summary
pyks¶
The goal of pyks
is to help do calculation KS statistic for a model.
The R version rawKS is hosted
from GitHub.
Installation¶
You can install the released version of pyks
from Anaconda
Cloud with:
conda
conda install -c jiaxiangbu pyks
or the released version of pyks
from Python Package
Index with:
conda
pip install pyks
or the development version from GitHub with:
conda
pip install git+https://github.com/JiaxiangBU/pyks
Citations¶
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
Disclaimers¶
**Code of Conduct**
Please note that the `pyks` project is released with a [Contributor Code
of Conduct](.github/CODE_OF_CONDUCT.md).
By contributing to this
project, you agree to abide by its terms.
**License**
MIT \u00a9 [Jiaxiang Li](LICENSE.md)
Examples¶
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()
perf(df1).table()