shapiq.explainer.ThresholdNNExplainer

class shapiq.explainer.ThresholdNNExplainer(model, class_index=None, data=None, index='SV', max_order=1)

Bases: NNExplainerBase

Explainer for threshold nearest neighbor models.

Implements the algorithm for efficiently computing exact Shapley values for threshold nearest neighbor models proposed by Wang et al.[1]. The algorithm has a runtime complexity of \(O(N)\) (when explaining a single data point), where \(N\) is the number of training samples.

References

[1]

Jiachen Tianhao Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, and Prateek Mittal. A privacy-friendly approach to data valuation. Advances in Neural Information Processing Systems, 36:60429–60467, 2023. URL: https://openreview.net/forum?id=FAZ3i0hvm0.

Initializes the class.

Parameters:

• model (RadiusNeighborsClassifier) – The NN model to explain. Must be an instance of sklearn.neighbors.KNeighborsClassifier or sklearn.neighbors.RadiusNeighborsClassifier. The model must not use multi-output classification, i.e. the y value provided to model.fit(X, y) must be a 1D vector.

• class_index (int | None) – The class index of the model to explain. Note that, as opposed to most Explainers, this must not be None!

• data (np.ndarray | None)

• index (ValidNNExplainerIndices)

• max_order (int)

Raises:

sklearn.exceptions.NotFittedError – The constructor was called with a model that hasn’t been fitted.

explain_function(x)

Explain a single prediction in terms of interaction values.

Parameters:

• x (ndarray[tuple[Any, ...], dtype[floating]]) – A numpy array of a data point to be explained.

• *args – Additional positional arguments passed to the explainer.

• **kwargs – Additional keyword-only arguments passed to the explainer.

Return type:

InteractionValues

Returns:

The interaction values of the prediction.

model: RadiusNeighborsClassifier

The model to be explained, either as a Model instance or a callable function.