- add `max_order=1` to `TabularExplainer` and `TreeExplainer` - fix `TreeExplainer.explain_X(..., n_jobs=2, random_state=0)`
1.0.0
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