def test_from_spec():
"""Test case to load key points from a spec function.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([
ReluLayer(),
FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,)))
])
layer_index = 1
region_of_interest = np.array([
[0.5, -3.0],
[1.0, -3.0],
[1.0, 9.0],
[0.5, 9.0],
])
spec_fn = lambda i: np.isclose(i[:, 0], 1.0).astype(np.float32)
patcher = NetPatcher.from_spec_function(network, layer_index,
region_of_interest, spec_fn)
assert patcher.network is network
assert patcher.layer_index is layer_index
assert len(patcher.inputs) == (4 + 2)
true_key_points = list(region_of_interest)
true_key_points += [np.array([0.5, 0.0]), np.array([1.0, 0.0])]
true_labels = [0, 1, 1, 0, 0, 1]
for true_point, true_label in zip(true_key_points, true_labels):
try:
i = next(i for i, point in enumerate(patcher.inputs)
if np.allclose(point, true_point))
except StopIteration:
assert False
assert true_label == patcher.labels[i]
def test_from_planes():
"""Test case to load key points from a set of labeled 2D polytopes.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([
ReluLayer(),
FullyConnectedLayer(np.eye(2), np.zeros(shape=(2,)))
])
layer_index = 1
planes = [
np.array([[-1.0, -3.0], [-0.5, -3.0], [-0.5, 9.0], [-1.0, 9.0]]),
np.array([[8.0, -2.0], [16.0, -2.0], [16.0, 6.0], [8.0, 6.0]]),
]
labels = [1, 0]
patcher = NetPatcher.from_planes(network, layer_index, planes, labels)
assert patcher.network is network
assert patcher.layer_index is layer_index
assert len(patcher.inputs) == (4 + 2) + (4 + 2)
true_key_points = list(planes[0])
true_key_points += [np.array([-1.0, 0.0]), np.array([-0.5, 0.0])]
true_key_points += list(planes[1])
true_key_points += [np.array([8.0, 0.0]), np.array([16.0, 0.0])]
true_labels = ([1] * 6) + ([0] * 6)
for true_point, true_label in zip(true_key_points, true_labels):
try:
i = next(i for i, point in enumerate(patcher.inputs)
if np.allclose(point, true_point))
except StopIteration:
assert False
assert true_label == patcher.labels[i]
def test_compute_from_network():
"""Tests the it works given an arbitrary network and planes.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([ReluLayer()])
planes = [np.array([[1.0, 2.0], [3.0, 4.0], [1.0, 2.0]]),
np.array([[3.0, 2.0], [7.0, 4.0], [5.0, 2.0]])]
classifier = PlanesClassifier(network, planes, preimages=True)
classifier.partial_compute()
assert classifier.partially_computed
transformed = network.transform_planes(planes, True, True)
assert all(all(np.allclose(actual_polytope[0], truth_polytope[0]) and
np.allclose(actual_polytope[1], truth_polytope[1])
for actual_polytope, truth_polytope in zip(actual, truth))
for actual, truth in zip(classifier.transformed_planes,
transformed))
classified = classifier.compute()
assert len(classified) == len(planes)
regions, labels = classified[0]
assert np.allclose(regions, planes[0])
assert np.allclose(labels, [1])
regions, labels = classified[1]
assert np.allclose(regions, planes[1])
assert np.allclose(labels, [0])
# Ensure it doesn't re-compute things it already knows.
assert classifier.compute() is classified
def test_compute_from_syrenn():
"""Tests the it works given an arbitrary network and planes.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([ReluLayer()])
planes = [np.array([[1.0, 2.0], [3.0, 4.0], [1.0, 2.0]]),
np.array([[3.0, 2.0], [7.0, 4.0], [5.0, 2.0]])]
transformed = network.transform_planes(planes, True, True)
classifier = PlanesClassifier.from_syrenn(transformed)
assert classifier.partially_computed
classified = classifier.compute()
assert len(classified) == len(planes)
regions, labels = classified[0]
assert np.allclose(regions, planes[0])
assert np.allclose(labels, [1])
regions, labels = classified[1]
assert np.allclose(regions, planes[1])
assert np.allclose(labels, [0])
# Ensure it doesn't re-compute things it already knows.
assert classifier.compute() is classified
def test_compute_from_network():
"""Tests the it works given an arbitrary network and lines.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([ReluLayer()])
lines = [(np.array([0.0, 1.0]), np.array([0.0, -1.0])),
(np.array([2.0, 3.0]), np.array([4.0, 3.0]))]
classifier = LinesClassifier(network, lines, preimages=True)
classifier.partial_compute()
exactlines = network.exactlines(lines, True, True)
assert all(
np.allclose(actual[0], truth[0]) and np.allclose(actual[1], truth[1])
for actual, truth in zip(classifier.transformed_lines, exactlines))
classified = classifier.compute()
assert len(classified) == len(lines)
regions, labels = classified[0]
assert np.allclose(regions,
[[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, -1.0]]])
assert np.allclose(labels, [1, 0])
regions, labels = classified[1]
assert np.allclose(regions,
[[[2.0, 3.0], [3.0, 3.0]], [[3.0, 3.0], [4.0, 3.0]]])
assert np.allclose(labels, [1, 0])
# Ensure it doesn't re-compute things it already knows.
assert classifier.compute() is classified
def test_compute_from_exactline_error():
"""Tests that it requires the plural exactline*s*(), not the singular.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([ReluLayer()])
exactline = network.exactline([-1.0, 1.0], [1.0, 0.0], True, True)
try:
classifier = LinesClassifier.from_exactlines(exactline)
assert False
except TypeError as e:
pass
def test_compute_from_network():
"""Tests the it works given an arbitrary network and lines.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([ReluLayer()])
lines = [(np.array([0.0, 1.0]), np.array([0.0, -1.0])),
(np.array([2.0, 3.0]), np.array([4.0, 3.0]))]
helper = IntegratedGradients(network, lines)
helper.partial_compute()
assert len(helper.exactlines) == len(lines)
assert np.allclose(helper.exactlines[0], [0.0, 0.5, 1.0])
assert np.allclose(helper.exactlines[1], [0.0, 1.0])
assert helper.n_samples == [2, 1]
attributions_0 = helper.compute_attributions(0)
assert len(attributions_0) == len(lines)
# The second component doesn't affect the 0-label at all, and the first
# component is 0 everywhere, so we have int_0^0 0.0dx = 0.0
assert np.allclose(attributions_0[0], [0.0, 0.0])
# Gradient of the 0-label is (1.0, 0.0) everywhere since its in the first
# orthant, and the partition has a size of (2.0, 0.0), so the IG is (2.0,
# 0.0).
assert np.allclose(attributions_0[1], [2.0, 0.0])
attributions_1 = helper.compute_attributions(1)
assert len(attributions_1) == len(lines)
# The gradient in the first partition is (0.0, 1.0) with a size of (0.0,
# -1.0) -> contribution of (0.0, -1.0). In the second partition, (0.0,
# 0.0)*(0.0, -1.0) = (0.0, 0.0).
assert np.allclose(attributions_1[0], [0.0, -1.0])
# Gradient is (0, 1) and the size is (2, 0) so IG is (0, 0).
assert np.allclose(attributions_1[1], [0.0, 0.0])
attributions_1_re = helper.compute_attributions(1)
# Ensure it doesn't re-compute the attributions.
assert attributions_1 is attributions_1_re
def test_compute_from_exactlines():
"""Tests the it works given pre-transformed lines.
"""
if not Network.has_connection():
pytest.skip("No server connected.")
network = Network([ReluLayer()])
lines = [(np.array([0.0, 1.0]), np.array([0.0, -1.0])),
(np.array([2.0, 3.0]), np.array([4.0, 3.0]))]
exactlines = network.exactlines(lines, True, True)
classifier = LinesClassifier.from_exactlines(exactlines)
classified = classifier.compute()
assert len(classified) == len(lines)
regions, labels = classified[0]
assert np.allclose(regions,
[[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, -1.0]]])
assert np.allclose(labels, [1, 0])
regions, labels = classified[1]
assert np.allclose(regions,
[[[2.0, 3.0], [3.0, 3.0]], [[3.0, 3.0], [4.0, 3.0]]])
assert np.allclose(labels, [1, 0])
