alibi.utils.gradients

Functions

num_grad_batch

num_grad_batch(func: Callable, X: numpy.ndarray, args: Tuple = (), eps: Union[float, numpy.ndarray] = 1e-08) -> numpy.ndarray

Calculate the numerical gradients of a vector-valued function (typically a prediction function in classification) with respect to a batch of arrays X.

Name	Type	Default	Description
func	Callable		Function to be differentiated.
X	numpy.ndarray		A batch of vectors at which to evaluate the gradient of the function.
args	Tuple	()
eps	Union[float, numpy.ndarray]	1e-08

Returns

• Type: numpy.ndarray

perturb

perturb(X: numpy.ndarray, eps: Union[float, numpy.ndarray] = 1e-08, proba: bool = False) -> Tuple[numpy.ndarray, numpy.ndarray]

Apply perturbation to instance or prediction probabilities. Used for numerical calculation of gradients.

Name	Type	Default	Description
X	numpy.ndarray		Array to be perturbed.
eps	Union[float, numpy.ndarray]	1e-08	Size of perturbation.
proba	bool	False	If True, the net effect of the perturbation needs to be 0 to keep the sum of the probabilities equal to 1.

Returns

• Type: Tuple[numpy.ndarray, numpy.ndarray]