Statistics tools for ML models and deployment
Project description
- Author:
ZeD@UChicago <zed.uchicago.edu>
- Description:
Tools for ML statistics
- Documentation:
- 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))
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
zedstat-0.0.125.tar.gz
(79.9 kB
view hashes)
Built Distribution
zedstat-0.0.125-py3-none-any.whl
(168.5 kB
view hashes)
Close
Hashes for zedstat-0.0.125-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | de119c4570592892d19ae0631ee70890815e532ad37d47e867d178a4ebcf290f |
|
MD5 | 3e5e3c75c80217e260924e09b12a0067 |
|
BLAKE2b-256 | 58ee355997975a5f2287b711612b64d9ffe55a9be0e5d68dfbeeedc094dfb188 |