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Important variables determined through data-based variable importance methods

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Welcome to the PermutationImportance library!

PermutationImportance is a Python package for Python 2.7 and 3.6+ which provides several methods for computing data-based predictor importance. The methods implemented are model-agnostic and can be used for any machine learning model in many stages of development. The complete documentation can be found at our Read The Docs.

Version History

  • Shuffled pandas dataframes now retain the proper row indexing
  • Fixed a bug where pandas dataframes were being unshuffled when concatenated
  • Added documentation and examples and ensured compatibility with Python 3.5+
  • Original scores are now also bootstrapped to match the other results
  • Corrected an issue with multithreading deadlock when returned scores were too large
  • Provided object to assist in constructing scoring strategies
    • Also added two new strategies with bootstrapping support
  • Metrics can now accept kwargs and support bootstrapping
  • Added support for Sequential Selection and completely revised backend for proper abstraction and extension
    • Return object now keeps track of (context, result) pairs
    • abstract_variable_importance enables implementation of custom variable importance methods
    • Backend is now correctly multithreaded (when specified) and is OS-independent
  • Revised return object of Permutation Importance to support easy retrieval of Breiman- and Lakshmanan-style importances
  • Published with pip support!

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