Computes Kendall's coefficient of concordance
Project description
kendall-w
Author: Ugo Loobuyck
Overview
Computes Kendall's coefficient of concordance for inter-annotator agreement in the case of item ranking.
Installation / Usage
To install use pip:
$ pip install kendall-w
Or clone the repo:
$ git clone https://github.com/ugolbck/kendall-w.git
$ python setup.py install
Example
from kendall_w import
annotations = [
[1, 1, 1, 2],
[2, 2, 2, 3],
[3, 3, 3, 1],
]
W = compute_w(annotations) # returns 0.4375 (fair overall agreement)
Contributions
All contributions are welcome.
TODO:
- Handle
pandas.DataFrame
as an input with the instructions in (https://github.com/ugolbck/kendall-w/blob/master/kendall_w/kendall_w.py) - Write unit tests for compute_w function
- Write code and unit tests for Kendall's tau function ?
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
kendall-w-0.0.2.tar.gz
(3.2 kB
view hashes)
Built Distribution
Close
Hashes for kendall_w-0.0.2-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d07f6c6a2c753d7c4cb6f144cd2d6f634846d1a503b54f75b72cae047468b2a5 |
|
MD5 | ff1a62ee38714c47ffe6f3922c60006e |
|
BLAKE2b-256 | b9344b7987859bd2dfaf2ce97b4a34919759afd6ee946c5d50d8bed9fc5a9ada |