def visualize(self, attribution, data, contribution_frac) -> FeatureOutput:
if self.visualization_transform:
text = self.visualization_transform(data)
else:
text = data
attribution = attribution.squeeze(0)
data = data.squeeze(0)
attribution = attribution.sum(dim=1)
# L-Infinity norm
attr_max = abs(attribution).max()
normalized_attribution = safe_div(attribution,
attr_max,
default_value=attribution)
modified = [x * 100 for x in normalized_attribution.tolist()]
return FeatureOutput(
name=self.name,
base=text,
modified=modified,
type=self.visualization_type(),
contribution=contribution_frac,
)
def _calculate_net_contrib(self, attrs_per_input_feature: List[Tensor]):
# get the net contribution per feature (input)
net_contrib = torch.stack(
[attrib.flatten().sum() for attrib in attrs_per_input_feature])
# normalise the contribution, s.t. sum(abs(x_i)) = 1
norm = torch.norm(net_contrib, p=1)
net_contrib = safe_div(net_contrib, norm, default_value=net_contrib)
return net_contrib.tolist()
def visualize(self, attribution, data, contribution_frac) -> FeatureOutput:
attribution = attribution.squeeze(0)
data = data.squeeze(0)
# L-2 norm
l2_norm = attribution.norm()
normalized_attribution = safe_div(attribution,
l2_norm,
default_value=attribution)
modified = [x * 100 for x in normalized_attribution.tolist()]
base = [
f"{c}: {d:.2f}" for c, d in zip(self.categories, data.tolist())
]
return FeatureOutput(
name=self.name,
base=base,
modified=modified,
type=self.visualization_type(),
contribution=contribution_frac,
)