Interrater agreement Phi, as an alternative to Kripperndorfs alpha, as described in https://github.com/AlessandroChecco/agreementphi
Project description
Agreement measure Phi
Source code for interrater agreement measure Phi. Live demo here: http://agreementmeasure.sheffield.ac.uk
Requirements
python 3+, pymc3 3.3+. See requirements files for tested working versions on linux and osx.
Installation  with pip
Simply run pip install agreement_phi
.
This will provide a module and a command line executable called run_phi
.
Installation  without pip
Download the folder.
Example  from command line
Prepare a csv file (no header, each row is a document, each column a rater), leaving empty the missing values. For example input.csv
:
1,2,,3
1,1,2,
4,3,2,1
And execute from the console run_phi file input.csv limits 1 4
.
More details obtained running run_phi h
:
usage: agreement_phi.py [h] f FILE [v] [l val val]
Phi Agreement Measure
optional arguments:
h, help show this help message and exit
f FILE, file FILE input FILE <REQUIRED>
v, verbose print verbose messages
l val val, limits val val Set limits <RECOMMENDED> (two values separated by a space)
Example  from python
Input is a numpy 2dimensional array with NaN for missing values, or equivalently a python list of lists (where each list is a set of ratings for a document, of the same length with nan padding as needed). Every row represents a different document, every column a different rating. Note that Phi does not take in account rater bias, so the order in which ratings appear for each document does not matter. For this reasons, missing values and a sparse representation is needed only when documents have different number of ratings.
Input example
import numpy as np
m_random = np.random.randint(5, size=(5, 10)).tolist()
m_random[0][1]=np.nan
or equivalently
m_random = np.random.randint(5, size=(5, 10)).astype(float)
m_random[0][1]=np.nan
Running the measure inference
from agreement_phi import run_phi
run_phi(data=m_random,limits=[0,4],keep_missing=True,fast=True,njobs=4,verbose=False,table=False,N=500)
data
[non optional] is the matrix or list of lists of input (all lists of the same length with nan padding if needed).
OPTIONAL PARAMETERS:
limits
defines the scale [automatically inferred by default]. It's a list with the minimum and maximum (included) of the scale.keep_missing
[automatically inferred by default based on number of NaNs] boolean. If you have many NaNs you might want to switch to False,fast
[default True] boolean. Whether to use or not the fast inferential technique.N
[default 1000] integer. Number of iterations. Increase it ifconvergence_test
is False.verbose
[default False] boolean. If True it shows more informationtable
[default False] boolean. If True more verbose output in form of a table.njobs
[default 1] integer. Number of parallel jobs. Set it equal to the number of CPUs available.binning
[default True] boolean. If False consider the values in the boundary of scale non binned: this is useful when using a discrete scale and the value in the boundaries should be considered adhering to the limits and not in the center of the corresponding bin. This is useful when the value of the boundaries have a strong meaning (for example [absolutely not, a bit, medium, totally]) where answering in the boundary of the scale is not in a bin as close as the second step in the scale.
Note that the code will try to infer the limits of the scale, but it's highly suggested to include them (in case some elements on the boundary are missing). For this example the parameter limits would be limits=[0,4]
.
Note that keep_missing
will be automatically inferred, but for highly inbalanced datasets (per document number of ratings distribution) it can be overriden by manually setting this option.
Output example
{'agreement': 0.023088447111559884, 'computation_time': 58.108173847198486, 'convergence_test': True, 'interval': array([0.03132854, 0.06889001])}
Where 'interval' represents the 95% Highest Posterior Density interval. If convergence_test is False we recommend to increase N.
References
If you use it for academic publications, please cite out paper:
Checco, A., Roitero, A., Maddalena, E., Mizzaro, S., & Demartini, G. (2017). Let’s Agree to Disagree: Fixing Agreement Measures for Crowdsourcing. In Proceedings of the Fifth AAAI Conference on Human Computation and Crowdsourcing (HCOMP17) (pp. 1120). AAAI Press.
@inproceedings{checco2017let,
title={Let’s Agree to Disagree: Fixing Agreement Measures for Crowdsourcing},
author={Checco, A and Roitero, A and Maddalena, E and Mizzaro, S and Demartini, G},
booktitle={Proceedings of the Fifth AAAI Conference on Human Computation and Crowdsourcing (HCOMP17)},
pages={1120},
year={2017},
organization={AAAI Press}
}
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