Base

pnpxai.explainers.base

ABC = abc.ABC if sys.version_info >= (3, 4) else abc.ABCMeta(str('ABC'), (), {}) module-attribute

NON_DISPLAYED_ATTRS = ['model', 'forward_arg_extractor', 'additional_forward_arg_extractor', 'device', 'n_classes', 'zennit_composite'] module-attribute

Explainer

Bases: ABC

Abstract base class for implementing attribution explanations for machine learning models.

This class provides methods for extracting forward arguments, loading baseline and feature mask functions, and applying them during attribution.

Parameters:

Name	Type	Description	Default
model	Module	The PyTorch model for which attribution is to be computed.	required
forward_arg_extractor	Optional[Callable[[Tuple[Tensor]], Union[Tensor, Tuple[Tensor]]]]	Optional function to extract forward arguments from inputs.	None
additional_forward_arg_extractor	Optional[Callable[[Tuple[Tensor]], Union[Tensor, Tuple[Tensor]]]]	Optional function to extract additional forward arguments.	None
**kwargs		Additional keyword arguments to pass to the constructor.	{}

Notes

• Subclasses must implement the attribute method to define how attributions are computed.
• The forward_arg_extractor and additional_forward_arg_extractor functions allow for customization in extracting forward arguments from the inputs.

EXPLANATION_TYPE: ExplanationType = 'attribution' class-attribute instance-attribute

SUPPORTED_MODULES = [] class-attribute instance-attribute

TUNABLES = {} class-attribute instance-attribute

model = model.eval() instance-attribute

forward_arg_extractor = forward_arg_extractor instance-attribute

additional_forward_arg_extractor = additional_forward_arg_extractor instance-attribute

device property

__init__(model: Module, forward_arg_extractor: Optional[ForwardArgumentExtractor] = None, additional_forward_arg_extractor: Optional[ForwardArgumentExtractor] = None, **kwargs) -> None

__repr__()

copy()

set_kwargs(**kwargs)

attribute(inputs: Union[Tensor, Tuple[Tensor]], targets: Tensor) -> Union[Tensor, Tuple[Tensor]] abstractmethod

Computes attributions for the given inputs and targets.

Parameters:

Name	Type	Description	Default
inputs	Union[Tensor, Tuple[Tensor]]	The inputs for the model.	required
targets	Tensor	The target labels.	required

Returns:

Type	Description
Union[Tensor, Tuple[Tensor]]	Union[Tensor, Tuple[Tensor]]: The computed attributions.

get_tunables() -> Dict[str, Tuple[type, dict]]

Returns a dictionary of tunable parameters for the explainer.

Returns:

Type	Description
Dict[str, Tuple[type, dict]]	Dict[str, Tuple[type, dict]]: Dictionary of tunable parameters.