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Categorisation of labeled data

Project description

===============================
binopt
===============================


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This 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.

installation
************
Install like any other python package::

pip install binopt --user

or::

git clone git@github.com:yhaddad/binopt.git
cd binopt/
pip install .


Getting started
***************

.. code-block:: python

sevent = 1000
bevent = 10000
X = np.concatenate((
expit(np.random.normal(+2.0, 2.0, sevent)),
expit(np.random.normal(-0.5, 2.0, bevent))
))
Y = np.concatenate((
np.ones(sevent),
np.zeros(bevent)
))
W = np.concatenate((np.ones(sevent), np.ones(bevent)))

binner = binopt.optimize_bin(
nbins=3, range=[0, 1],
drop_last_bin=True,
fix_upper=True,
fix_lower=False,
use_kde_density=True
)
opt = binner.fit(
X, Y, sample_weights=W,
method="Nelder-Mead",
breg=None, fom="AMS2"
)

print "bounds : ", opt.x
print "signif : ", binner.binned_score(opt.x)
print "Nsig : ", binner.binned_stats(opt.x)[0]
print "Nbkg : ", binner.binned_stats(opt.x)[1]


* Free software: GNU General Public License v3
* Documentation: https://binopt.readthedocs.io.


Features
--------

* TODO

Credits
---------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2017-04-06)
------------------

* First release on PyPI.

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