alibi.explainers.similarity.base

Constants

TYPE_CHECKING

TYPE_CHECKING: bool = False

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_pytorch

has_pytorch: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

has_tensorflow

has_tensorflow: bool = True

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

BaseSimilarityExplainer

Inherits from: Explainer, ABC, Base

Base class for similarity explainers.

Constructor

BaseSimilarityExplainer(self, predictor: 'Union[tensorflow.keras.Model, torch.nn.Module]', loss_fn: 'Union[Callable[[tensorflow.Tensor, tensorflow.Tensor], tensorflow.Tensor],\n                                   Callable[[torch.Tensor, torch.Tensor], torch.Tensor]]', sim_fn: Callable[[numpy.ndarray, numpy.ndarray], numpy.ndarray], precompute_grads: bool = False, backend: alibi.utils.frameworks.Framework = <Framework.TENSORFLOW: 'tensorflow'>, device: 'Union[int, str, torch.device, None]' = None, meta: Optional[dict] = None, verbose: bool = False)

Name	Type	Default	Description
predictor	Union[tensorflow.keras.Model, torch.nn.Module]		Model to be explained.
loss_fn	`Union[Callable[[tensorflow.Tensor, tensorflow.Tensor], tensorflow.Tensor],

                               Callable[[torch.Tensor, torch.Tensor], torch.Tensor]]` |  | Loss function. |

| sim_fn | Callable[[.[<class 'numpy.ndarray'>, <class 'numpy.ndarray'>]], numpy.ndarray] | | Similarity function. Takes two inputs and returns a similarity value. | | precompute_grads | bool | False | Whether to precompute and store the gradients when fitting. | | backend | alibi.utils.frameworks.Framework | <Framework.TENSORFLOW: 'tensorflow'> | Deep learning backend. | | device | Union[int, str, torch.device, None] | None | Device to be used. Will default to the same device the backend defaults to. | | meta | Optional[dict] | None | Metadata specific to explainers that inherit from this class. Should be initialized in the child class and passed in here. Is used in the __init__ of the base Explainer class. | | verbose | bool | False | |

Methods

fit

fit(X_train: Union[numpy.ndarray, List[typing.Any]], Y_train: numpy.ndarray) -> alibi.api.interfaces.Explainer

Name	Type	Default	Description
X_train	Union[numpy.ndarray, List[typing.Any]]		Training data.
Y_train	numpy.ndarray		Training labels.

Returns

• Type: alibi.api.interfaces.Explainer

reset_predictor

reset_predictor(predictor: Union[tensorflow.keras.Model, torch.nn.Module]) -> None

Name	Type	Default	Description
predictor	Union[tensorflow.keras.Model, torch.nn.Module]		The new predictor to use.

Returns

• Type: None