Mercurial > repos > shellac > sam_consensus_v3
diff 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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/tests/test_cluster.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,436 @@ +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}