alibi.explainers.counterfactual module

alibi.explainers.counterfactual.CounterFactual(*args, **kwargs)[source]

The class name CounterFactual is deprecated, please use Counterfactual.

class alibi.explainers.counterfactual.Counterfactual(predict_fn, shape, distance_fn='l1', target_proba=1.0, target_class='other', max_iter=1000, early_stop=50, lam_init=0.1, max_lam_steps=10, tol=0.05, learning_rate_init=0.1, feature_range=(- 10000000000.0, 10000000000.0), eps=0.01, init='identity', decay=True, write_dir=None, debug=False, sess=None)[source]

Bases: alibi.api.interfaces.Explainer

__init__(predict_fn, shape, distance_fn='l1', target_proba=1.0, target_class='other', max_iter=1000, early_stop=50, lam_init=0.1, max_lam_steps=10, tol=0.05, learning_rate_init=0.1, feature_range=(- 10000000000.0, 10000000000.0), eps=0.01, init='identity', decay=True, write_dir=None, debug=False, sess=None)[source]

Initialize counterfactual explanation method based on Wachter et al. (2017)

Parameters
  • predict_fn (Union[Callable[[ndarray], ndarray], Model]) – tensorflow model or any other model’s prediction function returning class probabilities.

  • shape (Tuple[int, ...]) – Shape of input data starting with batch size.

  • distance_fn (str) – Distance function to use in the loss term.

  • target_proba (float) – Target probability for the counterfactual to reach.

  • target_class (Union[str, int]) – Target class for the counterfactual to reach, one of 'other', 'same' or an integer denoting desired class membership for the counterfactual instance.

  • max_iter (int) – Maximum number of iterations to run the gradient descent for (inner loop).

  • early_stop (int) – Number of steps after which to terminate gradient descent if all or none of found instances are solutions.

  • lam_init (float) – Initial regularization constant for the prediction part of the Wachter loss.

  • max_lam_steps (int) – Maximum number of times to adjust the regularization constant (outer loop) before terminating the search.

  • tol (float) – Tolerance for the counterfactual target probability.

  • learning_rate_init – Initial learning rate for each outer loop of lambda.

  • feature_range (Union[Tuple, str]) – Tuple with min and max ranges to allow for perturbed instances. Min and max ranges can be float or numpy arrays with dimension (1 x nb of features) for feature-wise ranges.

  • eps (Union[float, ndarray]) – Gradient step sizes used in calculating numerical gradients, defaults to a single value for all features, but can be passed an array for feature-wise step sizes.

  • init (str) – Initialization method for the search of counterfactuals, currently must be 'identity'.

  • decay (bool) – Flag to decay learning rate to zero for each outer loop over lambda.

  • write_dir (Optional[str]) – Directory to write tensorboard files to.

  • debug (bool) – Flag to write tensorboard summaries for debugging.

  • sess (Optional[Session]) – Optional tensorflow session that will be used if passed instead of creating or inferring one internally.

explain(X)[source]

Explain an instance and return the counterfactual with metadata.

Parameters

X (ndarray) – Instance to be explained.

Return type

Explanation

Returns

explanationExplanation object containing the counterfactual with additional metadata as attributes. See usage at Counterfactual examples for details.

fit(X, y)[source]

Fit method - currently unused as the counterfactual search is fully unsupervised.

Parameters
  • X (ndarray) – Not used. Included for consistency.

  • y (Optional[ndarray]) – Not used. Included for consistency.

Return type

Counterfactual

Returns

self – Explainer itself.

meta: dict

Object metadata.

reset_predictor(predictor)[source]

Resets the predictor function/model.

Parameters

predictor (Union[Callable, Model]) – New predictor function/model.

Return type

None

setup: list