Metadata-Version: 2.1
Name: hep-ml
Version: 0.6.2
Summary: Machine Learning for High Energy Physics
Home-page: https://github.com/arogozhnikov/hep_ml
Author: Alex Rogozhnikov
License: Apache 2.0
Description: # hep_ml
        
        **hep_ml** provides specific machine learning tools for purposes of high energy physics.
        
        [![travis status](https://travis-ci.org/arogozhnikov/hep_ml.svg?branch=master)](https://travis-ci.org/arogozhnikov/hep_ml)
        [![PyPI version](https://badge.fury.io/py/hep-ml.svg)](https://badge.fury.io/py/hep-ml)
        [![Documentation](https://img.shields.io/badge/documentation-link-blue.svg)](https://arogozhnikov.github.io/hep_ml/)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1247391.svg)](https://doi.org/10.5281/zenodo.1247391)
        
        
        
        ![hep_ml, python library for high energy physics](https://github.com/arogozhnikov/hep_ml/blob/data/data_to_download/hep_ml_image.png)
        
        
        ### Main features
        
        * uniform classifiers - the classifiers with low correlation of predictions and mass (or some other variable, or even set of variables)
          * __uBoost__ optimized implementation inside
          * __UGradientBoosting__ (with different losses, specially __FlatnessLoss__ is of high interest)
        * measures of uniformity (see **hep_ml.metrics**)
        * advanced losses for classification, regression and ranking for __UGradientBoosting__ (see **hep_ml.losses**).  
        * **hep_ml.nnet** - theano-based flexible neural networks 
        * **hep_ml.reweight** - reweighting multidimensional distributions <br />
          (_multi_ here means 2, 3, 5 and more dimensions - see GBReweighter!)
        * **hep_ml.splot** - minimalistic sPlot-ting 
        * **hep_ml.speedup** - building models for fast classification (Bonsai BDT)
        * **sklearn**-compatibility of estimators.
        
        ### Installation
        
        Basic installation:
        
        ```bash
        pip install hep_ml
        ```
        
        If you're new to python and never used `pip`, first install scikit-learn [with these instructions](http://scikit-learn.org/stable/install.html).
        
        To use **latest development version**, clone it and install with `pip`:
        ```bash
        git clone https://github.com/arogozhnikov/hep_ml.git
        cd hep_ml
        pip install .
        ```
        
        Local testing: 
        ```bash
        nosetests tests/
        ```
        
        ### Links
        
        * [documentation](https://arogozhnikov.github.io/hep_ml/)
        * [notebooks, code examples](https://github.com/arogozhnikov/hep_ml/tree/master/notebooks)
            - you may need to install `ROOT` and `root_numpy` to run those 
        * [repository](https://github.com/arogozhnikov/hep_ml)
        * [issue tracker](https://github.com/arogozhnikov/hep_ml/issues)
        
        ### Related projects 
        Libraries you'll require to make your life easier and HEPpier.
        
        * [IPython Notebook](http://ipython.org/notebook.html) &mdash; web-shell for python
        * [scikit-learn](http://scikit-learn.org/)  &mdash; general-purpose library for machine learning in python
        * [numpy](http://www.numpy.org/)  &mdash; 'MATLAB in python', vector operation in python. 
            Use it you need to perform any number crunching. 
        * [theano](http://deeplearning.net/software/theano/)  &mdash; optimized vector analytical math engine in python
        * [ROOT](https://root.cern.ch/)  &mdash; main data format in high energy physics 
        * [root_numpy](http://rootpy.github.io/root_numpy/)  &mdash; python library to deal with ROOT files (without pain)
        
        
        ### License
        Apache 2.0, `hep_ml` is an open-source library.
        
        ### Platforms 
        Linux, Mac OS X and Windows are supported.
        
        **hep_ml** supports both python 2 and python 3.
        
Keywords: machine learning,supervised learning,uncorrelated methods of machine learning,high energy physics,particle physics
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2.7 
Classifier: Programming Language :: Python :: 3.5 
Classifier: Programming Language :: Python :: 3.6 
Classifier: Programming Language :: Python :: 3.7 
Description-Content-Type: text/markdown
