Metadata-Version: 1.1
Name: raredecay
Version: 1.2.1
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
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        # 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](http://mayou36.bitbucket.org/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](http://mayou36.bitbucket.org/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
        ```
        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
        
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 9
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 :: 2 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
