Shapiq

- substantially improves the runtime of all `Regression` approximators by a) a faster pre-computation of the regression matrices and b) a faster computation of the weighted least squares regression [340](https://github.com/mmschlk/shapiq/issues/340)
- removes `sample_replacements` parameter from `MarginalImputer` and removes the DeprecationWarning for it
- adds a trivial computation to `TreeSHAP-IQ` for trees that use only one feature in the tree (this works for decision stumps or trees splitting on only one feature multiple times). In such trees, the computation is trivial as the whole effect of $\nu(N) - \nu(\emptyset)$ is all on the main effect of the single feature and there is no interaction effect. This expands on the fix in v1.2.1 [286](https://github.com/mmschlk/shapiq/issues/286).
- fixes a bug with xgboost where feature names where trees that did not contain all features would lead `TreeExplainer` to fail
- fixes a bug with `stacked_bar_plot` where the higher order interactions were inflated by the lower order interactions, thus wrongly showing the higher order interactions as higher than they are
- fixes a bug where `InteractionValues.get_subset()` returns a faulty `coalition_lookup` dictionary pointing to indices outside the subset of players [336](https://github.com/mmschlk/shapiq/issues/336)
- updates default value of `TreeExplainer`'s `min_order` parameter from 1 to 0 to include the baseline value in the interaction values as per default
- adds the `RegressionFBII` approximator to estimate Faithful Banzhaf interactions via least squares regression [333](https://github.com/mmschlk/shapiq/pull/333). Additionally, FBII support was introduced in TabularExplainer and MonteCarlo-Approximator.
- adds a `RandomGame` class as part of `shapiq.games.benchmark` which always returns a random vector of integers between 0 and 100.

- changes python support to 3.10-3.13 [318](https://github.com/mmschlk/shapiq/pull/318)
- fixes a bug that prohibited importing shapiq in environments without write access [326](https://github.com/mmschlk/shapiq/issues/326)
- adds `ExtraTreeRegressors` to supported models [309](https://github.com/mmschlk/shapiq/pull/309)

- fixes bugs regarding plotting [315](https://github.com/mmschlk/shapiq/issues/315) and [#316](https://github.com/mmschlk/shapiq/issues/316)
- fixes a bug with TreeExplainer and Trees that consist of only one feature [286](https://github.com/mmschlk/shapiq/issues/286)
- fixes SV init with explainer for permutation, svarm, kernelshap, and unbiased kernelshap [319](https://github.com/mmschlk/shapiq/issues/319)
- adds a progress bar to `explain_X()` [324](https://github.com/mmschlk/shapiq/issues/324)

- adds ``shapiq.TabPFNExplainer`` as a specialized version of the ``shapiq.TabularExplainer`` which offers a streamlined variant of the explainer for the TabPFN model [301](https://github.com/mmschlk/shapiq/issues/301)
- handles ``explainer.explain()`` now through a common interface for all explainer classes which now need to implement a ``explain_function()`` method
- adds the baseline_value into the InteractionValues object's value storage for the ``()`` interaction if ``min_order=0`` (default usually) for all indices that are not ``SII(SII has another baseline value) such that the values are efficient (sum up to the model prediction) without the awkward handling of the baseline_value attribute
- renames ``game_fun`` parameter in ``shapiq.ExactComputer`` to ``game`` [297](https://github.com/mmschlk/shapiq/issues/297)
- adds a TabPFN example notebook to the documentation
- removes warning when class_index is not provided in explainers [298](https://github.com/mmschlk/shapiq/issues/298)
- adds the `sentence_plot` function to the `plot` module to visualize the contributions of words to a language model prediction in a sentence-like format
- makes abbreviations in the `plot` module optional [281](https://github.com/mmschlk/shapiq/issues/281)
- adds the `upset_plot` function to the `plot` module to visualize the interactions of higher-order [290](https://github.com/mmschlk/shapiq/issues/290)
- adds support for IsoForest models to explainer and tree explainer [278](https://github.com/mmschlk/shapiq/issues/278)
- adds support for sub-selection of players in the interaction values data class [276](https://github.com/mmschlk/shapiq/issues/276) which allows retrieving interaction values for a subset of players
- refactors game theory computations like `ExactComputer`, `MoebiusConverter`, `core`, among others to be more modular and flexible into the `game_theory` module [258](https://github.com/mmschlk/shapiq/issues/258)
- improves quality of the tests by adding many more semantic tests to the different interaction indices and computations [285](https://github.com/mmschlk/shapiq/pull/285)

Improvements and Ease of Use
- adds a `class_index` parameter to `TabularExplainer` and `Explainer` to specify the class index to be explained for classification models [271](https://github.com/mmschlk/shapiq/issues/271) (renames `class_label` parameter in TreeExplainer to `class_index`)
- adds support for `PyTorch` models to `Explainer` [272](https://github.com/mmschlk/shapiq/issues/272)
- adds new tests comparing `shapiq` outputs for SVs with alues computed with `shap`
- adds new tests for checking `shapiq` explainers with different types of models

Bug Fixes
- fixes a bug that `RandomForestClassifier` models were not working with the `TreeExplainer` [273](https://github.com/mmschlk/shapiq/issues/273)

New Features and Improvements
- adds computation of the Egalitarian Core (`EC`) and Egalitarian Least-Core (`ELC`) to the `ExactComputer` [182](https://github.com/mmschlk/shapiq/issues/182)
- adds `waterfall_plot` [34](https://github.com/mmschlk/shapiq/issues/34) that visualizes the contributions of features to the model prediction
- adds `BaselineImputer` [107](https://github.com/mmschlk/shapiq/issues/107) which is now responsible for handling the `sample_replacements` parameter. Added a DeprecationWarning for the parameter in `MarginalImputer`, which will be removed in the next release.
- adds `joint_marginal_distribution` parameter to `MarginalImputer` with default value `True` [261](https://github.com/mmschlk/shapiq/issues/261)
- renames explanation graph to `si_graph`
- `get_n_order` now has optional lower/upper limits for the order
- computing metrics for benchmarking now tries to resolve not-matching interaction indices and will throw a warning instead of a ValueError [179](https://github.com/mmschlk/shapiq/issues/179)
- add a legend to benchmark plots [170](https://github.com/mmschlk/shapiq/issues/170)
- refactored the `shapiq.games.benchmark` module into a separate `shapiq.benchmark` module by moving all but the benchmark games into the new module. This closes [169](https://github.com/mmschlk/shapiq/issues/169) and makes benchmarking more flexible and convenient.
- a `shapiq.Game` can now be called more intuitively with coalitions data types (tuples of int or str) and also allows to add `player_names` to the game at initialization [183](https://github.com/mmschlk/shapiq/issues/183)
- improve tests across the package

Documentation
- adds a notebook showing how to use custom tree models with the `TreeExplainer` [66](https://github.com/mmschlk/shapiq/issues/66)
- adds a notebook show how to use the `shapiq.Game` API to create custom games [184](https://github.com/mmschlk/shapiq/issues/184)
- adds a notebook showing hot to visualize interactions [252](https://github.com/mmschlk/shapiq/issues/252)
- adds a notebook showing how to compute Shapley values with `shapiq` [193](https://github.com/mmschlk/shapiq/issues/197)
- adds a notebook for conducting data valuation [190](https://github.com/mmschlk/shapiq/issues/190)
- adds a notebook showcasing introducing the Core and how to compute it with `shapiq` [191](https://github.com/mmschlk/shapiq/issues/191)

Bug Fixes
- fixes a bug with SIs not adding up to the model prediction because of wrong values in the empty set [264](https://github.com/mmschlk/shapiq/issues/264)
- fixes a bug that `TreeExplainer` did not have the correct baseline_value when using XGBoost models [250](https://github.com/mmschlk/shapiq/issues/250)
- fixes the force plot not showing and its baseline value