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:
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:
- Returns:
explanation – Explanation object containing the counterfactual with additional metadata as attributes. See usage at Counterfactual examples for details.