An LDA wrapper for explaining a blackbox classifier's predictions, as done in Oved, N., Feder, A. and Reichart, R. (2020) and presented in EMNLP 2020's blackbox workshop.
Currently supports only binary predictors.
The module was developed for domain-ruled data (see demo below), although data without domains is supported as well (see API below).
Get the latest version using git (recommended):
pip install git+https://github.com/EllRos/LDA-Explanation.git
In order to avoid git, get the latest wheel build (might not be updated, but should be):
- Download https://github.com/EllRos/LDA-Explanation/blob/main/dist/LDA_Explanation-0.0.1-py3-none-any.whl
- run
pip install LDA_Explanation-0.0.1-py3-none-any.whl
from the download directory.
Note: While typically wanting to just run pip install https://github.com/EllRos/LDA-Explanation/blob/main/dist/LDA_Explanation-0.0.1-py3-none-any.whl
,
this might cause a strange BadZipFile
error (even with pip cache disabled).
Installation requiers (and includes) the installation of the following libraries (of any version):
- NumPy
- Pandas
- Matplotlib
- Gensim
Also requires Python version of 3.6 and above.
API documentation: https://ellros.github.io/LDA-Explanation-docs/
Functionality and usage demonstration: https://ellros.github.io/LDA-Explanation/docs/demo/demo.html