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)