Merge pull request #442 from landonbutler/main

ProxySPEX Approximator

Author: mmschlk

CHANGELOG.md

@@ -28,6 +28,9 @@ This restructuring aims to improve maintainability and development scalability.
 ### Bugfixes
 - fixes a bug where RegressionFBII approximator was throwing an error when the index was `'BV'` or `'FBII'`.[#420](https://github.com/mmschlk/shapiq/pull/420)
 
+### New Features
+- adds the ProxySPEX (Proxy Sparse Explanation) module in `approximator.sparse` for even more efficient computation of sparse interaction values [#442](https://github.com/mmschlk/shapiq/pull/442)
+
 ### Removed Features
 - removes the ability to load `InteractionValues` from pickle files. This is now deprecated and will be removed in the next release. Use `InteractionValues.save(..., as_json=True)` to save interaction values as JSON files instead. [#413](https://github.com/mmschlk/shapiq/issues/413)
 - removes `coalition_lookup` and `value_storage` properties from `shapiq.Game` since the seperated view on game values and coalitions they belong to is now outdated. Use the `shapiq.Game.game_values` dictionary instead. [#430](https://github.com/mmschlk/shapiq/pull/430)

README.md

@@ -159,24 +159,24 @@ fsii_values.plot_force()               # plot the force plot
   <img width="800px" src="https://raw.githubusercontent.com/mmschlk/shapiq/main/docs/source/_static/images/fsii_tabpfn_force_plot_example.png" alt="Force Plot of FSII values as derived from the example tabpfn notebook">
 </p>
 
-### Use SPEX (SParse EXplainer) <img src="https://raw.githubusercontent.com/mmschlk/shapiq/main/docs/source/_static/images/spex_logo.png" alt="spex_logo" align="right" height="75px"/>
-For large-scale use-cases you can also check out the [👓``SPEX``](https://shapiq.readthedocs.io/en/latest/api/shapiq.approximator.sparse.html#shapiq.approximator.sparse.SPEX) approximator.
+### Use ProxySPEX (Proxy SParse EXplainer) <img src="https://raw.githubusercontent.com/mmschlk/shapiq/main/docs/source/_static/images/spex_logo.png" alt="spex_logo" align="right" height="75px"/>
+For large-scale use-cases you can also check out the [👓``ProxySPEX``](https://shapiq.readthedocs.io/en/latest/api/shapiq.approximator.sparse.html#shapiq.approximator.sparse.SPEX) approximator.
 
 ```python
 # load your data and model with large number of features
 data, model, n_features = ...
 
-# use the SPEX approximator directly
-approximator = shapiq.SPEX(n=n_features, index="FBII", max_order=2)
+# use the ProxySPEX approximator directly
+approximator = shapiq.ProxySPEX(n=n_features, index="FBII", max_order=2)
 fbii_scores = approximator.approximate(budget=2000, game=model.predict)
 
-# or use SPEX with an explainer
+# or use ProxySPEX with an explainer
 explainer = shapiq.Explainer(
     model=model,
     data=data,
     index="FBII",
     max_order=2,
-    approximator="spex"  # specify SPEX as approximator
+    approximator="proxyspex"  # specify ProxySPEX as approximator
 )
 explanation = explainer.explain(data[0])
 ```