def test_vertex_histogram():
"""Eigenvalue test for the Vertex Histogram Kernel."""
vh_kernel = VertexHistogram(verbose=verbose, normalize=normalize)
if verbose:
print_kernel("Vertex Histogram", vh_kernel, dataset_tr, dataset_te)
else:
positive_eig(vh_kernel, dataset)
def test_vertex_histogram():
"""Picklability test for the Vertex 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=('nl', 5))
vh_kernel = VertexHistogram(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel={"name": "vertex_histogram"},
verbose=verbose,
normalize=normalize)
vh_kernel.fit(train)
assert is_picklable(vh_kernel)
gk.fit(train)
assert is_picklable(gk)
def test_vertex_histogram():
"""Random input test for the Vertex 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=('nl', 5))
vh_kernel = VertexHistogram(verbose=verbose, normalize=normalize)
gk = GraphKernel(kernel="VH", verbose=verbose, normalize=normalize)
try:
vh_kernel.fit_transform(train)
vh_kernel.transform(test)
gk.fit_transform(train)
gk.transform(test)
assert True
except Exception as exception:
assert False, exception
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(
n_iter=4, n_jobs=N_JOBS, normalize=NORMALIZING_GRAPH_KERNELS),
"GK-WL-5":
# Classify the graphs of a real-world dataset using graph kernels
# Load the MUTAG dataset
# hint: use the fetch_dataset function of GraKeL
mutag = fetch_dataset("MUTAG", verbose=False)
G, y = mutag.data, mutag.target
# Split dataset into a training and a test set
# hint: use the train_test_split function of scikit-learn
G_train, G_test, y_train, y_test = train_test_split(G, y, test_size=0.1)
# Perform graph classification using different kernels and evaluate performance
gk = VertexHistogram()
K_train = gk.fit_transform(G_train)
K_test = gk.transform(G_test)
clf = SVC(kernel='precomputed', C=1) # Initialize SVM
clf.fit(K_train, y_train) # Train SVM
y_pred = clf.predict(K_test) # Predict
print("Classification accuracy using VertexHistogram",
accuracy_score(y_test, y_pred))
gk = ShortestPath(with_labels=True)
K_train = gk.fit_transform(G_train)
K_test = gk.transform(G_test)