Source code of the paper $\beta^3$-IRT: A New Item Response Model and its Applications
The source code was originally developed on:
- Python 2.7.12
- Tensorflow 1.2.0
- Edward 1.3.4
It was also tested on:
- Python 3.6.6
- Tensorflow 1.10.0
- Edward 1.3.5
which requires manually fixing the compatible issues of Tensorflow (> 1.2.0) in Edward and install Edward from the source.
There are two steps to run experiments:
-
Train classifiers and generate response data for the
$\beta^3$ IRT model, for example, run the following command:python gen_irt_data.py --dataset moons --data_size 400 --noise_fraction 0.2 --seed 42
-
The first step will automatically generate data files for the second step, and the file named with "irt_data_*.csv" is the input parameter of the command to run
$\beta^3$ IRT model, i.e.:python betairt_test.py --IRT_dfile irt_data_moons_s400_f20_sd42_m12.csv --a_prior_mean 1. --a_prior_std 1.
Biblatex entry:
@inproceedings{chen2019beta,
title={$\beta^3$-IRT: A New Item Response Model and its Applications},
author={Chen, Yu and Filho, Telmo Silva and Prud{\^e}ncio, Ricardo BC and Diethe, Tom and Flach, Peter},
booktitle={Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) },
year={2019}
}