def test_anchors(self):
x = Input((2, ))
z1 = Dense(2)(x)
z2 = Activation('relu')(z1)
y = Dense(1)(z2)
k_model = Model(x, y)
k_model.set_weights([
np.array([[1., 0.], [0., -1.]]),
np.array([0., 0.]),
np.array([[1.], [1.]]),
np.array([0.])
])
model = ModelWrapper(k_model)
infl_out = InternalInfluence(model,
Cut(2, anchor='out'),
ClassQoI(0),
PointDoi(),
multiply_activation=False)
infl_in = InternalInfluence(model,
Cut(2, anchor='in'),
ClassQoI(0),
PointDoi(),
multiply_activation=False)
res_out = infl_out.attributions(np.array([[1., 1.]]))
res_in = infl_in.attributions(np.array([[1., 1.]]))
self.assertEqual(res_out.shape, (1, 2))
self.assertEqual(res_in.shape, (1, 2))
self.assertTrue(np.allclose(res_out, np.array([[1., 1.]])))
self.assertTrue(np.allclose(res_in, np.array([[1., 0.]])))
def test_internal_multiple_inputs(self):
class ConcatenateLayer(Module):
def forward(this, x1, x2):
return cat((x1, x2), 1)
class M(Module):
def __init__(this):
super(M, this).__init__()
this.z1 = Linear(5, 6)
this.concat = ConcatenateLayer()
this.z3 = Linear(7, 7)
this.y = Linear(7, 3)
def forward(this, x1, x2):
x1 = this.z1(x1)
z = this.concat(x1, x2)
z = this.z3(z)
return this.y(z)
model = ModelWrapper(M(), [(5,), (1,)])
infl = InternalInfluence(
model, Cut('concat', anchor='in'), ClassQoI(1), PointDoi())
res = infl.attributions(
np.array([[1., 2., 3., 4., 5.]]).astype('float32'),
np.array([[1.]]).astype('float32'))
self.assertEqual(len(res), 2)
self.assertEqual(res[0].shape, (1, 6))
self.assertEqual(res[1].shape, (1, 1))
def test_internal_slice_multiple_layers(self):
class M(Module):
def __init__(this):
super(M, this).__init__()
this.cut_layer1 = Linear(5, 6)
this.cut_layer2 = Linear(1, 2)
this.z3 = Linear(2, 4)
this.z5 = Linear(10, 7)
this.y = Linear(7, 3)
def forward(this, x1, x2):
z1 = this.cut_layer1(x1)
z2 = this.cut_layer2(x2)
z3 = this.z3(z2)
z4 = cat((z1, z3), 1)
z5 = this.z5(z4)
return this.y(z5)
model = ModelWrapper(M(), [(5,), (1,)])
infl = InternalInfluence(
model, Cut(['cut_layer1', 'cut_layer2']), ClassQoI(1), PointDoi())
res = infl.attributions(
np.array([[1., 2., 3., 4., 5.]]).astype('float32'),
np.array([[1.]]).astype('float32'))
self.assertEqual(len(res), 2)
self.assertEqual(res[0].shape, (1, 6))
self.assertEqual(res[1].shape, (1, 2))
def test_internal_slice_multiple_layers(self):
graph = Graph()
with graph.as_default():
x1 = tf.placeholder('float32', (None, 5))
z1 = x1 @ tf.random.normal((5, 6))
x2 = tf.placeholder('float32', (None, 1))
z2 = x2 @ tf.random.normal((1, 2))
z3 = z2 @ tf.random.normal((2, 4))
z4 = tf.concat([z1, z3], axis=1)
z5 = z4 @ tf.random.normal((10, 7))
y = z5 @ tf.random.normal((7, 3))
model = ModelWrapper(
graph, [x1, x2], y, dict(cut_layer1=z1, cut_layer2=z2))
infl = InternalInfluence(
model, Cut(['cut_layer1', 'cut_layer2']), ClassQoI(1), PointDoi())
res = infl.attributions(
[np.array([[1., 2., 3., 4., 5.]]),
np.array([[1.]])])
self.assertEqual(len(res), 2)
self.assertEqual(res[0].shape, (1, 6))
self.assertEqual(res[1].shape, (1, 2))
def test_anchors(self):
class M(Module):
def __init__(this):
super(M, this).__init__()
this.z1 = Linear(2, 2)
this.z2 = ReLU()
this.y = Linear(2, 1)
this.z1.weight.data = B.as_tensor(
np.array([[1., 0.], [0., -1.]]).T)
this.z1.bias.data = B.as_tensor(np.array([0., 0.]))
this.y.weight.data = B.as_tensor(np.array([[1.], [1.]]).T)
this.y.bias.data = B.as_tensor(np.array([0.]))
def forward(this, x):
z1 = this.z1(x)
z2 = this.z2(z1)
return this.y(z2)
model = ModelWrapper(M(), (2,))
infl_out = InternalInfluence(
model,
Cut('z2', anchor='out'),
ClassQoI(0),
PointDoi(),
multiply_activation=False)
infl_in = InternalInfluence(
model,
Cut('z2', anchor='in'),
ClassQoI(0),
PointDoi(),
multiply_activation=False)
res_out = infl_out.attributions(np.array([[1., 1.]]))
res_in = infl_in.attributions(np.array([[1., 1.]]))
self.assertEqual(res_out.shape, (1, 2))
self.assertEqual(res_in.shape, (1, 2))
self.assertTrue(np.allclose(res_out, np.array([[1., 1.]])))
self.assertTrue(np.allclose(res_in, np.array([[1., 0.]])))
def test_catch_cut_index_error(self):
x = Input((2, ))
z1 = Dense(2)(x)
z2 = Activation('relu')(z1)
y = Dense(1)(z2)
model = ModelWrapper(Model(x, y))
with self.assertRaises(ValueError):
infl = InternalInfluence(model, Cut(4), ClassQoI(0), PointDoi())
infl.attributions(np.array([[1., 1.]]))
def test_linear_agreement_multiply_activation(self):
c = 1
infl = InternalInfluence(
self.model_lin,
InputCut(),
ClassQoI(c),
PointDoi(),
multiply_activation=True)
res = infl.attributions(self.x)
self.assertEqual(res.shape, (2, self.input_size))
self.assertTrue(np.allclose(res, self.model_lin_weights[:, c] * self.x))
def test_distributional_linearity_internal_influence(self):
x1, x2 = self.x[0:1], self.x[1:]
p1, p2 = 0.25, 0.75
class DistLinDoI(DoI):
'''
Represents the distribution of interest that weights `z` with
probability 1/4 and `z + diff` with probability 3/4.
'''
def __init__(self, diff):
super(DistLinDoI, self).__init__()
self.diff = diff
def __call__(self, z):
return [z, z + self.diff, z + self.diff, z + self.diff]
infl_pt = InternalInfluence(
self.model_deep,
Cut(self.layer2),
ClassQoI(0),
PointDoi(),
multiply_activation=False)
attr1 = infl_pt.attributions(x1)
attr2 = infl_pt.attributions(x2)
infl_dl = InternalInfluence(
self.model_deep,
Cut(self.layer2),
ClassQoI(0),
DistLinDoI(x2 - x1),
multiply_activation=False)
attr12 = infl_dl.attributions(x1)
self.assertTrue(np.allclose(attr12, p1 * attr1 + p2 * attr2))
def test_completeness_zero_baseline(self):
c = 2
infl = InternalInfluence(
self.model_deep,
InputCut(),
ClassQoI(c),
LinearDoi(resolution=100),
multiply_activation=True)
out_x = self.model_deep.fprop((self.x,))[0][:, c]
out_baseline = self.model_deep.fprop((self.baseline * 0,))[0][:, c]
res = infl.attributions(self.x)
self.assertTrue(
np.allclose(res.sum(axis=1), out_x - out_baseline, atol=5e-2))
def test_catch_cut_name_error(self):
graph = Graph()
with graph.as_default():
x = tf.placeholder('float32', (None, 2))
z1 = x @ tf.random.normal((2, 2))
z2 = relu(z1)
y = z2 @ tf.random.normal((2, 1))
model = ModelWrapper(graph, x, y)
with self.assertRaises(ValueError):
infl = InternalInfluence(
model, Cut('not_a_real_layer'), ClassQoI(0), PointDoi())
infl.attributions(np.array([[1., 1.]]))
def test_sensitivity(self):
c = 2
infl = InternalInfluence(
self.model_deep,
InputCut(),
ClassQoI(c),
LinearDoi(self.baseline),
multiply_activation=False)
out_x = self.model_deep.fprop((self.x[0:1],))[0][:, c]
out_baseline = self.model_deep.fprop((self.baseline,))[0][:, c]
if not np.allclose(out_x, out_baseline):
res = infl.attributions(self.x)
self.assertEqual(res.shape, (2, self.input_size))
self.assertNotEqual(res[0, 3], 0.)
def test_multiple_inputs(self):
x1 = Input((5, ))
z1 = Dense(6)(x1)
x2 = Input((1, ))
z2 = Concatenate()([z1, x2])
z3 = Dense(7)(z2)
y = Dense(3)(z3)
model = ModelWrapper(Model([x1, x2], y))
infl = InternalInfluence(model, InputCut(), ClassQoI(1), PointDoi())
res = infl.attributions(
[np.array([[1., 2., 3., 4., 5.]]),
np.array([[1.]])])
self.assertEqual(len(res), 2)
self.assertEqual(res[0].shape, (1, 5))
self.assertEqual(res[1].shape, (1, 1))
def test_completeness_internal_zero_baseline(self):
c = 2
infl = InternalInfluence(
self.model_deep,
Cut(self.layer2),
ClassQoI(c),
LinearDoi(resolution=100, cut=Cut(self.layer2)),
multiply_activation=True)
g = partial(
self.model_deep.fprop,
doi_cut=Cut(self.layer2),
intervention=np.zeros((2, 10)))
out_x = self.model_deep.fprop((self.x,))[0][:, c]
out_baseline = g((self.x,))[0][:, c]
res = infl.attributions(self.x)
self.assertTrue(
np.allclose(res.sum(axis=1), out_x - out_baseline, atol=5e-2))
def test_internal_slice_multiple_layers(self):
x1 = Input((5, ))
z1 = Dense(6, name='cut_layer1')(x1)
x2 = Input((1, ))
z2 = Dense(2, name='cut_layer2')(x2)
z3 = Dense(4)(z2)
z4 = Concatenate()([z1, z3])
z5 = Dense(7)(z4)
y = Dense(3)(z5)
model = ModelWrapper(Model([x1, x2], y))
infl = InternalInfluence(model, Cut(['cut_layer1', 'cut_layer2']),
ClassQoI(1), PointDoi())
res = infl.attributions(
[np.array([[1., 2., 3., 4., 5.]]),
np.array([[1.]])])
self.assertEqual(len(res), 2)
self.assertEqual(res[0].shape, (1, 6))
self.assertEqual(res[1].shape, (1, 2))
def test_catch_cut_name_error(self):
class M(Module):
def __init__(this):
super(M, this).__init__()
this.z1 = Linear(2, 2)
this.z2 = ReLU()
this.y = Linear(2, 1)
def forward(this, x):
z1 = this.z1(x)
z2 = this.z2(z1)
return this.y(z2)
model = ModelWrapper(M(), (2,))
with self.assertRaises(ValueError):
infl = InternalInfluence(
model, Cut('not_a_real_layer'), ClassQoI(0), PointDoi())
infl.attributions(np.array([[1., 1.]]).astype('float32'))
def test_multiple_inputs(self):
graph = Graph()
with graph.as_default():
x1 = tf.placeholder('float32', (None, 5))
z1 = x1 @ tf.random.normal((5, 6))
x2 = tf.placeholder('float32', (None, 1))
z2 = tf.concat([z1, x2], axis=1)
z3 = z2 @ tf.random.normal((7, 7))
y = z3 @ tf.random.normal((7, 3))
model = ModelWrapper(graph, [x1, x2], y)
infl = InternalInfluence(model, InputCut(), ClassQoI(1), PointDoi())
res = infl.attributions(
[np.array([[1., 2., 3., 4., 5.]]),
np.array([[1.]])])
self.assertEqual(len(res), 2)
self.assertEqual(res[0].shape, (1, 5))
self.assertEqual(res[1].shape, (1, 1))