def test_random_walk():
"""Random input test for the Simple Random Walk kernel."""
train, test = generate_dataset(n_graphs=100,
r_vertices=(10, 20),
r_connectivity=(0.4, 0.8),
r_weight_edges=(0.01, 12.0),
n_graphs_test=40,
random_state=rs,
features=None)
rw_kernel = RandomWalk(verbose=verbose, normalize=normalize)
try:
rw_kernel.fit_transform(train)
rw_kernel.transform(test)
assert True
except Exception as exception:
assert False, exception
y_pred = clf.predict(K_test) # Predict # Compute the classification accuracy # hint: use the accuracy_score function of scikit-learn ################## # your code here # print(accuracy_score(y_test, y_pred)) ################## # Use the random walk kernel and the pyramid match graph kernel to perform classification ################## # your code here # gk1 = RandomWalk() K_train1 = gk1.fit_transform(G_train) K_test1 = gk1.transform(G_test) clf1 = SVC(kernel='precomputed', C=1) # Initialize SVM clf1.fit(K_train, y_train) # Train SVM y_pred1 = clf1.predict(K_test) # Predict print(accuracy_score(y_test, y_pred1)) ################## ############## Question 3 # Classify the graphs of a real-world dataset using graph kernels # Load the MUTAG dataset # hint: use the fetch_dataset function of GraKeL