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CHANGELOG.md

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Changelog

v1.1.1 (2024-11-11)

Improvements and Ease of Use

  • adds a class_index parameter to TabularExplainer and Explainer to specify the class index to be explained for classification models #271 (renames class_label parameter in TreeExplainer to class_index)
  • adds support for PyTorch models to Explainer #272

Bug Fixes

  • fixes a bug that RandomForestClassifier models were not working with the TreeExplainer #273

v1.1.0 (2024-11-07)

New Features and Improvements

  • adds computation of the Egalitarian Core (EC) and Egalitarian Least-Core (ELC) to the ExactComputer #182
  • adds waterfall_plot #34 that visualizes the contributions of features to the model prediction
  • adds BaselineImputer #107 which is now responsible for handling the sample_replacements parameter. Added a DeprecationWarning for the parameter in MarginalImputer, which will be removed in the next release.
  • adds joint_marginal_distribution parameter to MarginalImputer with default value True #261
  • renames explanation graph to si_graph
  • get_n_order now has optional lower/upper limits for the order
  • computing metrics for benchmarking now tries to resolve not-matching interaction indices and will throw a warning instead of a ValueError #179
  • add a legend to benchmark plots #170
  • refactored the shapiq.games.benchmark module into a separate shapiq.benchmark module by moving all but the benchmark games into the new module. This closes #169 and makes benchmarking more flexible and convenient.
  • a shapiq.Game can now be called more intuitively with coalitions data types (tuples of int or str) and also allows to add player_names to the game at initialization #183
  • improve tests across the package

Documentation

  • adds a notebook showing how to use custom tree models with the TreeExplainer #66
  • adds a notebook show how to use the shapiq.Game API to create custom games #184
  • adds a notebook showing hot to visualize interactions #252
  • adds a notebook showing how to compute Shapley values with shapiq #193
  • adds a notebook for conducting data valuation #190
  • adds a notebook showcasing introducing the Core and how to compute it with shapiq #191

Bug Fixes

  • fixes a bug with SIs not adding up to the model prediction because of wrong values in the empty set #264
  • fixes a bug that TreeExplainer did not have the correct baseline_value when using XGBoost models #250
  • fixes the force plot not showing and its baseline value

v1.0.1 (2024-06-05)

  • add max_order=1 to TabularExplainer and TreeExplainer
  • fix TreeExplainer.explain_X(..., n_jobs=2, random_state=0)

v1.0.0 (2024-06-04)

Major release of the shapiq Python package including (among others):

  • approximator module implements over 10 approximators of Shapley values and interaction indices.
  • exact module implements a computer for over 10 game theoretic concepts like interaction indices or generalized values.
  • games module implements over 10 application benchmarks for the approximators.
  • explainer module includes a TabularExplainer and TreeExplainer for any-order feature interactions of machine learning model predictions.
  • interaction_values module implements a data class to store and analyze interaction values.
  • plot module allows visualizing interaction values.
  • datasets module loads datasets for testing and examples.

Documentation of shapiq with tutorials and API reference is available at https://shapiq.readthedocs.io