scikit-discovery

Scikit Discovery

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 for integration of a variety of scientific data sets

Scikit Discovery Overview

Install

pip install scikit-discovery

Documentation

See https://github.com/MITHaystack/scikit-discovery/tree/master/skdiscovery/docs

Contributors

Project lead: Victor Pankratius (MIT)
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

Download files

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Source Distribution

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