alibi.explainers.counterfactual

Constants

DEFAULT_DATA_CF

DEFAULT_DATA_CF: dict = {'cf': None, 'all': [], 'orig_class': None, 'orig_proba': None, 'success': None}

DEFAULT_META_CF

DEFAULT_META_CF: dict = {'name': None, 'type': ['blackbox', 'tensorflow', 'keras'], 'explanations': [...

logger

logger: logging.Logger = <Logger alibi.explainers.counterfactual (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

Counterfactual

Inherits from: Explainer, ABC, Base

Constructor

Counterfactual(self, predict_fn: Union[Callable[[numpy.ndarray], numpy.ndarray], keras.src.models.model.Model], shape: Tuple[int, ...], distance_fn: str = 'l1', target_proba: float = 1.0, target_class: Union[str, int] = 'other', max_iter: int = 1000, early_stop: int = 50, lam_init: float = 0.1, max_lam_steps: int = 10, tol: float = 0.05, learning_rate_init=0.1, feature_range: Union[Tuple, str] = (-10000000000.0, 10000000000.0), eps: Union[float, numpy.ndarray] = 0.01, init: str = 'identity', decay: bool = True, write_dir: Optional[str] = None, debug: bool = False, sess: Optional[tensorflow.python.client.session.Session] = None) -> None

Name	Type	Default	Description
predict_fn	Union[Callable[[.[<class 'numpy.ndarray'>]], numpy.ndarray], keras.src.models.model.Model]		tensorflow model or any other model's prediction function returning class probabilities.
shape	Tuple[int, .Ellipsis]		Shape of input data starting with batch size.
distance_fn	str	'l1'	Distance function to use in the loss term.
target_proba	float	1.0	Target probability for the counterfactual to reach.
target_class	Union[str, int]	'other'	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	1000	Maximum number of iterations to run the gradient descent for (inner loop).
early_stop	int	50	Number of steps after which to terminate gradient descent if all or none of found instances are solutions.
lam_init	float	0.1	Initial regularization constant for the prediction part of the Wachter loss.
max_lam_steps	int	10	Maximum number of times to adjust the regularization constant (outer loop) before terminating the search.
tol	float	0.05	Tolerance for the counterfactual target probability.
learning_rate_init		0.1	Initial learning rate for each outer loop of lambda.
feature_range	Union[Tuple, str]	(-10000000000.0, 10000000000.0)	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, numpy.ndarray]	0.01	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	'identity'	Initialization method for the search of counterfactuals, currently must be 'identity'.
decay	bool	True	Flag to decay learning rate to zero for each outer loop over lambda.
write_dir	Optional[str]	None	Directory to write tensorboard files to.
debug	bool	False	Flag to write tensorboard summaries for debugging.
sess	Optional[tensorflow.python.client.session.Session]	None	Optional tensorflow session that will be used if passed instead of creating or inferring one internally.

Methods

explain

explain(X: numpy.ndarray) -> alibi.api.interfaces.Explanation

Name	Type	Default	Description
X	numpy.ndarray		Instance to be explained.

Returns

• Type: alibi.api.interfaces.Explanation

fit

fit(X: numpy.ndarray, y: Optional[numpy.ndarray]) -> alibi.explainers.counterfactual.Counterfactual

Name	Type	Default	Description
X	numpy.ndarray		Not used. Included for consistency.
y	Optional[numpy.ndarray]		Not used. Included for consistency.

Returns

• Type: alibi.explainers.counterfactual.Counterfactual

reset_predictor

reset_predictor(predictor: Union[Callable, keras.src.models.model.Model]) -> None

Name	Type	Default	Description
predictor	Union[Callable, keras.src.models.model.Model]		New predictor function/model.

Returns

• Type: None

Functions

CounterFactual

CounterFactual(args, kwargs)

The class name CounterFactual is deprecated, please use Counterfactual.