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Everyone does it._\" - Geoff Hinton\n\n\n![Hypertools example](images/hypertools.gif)\n\n## Overview\n\nHyperTools is designed to facilitate\n[dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction)-based\nvisual explorations of high-dimensional data.  The basic pipeline is\nto feed in a high-dimensional dataset (or a series of high-dimensional\ndatasets) and, in a single function call, reduce the dimensionality of\nthe dataset(s) and create a plot.  The package is built atop many\nfamiliar friends, including [matplotlib](https://matplotlib.org/),\n[scikit-learn](http://scikit-learn.org/) and\n[seaborn](https://seaborn.pydata.org/).  Our package was recently\nfeatured on\n[Kaggle's No Free Hunch blog](http://blog.kaggle.com/2017/04/10/exploring-the-structure-of-high-dimensional-data-with-hypertools-in-kaggle-kernels/).  For a general overview, you may find [this talk](https://www.youtube.com/watch?v=hb_ER9RGtOM) useful (given as part of the [MIND Summer School](https://summer-mind.github.io) at Dartmouth).\n\n## Try it!\n\nClick the badge to launch a binder instance with example uses:\n\n[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org:/repo/contextlab/hypertools-paper-notebooks)\n\nor\n\nCheck the [repo](https://github.com/ContextLab/hypertools-paper-notebooks) of Jupyter notebooks from the HyperTools [paper](https://arxiv.org/abs/1701.08290).\n\n## Installation\n\nTo install the latest stable version run:\n\n`pip install hypertools`\n\nTo install the latest unstable version directly from GitHub, run:\n\n`pip install -U git+https://github.com/ContextLab/hypertools.git`\n\nOr alternatively, clone the repository to your local machine:\n\n`git clone https://github.com/ContextLab/hypertools.git`\n\nThen, navigate to the folder and type:\n\n`pip install -e .`\n\n(These instructions assume that you have [pip](https://pip.pypa.io/en/stable/installing/) installed on your system)\n\nNOTE: If you have been using the development version of 0.5.0, please clear your\ndata cache (/Users/yourusername/hypertools_data).\n\n## Requirements\n\n+ python\u003e=3.6\n+ PPCA\u003e=0.0.2\n+ scikit-learn\u003e=0.24.0\n+ pandas\u003e=0.18.0\n+ seaborn\u003e=0.8.1\n+ matplotlib\u003e=1.5.1\n+ scipy\u003e=1.0.0\n+ numpy\u003e=1.10.4\n+ umap-learn\u003e=0.4.6\n+ requests\n+ pytest (for development)\n+ ffmpeg (for saving animations)\n\n## Documentation\n\nCheck out our [readthedocs](http://hypertools.readthedocs.io/en/latest/) page for further documentation, complete API details, and additional examples.\n\n## Citing\n\nWe wrote a short JMLR paper about HyperTools, which you can read [here](http://jmlr.org/papers/v18/17-434.html), or you can check out a (longer) preprint [here](https://arxiv.org/abs/1701.08290). We also have a repository with example notebooks from the paper [here](https://github.com/ContextLab/hypertools-paper-notebooks).\n\nPlease cite as:\n\n`Heusser AC, Ziman K, Owen LLW, Manning JR (2018) HyperTools: A Python toolbox for gaining geometric insights into high-dimensional data.  Journal of Machine Learning Research, 18(152): 1--6.`\n\nHere is a bibtex formatted reference:\n\n```bibtex\n@ARTICLE {,\n    author  = {Andrew C. Heusser and Kirsten Ziman and Lucy L. W. Owen and Jeremy R. Manning},    \n    title   = {HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data},    \n    journal = {Journal of Machine Learning Research},\n    year    = {2018},\n    volume  = {18},\t\n    number  = {152},\t\n    pages   = {1-6},\t\n    url     = {http://jmlr.org/papers/v18/17-434.html}\t\n}\n```\n\n## Contributing\n\n[![Join the chat at https://gitter.im/hypertools/Lobby](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/hypertools/Lobby)\n\nIf you'd like to contribute, please first read our [Code of Conduct](https://www.mozilla.org/en-US/about/governance/policies/participation/).\n\nFor specific information on how to contribute to the project, please see our [Contributing](https://github.com/ContextLab/hypertools/blob/master/CONTRIBUTING.md) page.\n## Testing\n\n[![Build Status](https://travis-ci.org/ContextLab/hypertools.svg?branch=master)](https://travis-ci.org/ContextLab/hypertools)\n\n\nTo test HyperTools, install pytest (`pip install pytest`) and run `pytest` in the HyperTools folder\n\n## Examples\n\nSee [here](http://hypertools.readthedocs.io/en/latest/auto_examples/index.html) for more examples.\n\n## Plot\n\n```python\nimport hypertools as hyp\nhyp.plot(list_of_arrays, '.', group=list_of_labels)\n```\n\n![Plot example](images/plot.gif)\n\n## Align\n\n```python\nimport hypertools as hyp\nhyp.plot(list_of_arrays, align='hyper')\n```\n\n### BEFORE\n\n![Align before example](images/align_before.gif)\n\n### AFTER\u003c/center\u003e\n\n![Align after example](images/align_after.gif)\n\n\n## Cluster\n\n```python\nimport hypertools as hyp\nhyp.plot(array, '.', n_clusters=10)\n```\n\n![Cluster Example](images/cluster_example.png)\n\n\n## Describe\n\n```python\nimport hypertools as hyp\nhyp.tools.describe(list_of_arrays, reduce='PCA', max_dims=14)\n```\n![Describe Example](images/describe_example.png)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcontextlab%2Fhypertools","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcontextlab%2Fhypertools","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcontextlab%2Fhypertools/lists"}