Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/tests/test_similarity.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
| author | shellac |
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| date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/tests/test_similarity.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,715 @@ +import pytest + +import networkx as nx +from networkx.algorithms.similarity import ( + graph_edit_distance, + optimal_edit_paths, + optimize_graph_edit_distance, +) +from networkx.generators.classic import ( + circular_ladder_graph, + cycle_graph, + path_graph, + wheel_graph, +) + + +def nmatch(n1, n2): + return n1 == n2 + + +def ematch(e1, e2): + return e1 == e2 + + +def getCanonical(): + G = nx.Graph() + G.add_node("A", label="A") + G.add_node("B", label="B") + G.add_node("C", label="C") + G.add_node("D", label="D") + G.add_edge("A", "B", label="a-b") + G.add_edge("B", "C", label="b-c") + G.add_edge("B", "D", label="b-d") + return G + + +class TestSimilarity: + @classmethod + def setup_class(cls): + global numpy + global scipy + numpy = pytest.importorskip("numpy") + scipy = pytest.importorskip("scipy") + + def test_graph_edit_distance_roots_and_timeout(self): + G0 = nx.star_graph(5) + G1 = G0.copy() + pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2]) + pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2, 3, 4]) + pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 3)) + pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(3, 9)) + pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 9)) + assert graph_edit_distance(G0, G1, roots=(1, 2)) == 0 + assert graph_edit_distance(G0, G1, roots=(0, 1)) == 8 + assert graph_edit_distance(G0, G1, roots=(1, 2), timeout=5) == 0 + assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=5) == 8 + assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=0.0001) is None + # test raise on 0 timeout + pytest.raises(nx.NetworkXError, graph_edit_distance, G0, G1, timeout=0) + + def test_graph_edit_distance(self): + G0 = nx.Graph() + G1 = path_graph(6) + G2 = cycle_graph(6) + G3 = wheel_graph(7) + + assert graph_edit_distance(G0, G0) == 0 + assert graph_edit_distance(G0, G1) == 11 + assert graph_edit_distance(G1, G0) == 11 + assert graph_edit_distance(G0, G2) == 12 + assert graph_edit_distance(G2, G0) == 12 + assert graph_edit_distance(G0, G3) == 19 + assert graph_edit_distance(G3, G0) == 19 + + assert graph_edit_distance(G1, G1) == 0 + assert graph_edit_distance(G1, G2) == 1 + assert graph_edit_distance(G2, G1) == 1 + assert graph_edit_distance(G1, G3) == 8 + assert graph_edit_distance(G3, G1) == 8 + + assert graph_edit_distance(G2, G2) == 0 + assert graph_edit_distance(G2, G3) == 7 + assert graph_edit_distance(G3, G2) == 7 + + assert graph_edit_distance(G3, G3) == 0 + + def test_graph_edit_distance_node_match(self): + G1 = cycle_graph(5) + G2 = cycle_graph(5) + for n, attr in G1.nodes.items(): + attr["color"] = "red" if n % 2 == 0 else "blue" + for n, attr in G2.nodes.items(): + attr["color"] = "red" if n % 2 == 1 else "blue" + assert graph_edit_distance(G1, G2) == 0 + assert ( + graph_edit_distance( + G1, G2, node_match=lambda n1, n2: n1["color"] == n2["color"] + ) + == 1 + ) + + def test_graph_edit_distance_edge_match(self): + G1 = path_graph(6) + G2 = path_graph(6) + for e, attr in G1.edges.items(): + attr["color"] = "red" if min(e) % 2 == 0 else "blue" + for e, attr in G2.edges.items(): + attr["color"] = "red" if min(e) // 3 == 0 else "blue" + assert graph_edit_distance(G1, G2) == 0 + assert ( + graph_edit_distance( + G1, G2, edge_match=lambda e1, e2: e1["color"] == e2["color"] + ) + == 2 + ) + + def test_graph_edit_distance_node_cost(self): + G1 = path_graph(6) + G2 = path_graph(6) + for n, attr in G1.nodes.items(): + attr["color"] = "red" if n % 2 == 0 else "blue" + for n, attr in G2.nodes.items(): + attr["color"] = "red" if n % 2 == 1 else "blue" + + def node_subst_cost(uattr, vattr): + if uattr["color"] == vattr["color"]: + return 1 + else: + return 10 + + def node_del_cost(attr): + if attr["color"] == "blue": + return 20 + else: + return 50 + + def node_ins_cost(attr): + if attr["color"] == "blue": + return 40 + else: + return 100 + + assert ( + graph_edit_distance( + G1, + G2, + node_subst_cost=node_subst_cost, + node_del_cost=node_del_cost, + node_ins_cost=node_ins_cost, + ) + == 6 + ) + + def test_graph_edit_distance_edge_cost(self): + G1 = path_graph(6) + G2 = path_graph(6) + for e, attr in G1.edges.items(): + attr["color"] = "red" if min(e) % 2 == 0 else "blue" + for e, attr in G2.edges.items(): + attr["color"] = "red" if min(e) // 3 == 0 else "blue" + + def edge_subst_cost(gattr, hattr): + if gattr["color"] == hattr["color"]: + return 0.01 + else: + return 0.1 + + def edge_del_cost(attr): + if attr["color"] == "blue": + return 0.2 + else: + return 0.5 + + def edge_ins_cost(attr): + if attr["color"] == "blue": + return 0.4 + else: + return 1.0 + + assert ( + graph_edit_distance( + G1, + G2, + edge_subst_cost=edge_subst_cost, + edge_del_cost=edge_del_cost, + edge_ins_cost=edge_ins_cost, + ) + == 0.23 + ) + + def test_graph_edit_distance_upper_bound(self): + G1 = circular_ladder_graph(2) + G2 = circular_ladder_graph(6) + assert graph_edit_distance(G1, G2, upper_bound=5) is None + assert graph_edit_distance(G1, G2, upper_bound=24) == 22 + assert graph_edit_distance(G1, G2) == 22 + + def test_optimal_edit_paths(self): + G1 = path_graph(3) + G2 = cycle_graph(3) + paths, cost = optimal_edit_paths(G1, G2) + assert cost == 1 + assert len(paths) == 6 + + def canonical(vertex_path, edge_path): + return ( + tuple(sorted(vertex_path)), + tuple(sorted(edge_path, key=lambda x: (None in x, x))), + ) + + expected_paths = [ + ( + [(0, 0), (1, 1), (2, 2)], + [((0, 1), (0, 1)), ((1, 2), (1, 2)), (None, (0, 2))], + ), + ( + [(0, 0), (1, 2), (2, 1)], + [((0, 1), (0, 2)), ((1, 2), (1, 2)), (None, (0, 1))], + ), + ( + [(0, 1), (1, 0), (2, 2)], + [((0, 1), (0, 1)), ((1, 2), (0, 2)), (None, (1, 2))], + ), + ( + [(0, 1), (1, 2), (2, 0)], + [((0, 1), (1, 2)), ((1, 2), (0, 2)), (None, (0, 1))], + ), + ( + [(0, 2), (1, 0), (2, 1)], + [((0, 1), (0, 2)), ((1, 2), (0, 1)), (None, (1, 2))], + ), + ( + [(0, 2), (1, 1), (2, 0)], + [((0, 1), (1, 2)), ((1, 2), (0, 1)), (None, (0, 2))], + ), + ] + assert {canonical(*p) for p in paths} == {canonical(*p) for p in expected_paths} + + def test_optimize_graph_edit_distance(self): + G1 = circular_ladder_graph(2) + G2 = circular_ladder_graph(6) + bestcost = 1000 + for cost in optimize_graph_edit_distance(G1, G2): + assert cost < bestcost + bestcost = cost + assert bestcost == 22 + + # def test_graph_edit_distance_bigger(self): + # G1 = circular_ladder_graph(12) + # G2 = circular_ladder_graph(16) + # assert_equal(graph_edit_distance(G1, G2), 22) + + def test_selfloops(self): + G0 = nx.Graph() + G1 = nx.Graph() + G1.add_edges_from((("A", "A"), ("A", "B"))) + G2 = nx.Graph() + G2.add_edges_from((("A", "B"), ("B", "B"))) + G3 = nx.Graph() + G3.add_edges_from((("A", "A"), ("A", "B"), ("B", "B"))) + + assert graph_edit_distance(G0, G0) == 0 + assert graph_edit_distance(G0, G1) == 4 + assert graph_edit_distance(G1, G0) == 4 + assert graph_edit_distance(G0, G2) == 4 + assert graph_edit_distance(G2, G0) == 4 + assert graph_edit_distance(G0, G3) == 5 + assert graph_edit_distance(G3, G0) == 5 + + assert graph_edit_distance(G1, G1) == 0 + assert graph_edit_distance(G1, G2) == 0 + assert graph_edit_distance(G2, G1) == 0 + assert graph_edit_distance(G1, G3) == 1 + assert graph_edit_distance(G3, G1) == 1 + + assert graph_edit_distance(G2, G2) == 0 + assert graph_edit_distance(G2, G3) == 1 + assert graph_edit_distance(G3, G2) == 1 + + assert graph_edit_distance(G3, G3) == 0 + + def test_digraph(self): + G0 = nx.DiGraph() + G1 = nx.DiGraph() + G1.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("D", "A"))) + G2 = nx.DiGraph() + G2.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("A", "D"))) + G3 = nx.DiGraph() + G3.add_edges_from((("A", "B"), ("A", "C"), ("B", "D"), ("C", "D"))) + + assert graph_edit_distance(G0, G0) == 0 + assert graph_edit_distance(G0, G1) == 8 + assert graph_edit_distance(G1, G0) == 8 + assert graph_edit_distance(G0, G2) == 8 + assert graph_edit_distance(G2, G0) == 8 + assert graph_edit_distance(G0, G3) == 8 + assert graph_edit_distance(G3, G0) == 8 + + assert graph_edit_distance(G1, G1) == 0 + assert graph_edit_distance(G1, G2) == 2 + assert graph_edit_distance(G2, G1) == 2 + assert graph_edit_distance(G1, G3) == 4 + assert graph_edit_distance(G3, G1) == 4 + + assert graph_edit_distance(G2, G2) == 0 + assert graph_edit_distance(G2, G3) == 2 + assert graph_edit_distance(G3, G2) == 2 + + assert graph_edit_distance(G3, G3) == 0 + + def test_multigraph(self): + G0 = nx.MultiGraph() + G1 = nx.MultiGraph() + G1.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"))) + G2 = nx.MultiGraph() + G2.add_edges_from((("A", "B"), ("B", "C"), ("B", "C"), ("A", "C"))) + G3 = nx.MultiGraph() + G3.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"), ("A", "C"), ("A", "C"))) + + assert graph_edit_distance(G0, G0) == 0 + assert graph_edit_distance(G0, G1) == 6 + assert graph_edit_distance(G1, G0) == 6 + assert graph_edit_distance(G0, G2) == 7 + assert graph_edit_distance(G2, G0) == 7 + assert graph_edit_distance(G0, G3) == 8 + assert graph_edit_distance(G3, G0) == 8 + + assert graph_edit_distance(G1, G1) == 0 + assert graph_edit_distance(G1, G2) == 1 + assert graph_edit_distance(G2, G1) == 1 + assert graph_edit_distance(G1, G3) == 2 + assert graph_edit_distance(G3, G1) == 2 + + assert graph_edit_distance(G2, G2) == 0 + assert graph_edit_distance(G2, G3) == 1 + assert graph_edit_distance(G3, G2) == 1 + + assert graph_edit_distance(G3, G3) == 0 + + def test_multidigraph(self): + G1 = nx.MultiDiGraph() + G1.add_edges_from( + ( + ("hardware", "kernel"), + ("kernel", "hardware"), + ("kernel", "userspace"), + ("userspace", "kernel"), + ) + ) + G2 = nx.MultiDiGraph() + G2.add_edges_from( + ( + ("winter", "spring"), + ("spring", "summer"), + ("summer", "autumn"), + ("autumn", "winter"), + ) + ) + + assert graph_edit_distance(G1, G2) == 5 + assert graph_edit_distance(G2, G1) == 5 + + # by https://github.com/jfbeaumont + def testCopy(self): + G = nx.Graph() + G.add_node("A", label="A") + G.add_node("B", label="B") + G.add_edge("A", "B", label="a-b") + assert ( + graph_edit_distance(G, G.copy(), node_match=nmatch, edge_match=ematch) == 0 + ) + + def testSame(self): + G1 = nx.Graph() + G1.add_node("A", label="A") + G1.add_node("B", label="B") + G1.add_edge("A", "B", label="a-b") + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_edge("A", "B", label="a-b") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 0 + + def testOneEdgeLabelDiff(self): + G1 = nx.Graph() + G1.add_node("A", label="A") + G1.add_node("B", label="B") + G1.add_edge("A", "B", label="a-b") + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_edge("A", "B", label="bad") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 + + def testOneNodeLabelDiff(self): + G1 = nx.Graph() + G1.add_node("A", label="A") + G1.add_node("B", label="B") + G1.add_edge("A", "B", label="a-b") + G2 = nx.Graph() + G2.add_node("A", label="Z") + G2.add_node("B", label="B") + G2.add_edge("A", "B", label="a-b") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 + + def testOneExtraNode(self): + G1 = nx.Graph() + G1.add_node("A", label="A") + G1.add_node("B", label="B") + G1.add_edge("A", "B", label="a-b") + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_edge("A", "B", label="a-b") + G2.add_node("C", label="C") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 + + def testOneExtraEdge(self): + G1 = nx.Graph() + G1.add_node("A", label="A") + G1.add_node("B", label="B") + G1.add_node("C", label="C") + G1.add_node("C", label="C") + G1.add_edge("A", "B", label="a-b") + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("A", "C", label="a-c") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 + + def testOneExtraNodeAndEdge(self): + G1 = nx.Graph() + G1.add_node("A", label="A") + G1.add_node("B", label="B") + G1.add_edge("A", "B", label="a-b") + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("A", "C", label="a-c") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 + + def testGraph1(self): + G1 = getCanonical() + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("D", label="D") + G2.add_node("E", label="E") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("B", "D", label="b-d") + G2.add_edge("D", "E", label="d-e") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 3 + + def testGraph2(self): + G1 = getCanonical() + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_node("D", label="D") + G2.add_node("E", label="E") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("B", "C", label="b-c") + G2.add_edge("C", "D", label="c-d") + G2.add_edge("C", "E", label="c-e") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 4 + + def testGraph3(self): + G1 = getCanonical() + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_node("D", label="D") + G2.add_node("E", label="E") + G2.add_node("F", label="F") + G2.add_node("G", label="G") + G2.add_edge("A", "C", label="a-c") + G2.add_edge("A", "D", label="a-d") + G2.add_edge("D", "E", label="d-e") + G2.add_edge("D", "F", label="d-f") + G2.add_edge("D", "G", label="d-g") + G2.add_edge("E", "B", label="e-b") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 12 + + def testGraph4(self): + G1 = getCanonical() + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_node("D", label="D") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("B", "C", label="b-c") + G2.add_edge("C", "D", label="c-d") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 + + def testGraph4_a(self): + G1 = getCanonical() + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_node("D", label="D") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("B", "C", label="b-c") + G2.add_edge("A", "D", label="a-d") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 + + def testGraph4_b(self): + G1 = getCanonical() + G2 = nx.Graph() + G2.add_node("A", label="A") + G2.add_node("B", label="B") + G2.add_node("C", label="C") + G2.add_node("D", label="D") + G2.add_edge("A", "B", label="a-b") + G2.add_edge("B", "C", label="b-c") + G2.add_edge("B", "D", label="bad") + assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 + + def test_simrank_no_source_no_target(self): + G = nx.cycle_graph(5) + expected = { + 0: { + 0: 1, + 1: 0.3951219505902448, + 2: 0.5707317069281646, + 3: 0.5707317069281646, + 4: 0.3951219505902449, + }, + 1: { + 0: 0.3951219505902448, + 1: 1, + 2: 0.3951219505902449, + 3: 0.5707317069281646, + 4: 0.5707317069281646, + }, + 2: { + 0: 0.5707317069281646, + 1: 0.3951219505902449, + 2: 1, + 3: 0.3951219505902449, + 4: 0.5707317069281646, + }, + 3: { + 0: 0.5707317069281646, + 1: 0.5707317069281646, + 2: 0.3951219505902449, + 3: 1, + 4: 0.3951219505902449, + }, + 4: { + 0: 0.3951219505902449, + 1: 0.5707317069281646, + 2: 0.5707317069281646, + 3: 0.3951219505902449, + 4: 1, + }, + } + actual = nx.simrank_similarity(G) + assert expected == actual + + # For a DiGraph test, use the first graph from the paper cited in + # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126 + G = nx.DiGraph() + G.add_node(0, label="Univ") + G.add_node(1, label="ProfA") + G.add_node(2, label="ProfB") + G.add_node(3, label="StudentA") + G.add_node(4, label="StudentB") + G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)]) + + expected = { + 0: {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443}, + 1: {0: 0.0, 1: 1, 2: 0.4135512472705618, 3: 0.0, 4: 0.10586911930126384}, + 2: { + 0: 0.1323363991265798, + 1: 0.4135512472705618, + 2: 1, + 3: 0.04234764772050554, + 4: 0.08822426608438655, + }, + 3: {0: 0.0, 1: 0.0, 2: 0.04234764772050554, 3: 1, 4: 0.3308409978164495}, + 4: { + 0: 0.03387811817640443, + 1: 0.10586911930126384, + 2: 0.08822426608438655, + 3: 0.3308409978164495, + 4: 1, + }, + } + # Use the importance_factor from the paper to get the same numbers. + actual = nx.algorithms.similarity.simrank_similarity(G, importance_factor=0.8) + assert expected == actual + + def test_simrank_source_no_target(self): + G = nx.cycle_graph(5) + expected = { + 0: 1, + 1: 0.3951219505902448, + 2: 0.5707317069281646, + 3: 0.5707317069281646, + 4: 0.3951219505902449, + } + actual = nx.simrank_similarity(G, source=0) + assert expected == actual + + # For a DiGraph test, use the first graph from the paper cited in + # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126 + G = nx.DiGraph() + G.add_node(0, label="Univ") + G.add_node(1, label="ProfA") + G.add_node(2, label="ProfB") + G.add_node(3, label="StudentA") + G.add_node(4, label="StudentB") + G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)]) + + expected = {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443} + # Use the importance_factor from the paper to get the same numbers. + actual = nx.algorithms.similarity.simrank_similarity( + G, importance_factor=0.8, source=0 + ) + assert expected == actual + + def test_simrank_source_and_target(self): + G = nx.cycle_graph(5) + expected = 1 + actual = nx.simrank_similarity(G, source=0, target=0) + + # For a DiGraph test, use the first graph from the paper cited in + # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126 + G = nx.DiGraph() + G.add_node(0, label="Univ") + G.add_node(1, label="ProfA") + G.add_node(2, label="ProfB") + G.add_node(3, label="StudentA") + G.add_node(4, label="StudentB") + G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)]) + + expected = 0.1323363991265798 + # Use the importance_factor from the paper to get the same numbers. + # Use the pair (0,2) because (0,0) and (0,1) have trivial results. + actual = nx.algorithms.similarity.simrank_similarity( + G, importance_factor=0.8, source=0, target=2 + ) + assert expected == actual + + def test_simrank_numpy_no_source_no_target(self): + G = nx.cycle_graph(5) + expected = numpy.array( + [ + [ + 1.0, + 0.3947180735764555, + 0.570482097206368, + 0.570482097206368, + 0.3947180735764555, + ], + [ + 0.3947180735764555, + 1.0, + 0.3947180735764555, + 0.570482097206368, + 0.570482097206368, + ], + [ + 0.570482097206368, + 0.3947180735764555, + 1.0, + 0.3947180735764555, + 0.570482097206368, + ], + [ + 0.570482097206368, + 0.570482097206368, + 0.3947180735764555, + 1.0, + 0.3947180735764555, + ], + [ + 0.3947180735764555, + 0.570482097206368, + 0.570482097206368, + 0.3947180735764555, + 1.0, + ], + ] + ) + actual = nx.simrank_similarity_numpy(G) + numpy.testing.assert_allclose(expected, actual, atol=1e-7) + + def test_simrank_numpy_source_no_target(self): + G = nx.cycle_graph(5) + expected = numpy.array( + [ + 1.0, + 0.3947180735764555, + 0.570482097206368, + 0.570482097206368, + 0.3947180735764555, + ] + ) + actual = nx.simrank_similarity_numpy(G, source=0) + numpy.testing.assert_allclose(expected, actual, atol=1e-7) + + def test_simrank_numpy_source_and_target(self): + G = nx.cycle_graph(5) + expected = 1.0 + actual = nx.simrank_similarity_numpy(G, source=0, target=0) + numpy.testing.assert_allclose(expected, actual, atol=1e-7)