New ML tool HiPlot offers interactive high-dimensional data visualization

In a blog post, Facebook AI announced its newly open sourced tool HiPlot for data visualization tasks. It is designed to be lightweight and should help machine learning researchers discover patterns and correlations in their high-dimensional data.

As the blog states, HiPlot is being used at Facebook AI “to explore and efficiently analyze hyperparameter tuning of deep neural networks with dozens of hyperparameters.”

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HiPlot can be used either with Jupyter Notebook for Python data or as a webserver when data is stored in a CSV file. It is available on PyPI and can be installed via pip install hiplot, or as npm package.

Interactivity, extendability and more

HiPlot is designed to offer some advantages compared to similiar visualization tools, one of which is interactivity. This is made possible by displaying parallel plots, which allow researchers to customize the visualization as needed.

For example, parallel plots let you reorder or remove axes or extract a certain data selection. See the interactive demo to try this for yourself.

While HiPlot’s web server can parse CSV or JSON files, a custom Python parser can be used as well. In addition, HiPlot is compatible with the logs of other Facebook AI libraries including wav2letter@anywhere, Nevergrad and fairseq.

Furthermore, HiPlot provides visualization for population-based training, a genetic algorithm.

Sample visualization

On GitHub, there is an example for using HiPlot with Jupyter Notebook. The following input creates the HiPlot visualization as seen below:

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()

Source.

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The Facebook AI blog and the GitHub repository provide further details on HiPlot.

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