alibi.explainers.backends.cfrl_base

This module contains utility functions for the Counterfactual with Reinforcement Learning base class, :py:class:alibi.explainers.cfrl_base, that are common for both Tensorflow and Pytorch backends.

CounterfactualRLDataset

Inherits from: ABC

Methods

predict_batches

predict_batches(X: numpy.ndarray, predictor: Callable, batch_size: int) -> numpy.ndarray

Name	Type	Default	Description
X	numpy.ndarray		Input to be classified.
predictor	Callable		Prediction function.
batch_size	int		Maximum batch size to be used during each inference step.

Returns

• Type: numpy.ndarray

Functions

generate_empty_condition

generate_empty_condition(X: typing.Any) -> None

Empty conditioning.

Name	Type	Default	Description
X	typing.Any		Input instance.

Returns

• Type: None

get_classification_reward

get_classification_reward(Y_pred: numpy.ndarray, Y_true: numpy.ndarray)

Computes classification reward per instance given the prediction output and the true label. The classification reward is a sparse/binary reward: 1 if the most likely classes from the prediction output and the label match, 0 otherwise.

Name	Type	Default	Description
Y_pred	numpy.ndarray		Prediction output as a distribution over the possible classes.
Y_true	numpy.ndarray		True label as a distribution over the possible classes.

get_hard_distribution

get_hard_distribution(Y: numpy.ndarray, num_classes: Optional[int] = None) -> numpy.ndarray

Constructs the hard label distribution (one-hot encoding).

Name	Type	Default	Description
Y	numpy.ndarray		Prediction array. Can be soft or hard label distribution, or a label.
num_classes	Optional[int]	None	Number of classes to be considered.

Returns

• Type: numpy.ndarray

identity_function

identity_function(X: typing.Any) -> typing.Any

Identity function.

Name	Type	Default	Description
X	typing.Any		Input instance.

Returns

• Type: typing.Any