def test_spectral_embedding_two_components(seed=36):
# Test spectral embedding with two components
random_state = np.random.RandomState(seed)
n_sample = 100
affinity = np.zeros(shape=[n_sample * 2, n_sample * 2])
# first component
affinity[0:n_sample, 0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
# second component
affinity[n_sample::, n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2
# Test of internal _graph_connected_component before connection
component = _graph_connected_component(affinity, 0)
assert_true(component[:n_sample].all())
assert_true(not component[n_sample:].any())
component = _graph_connected_component(affinity, -1)
assert_true(not component[:n_sample].any())
assert_true(component[n_sample:].all())
# connection
affinity[0, n_sample + 1] = 1
affinity[n_sample + 1, 0] = 1
affinity.flat[:: 2 * n_sample + 1] = 0
affinity = 0.5 * (affinity + affinity.T)
true_label = np.zeros(shape=2 * n_sample)
true_label[0:n_sample] = 1
se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed", random_state=np.random.RandomState(seed))
embedded_coordinate = se_precomp.fit_transform(affinity)
# Some numpy versions are touchy with types
embedded_coordinate = se_precomp.fit_transform(affinity.astype(np.float32))
# thresholding on the first components using 0.
label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
assert_equal(normalized_mutual_info_score(true_label, label_), 1.0)
def test_spectral_embedding_two_components(seed=36):
# Test spectral embedding with two components
random_state = np.random.RandomState(seed)
n_sample = 100
affinity = np.zeros(shape=[n_sample * 2, n_sample * 2])
# first component
affinity[0:n_sample,
0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
# second component
affinity[n_sample::,
n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2
# Test of internal _graph_connected_component before connection
component = _graph_connected_component(affinity, 0)
assert component[:n_sample].all()
assert not component[n_sample:].any()
component = _graph_connected_component(affinity, -1)
assert not component[:n_sample].any()
assert component[n_sample:].all()
# connection
affinity[0, n_sample + 1] = 1
affinity[n_sample + 1, 0] = 1
affinity.flat[::2 * n_sample + 1] = 0
affinity = 0.5 * (affinity + affinity.T)
true_label = np.zeros(shape=2 * n_sample)
true_label[0:n_sample] = 1
se_precomp = SpectralEmbedding(n_components=1,
affinity="precomputed",
random_state=np.random.RandomState(seed))
embedded_coordinate = se_precomp.fit_transform(affinity)
# Some numpy versions are touchy with types
embedded_coordinate = \
se_precomp.fit_transform(affinity.astype(np.float32))
# thresholding on the first components using 0.
label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
assert normalized_mutual_info_score(true_label, label_) == 1.0
def test_sparse_graph_connected_component():
rng = np.random.RandomState(42)
n_samples = 300
boundaries = [0, 42, 121, 200, n_samples]
p = rng.permutation(n_samples)
connections = []
for start, stop in zip(boundaries[:-1], boundaries[1:]):
group = p[start:stop]
# Connect all elements within the group at least once via an
# arbitrary path that spans the group.
for i in range(len(group) - 1):
connections.append((group[i], group[i + 1]))
# Add some more random connections within the group
min_idx, max_idx = 0, len(group) - 1
n_random_connections = 1000
source = rng.randint(min_idx, max_idx, size=n_random_connections)
target = rng.randint(min_idx, max_idx, size=n_random_connections)
connections.extend(zip(group[source], group[target]))
# Build a symmetric affinity matrix
row_idx, column_idx = tuple(np.array(connections).T)
data = rng.uniform(.1, 42, size=len(connections))
affinity = sparse.coo_matrix((data, (row_idx, column_idx)))
affinity = 0.5 * (affinity + affinity.T)
for start, stop in zip(boundaries[:-1], boundaries[1:]):
component_1 = _graph_connected_component(affinity, p[start])
component_size = stop - start
assert component_1.sum() == component_size
# We should retrieve the same component mask by starting by both ends
# of the group
component_2 = _graph_connected_component(affinity, p[stop - 1])
assert component_2.sum() == component_size
assert_array_equal(component_1, component_2)
def test_sparse_graph_connected_component():
rng = np.random.RandomState(42)
n_samples = 300
boundaries = [0, 42, 121, 200, n_samples]
p = rng.permutation(n_samples)
connections = []
for start, stop in zip(boundaries[:-1], boundaries[1:]):
group = p[start:stop]
# Connect all elements within the group at least once via an
# arbitrary path that spans the group.
for i in range(len(group) - 1):
connections.append((group[i], group[i + 1]))
# Add some more random connections within the group
min_idx, max_idx = 0, len(group) - 1
n_random_connections = 1000
source = rng.randint(min_idx, max_idx, size=n_random_connections)
target = rng.randint(min_idx, max_idx, size=n_random_connections)
connections.extend(zip(group[source], group[target]))
# Build a symmetric affinity matrix
row_idx, column_idx = tuple(np.array(connections).T)
data = rng.uniform(.1, 42, size=len(connections))
affinity = sparse.coo_matrix((data, (row_idx, column_idx)))
affinity = 0.5 * (affinity + affinity.T)
for start, stop in zip(boundaries[:-1], boundaries[1:]):
component_1 = _graph_connected_component(affinity, p[start])
component_size = stop - start
assert_equal(component_1.sum(), component_size)
# We should retrieve the same component mask by starting by both ends
# of the group
component_2 = _graph_connected_component(affinity, p[stop - 1])
assert_equal(component_2.sum(), component_size)
assert_array_equal(component_1, component_2)
