summary

The goal of pyks is to help do calculation KS statistic for a model. The R version rawKS is hosted from GitHub.

You can install the released version of pyks from Anaconda Cloud with:

conda install -c jiaxiangbu pyks 

or the released version of pyks from Python Package Index with:

pip install pyks

or the development version from GitHub with:

pip install git+https://github.com/JiaxiangBU/pyks

If you use pyks, I would be very grateful if you can add a citation in your published work. By citing pyks, beyond acknowledging the work, you contribute to make it more visible and guarantee its growing and sustainability. For citation, please use the BibTex or the citation content.

@misc{jiaxiang_li_2019_3351276,
  author       = {Jiaxiang Li},
  title        = {JiaxiangBU/pyks: pyks 1.1.3},
  month        = jul,
  year         = 2019,
  doi          = {10.5281/zenodo.3351276},
  url          = {https://doi.org/10.5281/zenodo.3351276}
}

Jiaxiang Li. (2019, July 25). JiaxiangBU/pyks: pyks 1.1.3 (Version v1.1.3). Zenodo. http://doi.org/10.5281/zenodo.3351276

import pandas as pd
import numpy as np

df1 = pd.read_csv('refs/two_class_example.csv')

from pyKS.ks import perf

perf(df1).chart()

0.727689153693382

The function `plot` is depreciated, use `perf.plot()`



<Figure size 432x288 with 0 Axes>

perf(df1).table()

The function `summary` is depreciated, use `perf.table()`

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