`wildboar.explain.counterfactual._sf`#

Module Contents#

`PredictionPaths`
`ShapeletForestCounterfactual`	Counterfactual explanations for shapelet forest classifiers

`MIN_MATCHING_DISTANCE`
`euclidean_distance`

class wildboar.explain.counterfactual._sf.PredictionPaths(classes)#

class wildboar.explain.counterfactual._sf.ShapeletForestCounterfactual(*, epsilon=1.0, batch_size=1, random_state=10)#

Counterfactual explanations for shapelet forest classifiers

paths_#

A dictionary of prediction paths per label

Notes

This implementation only supports the reversible algorithm described by Karlsson (2020)

Warning

Only shapelet forests fit with the Euclidean distance is supported i.e., metric="euclidean"

References

Karlsson, I., Rebane, J., Papapetrou, P., & Gionis, A. (2020).: Locally and globally explainable time series tweaking. Knowledge and Information Systems, 62(5), 1671-1700.
Karlsson, I., Rebane, J., Papapetrou, P., & Gionis, A. (2018).: Explainable time series tweaking via irreversible and reversible temporal transformations. In 2018 IEEE International Conference on Data Mining (ICDM)

fit(estimator)#

Fit the counterfactual to a given estimator

Parameters:: estimator (object) – An estimator for which counterfactual explanations are produced
Return type:: self

transform(x, y)#

Transform the i:th sample in x to a sample that would be labeled as the i:th label in y

Parameters:

x (array-like of shape (n_samples, n_timestep) or (n_samples, n_dimension, n_timestep)) – The samples to generate counterfactual explanations for
y (array-like of shape (n_samples,)) – The desired label of the counterfactual sample

Returns:

counterfactuals (ndarray of same shape as x) – The counterfactual for each sample. If success[i] == False, then the value of counterfactuals[i] is undefined.
success (ndarray of shape (n_samples,)) – Boolean vector indicating successful transformations.