def test_compute_laplacian_matrix_args(almost_equal_decimals=5):
input_types = ['data', 'adjacency', 'affinity']
params = [{}, {'radius': 4}, {'radius': 5}]
lapl_params = [{}, {'scaling_epps': 4}, {'scaling_epps': 10}]
adjacency_method = 'auto'
affinity_method = 'auto'
for laplacian_method in laplacian_methods():
X = np.random.uniform(size=(10, 2))
D = compute_adjacency_matrix(X, adjacency_method, **params[1])
A = compute_affinity_matrix(D, affinity_method, **params[1])
for init_params in lapl_params:
for kwarg_params in lapl_params:
true_params = init_params.copy()
true_params.update(kwarg_params)
laplacian_true = compute_laplacian_matrix(
A, laplacian_method, **true_params)
for input in input_types:
G = Geometry(adjacency_method=adjacency_method,
adjacency_kwds=params[1],
affinity_method=affinity_method,
affinity_kwds=params[1],
laplacian_method=laplacian_method,
laplacian_kwds=lapl_params[0])
if input in ['data']:
G.set_data_matrix(X)
if input in ['adjacency']:
G.set_adjacency_matrix(D)
else:
G.set_affinity_matrix(A)
laplacian_queried = G.compute_laplacian_matrix(**kwarg_params)
assert_array_almost_equal(laplacian_true.todense(),
laplacian_queried.todense(),
almost_equal_decimals)
def test_compute_adjacency_matrix_args(almost_equal_decimals=5):
""" test the compute_adjacency_matrix parameter arguments """
input_types = ['data', 'adjacency', 'affinity']
params = [{'radius':1}, {'radius':2}]
for adjacency_method in adjacency_methods():
if adjacency_method == 'pyflann':
try:
import pyflann as pyf
except ImportError:
raise SkipTest("pyflann not installed.")
X = np.random.uniform(size=(10, 2))
D = compute_adjacency_matrix(X, adjacency_method, **params[1])
A = compute_affinity_matrix(D, radius=1)
for init_params in params:
for kwarg_params in params:
true_params = init_params.copy()
true_params.update(kwarg_params)
adjacency_true = compute_adjacency_matrix(X, adjacency_method, **true_params)
G = Geometry(adjacency_method=adjacency_method, adjacency_kwds=init_params)
for input in input_types:
G = Geometry(adjacency_kwds = init_params)
if input in ['data']:
G.set_data_matrix(X)
adjacency_queried = G.compute_adjacency_matrix(**kwarg_params)
assert_allclose(adjacency_true.toarray(),
adjacency_queried.toarray(), rtol=1E-5)
else:
if input in ['adjacency']:
G.set_adjacency_matrix(D)
else:
G.set_affinity_matrix(A)
msg = distance_error_msg
assert_raise_message(ValueError, msg, G.compute_adjacency_matrix)
def test_compute_affinity_matrix_args(almost_equal_decimals=5):
""" test the compute_affinity_matrix parameter arguments """
input_types = ['data', 'adjacency', 'affinity']
params = [{'radius': 4}, {'radius': 5}]
adjacency_method = 'auto'
for affinity_method in affinity_methods():
X = np.random.uniform(size=(10, 2))
D = compute_adjacency_matrix(X, adjacency_method, **params[1])
A = compute_affinity_matrix(D, affinity_method, **params[1])
for init_params in params:
for kwarg_params in params:
true_params = init_params.copy()
true_params.update(kwarg_params)
affinity_true = compute_affinity_matrix(
D, adjacency_method, **true_params)
for input in input_types:
G = Geometry(adjacency_method=adjacency_method,
adjacency_kwds=params[1],
affinity_method=affinity_method,
affinity_kwds=init_params)
if input in ['data', 'adjacency']:
if input in ['data']:
G.set_data_matrix(X)
else:
G.set_adjacency_matrix(D)
affinity_queried = G.compute_affinity_matrix(
**kwarg_params)
assert_array_almost_equal(affinity_true.todense(),
affinity_queried.todense(),
almost_equal_decimals)
else:
G.set_affinity_matrix(A)
msg = distance_error_msg
assert_raise_message(ValueError, msg,
G.compute_affinity_matrix)
def test_compute_laplacian_matrix_args(almost_equal_decimals=5):
input_types = ['data', 'adjacency', 'affinity']
params = [{}, {'radius':4}, {'radius':5}]
lapl_params = [{}, {'scaling_epps':4}, {'scaling_epps':10}]
adjacency_method = 'auto'
affinity_method = 'auto'
for laplacian_method in laplacian_methods():
X = np.random.uniform(size=(10, 2))
D = compute_adjacency_matrix(X, adjacency_method, **params[1])
A = compute_affinity_matrix(D, affinity_method, **params[1])
for init_params in lapl_params:
for kwarg_params in lapl_params:
true_params = init_params.copy()
true_params.update(kwarg_params)
laplacian_true = compute_laplacian_matrix(A, laplacian_method, **true_params)
for input in input_types:
G = Geometry(adjacency_method = adjacency_method,
adjacency_kwds = params[1],
affinity_method = affinity_method,
affinity_kwds = params[1],
laplacian_method = laplacian_method,
laplacian_kwds = lapl_params[0])
if input in ['data']:
G.set_data_matrix(X)
if input in ['adjacency']:
G.set_adjacency_matrix(D)
else:
G.set_affinity_matrix(A)
laplacian_queried = G.compute_laplacian_matrix(**kwarg_params)
assert_array_almost_equal(laplacian_true.todense(), laplacian_queried.todense(), almost_equal_decimals)
def test_compute_affinity_matrix_args(almost_equal_decimals=5):
""" test the compute_affinity_matrix parameter arguments """
input_types = ['data', 'adjacency', 'affinity']
params = [{'radius':4}, {'radius':5}]
adjacency_method = 'auto'
for affinity_method in affinity_methods():
X = np.random.uniform(size=(10, 2))
D = compute_adjacency_matrix(X, adjacency_method, **params[1])
A = compute_affinity_matrix(D, affinity_method, **params[1])
for init_params in params:
for kwarg_params in params:
true_params = init_params.copy()
true_params.update(kwarg_params)
affinity_true = compute_affinity_matrix(D, adjacency_method,
**true_params)
for input in input_types:
G = Geometry(adjacency_method = adjacency_method,
adjacency_kwds = params[1],
affinity_method = affinity_method,
affinity_kwds = init_params)
if input in ['data', 'adjacency']:
if input in ['data']:
G.set_data_matrix(X)
else:
G.set_adjacency_matrix(D)
affinity_queried = G.compute_affinity_matrix(**kwarg_params)
assert_array_almost_equal(affinity_true.todense(), affinity_queried.todense(), almost_equal_decimals)
else:
G.set_affinity_matrix(A)
msg = distance_error_msg
assert_raise_message(ValueError, msg, G.compute_affinity_matrix)
def test_geometry_update():
""" Test passing geometry object then independently update a parameter and confirm that the embedding
geometry is also updated """
g1 = Geometry(adjacency_method = 'auto',
adjacency_kwds = {'radius':4},
affinity_method = 'auto',
affinity_kwds = {'radius':4},
laplacian_method = 'geometric',
laplacian_kwds = {'scaling_eps':4})
base_embedding = BaseEmbedding(geom=g1)
X = np.random.rand(10, 2)
# Now update g1 -- object that was passed
g1.set_data_matrix(X)
# confirm internal object is updated
assert_allclose(g1.X, base_embedding.geom.X)
def test_geometry_update():
""" Test passing geometry object then independently update a parameter and confirm that the embedding
geometry is also updated """
g1 = Geometry(adjacency_method='auto',
adjacency_kwds={'radius': 4},
affinity_method='auto',
affinity_kwds={'radius': 4},
laplacian_method='geometric',
laplacian_kwds={'scaling_eps': 4})
base_embedding = BaseEmbedding(geom=g1)
X = np.random.rand(10, 2)
# Now update g1 -- object that was passed
g1.set_data_matrix(X)
# confirm internal object is updated
assert_allclose(g1.X, base_embedding.geom.X)
def test_geometry_object():
""" Test passing a geometry object and confirm the output """
g1 = Geometry(adjacency_method='auto',
adjacency_kwds={'radius': 4},
affinity_method='auto',
affinity_kwds={'radius': 4},
laplacian_method='geometric',
laplacian_kwds={'scaling_eps': 4})
base_embedding = BaseEmbedding(geom=g1).fit_geometry()
assert (g1.__dict__ == base_embedding.geom_.__dict__)
def test_embeddings_fit_vs_transform():
rand = np.random.RandomState(42)
X = rand.rand(100, 5)
geom = Geometry(adjacency_kwds = {'radius':1.0},
affinity_kwds = {'radius':1.0})
def check_embedding(Embedding, n_components):
model = Embedding(n_components=n_components,
geom=geom, random_state=rand)
embedding = model.fit_transform(X)
assert model.embedding_.shape == (X.shape[0], n_components)
assert_allclose(embedding, model.embedding_)
for Embedding in EMBEDDINGS:
for n_components in [1, 2, 3]:
yield check_embedding, Embedding, n_components
def test_compute_adjacency_matrix_args(almost_equal_decimals=5):
""" test the compute_adjacency_matrix parameter arguments """
input_types = ['data', 'adjacency', 'affinity']
params = [{'radius': 1}, {'radius': 2}]
for adjacency_method in adjacency_methods():
if adjacency_method == 'pyflann':
try:
import pyflann as pyf
except ImportError:
raise SkipTest("pyflann not installed.")
X = np.random.uniform(size=(10, 2))
D = compute_adjacency_matrix(X, adjacency_method, **params[1])
A = compute_affinity_matrix(D, radius=1)
for init_params in params:
for kwarg_params in params:
true_params = init_params.copy()
true_params.update(kwarg_params)
adjacency_true = compute_adjacency_matrix(
X, adjacency_method, **true_params)
G = Geometry(adjacency_method=adjacency_method,
adjacency_kwds=init_params)
for input in input_types:
G = Geometry(adjacency_kwds=init_params)
if input in ['data']:
G.set_data_matrix(X)
adjacency_queried = G.compute_adjacency_matrix(
**kwarg_params)
assert_allclose(adjacency_true.toarray(),
adjacency_queried.toarray(),
rtol=1E-5)
else:
if input in ['adjacency']:
G.set_adjacency_matrix(D)
else:
G.set_affinity_matrix(A)
msg = distance_error_msg
assert_raise_message(ValueError, msg,
G.compute_adjacency_matrix)
