def test_odd_sth():
"""Eigenvalue test for the ODD-STh kernel."""
odd_sth_kernel = OddSth(verbose=verbose, normalize=normalize)
if verbose:
print_kernel("ODD-STh", odd_sth_kernel, dataset_tr, dataset_te)
else:
positive_eig(odd_sth_kernel, dataset)
def test_odd_sth():
"""Picklability test for the ODD-STh kernel [+ generic-wrapper]."""
train, _ = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 4))
odd_sth_kernel = OddSth(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel={"name": "odd_sth"},
verbose=verbose,
normalize=normalize)
odd_sth_kernel.fit(train)
assert is_picklable(odd_sth_kernel)
gk.fit(train)
assert is_picklable(gk)
def test_odd_sth():
"""Random input test for the ODD-STh kernel [+ generic-wrapper]."""
train, test = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(1, 1),
n_graphs_test=40,
random_state=rs,
features=('nl', 4))
odd_sth_kernel = OddSth(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel="ODD", verbose=verbose, normalize=normalize)
try:
odd_sth_kernel.fit_transform(train)
odd_sth_kernel.transform(test)
gk.fit_transform(train)
gk.transform(test)
assert True
except Exception as exception:
assert False, exception
"GK-GraphHopper":
lambda: GraphHopper(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-PyramidMatch":
lambda: PyramidMatch(normalize=NORMALIZING_GRAPH_KERNELS), # Error with PG
"GK-LovaszTheta":
lambda: LovaszTheta(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-SvmTheta":
lambda: SvmTheta(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-Propagation":
lambda: Propagation(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-PropagationA":
lambda: PropagationAttr(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-MScLaplacian":
lambda: MultiscaleLaplacian(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-OddSth":
lambda: OddSth(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-SubgraphMatching":
lambda: SubgraphMatching(normalize=NORMALIZING_GRAPH_KERNELS
), # taking too long
}
def test_prediction_on_Grakel_kernels(graphs: pd.DataFrame,
y_column: str,
cv_sets=None,
ignore_kernels=None):
if cv_sets is None:
cv = 10
print("Using 10-fold cross validation...")
else:
cv = cv_sets