attribench.masking.TabularMasker

class attribench.masking.TabularMasker(mask_value=0.0)

Bases: ImageMasker

Parameters:

masking_levelstr

Either "feature" or "pixel". If "feature", the masker will mask features. If "pixel", the masker will mask pixels.

Raises:

ValueError

If masking_level is not "feature" or "pixel".

Methods

get_num_features	Return the number of features in the samples.
mask_bot	Mask the k least important features, according to the attributions.
mask_rand	Mask k random features.
mask_top	Mask the k most important features, according to the attributions.
set_batch	Set the batch of samples and attributions to use for masking.

get_num_features()

Return the number of features in the samples.

Returns:

int

Number of features in the samples.

mask_bot(k)

Mask the k least important features, according to the attributions.

Parameters:

kint

Number of features to mask.

Returns:

torch.Tensor

Samples with the bottom k features masked.

mask_rand(k, return_indices=False)

Mask k random features.

Parameters:

kint

Number of features to mask.

return_indicesbool, optional

Whether to return the indices of the masked features, by default False

Returns:

torch.Tensor

Samples with k random features masked.

Return type:

Union[Tensor, Tuple[Tensor, Tensor]]

mask_top(k)

Mask the k most important features, according to the attributions.

Parameters:

kint

Number of features to mask.

Returns:

torch.Tensor

Samples with the top k features masked.

set_batch(samples, attributions=None, segmented_samples=None)

Set the batch of samples and attributions to use for masking. Optionally also set the segmented samples.

Parameters:

samplestorch.Tensor

Samples to use for masking.

attributionstorch.Tensor, optional

Attributions to use for masking, by default None If None, the mask_top() and mask_bot() methods will not be available.

segmented_samplestorch.Tensor, optional

Segmented samples to use for masking, by default None