def test_marginalized_graph_kernel_2nd_launch(benchmark, batch): graphs = [Graph.from_networkx(g, weight='weight') for g in make_graphs(batch, 48)] knode = TensorProduct(label=KroneckerDelta(0.5)) kedge = TensorProduct(label=KroneckerDelta(0.5)) kernel = MarginalizedGraphKernel(knode, kedge) def fun(): kernel(graphs, nodal=False) benchmark.pedantic(fun, iterations=3, rounds=3, warmup_rounds=0)
def test_mlgk_typecheck(): node_kernel = Constant(1.0) edge_kernel = Constant(1.0) mlgk = MarginalizedGraphKernel(node_kernel, edge_kernel, q=0.5) G = [ Graph.from_networkx(unlabeled_graph1), Graph.from_networkx(labeled_graph1), Graph.from_networkx(weighted_graph1, weight='w') ] with pytest.raises(TypeError): mlgk([G[0], G[1]]) with pytest.raises(TypeError): mlgk([G[0], G[2]]) with pytest.raises(TypeError): mlgk([G[1], G[2]]) with pytest.raises(TypeError): mlgk([G[1], G[0]]) with pytest.raises(TypeError): mlgk([G[2], G[0]]) with pytest.raises(TypeError): mlgk([G[2], G[1]])
def test_mlgk_self_loops(): kedge = Constant(1.0) knode = Constant(1.0) q = 0.1 mlgk = MarginalizedGraphKernel(knode, kedge, q=q) np.random.seed(2) for i in range(10): n = np.random.randint(4, 20) A = np.random.randn(n, n) A = A + A.T G = [Graph.from_networkx(nx.from_numpy_array(A), weight='weight')] K = mlgk(G).item() K0 = MLGK(G[0], knode, kedge, q, q, nodal=False) assert (K == pytest.approx(K0, 5e-4))
def test_mlgk_fixed_hyperparameters(): g = nx.Graph() g.add_node(0, feature=0) g.add_node(1, feature=1) g.add_node(2, feature=0) g.add_edge(0, 1, attribute=1.0) g.add_edge(0, 2, attribute=2.0) G = [Graph.from_networkx(g)] knodeV = TensorProduct(feature=KroneckerDelta(0.5)) knodeF = TensorProduct(feature=KroneckerDelta(0.5, h_bounds='fixed')) kedgeV = TensorProduct(attribute=SquareExponential(1.0)) kedgeF = TensorProduct( attribute=SquareExponential(1.0, length_scale_bounds='fixed')) kernelVV = MarginalizedGraphKernel(knodeV, kedgeV) kernelVF = MarginalizedGraphKernel(knodeV, kedgeF) kernelFV = MarginalizedGraphKernel(knodeF, kedgeV) kernelFF = MarginalizedGraphKernel(knodeF, kedgeF) assert (len(kernelVV.theta) == len(kernelVF.theta) + 1) assert (len(kernelVV.theta) == len(kernelFV.theta) + 1) assert (len(kernelVV.theta) == len(kernelFF.theta) + 2) assert (len(kernelVV.bounds) == len(kernelVF.bounds) + 1) assert (len(kernelVV.bounds) == len(kernelFV.bounds) + 1) assert (len(kernelVV.bounds) == len(kernelFF.bounds) + 2) Rvv, dRvv = kernelVV(G, eval_gradient=True) Rvf, dRvf = kernelVF(G, eval_gradient=True) Rfv, dRfv = kernelFV(G, eval_gradient=True) Rff, dRff = kernelFF(G, eval_gradient=True) assert (Rvv == pytest.approx(Rvf)) assert (Rvv == pytest.approx(Rfv)) assert (Rvv == pytest.approx(Rff)) assert (dRvv.shape[2] == dRvf.shape[2] + 1) assert (dRvv.shape[2] == dRfv.shape[2] + 1) assert (dRvv.shape[2] == dRff.shape[2] + 2) assert (dRvv[:, :, kernelVF.active_theta_mask] == pytest.approx(dRvf)) assert (dRvv[:, :, kernelFV.active_theta_mask] == pytest.approx(dRfv)) assert (dRvv[:, :, kernelFF.active_theta_mask] == pytest.approx(dRff))
def test_mlgk_large(): g = nx.Graph() n = 24 for i, row in enumerate(np.random.randint(0, 2, (n, n))): g.add_node(i, type=0) for j, pred in enumerate(row[:i]): if pred: g.add_edge(i, j, weight=1) dfg = Graph.from_networkx(g, weight='weight') q = 0.5 node_kernel = TensorProduct(type=KroneckerDelta(1.0)) edge_kernel = Constant(1.0) mlgk = MarginalizedGraphKernel(node_kernel, edge_kernel, q=q) dot = mlgk([dfg]) gold = MLGK(dfg, node_kernel, edge_kernel, q, q) assert (dot.shape == (1, 1)) assert (dot.item() == pytest.approx(gold))
def test_mlgk_dtype(): g = nx.Graph() n = 8 for i, row in enumerate(np.random.randint(0, 2, (n, n))): g.add_node(i, type=0) for j, pred in enumerate(row[:i]): if pred: g.add_edge(i, j, weight=1) dfg = Graph.from_networkx(g, weight='weight') q = 0.5 node_kernel = TensorProduct(type=KroneckerDelta(1.0)) edge_kernel = Constant(1.0) for dtype in [np.float, np.float32, np.float64]: mlgk = MarginalizedGraphKernel(node_kernel, edge_kernel, q=q, dtype=dtype) assert (mlgk([dfg]).dtype == dtype) assert (mlgk.diag([dfg]).dtype == dtype)
g2.add_node(2) g2.add_edge(0, 1) g2.add_edge(1, 2) # 0 --- 1 # \ / # 2 g3 = nx.Graph() g3.add_node(0) g3.add_node(1) g3.add_node(2) g3.add_edge(0, 1) g3.add_edge(0, 2) g3.add_edge(1, 2) # define trivial node and edge kernelets knode = Constant(1.0) kedge = Constant(1.0) # compose the marginalized graph kernel and compute pairwise similarity mlgk = MarginalizedGraphKernel(knode, kedge, q=0.05) R = mlgk([Graph.from_networkx(g) for g in [g1, g2, g3]]) # normalize the similarity matrix d = np.diag(R)**-0.5 K = np.diag(d).dot(R).dot(np.diag(d)) # all entries should be approximately 1 plus round-off error print(K)
g1 = nx.Graph() g1.add_node(0, category=(1, 2), symbol=1) g1.add_node(1, category=(2, ), symbol=2) g1.add_edge(0, 1, w=1.0, spectra=[0.5, 0.2]) g2 = nx.Graph() g2.add_node(0, category=(1, 3), symbol=1) g2.add_node(1, category=(2, 3, 5), symbol=2) g2.add_node(2, category=(1, ), symbol=1) g2.add_edge(0, 1, w=2.0, spectra=[0.1, 0.9, 1.5]) g2.add_edge(0, 2, w=0.5, spectra=[0.4]) g2.add_edge(1, 2, w=0.5, spectra=[0.3, 0.6]) # Define node and edge base kernels using the R-convolution framework # Reference: Haussler, David. Convolution kernels on discrete structures. 1999. knode = TensorProduct(symbol=KroneckerDelta(0.5), category=Convolution(KroneckerDelta(0.5))) kedge = TensorProduct(spectra=Convolution(SquareExponential(0.3))) # compose the marginalized graph kernel and compute pairwise similarity mlgk = MarginalizedGraphKernel(knode, kedge, q=0.05) R = mlgk([Graph.from_networkx(g, weight='w') for g in [g1, g2]]) # normalize the similarity matrix d = np.diag(R)**-0.5 K = np.diag(d).dot(R).dot(np.diag(d)) print(K)
weighted_graph1 = nx.Graph(title='H2O') weighted_graph1.add_node('O1', hybridization=Hybrid.SP2, charge=1) weighted_graph1.add_node('H1', hybridization=Hybrid.SP3, charge=-1) weighted_graph1.add_node('H2', hybridization=Hybrid.SP, charge=2) weighted_graph1.add_edge('O1', 'H1', order=1, length=0.5, w=1.0) weighted_graph1.add_edge('O1', 'H2', order=2, length=1.0, w=2.0) weighted_graph2 = nx.Graph(title='H2') weighted_graph2.add_node('H1', hybridization=Hybrid.SP, charge=1) weighted_graph2.add_node('H2', hybridization=Hybrid.SP, charge=1) weighted_graph2.add_edge('H1', 'H2', order=2, length=1.0, w=3.0) case_dict = { 'unlabeled': { 'graphs': [ Graph.from_networkx(unlabeled_graph1), Graph.from_networkx(unlabeled_graph2) ], 'knode': Constant(1.0), 'kedge': Constant(1.0), 'q': [0.01, 0.05, 0.1, 0.5] }, 'labeled': { 'graphs': [ Graph.from_networkx(labeled_graph1), Graph.from_networkx(labeled_graph2) ], 'knode': TensorProduct(hybridization=KroneckerDelta(0.3, 1.0),