install raredecay -c mayou36\n\n\n\nTo make sure you can convert ROOT-NTuples, use\n\n::\n\n pip install raredecay[root] # *use raredecay\\[root\\] in a zsh-console*\n\nor, instead of root/additionally (comma separated) ``reweight`` or\n``reweight`` for the specific functionalities.\n\n\n.. |Code Health| image:: https://landscape.io/github/mayou36/raredecay/master/landscape.svg?style=flat\n :target: https://landscape.io/github/mayou36/raredecay/master\n.. |Build Status| image:: https://travis-ci.org/mayou36/raredecay.svg?branch=master\n :target: https://travis-ci.org/mayou36/raredecay\n.. |PyPI version| image:: https://badge.fury.io/py/raredecay.svg\n :target: https://badge.fury.io/py/raredecay\n.. |Dependency Status| image:: https://www.versioneye.com/user/projects/58273f1df09d22004f5914f9/badge.svg?style=flat-square\n :target: https://www.versioneye.com/user/projects/58273f1df09d22004f5914f9\n\n\n",
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