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HiPlot - High dimensional Interactive Plotting CircleCI

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License: MIT PyPI download month PyPI version docs Open In Colab

HiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.

Try a demo now with sweep data or upload your CSV or Open In Colab

There are several modes to HiPlot:

  • As a web-server (if your data is a CSV for instance)
  • In a jupyter notebook (to visualize python data), or in Streamlit apps
  • In CLI to render standalone HTML
pip install -U hiplot  # Or for conda users: conda install -c conda-forge hiplot

If you have a jupyter notebook, you can get started with something as simple as:

import hiplot as hip
data = [{'dropout':0.1, 'lr': 0.001, 'loss': 10.0, 'optimizer': 'SGD'},
        {'dropout':0.15, 'lr': 0.01, 'loss': 3.5, 'optimizer': 'Adam'},
        {'dropout':0.3, 'lr': 0.1, 'loss': 4.5, 'optimizer': 'Adam'}]
hip.Experiment.from_iterable(data).display()

See the live result

Result

Links

Citing

@misc{hiplot,
    author = {Haziza, D. and Rapin, J. and Synnaeve, G.},
    title = {{Hiplot, interactive high-dimensionality plots}},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/facebookresearch/hiplot}},
}

Credits

Inspired by and based on code from Kai Chang, Mike Bostock and Jason Davies.

External contributors (please add your name when you submit your first pull request):

License

HiPlot is MIT licensed, as found in the LICENSE file.