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Release Notes: APLR Version 10.13.0

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@mathias-von-ottenbreit mathias-von-ottenbreit released this 21 Sep 19:24
· 8 commits to main since this release
10.13.0
0284683

This release introduces a major new feature for model interpretation through visualization, along with several documentation improvements and a key enhancement for the classification module.

New Features

  • Visualizing Model Components: A new method, plot_affiliation_shape, has been added to the APLRRegressor class. This allows for easy one-line plotting of main effects (as line plots) and two-way interactions (as heatmaps) directly from a fitted model object. This greatly simplifies model interpretation and debugging.

  • Optional Plotting Dependencies: To support the new plotting feature, pandas and matplotlib can now be installed as optional dependencies using pip install aplr[plots]. The core library remains lightweight for users who do not require plotting.

Improvements

  • Classification Model Interpretation: The get_logit_model method has been improved to return a full-featured Python APLRRegressor object. This enables the use of Python-specific methods, making it straightforward to visualize model components for each category by calling the new plot_affiliation_shape method on the returned logit model.

  • Example Scripts: All example scripts have been updated to use the new plot_affiliation_shape method, making them cleaner and demonstrating the current best practices for model interpretation.

Documentation

  • The API Reference for APLRRegressor has been updated with detailed documentation for the new plot_affiliation_shape method.
  • The main README.md now includes instructions for installing the optional plotting dependencies.
  • The Model Interpretation guides for both regression and classification have been significantly improved to reflect the new, simplified plotting workflow and to provide clearer instructions.
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