def test_lle_simple_grid():
rng = np.random.RandomState(0)
# grid of equidistant points in 2D, out_dim = n_dim
X = np.array(list(product(range(5), repeat=2)))
out_dim = 2
clf = manifold.LocallyLinearEmbedding(n_neighbors=5, out_dim=out_dim)
tol = .1
N = barycenter_kneighbors_graph(X, clf.n_neighbors).todense()
reconstruction_error = np.linalg.norm(np.dot(N, X) - X, 'fro')
assert_lower(reconstruction_error, tol)
for solver in eigen_solvers:
clf.set_params(eigen_solver=solver)
clf.fit(X)
assert clf.embedding_.shape[1] == out_dim
reconstruction_error = np.linalg.norm(
np.dot(N, clf.embedding_) - clf.embedding_, 'fro')**2
# FIXME: ARPACK fails this test ...
if solver != 'arpack':
assert_lower(reconstruction_error, tol)
assert_almost_equal(clf.reconstruction_error_,
reconstruction_error,
decimal=4)
# re-embed a noisy version of X using the transform method
noise = rng.randn(*X.shape) / 100
X_reembedded = clf.transform(X + noise)
assert_lower(np.linalg.norm(X_reembedded - clf.embedding_), tol)
def test_lle_simple_grid():
rng = np.random.RandomState(0)
# grid of equidistant points in 2D, out_dim = n_dim
X = np.array(list(product(range(5), repeat=2)))
out_dim = 2
clf = manifold.LocallyLinearEmbedding(n_neighbors=5, out_dim=out_dim)
tol = .1
N = barycenter_kneighbors_graph(X, clf.n_neighbors).todense()
reconstruction_error = np.linalg.norm(np.dot(N, X) - X, 'fro')
assert_lower(reconstruction_error, tol)
for solver in eigen_solvers:
clf.set_params(eigen_solver=solver)
clf.fit(X)
assert clf.embedding_.shape[1] == out_dim
reconstruction_error = np.linalg.norm(
np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
# FIXME: ARPACK fails this test ...
if solver != 'arpack':
assert_lower(reconstruction_error, tol)
assert_almost_equal(clf.reconstruction_error_,
reconstruction_error, decimal=4)
# re-embed a noisy version of X using the transform method
noise = rng.randn(*X.shape) / 100
X_reembedded = clf.transform(X + noise)
assert_lower(np.linalg.norm(X_reembedded - clf.embedding_), tol)
def test_lle_manifold():
# similar test on a slightly more complex manifold
X = np.array(list(product(range(20), repeat=2)))
X = np.c_[X, X[:, 0] ** 2 / 20]
out_dim = 2
clf = manifold.LocallyLinearEmbedding(n_neighbors=5, out_dim=out_dim)
tol = 1.5
N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
reconstruction_error = np.linalg.norm(np.dot(N, X) - X)
assert_lower(reconstruction_error, tol)
for solver in eigen_solvers:
clf.set_params(eigen_solver=solver)
clf.fit(X)
assert clf.embedding_.shape[1] == out_dim
reconstruction_error = np.linalg.norm(
np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
details = "solver: " + solver
assert_lower(reconstruction_error, tol, details=details)
assert_lower(np.abs(clf.reconstruction_error_ - reconstruction_error),
tol * reconstruction_error, details=details)
def test_isomap_reconstruction_error():
# Same setup as in test_isomap_simple_grid, with an added dimension
N_per_side = 5
Npts = N_per_side**2
n_neighbors = Npts - 1
# grid of equidistant points in 2D, out_dim = n_dim
X = np.array(list(product(range(N_per_side), repeat=2)))
# add noise in a third dimension
rng = np.random.RandomState(0)
noise = 0.1 * rng.randn(Npts, 1)
X = np.concatenate((X, noise), 1)
# compute input kernel
G = neighbors.kneighbors_graph(X, n_neighbors, mode='distance').toarray()
centerer = preprocessing.KernelCenterer()
K = centerer.fit_transform(-0.5 * G**2)
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_neighbors=n_neighbors,
out_dim=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
# compute output kernel
G_iso = neighbors.kneighbors_graph(clf.embedding_,
n_neighbors,
mode='distance').toarray()
K_iso = centerer.fit_transform(-0.5 * G_iso**2)
# make sure error agrees
reconstruction_error = np.linalg.norm(K - K_iso) / Npts
assert_almost_equal(reconstruction_error,
clf.reconstruction_error())
def test_isomap_reconstruction_error():
# Same setup as in test_isomap_simple_grid, with an added dimension
N_per_side = 5
Npts = N_per_side ** 2
n_neighbors = Npts - 1
# grid of equidistant points in 2D, out_dim = n_dim
X = np.array(list(product(range(N_per_side), repeat=2)))
# add noise in a third dimension
rng = np.random.RandomState(0)
noise = 0.1 * rng.randn(Npts, 1)
X = np.concatenate((X, noise), 1)
# compute input kernel
G = neighbors.kneighbors_graph(X, n_neighbors,
mode='distance').toarray()
centerer = preprocessing.KernelCenterer()
K = centerer.fit_transform(-0.5 * G ** 2)
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_neighbors=n_neighbors, out_dim=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
# compute output kernel
G_iso = neighbors.kneighbors_graph(clf.embedding_,
n_neighbors,
mode='distance').toarray()
K_iso = centerer.fit_transform(-0.5 * G_iso ** 2)
# make sure error agrees
reconstruction_error = np.linalg.norm(K - K_iso) / Npts
assert_almost_equal(reconstruction_error,
clf.reconstruction_error())
def test_lle_manifold():
# similar test on a slightly more complex manifold
X = np.array(list(product(range(20), repeat=2)))
X = np.c_[X, X[:, 0]**2 / 20]
out_dim = 2
clf = manifold.LocallyLinearEmbedding(n_neighbors=5, out_dim=out_dim)
tol = 1.5
N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
reconstruction_error = np.linalg.norm(np.dot(N, X) - X)
assert_lower(reconstruction_error, tol)
for solver in eigen_solvers:
clf.set_params(eigen_solver=solver)
clf.fit(X)
assert clf.embedding_.shape[1] == out_dim
reconstruction_error = np.linalg.norm(
np.dot(N, clf.embedding_) - clf.embedding_, 'fro')**2
details = "solver: " + solver
assert_lower(reconstruction_error, tol, details=details)
assert_lower(np.abs(clf.reconstruction_error_ - reconstruction_error),
tol * reconstruction_error,
details=details)
def test_isomap_simple_grid():
# Isomap should preserve distances when all neighbors are used
N_per_side = 5
Npts = N_per_side**2
n_neighbors = Npts - 1
# grid of equidistant points in 2D, out_dim = n_dim
X = np.array(list(product(range(N_per_side), repeat=2)))
# distances from each point to all others
G = neighbors.kneighbors_graph(X, n_neighbors, mode='distance').toarray()
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_neighbors=n_neighbors,
out_dim=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
G_iso = neighbors.kneighbors_graph(clf.embedding_,
n_neighbors,
mode='distance').toarray()
assert_array_almost_equal(G, G_iso)
def test_isomap_simple_grid():
# Isomap should preserve distances when all neighbors are used
N_per_side = 5
Npts = N_per_side ** 2
n_neighbors = Npts - 1
# grid of equidistant points in 2D, out_dim = n_dim
X = np.array(list(product(range(N_per_side), repeat=2)))
# distances from each point to all others
G = neighbors.kneighbors_graph(X, n_neighbors,
mode='distance').toarray()
for eigen_solver in eigen_solvers:
for path_method in path_methods:
clf = manifold.Isomap(n_neighbors=n_neighbors, out_dim=2,
eigen_solver=eigen_solver,
path_method=path_method)
clf.fit(X)
G_iso = neighbors.kneighbors_graph(clf.embedding_,
n_neighbors,
mode='distance').toarray()
assert_array_almost_equal(G, G_iso)
