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.

There are 2 modes to HiPlot:

• As a web-server (if your data is a CSV for instance)
• In a jupyter notebook (to visualize python data)

pip install 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()

• Blog post: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/
• Documentation: https://facebookresearch.github.io/hiplot/index.html
• Pypi package: https://pypi.org/project/hiplot/
• Conda package: https://anaconda.org/conda-forge/hiplot
• NPM package: https://www.npmjs.com/package/hiplot

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

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