def test_edge_histogram():
"""Eigenvalue test for the Edge Histogram Kernel."""
eh_kernel = EdgeHistogram(verbose=verbose, normalize=normalize)
if verbose:
print_kernel("Edge Histogram", eh_kernel, dataset_tr, dataset_te)
else:
positive_eig(eh_kernel, dataset)
def test_edge_histogram():
"""Picklability test for the Edge Histogram 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=('el', 4))
eh_kernel = EdgeHistogram(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel={"name": "edge_histogram"},
verbose=verbose,
normalize=normalize)
eh_kernel.fit(train)
assert is_picklable(eh_kernel)
gk.fit(train)
assert is_picklable(gk)
def gk_function(algorithm, graphs, par):
""" Function to run the kernel on the param grid. Since different
kernels have different numbers of parameters, this is necessary. """
print("parameters", par)
if algorithm == "SP_gkl":
gk = ShortestPath(with_labels=True).fit_transform(graphs)
elif algorithm == "EH_gkl":
gk = EdgeHistogram().fit_transform(graphs)
elif algorithm == "WL_gkl":
gk = WeisfeilerLehman(n_iter=par).fit_transform(graphs)
elif algorithm == "RW_gkl":
lam, p = par
gk = RandomWalkLabeled(lamda=lam, p=p).fit_transform(graphs)
elif algorithm == "CSM_gkl":
c, k = par
# testing lambda function. c should reset for each iteration
gk = SubgraphMatching(
k=k,
ke=lambda p1, p2: ke_kernel(p1, p2, c), # inline lambda
kv=kv_kernel
).fit_transform(graphs)
return(gk)
def test_edge_histogram():
"""Random input test for the Edge Histogram 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=('el', 4))
eh_kernel = EdgeHistogram(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel="EH", verbose=verbose, normalize=normalize)
try:
eh_kernel.fit_transform(train)
eh_kernel.transform(test)
gk.fit_transform(train)
gk.transform(test)
assert True
except Exception as exception:
assert False, exception
data = {
score_type: scores[score_field]
for score_type, score_field in zip(scoring, score_fields)
}
data["method"] = method_id
data["time"] = graphs[f"timings_{kernel_set}_{level}"].sum()
results = results.append(pd.DataFrame(data), ignore_index=True)
return results
GRAKEL_KERNELS = {
"GK-SPath":
lambda: ShortestPath(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-EHist":
lambda: EdgeHistogram(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-VHist":
lambda: VertexHistogram(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-GSamp":
lambda: GraphletSampling(normalize=NORMALIZING_GRAPH_KERNELS),
"GK-WL-1":
lambda: WeisfeilerLehman(
n_iter=1, n_jobs=N_JOBS, normalize=NORMALIZING_GRAPH_KERNELS),
"GK-WL-2":
lambda: WeisfeilerLehman(
n_iter=2, n_jobs=N_JOBS, normalize=NORMALIZING_GRAPH_KERNELS),
"GK-WL-3":
lambda: WeisfeilerLehman(
n_iter=3, n_jobs=N_JOBS, normalize=NORMALIZING_GRAPH_KERNELS),
"GK-WL-4":
lambda: WeisfeilerLehman(