quica 0.2.5

Description:

quica 0.2.5

Quick Inter Coder Agreement in Python
Quica (Quick Inter Coder Agreement in Python) is a tool to run inter coder agreement pipelines in an easy and effective ways.
Multiple measures are run and results are collected in a single table than can be easily exported in Latex.
quica supports binary or multiple coders.




Quick Inter Coder Agreement in Python

Free software: MIT license
Documentation: https://quica.readthedocs.io.


Installation
pip install -U quica


Get Quick Agreement
If you already have a python dataframe you can run Quica with few liens of code! Let’s assume you have two
coders; we will create a pandas dataframe just to show how to use the library. As for now, we support only integer values
and we still have not included weighting.
from quica.quica import Quica
import pandas as pd

coder_1 = [0, 1, 0, 1, 0, 1]
coder_3 = [0, 1, 0, 1, 0, 0]

dataframe = pd.DataFrame({"coder1" : coder_1,
"coder3" : coder_3})

quica = Quica(dataframe=dataframe)
print(quica.get_results())
This is the expected output:
Out[1]:
score
names
krippendorff 0.685714
fleiss 0.666667
scotts 0.657143
raw 0.833333
mace 0.426531
cohen 0.666667
It was pretty easy to get all the scores, right? What if we do not have a pandas dataframe? what if we want to directly get
the latex table to put into the paper? worry not, my friend: it’s easier done than said!
from quica.measures.irr import *
from quica.dataset.dataset import IRRDataset
from quica.quica import Quica

coder_1 = [0, 1, 0, 1, 0, 1]
coder_3 = [0, 1, 0, 1, 0, 0]

disagreeing_coders = [coder_1, coder_3]
disagreeing_dataset = IRRDataset(disagreeing_coders)

quica = Quica(disagreeing_dataset)

print(quica.get_results())
print(quica.get_latex())
you should get this in output, note that the latex table requires the booktabs package:
Out[1]:
score
names
krippendorff 0.685714
fleiss 0.666667
scotts 0.657143
raw 0.833333
mace 0.426531
cohen 0.666667

Out[2]:

\begin{tabular}{lr}
\toprule
{} & score \\
names & \\
\midrule
krippendorff & 0.685714 \\
fleiss & 0.666667 \\
scotts & 0.657143 \\
raw & 0.833333 \\
mace & 0.426531 \\
cohen & 0.666667 \\
\bottomrule
\end{tabular}


Features
from quica.measures.irr import *
from quica.dataset.dataset import IRRDataset
from quica.quica import Quica

coder_1 = [0, 1, 0, 1, 0, 1]
coder_2 = [0, 1, 0, 1, 0, 1]
coder_3 = [0, 1, 0, 1, 0, 0]

agreeing_coders = [coder_1, coder_2]
agreeing_dataset = IRRDataset(agreeing_coders)

disagreeing_coders = [coder_1, coder_3]
disagreeing_dataset = IRRDataset(disagreeing_coders)

kri = Krippendorff()
cohen = CohensK()

assert kri.compute_irr(agreeing_dataset) == 1
assert kri.compute_irr(agreeing_dataset) == 1
assert cohen.compute_irr(disagreeing_dataset) < 1
assert cohen.compute_irr(disagreeing_dataset) < 1


Supported Algorithms


MACE (Multi-Annotator Competence Estimation)

Hovy, D., Berg-Kirkpatrick, T., Vaswani, A., & Hovy, E. (2013, June). Learning whom to trust with MACE. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1120-1130).
We define the inter coder agreeement as the average competence of the users.




Krippendorff’s Alpha
Cohens’ K
Fleiss’ K
Scotts’ PI
Raw Agreement: Standard Accuracy



Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template. Thanks to Pietro Lesci and Dirk Hovy
for their implementation of MACE.



History

0.1.0 (2020-11-08)

New API to get the output
Fixed test cases
Extended documentation on the README file



0.1.0 (2020-11-05)

First release on PyPI.