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Fisher Scoring v2.0.2

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@xRiskLab xRiskLab released this 26 Oct 14:44
· 9 commits to main since this release
v2.0.2
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Fisher Scoring v2.0.2

Changelog

Overview

The Fisher Scoring package has undergone significant performance improvements and feature updates from v2.0 onward, enhancing matrix calculation efficiency, model speed, and usability across all regression types.

New in v2.0.2

  • FisherScoringMultinomialRegression: Enhanced flexibility by removing fixed data types for numpy arrays, improving compatibility with diverse datasets.
  • Code Optimization: Refined matrix operations to maintain peak performance across Fisher Scoring modules.
  • Documentation & Image Display: Adjusted README formatting and hosted images to ensure accurate display on PyPI, enhancing readability and visual guidance.

Summary of Major Changes Since v2.0

Performance Improvements

With optimized matrix calculations, all models now exhibit faster training times and reduced memory usage. The streamlined operations provide up to 290x speed improvements:

  • Multinomial Logistic Regression: Reduced training time from 125.10s to 0.43s (~290x speedup).
  • Logistic Regression: Reduced training time from 0.24s to 0.05s (~5x speedup).
  • Focal Loss Logistic Regression: Reduced training time from 0.26s to 0.01s (~26x speedup).

Fixes and Usability Enhancements

  • Verbose Parameter for Focal Loss: Fixed the verbose logging for FisherScoringFocalRegression, ensuring informative output during model training.
  • Improved Documentation: Expanded README to provide clear installation instructions, feature descriptions, and example usage for all models, enhancing user experience.
  • External Image Display on PyPI: Ensured hosted images display properly in the README for a polished PyPI appearance.
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