Metadata-Version: 2.1
Name: cvplot
Version: 0.0.2
Summary: Understand machine learning models with Contribution-Value plots
Home-page: https://github.com/iamDecode/cvplot
Author: Dennis Collaris
Author-email: d.collaris@me.com
License: UNKNOWN
Description: # <img src="https://explaining.ml/images/cvplots/logo.png" height="35"/> Contribution-Value plots
        
        The Contribution-Value plot is a visual encoding for interpreting machine learning models. [[more information]](https://explaining.ml/cvplots)
        
        
        ## Demo
        
        <img src="https://user-images.githubusercontent.com/1223300/132945671-1e7c0b64-fb63-46b3-ac5f-3fa19808242c.png" width="600"/>
        
        
        ## 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:
        
        ```bibtex
        @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](LICENSE) file for details.
        
Keywords: ipython,jupyter,widgets
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: IPython
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Multimedia :: Graphics
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
