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
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()
- Documentation: https://facebookresearch.github.io/hiplot/index.html
- Pypi package: https://pypi.org/project/hiplot/
- NPM package: https://www.npmjs.com/package/hiplot
Inspired by and based on code from Kai Chang, Mike Bostock and Jason Davies.