cvplot 0.0.2

Understand machine learning models with Contribution-Value plots

Contribution-Value plots

The Contribution-Value plot is a visual encoding for interpreting machine learning models. [more information]

Demo

Installation

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

Development

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.

Citation

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}
}

License

This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.