def gk_test_random_walk_sylvester():
XX = list(zip(k * [X], k * [L]))
gk = GraphKernel(kernel={
"name": "random_walk",
"lamda": 0.1,
"method_type": "sylvester"
})
gkf = gk.fit(XX)
print("Sylvester:", gkf.transform())
def gk_test_graphlets_sampling():
XX = list(zip(k * [X], k * [L]))
gk = GraphKernel(
kernel={
"name": "graphlets_sampling",
"k": 5,
"delta": 0.05,
"epsilon": 0.05,
"a": -1
})
gkf = gk.fit(XX)
print("Graphlets Sampling:", gkf.transform())
def gk_test_weisfeiler_lehman():
XX = list(zip(k * [X], k * [L]))
base_kernel = dict()
base_kernel["dirac"] = {"name": "dirac"}
base_kernel["shortest path"] = {"name": "shortest_path"}
base_kernel["subtree"] = {"name": "subtree_rg"}
for key in base_kernel.keys():
gk = GraphKernel(kernel=[{
"name": "weisfeiler_lehman",
"niter": 5
}, base_kernel[key]])
gkf = gk.fit(XX)
print("Weisfeiler_lehman - " + str(key) + ":", gkf.transform())
def test_odd_sth():
"""Test the ODD-STh kernel [decorator]."""
gk = GraphKernel(kernel={"name": "odd_sth"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("ODD-STh", gk, dataset_tr, dataset_te)
def test_svm_theta():
"""Test the SVM-theta kernel [decorator]."""
gk = GraphKernel(kernel={"name": "svm_theta"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("SVM-theta", gk, dataset_tr, dataset_te)
def test_propagation():
"""Test the Propagation kernel [decorator]."""
gk = GraphKernel(kernel={"name": "propagation"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Propagation", gk, dataset_tr, dataset_te)
def test_random_walk():
"""Test the Simple random walk kernel [decorator]."""
gk = GraphKernel(kernel={"name": "random_walk"},
verbose=verbose,
normalize=normalize)
if verbose:
print_kernel_decorator("Random Walk", gk, dataset_tr, dataset_te)
def test_multiscale_laplacian():
"""Test the Multiscale Laplacian kernel [decorator]."""
gk = GraphKernel(kernel="ML", verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Multiscale Laplacian", gk, dataset_attr_tr,
dataset_attr_te)
def test_pyramid_match():
"""Test the Pyramid Match kernel [decorator]."""
gk = GraphKernel(kernel={"name": "pyramid_match"},
verbose=verbose,
normalize=normalize)
if verbose:
print_kernel_decorator("Pyramid Match", gk, dataset_tr, dataset_te)
def test_weisfeiler_lehman():
"""Test the Weisfeiler Lehman kernel [decorator]."""
gk = GraphKernel(kernel=[{"name": "weisfeiler_lehman"},
{"name": "vertex_histogram"}],
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("WL/Subtree", gk, dataset_tr, dataset_te)
def test_shortest_path():
"""Test the Shortest Path kernel [decorator]."""
gk = GraphKernel(kernel={"name": "shortest_path"},
verbose=verbose,
normalize=normalize)
if verbose:
print_kernel_decorator("Shortest Path", gk, dataset_tr, dataset_te)
def test_vertex_histogram():
"""Test the Vertex Histogram kernel [decorator]."""
gk = GraphKernel(kernel={"name": "vertex_histogram"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Vertex Histogram",
gk, dataset_tr, dataset_te)
def test_core_framework():
"""Test the Core Framework kernel [decorator]."""
kernel = [{"name": "core_framework"}, {"name": "weisfeiler_lehman"},
{"name": "vertex_histogram"}]
gk = GraphKernel(kernel=kernel, verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Core Framework",
gk, dataset_tr, dataset_te)
def test_graph_hopper():
"""Test the Graph Hopper kernel [decorator]."""
gk = GraphKernel(kernel={"name": "graph_hopper"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Graph Hopper",
gk, dataset_attr_tr, dataset_attr_te)
def test_multiscale_laplacian_fast():
"""Test the fast Multiscale Laplacian kernel [decorator]."""
gk = GraphKernel(kernel={"name": "multiscale_laplacian", "which": "fast"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Multiscale Laplacian Fast",
gk, dataset_attr_tr, dataset_attr_te)
def test_subgraph_matching():
"""Test the Subgraph Matching kernel [decorator]."""
gk = GraphKernel(kernel={"name": "subgraph_matching"},
verbose=verbose,
normalize=normalize)
if verbose:
print_kernel_decorator("Subgraph Matching", gk, dataset_tr,
dataset_te)
def test_neighborhood_hash():
"""Test the Neighborhood Hash kernel [decorator]."""
gk = GraphKernel(kernel={"name": "neighborhood_hash"},
verbose=verbose,
normalize=normalize)
if verbose:
print_kernel_decorator("Neighborhood Hash", gk, dataset_tr,
dataset_te)
def test_graphlet_sampling():
"""Test the Graphlet Sampling Kernel [decorator]."""
gk = GraphKernel(kernel={"name": "graphlet_sampling",
"sampling": {"n_samples": 200}},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Graphlet Sampling", gk, dataset_tr, dataset_te)
def test_neighborhood_pairwise_distance():
"""Test the Neighborhood Subgraph Pairwise Distance kernel [decorator]."""
gk = GraphKernel(kernel={
"name": "neighborhood_subgraph_pairwise_distance"},
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("NSPD", gk, dataset_tr, dataset_te)
def test_hadamard_code():
"""Test the Hadamard Code kernel [decorator]."""
gk = GraphKernel(kernel=[{"name": "hadamard_code"},
{"name": "subtree_wl"}],
verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("Hadamard-Code/Subtree-WL [Simple]",
gk, dataset_tr, dataset_te)
def test_lovasz_theta():
"""Test the Lovasz-theta kernel [decorator]."""
try:
gk = GraphKernel(kernel={"name": "lovasz_theta"},
verbose=verbose, normalize=normalize)
except ImportError:
return
if verbose:
print_kernel_decorator("Lovasz-theta", gk, dataset_tr, dataset_te)
def gk_test_shortest_path():
XX = list(zip(k * [X], k * [L]))
gk = GraphKernel(kernel={
"name": "shortest_path",
"algorithm_type": "dijkstra"
})
gkf = gk.fit(XX)
print("Dijkstra:", gkf.transform())
gk = GraphKernel(kernel={
"name": "shortest_path",
"algorithm_type": "floyd_warshall"
})
gkf = gk.fit(XX)
print("Floyd Warshall:", gkf.transform())
gk = GraphKernel(kernel={
"name": "shortest_path",
"algorithm_type": "auto"
})
gkf = gk.fit(XX)
print("Auto:", gkf.transform())
def gk_test_subtree_rg():
XX = list(zip(k * [X], k * [L]))
gk = GraphKernel(kernel={"name": "subtree_rg", "h": 5})
gkf = gk.fit(XX)
print("Subtree [RG]:", gkf.transform())
def gk_test_dirac():
XX = list(zip(k * [X], k * [L]))
gk = GraphKernel(kernel={"name": "dirac"})
gkf = gk.fit(XX)
print("Dirac:", gkf.transform())
def test_weisfeiler_lehman_optimal_assignment():
"""Test the Weisfeiler Lehman Optimal Assignment kernel [decorator]."""
gk = GraphKernel(kernel="WL-OA", verbose=verbose, normalize=normalize)
if verbose:
print_kernel_decorator("WL-OA", gk, dataset_tr, dataset_te)
