def _shapley_test_assert(
self,
model: Callable,
test_input: TensorOrTupleOfTensors,
expected_attr,
feature_mask: Optional[TensorOrTupleOfTensors] = None,
additional_input: Any = None,
perturbations_per_eval: Tuple[int, ...] = (1,),
baselines: Optional[
Union[Tensor, int, float, Tuple[Union[Tensor, int, float], ...]]
] = None,
target: Optional[int] = 0,
n_samples: int = 100,
delta: float = 1.0,
) -> None:
for batch_size in perturbations_per_eval:
shapley_samp = ShapleyValueSampling(model)
attributions = shapley_samp.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
)
assertTensorTuplesAlmostEqual(
self, attributions, expected_attr, delta=delta, mode="max"
)
def _shapley_test_assert(
self,
model: Callable,
test_input: TensorOrTupleOfTensorsGeneric,
expected_attr,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
additional_input: Any = None,
perturbations_per_eval: Tuple[int, ...] = (1, ),
baselines: BaselineType = None,
target: Union[None, int] = 0,
n_samples: int = 100,
delta: float = 1.0,
test_true_shapley: bool = True,
show_progress: bool = False,
) -> None:
for batch_size in perturbations_per_eval:
shapley_samp = ShapleyValueSampling(model)
attributions = shapley_samp.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
show_progress=show_progress,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_attr,
delta=delta,
mode="max")
if test_true_shapley:
shapley_val = ShapleyValues(model)
attributions = shapley_val.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
show_progress=show_progress,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_attr,
mode="max",
delta=0.001)