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Statistics tools for ML models and deployment

Project description

https://zed.uchicago.edu/logo/logo_zedstat.png https://zenodo.org/badge/529991779.svg
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:

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))

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