===============
zedstat
===============
.. image:: https://zed.uchicago.edu/logo/logo_zedstat.png
:height: 150px
:align: center
.. image:: https://zenodo.org/badge/529991779.svg
:target: https://zenodo.org/badge/latestdoi/529991779
.. class:: no-web no-pdf
:Author: ZeD@UChicago <zed.uchicago.edu>
:Description: Tools for ML statistics
:Documentation: https://zeroknowledgediscovery.github.io/zedstat/
:Example: https://github.com/zeroknowledgediscovery/zedstat/blob/master/examples/example1.ipynb
**Usage:**
.. code-block::
from zedstat import zedstat
zt=zedstat.processRoc(df=pd.read_csv('roc.csv'),
order=3,
total_samples=100000,
positive_samples=100,
alpha=0.01,
prevalence=.002)
zt.smooth(STEP=0.001)
zt.allmeasures(interpolate=True)
zt.usample(precision=3)
zt.getBounds()
print(zt.auc())
# find the high precision and high sensitivity operating points
zt.operating_zone(LRminus=.65)
rf0,txt0=zt.interpret(fpr=zt._operating_zone.fpr.values[0],number_of_positives=10)
rf1,txt1=zt.interpret(fpr=zt._operating_zone.fpr.values[1],number_of_positives=10)
display(zt._operating_zone)
print('high precision operation:\n','\n '.join(txt0))
print('high recall operation:\n','\n '.join(txt1))
Raw data
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