def _assert_delta(test: BaseTest, delta: Tensor) -> None:
delta_condition = all(abs(delta.numpy().flatten()) < 0.0006)
test.assertTrue(
delta_condition,
"Sum of SHAP values {} does"
" not match the difference of endpoints.".format(delta),
)
def _scale_input(input: Tensor, baseline: Union[Tensor, int, float],
rand_coefficient: Tensor) -> Tensor:
# batch size
bsz = input.shape[0]
inp_shape_wo_bsz = input.shape[1:]
inp_shape = (bsz, ) + tuple([1] * len(inp_shape_wo_bsz))
# expand and reshape the indices
rand_coefficient = rand_coefficient.view(inp_shape)
input_baseline_scaled = (
rand_coefficient * input +
(1.0 - rand_coefficient) * baseline).requires_grad_()
return input_baseline_scaled
def _assert_attribution_delta(
test: BaseTest,
inputs: Union[Tensor, Tuple[Tensor, ...]],
attributions: Union[Tensor, Tuple[Tensor, ...]],
n_samples: int,
delta: Tensor,
is_layer: bool = False,
) -> None:
if not is_layer:
for input, attribution in zip(inputs, attributions):
test.assertEqual(attribution.shape, input.shape)
if isinstance(inputs, tuple):
bsz = inputs[0].shape[0]
else:
bsz = inputs.shape[0]
test.assertEqual([bsz * n_samples], list(delta.shape))
delta = torch.mean(delta.reshape(bsz, -1), dim=1)
_assert_delta(test, delta)