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A package for Computer-Aided Discovery

Project description

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

Project details


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Files for scikit-discovery, version 0.9.18
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