PermutationImportance 1.2.1.7

Important variables determined through data-based variable importance methods

PermutationImportance

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

• 1.2.1.7: Fixed a bug where pandas dataframes were being unshuffled when concatenated
• 1.2.1.5: Added documentation and examples and ensured compatibility with Python 3.5+
• 1.2.1.4: Original scores are now also bootstrapped to match the other results
• 1.2.1.3: Corrected an issue with multithreading deadlock when returned scores were too large
• 1.2.1.1: Provided object to assist in constructing scoring strategies
  • Also added two new strategies with bootstrapping support
• 1.2.1.0: Metrics can now accept kwargs and support bootstrapping
• 1.2.0.0: 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
• 1.1.0.0: Revised return object of Permutation Importance to support easy retrieval of Breiman- and Lakshmanan-style importances
• 1.0.0.0: Published with pip support!