The Contribution-Value plot is a visual encoding for interpreting machine learning models. [more information]
To install use pip:
$ pip install cvplot
If you use jupyter lab, also run:
$ jupyter labextension install cvplot
for classic jupyter notebooks, run:
jupyter nbextension install --py --symlink --overwrite --sys-prefix cvplot
jupyter nbextension enable --py --sys-prefix cvplot
For a development installation (requires npm or yarn),
$ git clone https://github.com/iamDecode/cvplot.git
$ cd cvplot
You may want to (create and) activate a virtual environment before continuing with:
$ pip install -e .
$ jupyter labextension install js
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix cvplot
$ jupyter nbextension enable --py --sys-prefix cvplot
When actively developing your extension, build Jupyter Lab with the command:
$ jupyter lab --watch
This takes a minute or so to get started, but then automatically rebuilds JupyterLab when your javascript changes.
If you want to refer to our visualization, please cite our paper using the following BibTeX entry:
@article{collaris2021comparative, title={Comparative Evaluation of Contribution-Value Plots for Machine Learning Understanding}, author={Collaris, Dennis and van Wijk, Jarke J.}, journal={Journal of Visualization}, year={2021}, issn={1875-8975}, doi={10.1007/s12650-021-00776-w}, url={https://doi.org/10.1007/s12650-021-00776-w} }
This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.