def test_relu_deepliftshap_baselines_as_function(self):
model = ReLULinearDeepLiftModel()
x1 = torch.tensor([[-10.0, 1.0, -5.0]])
x2 = torch.tensor([[3.0, 3.0, 1.0]])
def gen_baselines():
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
return (b1, b2)
def gen_baselines_with_inputs(inputs):
b1 = inputs[0].mean(0, keepdim=True)
b2 = inputs[1].mean(0, keepdim=True)
return (b1, b2)
def gen_baselines_returns_array():
b1 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
b2 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
return (b1, b2)
inputs = (x1, x2)
dl_shap = DeepLiftShap(model)
self._deeplift_assert(model, dl_shap, inputs, gen_baselines)
self._deeplift_assert(model, dl_shap, inputs, gen_baselines_with_inputs)
with self.assertRaises(AssertionError):
self._deeplift_assert(
model, DeepLiftShap(model), inputs, gen_baselines_returns_array
)
baselines = gen_baselines()
attributions = dl_shap.attribute(inputs, baselines)
attributions_with_func = dl_shap.attribute(inputs, gen_baselines)
assertTensorAlmostEqual(self, attributions[0], attributions_with_func[0])
assertTensorAlmostEqual(self, attributions[1], attributions_with_func[1])
def test_relu_deepliftshap_baselines_as_func(self) -> None:
model = ReLULinearDeepLiftModel(inplace=True)
x1 = torch.tensor([[-10.0, 1.0, -5.0]])
x2 = torch.tensor([[3.0, 3.0, 1.0]])
def gen_baselines() -> Tuple[Tensor, ...]:
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
return (b1, b2)
def gen_baselines_scalar() -> Tuple[float, ...]:
return (0.0, 0.0001)
def gen_baselines_with_inputs(
inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]:
b1 = torch.cat([inputs[0], inputs[0] - 10])
b2 = torch.cat([inputs[1], inputs[1] - 10])
return (b1, b2)
def gen_baselines_returns_array() -> Tuple[List[List[float]], ...]:
b1 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
b2 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
return (b1, b2)
inputs = (x1, x2)
dl_shap = DeepLiftShap(model)
self._deeplift_assert(model, dl_shap, inputs, gen_baselines)
self._deeplift_assert(model, dl_shap, inputs,
gen_baselines_with_inputs)
with self.assertRaises(AssertionError):
self._deeplift_assert(model, DeepLiftShap(model), inputs,
gen_baselines_returns_array)
with self.assertRaises(AssertionError):
self._deeplift_assert(model, dl_shap, inputs, gen_baselines_scalar)
baselines = gen_baselines()
attributions = dl_shap.attribute(inputs, baselines)
attributions_with_func = dl_shap.attribute(inputs, gen_baselines)
assertTensorAlmostEqual(self, attributions[0],
attributions_with_func[0])
assertTensorAlmostEqual(self, attributions[1],
attributions_with_func[1])
def test_relu_deepliftshap_with_hypothetical_contrib_func(self) -> None:
model = Conv1dDeepLiftModel()
rand_seq_data = torch.abs(torch.randn(2, 4, 1000))
rand_seq_ref = torch.abs(torch.randn(3, 4, 1000))
dls = DeepLiftShap(model)
attr = dls.attribute(
rand_seq_data,
rand_seq_ref,
custom_attribution_func=_hypothetical_contrib_func,
target=(0, 0),
)
self.assertEqual(attr.shape, rand_seq_data.shape)
def test_relu_deepliftshap_with_custom_attr_func(self):
def custom_attr_func(multipliers, inputs, baselines):
return tuple(multiplier * 0.0 for multiplier in multipliers)
model = ReLULinearDeepLiftModel()
x1 = torch.tensor([[-10.0, 1.0, -5.0]])
x2 = torch.tensor([[3.0, 3.0, 1.0]])
b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
inputs = (x1, x2)
baselines = (b1, b2)
dls = DeepLiftShap(model)
attr_w_func = dls.attribute(inputs,
baselines,
custom_attribution_func=custom_attr_func)
assertTensorAlmostEqual(self, attr_w_func[0], [[0.0, 0.0, 0.0]], 0.0)
assertTensorAlmostEqual(self, attr_w_func[1], [[0.0, 0.0, 0.0]], 0.0)
