Mercurial > repos > shellac > sam_consensus_v3
view env/lib/python3.9/site-packages/networkx/algorithms/tests/test_cluster.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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import networkx as nx class TestTriangles: def test_empty(self): G = nx.Graph() assert list(nx.triangles(G).values()) == [] def test_path(self): G = nx.path_graph(10) assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] assert nx.triangles(G) == { 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, } def test_cubical(self): G = nx.cubical_graph() assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0] assert nx.triangles(G, 1) == 0 assert list(nx.triangles(G, [1, 2]).values()) == [0, 0] assert nx.triangles(G, 1) == 0 assert nx.triangles(G, [1, 2]) == {1: 0, 2: 0} def test_k5(self): G = nx.complete_graph(5) assert list(nx.triangles(G).values()) == [6, 6, 6, 6, 6] assert sum(nx.triangles(G).values()) / 3.0 == 10 assert nx.triangles(G, 1) == 6 G.remove_edge(1, 2) assert list(nx.triangles(G).values()) == [5, 3, 3, 5, 5] assert nx.triangles(G, 1) == 3 class TestDirectedClustering: def test_clustering(self): G = nx.DiGraph() assert list(nx.clustering(G).values()) == [] assert nx.clustering(G) == {} def test_path(self): G = nx.path_graph(10, create_using=nx.DiGraph()) assert list(nx.clustering(G).values()) == [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] assert nx.clustering(G) == { 0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, } def test_k5(self): G = nx.complete_graph(5, create_using=nx.DiGraph()) assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1] assert nx.average_clustering(G) == 1 G.remove_edge(1, 2) assert list(nx.clustering(G).values()) == [ 11.0 / 12.0, 1.0, 1.0, 11.0 / 12.0, 11.0 / 12.0, ] assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 11.0 / 12.0} G.remove_edge(2, 1) assert list(nx.clustering(G).values()) == [ 5.0 / 6.0, 1.0, 1.0, 5.0 / 6.0, 5.0 / 6.0, ] assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337} def test_triangle_and_edge(self): G = nx.cycle_graph(3, create_using=nx.DiGraph()) G.add_edge(0, 4) assert nx.clustering(G)[0] == 1.0 / 6.0 class TestDirectedWeightedClustering: def test_clustering(self): G = nx.DiGraph() assert list(nx.clustering(G, weight="weight").values()) == [] assert nx.clustering(G) == {} def test_path(self): G = nx.path_graph(10, create_using=nx.DiGraph()) assert list(nx.clustering(G, weight="weight").values()) == [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] assert nx.clustering(G, weight="weight") == { 0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, } def test_k5(self): G = nx.complete_graph(5, create_using=nx.DiGraph()) assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1] assert nx.average_clustering(G, weight="weight") == 1 G.remove_edge(1, 2) assert list(nx.clustering(G, weight="weight").values()) == [ 11.0 / 12.0, 1.0, 1.0, 11.0 / 12.0, 11.0 / 12.0, ] assert nx.clustering(G, [1, 4], weight="weight") == {1: 1.0, 4: 11.0 / 12.0} G.remove_edge(2, 1) assert list(nx.clustering(G, weight="weight").values()) == [ 5.0 / 6.0, 1.0, 1.0, 5.0 / 6.0, 5.0 / 6.0, ] assert nx.clustering(G, [1, 4], weight="weight") == { 1: 1.0, 4: 0.83333333333333337, } def test_triangle_and_edge(self): G = nx.cycle_graph(3, create_using=nx.DiGraph()) G.add_edge(0, 4, weight=2) assert nx.clustering(G)[0] == 1.0 / 6.0 assert nx.clustering(G, weight="weight")[0] == 1.0 / 12.0 class TestWeightedClustering: def test_clustering(self): G = nx.Graph() assert list(nx.clustering(G, weight="weight").values()) == [] assert nx.clustering(G) == {} def test_path(self): G = nx.path_graph(10) assert list(nx.clustering(G, weight="weight").values()) == [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] assert nx.clustering(G, weight="weight") == { 0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, } def test_cubical(self): G = nx.cubical_graph() assert list(nx.clustering(G, weight="weight").values()) == [ 0, 0, 0, 0, 0, 0, 0, 0, ] assert nx.clustering(G, 1) == 0 assert list(nx.clustering(G, [1, 2], weight="weight").values()) == [0, 0] assert nx.clustering(G, 1, weight="weight") == 0 assert nx.clustering(G, [1, 2], weight="weight") == {1: 0, 2: 0} def test_k5(self): G = nx.complete_graph(5) assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1] assert nx.average_clustering(G, weight="weight") == 1 G.remove_edge(1, 2) assert list(nx.clustering(G, weight="weight").values()) == [ 5.0 / 6.0, 1.0, 1.0, 5.0 / 6.0, 5.0 / 6.0, ] assert nx.clustering(G, [1, 4], weight="weight") == { 1: 1.0, 4: 0.83333333333333337, } def test_triangle_and_edge(self): G = nx.cycle_graph(3) G.add_edge(0, 4, weight=2) assert nx.clustering(G)[0] == 1.0 / 3.0 assert nx.clustering(G, weight="weight")[0] == 1.0 / 6.0 class TestClustering: def test_clustering(self): G = nx.Graph() assert list(nx.clustering(G).values()) == [] assert nx.clustering(G) == {} def test_path(self): G = nx.path_graph(10) assert list(nx.clustering(G).values()) == [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] assert nx.clustering(G) == { 0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, } def test_cubical(self): G = nx.cubical_graph() assert list(nx.clustering(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0] assert nx.clustering(G, 1) == 0 assert list(nx.clustering(G, [1, 2]).values()) == [0, 0] assert nx.clustering(G, 1) == 0 assert nx.clustering(G, [1, 2]) == {1: 0, 2: 0} def test_k5(self): G = nx.complete_graph(5) assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1] assert nx.average_clustering(G) == 1 G.remove_edge(1, 2) assert list(nx.clustering(G).values()) == [ 5.0 / 6.0, 1.0, 1.0, 5.0 / 6.0, 5.0 / 6.0, ] assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337} class TestTransitivity: def test_transitivity(self): G = nx.Graph() assert nx.transitivity(G) == 0.0 def test_path(self): G = nx.path_graph(10) assert nx.transitivity(G) == 0.0 def test_cubical(self): G = nx.cubical_graph() assert nx.transitivity(G) == 0.0 def test_k5(self): G = nx.complete_graph(5) assert nx.transitivity(G) == 1.0 G.remove_edge(1, 2) assert nx.transitivity(G) == 0.875 class TestSquareClustering: def test_clustering(self): G = nx.Graph() assert list(nx.square_clustering(G).values()) == [] assert nx.square_clustering(G) == {} def test_path(self): G = nx.path_graph(10) assert list(nx.square_clustering(G).values()) == [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] assert nx.square_clustering(G) == { 0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, } def test_cubical(self): G = nx.cubical_graph() assert list(nx.square_clustering(G).values()) == [ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, ] assert list(nx.square_clustering(G, [1, 2]).values()) == [0.5, 0.5] assert nx.square_clustering(G, [1])[1] == 0.5 assert nx.square_clustering(G, [1, 2]) == {1: 0.5, 2: 0.5} def test_k5(self): G = nx.complete_graph(5) assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1] def test_bipartite_k5(self): G = nx.complete_bipartite_graph(5, 5) assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] def test_lind_square_clustering(self): """Test C4 for figure 1 Lind et al (2005)""" G = nx.Graph( [ (1, 2), (1, 3), (1, 6), (1, 7), (2, 4), (2, 5), (3, 4), (3, 5), (6, 7), (7, 8), (6, 8), (7, 9), (7, 10), (6, 11), (6, 12), (2, 13), (2, 14), (3, 15), (3, 16), ] ) G1 = G.subgraph([1, 2, 3, 4, 5, 13, 14, 15, 16]) G2 = G.subgraph([1, 6, 7, 8, 9, 10, 11, 12]) assert nx.square_clustering(G, [1])[1] == 3 / 75.0 assert nx.square_clustering(G1, [1])[1] == 2 / 6.0 assert nx.square_clustering(G2, [1])[1] == 1 / 5.0 def test_average_clustering(): G = nx.cycle_graph(3) G.add_edge(2, 3) assert nx.average_clustering(G) == (1 + 1 + 1 / 3.0) / 4.0 assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3.0) / 4.0 assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3.0) / 3.0 class TestGeneralizedDegree: def test_generalized_degree(self): G = nx.Graph() assert nx.generalized_degree(G) == {} def test_path(self): G = nx.path_graph(5) assert nx.generalized_degree(G, 0) == {0: 1} assert nx.generalized_degree(G, 1) == {0: 2} def test_cubical(self): G = nx.cubical_graph() assert nx.generalized_degree(G, 0) == {0: 3} def test_k5(self): G = nx.complete_graph(5) assert nx.generalized_degree(G, 0) == {3: 4} G.remove_edge(0, 1) assert nx.generalized_degree(G, 0) == {2: 3}