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    "info": {
        "author": "Yacine Haddad",
        "author_email": "yhaddad@cern.ch",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7"
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        "description": "===============================\nbinopt\n===============================\n\n\n.. image:: https://img.shields.io/pypi/v/binopt.svg\n        :target: https://pypi.python.org/pypi/binopt\n\n\n.. image:: https://img.shields.io/travis/yhaddad/binopt.svg\n        :target: https://travis-ci.org/yhaddad/binopt\n\n\n.. image:: https://readthedocs.org/projects/binopt/badge/?version=latest\n        :target: https://binopt.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\n.. image:: https://pyup.io/repos/github/yhaddad/binopt/shield.svg\n     :target: https://pyup.io/repos/github/yhaddad/binopt/\n     :alt: Updates\n\n.. image:: https://zenodo.org/badge/86721620.svg\n   :target: https://zenodo.org/badge/latestdoi/86721620\n\n\nThis package is aiming to categorize labeled data in terms of a global figure of merit. In high energy physics, categorization of collision data is done by maximizing the discovery significance. This package run on unbinned binary datasets.\n\ninstallation\n************\nInstall like any other python package::\n    \n    pip install binopt --user\n\nor::\n    \n    git clone git@github.com:yhaddad/binopt.git\n    cd binopt/\n    pip install .\n\n\nGetting started\n***************\n\n.. code-block:: python\n\n   sevent = 1000\n   bevent = 10000\n   X = np.concatenate((\n                expit(np.random.normal(+2.0, 2.0, sevent)),\n                expit(np.random.normal(-0.5, 2.0, bevent))\n   ))\n   Y = np.concatenate((\n                np.ones(sevent),\n                np.zeros(bevent)\n   ))\n   W = np.concatenate((np.ones(sevent), np.ones(bevent)))\n\n   binner = binopt.optimize_bin(\n                nbins=3, range=[0, 1],\n                drop_last_bin=True,\n                fix_upper=True,\n                fix_lower=False,\n                use_kde_density=True\n   )\n   opt = binner.fit(\n                X, Y, sample_weights=W,\n                method=\"Nelder-Mead\",\n                breg=None, fom=\"AMS2\"\n   )\n\n   print \"bounds : \", opt.x\n   print \"signif : \", binner.binned_score(opt.x)\n   print \"Nsig   : \", binner.binned_stats(opt.x)[0]\n   print \"Nbkg   : \", binner.binned_stats(opt.x)[1]\n\n\n* Free software: GNU General Public License v3\n* Documentation: https://binopt.readthedocs.io.\n\n\nFeatures\n--------\n\n* TODO\n\nCredits\n---------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.0 (2017-04-06)\n------------------\n\n* First release on PyPI.\n",
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        "keywords": "binopt",
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        "name": "binopt",
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        "summary": "Categorisation of labeled data",
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