def test_basic_multilayer_wo_mult_by_inputs(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, 20.0, 10.0]])
baselines = torch.randn(2, 3)
self._assert_attributions(model, model.linear1, inputs, baselines, 0, 60)
def test_basic_multilayer(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, 20.0, 10.0]])
baselines = torch.zeros(2, 3)
ngs = NeuronGradientShap(model, model.linear1, multiply_by_inputs=False)
attr = ngs.attribute(inputs, 0, baselines=baselines, stdevs=0.0)
self.assertFalse(ngs.multiplies_by_inputs)
assertTensorAlmostEqual(self, attr, [1.0, 1.0, 1.0])
def test_basic_multilayer(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, -20.0, 10.0]])
baselines = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0],
[2.0, 2.0, 2.0]])
expected = [[-8.4, 0.0]]
self._assert_attributions(model, model.linear2, inputs, baselines, 0,
expected)
def test_basic_multilayer_wo_mult_by_inputs_agg_neurons(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, 20.0, 10.0]])
baselines = torch.randn(2, 3)
self._assert_attributions(model, model.linear1, inputs, baselines,
(slice(0, 1, 1), ), 60)
self._assert_attributions(model, model.linear1, inputs, baselines,
lambda x: x[:, 0:1], 60)
def test_basic_multilayer_wo_multiplying_by_inputs(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, -20.0, 10.0]])
baselines = torch.zeros(3, 3)
lgs = LayerGradientShap(model, model.linear2, multiply_by_inputs=False)
attrs = lgs.attribute(
inputs,
baselines,
target=0,
stdevs=0.0,
)
assertTensorAlmostEqual(self, attrs, torch.tensor([[1.0, 0.0]]))
def test_basic_multi_tensor_output(self) -> None:
model = BasicModel_MultiLayer(multi_input_module=True)
model.eval()
inputs = torch.tensor([[0.0, 100.0, 0.0]])
expected = ([90.0, 100.0, 100.0, 100.0], [90.0, 100.0, 100.0, 100.0])
self._assert_attributions(
model,
model.multi_relu,
inputs,
torch.zeros_like(inputs),
0,
expected,
n_samples=5,
)
def test_basic_multilayer_with_add_args(self) -> None:
model = BasicModel_MultiLayer(inplace=True)
model.eval()
inputs = torch.tensor([[1.0, -20.0, 10.0]])
add_args = torch.ones(1, 3)
baselines = torch.randn(30, 3)
expected = [[-13.9510, 0.0]]
self._assert_attributions(model,
model.linear2,
inputs,
baselines,
0,
expected,
add_args=add_args)
def test_basic_multilayer_compare_w_inp_features(self) -> None:
model = BasicModel_MultiLayer()
model.eval()
inputs = torch.tensor([[10.0, 20.0, 10.0]])
baselines = torch.randn(30, 3)
gs = GradientShap(model)
expected, delta = gs.attribute(inputs,
baselines,
target=0,
return_convergence_delta=True)
self.setUp()
self._assert_attributions(
model,
model.linear0,
inputs,
baselines,
0,
(expected, ),
expected_delta=delta,
attribute_to_layer_input=True,
)
