igc.Gradients

class igc.Gradients(module, dataset, dtld_kwargs=None, forward_method_name=None, forward_method_kwargs=None, dtype=torch.float32, dtype_cat=torch.int32)

Bases: AbstractAttributionMethod

Gradients.

add_embedding_method(embedding_method_name, embedding_method_kwargs=None, embedding_n_cat=None)

Add an embedding method to preprocess categorical inputs.

Warning

This effect of this method must not excluded from the forward method defined by forward_method_name at initialization.

Parameters:

• embedding_method_name (str) – Name of the embedding method of the module.

• embedding_method_kwargs (dict) – Additional keyword arguments to the embedding method of the module.

• embedding_n_cat (int) – Number of categorical inputs. If None, this value is inferred from the input data types (torch.int16, torch.int32, torch.int64).

Return type:

self

compute(x, y_idx=None, batch_size=None, x_seed=None)

Compute gradients.

Parameters:

• x (None | int | ArrayLike | tuple(ArrayLike))

• y_idx (None | int | ArrayLike)

• batch_size (int | tuple(int))

• x_seed (None | int)

Return type:

tuple(ArrayLike)