- started work on 188 which overhauls the existing
shapiq
tutorial notebooks and fixes 199 - added Exact Core computation closing #182
- refactored the
shapiq.games.benchmark
module into a separateshapiq.benchmark
module by moving all but the benchmark games into the new modul. This closes #169 and makes benchmarking more flexible and convenient. - add waterfall plot as described in #34
- add a legend to benchmark plots #170
- fix the force plot not showing and its baseline value
- improve tests for plots and benchmarks
- renames explanation graph to
si_graph
get_n_order
now has optional lower/upper limits for the order- computing metrics now tries to resolve not-matching interaction indices and will throw a warning instead of a ValueError #179
- ...
- add
max_order=1
toTabularExplainer
andTreeExplainer
- fix
TreeExplainer.explain_X(..., n_jobs=2, random_state=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 aTabularExplainer
andTreeExplainer
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