Skip to main content

High dimensional Interactive Plotting tool

Project description

HiPlot - High dimensional Interactive Plotting CircleCI

Logo

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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hiplot-0.1.32.tar.gz (847.6 kB view hashes)

Uploaded source

Built Distribution

hiplot-0.1.32-py3-none-any.whl (862.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page