alibi.explainers.similarity.grad

Gradient-based explainer.

This module implements the gradient-based explainers grad-dot and grad-cos.

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.

DEFAULT_DATA_SIM

DEFAULT_DATA_SIM: dict = {'scores': None, 'ordered_indices': None, 'most_similar': None, 'least_simila...

DEFAULT_META_SIM

DEFAULT_META_SIM: dict = {'name': None, 'type': ['whitebox'], 'explanations': ['local'], 'params': {},...

GradientSimilarity

Inherits from: BaseSimilarityExplainer, Explainer, ABC, Base

Constructor

GradientSimilarity(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: typing_extensions.Literal['grad_dot', 'grad_cos', 'grad_asym_dot'] = 'grad_dot', task: typing_extensions.Literal['classification', 'regression'] = 'classification', precompute_grads: bool = False, backend: typing_extensions.Literal['tensorflow', 'pytorch'] = 'tensorflow', device: 'Union[int, str, torch.device, None]' = None, verbose: bool = False)

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

                               Callable[[torch.Tensor, torch.Tensor], torch.Tensor]]` |  | Loss function used. The gradient of the loss function is used to compute the similarity between the test instances and the training set. |

| sim_fn | Literal[grad_dot, grad_cos, grad_asym_dot] | 'grad_dot' | Similarity function to use. The 'grad_dot' similarity function computes the dot product of the gradients, see :py:func:alibi.explainers.similarity.metrics.dot. The 'grad_cos' similarity function computes the cosine similarity between the gradients, see :py:func:alibi.explainers.similarity.metrics.cos. The 'grad_asym_dot' similarity function is similar to 'grad_dot' but is asymmetric, see :py:func:alibi.explainers.similarity.metrics.asym_dot. | | task | Literal[classification, regression] | 'classification' | Type of task performed by the model. If the task is 'classification', the target value passed to the explain method of the test instance can be specified either directly or left as None, if left None we use the model's maximum prediction. If the task is 'regression', the target value of the test instance must be specified directly. | | precompute_grads | bool | False | Whether to precompute the gradients. If False, gradients are computed on the fly otherwise we precompute them which can be faster when it comes to computing explanations. Note this option may be memory intensive if the model is large. | | backend | Literal[tensorflow, pytorch] | 'tensorflow' | Backend to use. | | device | Union[int, str, torch.device, None] | None | Device to use. If None, the default device for the backend is used. If using pytorch backend see pytorch device docs <https://pytorch.org/docs/stable/tensor_attributes.html#torch-device>_ for correct options. Note that in the pytorch backend case this parameter can be a torch.device. If using tensorflow backend see tensorflow docs <https://www.tensorflow.org/api_docs/python/tf/device>_ for correct options. | | verbose | bool | False | Whether to print the progress of the explainer. |

Methods

explain

explain(X: Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]], Y: Optional[Union[np.ndarray, tensorflow.Tensor, torch.Tensor]] = None) -> Explanation

Name	Type	Default	Description
X	Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]]		X can be a numpy array, tensorflow tensor, pytorch tensor of the same shape as the training data or a list of objects, with or without a leading batch dimension. If the batch dimension is missing it's added.
Y	Optional[Union[np.ndarray, tensorflow.Tensor, torch.Tensor]]	None	Y can be a numpy array, tensorflow tensor or a pytorch tensor. In the case of a regression task, the Y argument must be present. If the task is classification then Y defaults to the model prediction.

Returns

• Type: Explanation

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

Task

Inherits from: str, Enum

Enum of supported tasks.