Release notes

2.0.0 (2025-11-24)

  • Suspended support fro UARFF files

  • Significant improvement of the backbone decision tree generation mechanism by moving away from object-oriented structure and focusing purely on Python-optimized structures (dicts)

  • Parsing dataframe is up to 10 times faster (usually more like 5x)

  • Fitting model using SHAP values, but without oversampling is up to 4 times faster

  • Oversampling alone is up 30 times faster

  • Fixes in dependencies related to upgraded numpy and shap packages.

1.3.2 (2025-02-05)

  • Fixes in dependencies

  • Fixes in numpy float representation of dataframes read from UARFF

  • Added more examples to documentation

1.3.0 (2024-09-06)

  • Fixed catBoost problem and minor visualization bugs

  • Fixed class balancing code

  • Added labelling of phantom branches in the leaves

  • Fixed bug with additional columns added when parity==’local’

  • Improved SHAP-guided sampling

  • Bugfixes in importance samplers

  • Added medoid CF for categorical variables

  • Fixed invalid requirements in build file

  • gower replaced with gower-multiprocessing

  • Fixed visualization issue when no instance nor counterfactual is passed.

  • Fixed issue with categorical variables.

  • Fixed with gower installed without multiprocessing

  • Added balancing to BOTH sampling strategy, to overcome heavy imbalance generated by SHAP-sampler

1.2.0 (2024-04-30)

  • Fixes in SHAP guided sampler

  • Fixed in export to HMR

  • Added documentation section on visualization

1.1.0 (2024-03-08)

  • Changed modularization (internal, so it will not affect usage). Samplers were moved to separate module

1.0.3 (2024-03-01)

  • Fixes in tree visualization

  • Improved documentation

1.0.1 (2024-02-23)

  • Added pypl installation

  • Added sphinx documentation