"To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say 'fourteen' very loudly. Everyone does it." - Geoff Hinton
HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. The package is built atop many familiar friends, including matplotlib, scikit-learn and seaborn. Our package was recently featured on Kaggle's No Free Hunch blog.
Click the badge to launch a binder instance with example uses:
or
Check the repo of Jupyter notebooks from the HyperTools paper.
pip install hypertools
or
To install from this repo:
git clone https://github.com/ContextLab/hypertools.git
Then, navigate to the folder and type:
pip install -e .
(this assumes you have pip installed on your system)
- python 2.7, 3.4+
- PPCA>=0.0.2
- scikit-learn>=0.18.1
- pandas>=0.18.0
- seaborn>=0.7.1
- matplotlib>=1.5.1
- scipy>=0.17.1
- numpy>=1.10.4
- future
- pytest (for development)
- ffmpeg (for saving animations)
If installing from github (instead of pip), you must also install the requirements:
pip install -r requirements.txt
Check out our readthedocs here.
We wrote a paper about HyperTools, which you can read here. We also have a repo with example notebooks from the paper here.
Please cite as:
Heusser AC, Ziman K, Owen LLW, Manning JR (2017) HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data. arXiv: 1701.08290
Here is a bibtex formatted reference:
@ARTICLE {,
author = "A C Heusser and K Ziman and L L W Owen and J R Manning",
title = "HyperTools: A Python toolbox for visualizing and manipulating high-dimensional data",
journal = "arXiv",
year = "2017",
volume = "1701",
number = "08290",
month = "jan"
}
If you'd like to contribute, please first read our Code of Conduct.
For specific information on how to contribute to the project, please see our Contributing page.
To test HyperTools, install pytest (pip install pytest
) and run pytest
in the HyperTools folder
See here for more examples.
import hypertools as hyp
hyp.plot(list_of_arrays, 'o', group=list_of_labels)
import hypertools as hyp
aligned_list = hyp.tools.align(list_of_arrays)
hyp.plot(aligned_list)
import hypertools as hyp
hyp.plot(array, 'o', n_clusters=10)
import hypertools as hyp
hyp.tools.describe_pca(list_of_arrays)