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"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"
}

(Some text borrowed from the Matplotlib contributing guide.)

If you are reporting a bug, please do your best to include the following:

1. A short, top-level summary of the bug. In most cases, this should be 1-2 sentences.
2. A short, self-contained code snippet to reproduce the bug, ideally allowing a simple copy and paste to reproduce. Please do your best to reduce the code snippet to the minimum required.
3. The actual outcome of the code snippet
4. The expected outcome of the code snippet

The preferred way to contribute to HyperTools is to fork the main repository on GitHub, then submit a pull request.

• If your pull request addresses an issue, please use the title to describe the issue and mention the issue number in the pull request description to ensure a link is created to the original issue.

• All public methods should be documented in the README.

• Each high-level plotting function should have a simple example in the examples folder. This should be as simple as possible to demonstrate the method.

• Changes (both new features and bugfixes) should be tested using pytest. Add tests for your new feature to the tests/ repo folder.

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)