https://github.com/mithaystack/scikit-discovery
Scikit Discovery: Python Toolkit for Computer-Aided Discovery
https://github.com/mithaystack/scikit-discovery
Last synced: 12 days ago
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Scikit Discovery: Python Toolkit for Computer-Aided Discovery
- Host: GitHub
- URL: https://github.com/mithaystack/scikit-discovery
- Owner: MITHaystack
- License: other
- Created: 2017-06-09T19:44:55.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-11-07T18:30:39.000Z (over 6 years ago)
- Last Synced: 2024-10-11T09:19:51.104Z (7 months ago)
- Language: Python
- Homepage:
- Size: 13.4 MB
- Stars: 16
- Watchers: 11
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
- Explore scientific data with a set of tools for human-guided or automated discovery
- Design & configure data processing pipelines
- Define the parameter ranges for your algorithms, available algorithmic choices, and the framework will generate pipeline instances for you
- Use automatically perturbed data processing pipelines to create different data products.
- Easy to use with [scikit-dataaccess](https://github.com/MITHaystack/scikit-dataaccess) for integration of a variety of scientific data sets
### Install
```
pip install scikit-discovery
```
### Documentation
See
### Contributors
Project lead: [Victor Pankratius (MIT)](http://www.victorpankratius.com)
Contributors: Cody M. Rude, Justin D. Li, David M. Blair, Michael G. Gowanlock, Evan Wojciechowski, Victor Pankratius
### Acknowledgements
We acknowledge support from NASA AIST14-NNX15AG84G, NASA AIST16-80NSSC17K0125, NSF ACI-1442997, NSF AGS-1343967, and Amazon AWS computing access support.
## Examples
Example code with complete science case studies are available as Jupyter Notebooks at:
[/MITHaystack/science-casestudies](https://github.com/MITHaystack/science-casestudies)