def test_laplacian_eigenmap_precomputed_affinity(seed=36):
# Test laplacian eigenmap with precomputed kernel
gamma = 1.0
embedding_precomp = LaplacianEigenmap(n_components=2, affinity="precomputed",
random_state=np.random.RandomState(seed))
embedding_rbf = LaplacianEigenmap(n_components=2, affinity="rbf",
gamma=gamma,
random_state=np.random.RandomState(seed))
embed_precomp = embedding_precomp.fit_transform(rbf_kernel(S, gamma=gamma))
embed_rbf = embedding_rbf.fit_transform(S)
assert_array_almost_equal(
embedding_precomp.affinity_matrix_, embedding_rbf.affinity_matrix_)
assert_true(_check_with_col_sign_flipping(embed_precomp, embed_rbf, 0.05))
def test_laplacian_eigenmap_amg_solver(seed=36):
# Test laplacian eigenmap with amg solver
try:
from pyamg import smoothed_aggregation_solver
except ImportError:
raise SkipTest("pyamg not available.")
embedding_amg = LaplacianEigenmap(n_components=2, affinity="nearest_neighbors",
eigen_solver="amg", n_neighbors=5,
random_state=np.random.RandomState(seed))
embedding_arpack = LaplacianEigenmap(n_components=2, affinity="nearest_neighbors",
eigen_solver="arpack", n_neighbors=5,
random_state=np.random.RandomState(seed))
embed_amg = embedding_amg.fit_transform(S)
embed_arpack = embedding_arpack.fit_transform(S)
assert_true(_check_with_col_sign_flipping(embed_amg, embed_arpack, 0.05))
def test_laplacian_eigenmap_callable_affinity(seed=36):
# Test laplacian eigenmap with callable affinity
gamma = 0.9
kern = rbf_kernel(S, gamma=gamma)
embedding_callable = LaplacianEigenmap(n_components=2,
affinity=(
lambda x: rbf_kernel(x, gamma=gamma)),
gamma=gamma,
random_state=np.random.RandomState(seed))
embedding_rbf = LaplacianEigenmap(n_components=2, affinity="rbf",
gamma=gamma,
random_state=np.random.RandomState(seed))
embed_rbf = embedding_rbf.fit_transform(S)
embed_callable = embedding_callable.fit_transform(S)
assert_array_almost_equal(
embedding_callable.affinity_matrix_, embedding_rbf.affinity_matrix_)
assert_array_almost_equal(kern, embedding_rbf.affinity_matrix_)
assert_true(
_check_with_col_sign_flipping(embed_rbf, embed_callable, 0.05))
def test_laplacian_eigenmap_two_components(seed=36):
# Test laplacian eigenmap 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
embedding_precomp = LaplacianEigenmap(n_components=1, affinity="precomputed",
random_state=np.random.RandomState(seed))
embedded_coordinate = embedding_precomp.fit_transform(affinity)
# Some numpy versions are touchy with types
embedded_coordinate = \
embedding_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)