alibi.explainers.backends.pytorch.cfrl_tabular

This module contains utility functions for the Counterfactual with Reinforcement Learning tabular class, :py:class:alibi.explainers.cfrl_tabular, for the Pytorch backend.

Functions

consistency_loss

consistency_loss(Z_cf_pred: torch.Tensor, Z_cf_tgt: torch.Tensor, kwargs)

Computes heterogeneous consistency loss.

Name	Type	Default	Description
Z_cf_pred	torch.Tensor		Predicted counterfactual embedding.
Z_cf_tgt	torch.Tensor		Counterfactual embedding target.

l0_ohe

l0_ohe(input: torch.Tensor, target: torch.Tensor, reduction: str = 'none') -> torch.Tensor

Computes the L0 loss for a one-hot encoding representation.

Name	Type	Default	Description
input	torch.Tensor		Input tensor.
target	torch.Tensor		Target tensor
reduction	str	'none'	Specifies the reduction to apply to the output: 'none'

Returns

• Type: torch.Tensor

l1_loss

l1_loss(input: torch.Tensor, target: torch.Tensor, reduction: str = 'none') -> torch.Tensor

Computes L1 loss.

Name	Type	Default	Description
input	torch.Tensor		Input tensor.
target	torch.Tensor		Target tensor.
reduction	str	'none'	Specifies the reduction to apply to the output: 'none'

Returns

• Type: torch.Tensor

sample_differentiable

sample_differentiable(X_hat_split: List[torch.Tensor], category_map: Dict[int, List[str]]) -> List[torch.Tensor]

Samples differentiable reconstruction.

Name	Type	Default	Description
X_hat_split	List[torch.Tensor]		List of reconstructed columns form the auto-encoder.
category_map	Dict[int, List[str]]		Dictionary of category mapping. The keys are column indexes and the values are lists containing the possible values for an attribute.

Returns

• Type: List[torch.Tensor]

sparsity_loss

sparsity_loss(X_hat_split: List[torch.Tensor], X_ohe: torch.Tensor, category_map: Dict[int, List[str]], weight_num: float = 1.0, weight_cat: float = 1.0)

Computes heterogeneous sparsity loss.

Name	Type	Default	Description
X_hat_split	List[torch.Tensor]		List of one-hot encoded reconstructed columns form the auto-encoder.
X_ohe	torch.Tensor		One-hot encoded representation of the input.
category_map	Dict[int, List[str]]		Dictionary of category mapping. The keys are column indexes and the values are lists containing the possible values for an attribute.
weight_num	float	1.0	Numerical loss weight.
weight_cat	float	1.0	Categorical loss weight.