v1.0.1 (2024-06-05)

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

shapiq 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

Update v.0.0.6-alpha

Highlights

The highlights of this release are as follows.

Random Forest Support for TreeExplainer

We add support for RandomForestClassifier and RandomForestRegressor as provided by sklearn.ensemble in the TreeExplainer. This is discussed in #55.

TreeExplainer Bugfix

We fix a package-breaking bug that made it impossible to use the TreeExplainer class.

Additional Unittests

We add additional unittests that fully covers the TreeExplainer in its current form.

What's Changed

• Bugfix and Support for Random Forests in TreeExplainer by @mmschlk in #59

Full Changelog: v.0.0.5-alpha...v.0.0.6-alpha

Update v.0.0.5-alpha

Highlights

Since the last release already is some time back, lot's has changed. The highlights are as follows:

TreeExplainer based on TreeSHAP-IQ

shapiq now is equipped with a TreeExplainer. The TreeExplainer is based on the TreeSHAP-IQ algorithm proposed in this paper. It is a model-specific method to compute Shapley interactions of all kinds and any-order. Since it is based on the linear TreeSHAP algorithm it is quite fast. The work is not yet finished on the TreeExplainer, since it only accepts very basic tree models from sklearn as input. More to follow on this front.

The first visualizations are added.

We added a new plot to the visualizations: the stacked bar chart (underwhelming name). This plot is illustrated in the TreeSHAP-IQ paper and Sebastian Bordt and Ulrike von Luxburg's AISTATS paper. The stacked bar charts shows how much interaction is present in a specific instance and is based on the k-SII values.

Major Refactoring

The codebase has changed quite drastically and many objects have been renamed (e.g. InteractionExplainer has been renamed to Tabular Explainer to better fit the more specific intend of this class). The InteractionValues (the main data structure of the package) has become much more powerful. You can now multiply scalar values to the interaction scores and even add two objects together.

Welcoming new Collaborators

We still are looking for all the help that we can get! If you want to contribute please check out the tutorial and our project.

What's Changed

• Add nSII and SII Regression Estimator by @mmschlk in #22
• Merge Dev by @mmschlk in #23
• Add initial explainer. by @mmschlk in #25
• Development by @mmschlk in #27
• Merge by @mmschlk in #28
• Add additional Tests by @mmschlk in #29
• Adds Stacked Bar Plot and tidyies up Network Plot. by @mmschlk in #41
• Renamed nSII to k-SII and refactors base approximators by @mmschlk in #42
• Add Collaborator Tutorial and Code of Conduct by @mmschlk in #46
• adds code-quality check and closes #45 by @mmschlk in #47
• adds TreeExplainer with TreeSHAP-IQ by @mmschlk in #51

Full Changelog: v.0.0.4-alpha...v.0.0.5-alpha

Update v.0.0.4-alpha

New Approximators

This release adds new approximation methods to shapiq and makes all interaction calculations faster and more memory efficient.

ShapIQ approximator

This release adds the shapiq.approximator.ShapIQ approximator as proposed in this paper (NeurIPS'23).
ShapIQ can approximate any cardinal interaction index (CII) like the Shapley Interaction Index (SII), the Shapley Taylor Index (STI), or the Faithful Shapley Interaction index (FSI).

Regression Estimator for FSI

The shapiq.approximator.RegressionFSI regression estimator, which is only available for FSI, was proposed in this paper (JMLR'23). It is similar to KernelSHAP in that it leverages a weighted least squares representation for the interaction index and solves this by estimating this regression problem.

Permutation Sampling for STI

The permutation sampling currently implemented for the SII (shapiq.approximator.PermutationSamplingSII) can also be extended to the STI. The new shapiq.approximator.PermutationSamplingSTI uses the traditional permutation sampling approach to compute STI scores.

List of PRs

• Add FSI and STI approximation methods by @mmschlk in #7
• Add SHAP-IQ approximator by @mmschlk in #10
• Tests and Efficiency by @mmschlk in #18
• Updates docs. by @mmschlk in #20

Full Changelog: v0.0.3-alpha...v.0.0.4-alpha

Update v.0.0.3-alpha

What's Changed

• Development by @mmschlk in #6
• Development by @FFmgll in 7e5bc06

New Contributors

• @mmschlk made their first contribution in #6
• @FFmgll made their first contribution in 7e5bc06

Full Changelog: v0.0.2-alpha...v0.0.3-alpha

0.0.2

Adds network plot functionality and updates docs. This release is mainly used to test the workflows and publish of docs and automatic update to pypi.