HyperTools: A Python toolbox for gaining insights into high-dimensional data (opens in new tab)

Quote of the day from HyperTools README https://github.com/ContextLab/hypertools

"To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say "fourteen" very loudly. Everyone does it." -- Geoff Hinton

Looks neat and wonderful. It’s always impossible to compress all structure from high dimensions to 2D or 3D via something like PCA or t-SNE, and the focus on “geometric” insight is also encouraging. Cool/appropriate name too, nice choice of well-supported dependencies, and minimalist design are all appealing. This could become a go-to toolkit for early stage exploration, while it’s also pretty and smart enough to wow a coworker / investor. Hope it continues to really develop conceptually and practically!

The first example

http://hypertools.readthedocs.io/en/latest/auto_examples/plo...

seems a little disappointing -- it seems to exemplify more than solve the problems of representing 3D data on a 2D screen. (Is it interactively rotatable or something? I did run it locally in a notebook and it didn't appear to be.)

EdX has a good course on this (tho' taught in R) https://www.edx.org/course/high-dimensional-data-analysis-ha...

I can't see anything in the tutorials or examples that makes this a good library for visualisation of high-dimensional data. It seems somewhat equivalent to base graphics in R, which is much less useful for visualising high-dimensional data than anything based on Wilkinson's grammar of graphics, like ggplot2 in R.

Would this be useful for word vectors/embeddings?