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        "author": "Alex Rogozhnikov",
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        "description": "# hep_ml\n\n**hep_ml** provides specific machine learning tools for purposes of high energy physics.\n\n[![travis status](https://travis-ci.org/arogozhnikov/hep_ml.svg?branch=master)](https://travis-ci.org/arogozhnikov/hep_ml)\n[![appveyor status](https://ci.appveyor.com/api/projects/status/kxatlw869t9ibbo3?svg=true)](https://ci.appveyor.com/project/arogozhnikov/hep-ml)\n[![PyPI version](https://badge.fury.io/py/hep_ml.svg)](http://badge.fury.io/py/hep_ml)\n[![Documentation](https://img.shields.io/badge/documentation-link-blue.svg)](https://arogozhnikov.github.io/hep_ml/)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1247391.svg)](https://doi.org/10.5281/zenodo.1247391)\n\n\n\n![hep_ml, python library for high energy physics](https://github.com/arogozhnikov/hep_ml/blob/data/data_to_download/hep_ml_image.png)\n\n\n### Main points\n* uniform classifiers - the classifiers with low correlation of predictions and mass (or some other variable, or even set of variables)\n  * __uBoost__ optimized implementation inside\n  * __UGradientBoosting__ (with different losses, specially __FlatnessLoss__ is very interesting)\n* measures of uniformity (see **hep_ml.metrics**)\n* advanced losses for classification, regression and ranking for __UGradientBoosting__ (see **hep_ml.losses**).  \n* **hep_ml.nnet** - theano-based flexible neural networks \n* **hep_ml.reweight** - reweighting multidimensional distributions <br />\n  (_multi_ here means 2, 3, 5 and more dimensions - see GBReweighter!)\n* **hep_ml.splot** - minimalistic sPlot-ting \n* **hep_ml.speedup** - building models for fast classification (Bonsai BDT)\n* **sklearn**-compatibility of estimators.\n\n### Installation\n\nBasic installation:\n\n```bash\npip install hep_ml\n```\n\nIf you're new to python and never used `pip`, first install scikit-learn [with these instructions](http://scikit-learn.org/stable/install.html).\n\nTo use **latest development version**, clone it and install with `pip`:\n```bash\ngit clone https://github.com/arogozhnikov/hep_ml.git\ncd hep_ml\nsudo pip install .\n```\n\n### Links\n\n* [documentation](https://arogozhnikov.github.io/hep_ml/)\n* [notebooks, code examples](https://github.com/arogozhnikov/hep_ml/tree/master/notebooks)\n    - you may need to install `ROOT` and `root_numpy` to run those \n* [repository](https://github.com/arogozhnikov/hep_ml)\n* [issue tracker](https://github.com/arogozhnikov/hep_ml/issues)\n\n### Related projects \nLibraries you'll require to make your life easier and HEPpier.\n\n* [IPython Notebook](http://ipython.org/notebook.html) &mdash; web-shell for python\n* [scikit-learn](http://scikit-learn.org/)  &mdash; general-purpose library for machine learning in python\n* [numpy](http://www.numpy.org/)  &mdash; 'MATLAB in python', vector operation in python. \n    Use it you need to perform any number crunching. \n* [theano](http://deeplearning.net/software/theano/)  &mdash; optimized vector analytical math engine in python\n* [ROOT](https://root.cern.ch/)  &mdash; main data format in high energy physics \n* [root_numpy](http://rootpy.github.io/root_numpy/)  &mdash; python library to deal with ROOT files (without pain)\n\n\n### License\nApache 2.0, `hep_ml` is an open-source library.\n\n### Platforms \nLinux, Mac OS X and Windows are supported.\n\n**hep_ml** supports both python 2 and python 3.\n\n\n",
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