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Diffstat (limited to '.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_cluster.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_cluster.py | 549 |
1 files changed, 549 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_cluster.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_cluster.py new file mode 100644 index 00000000..b656ba81 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/networkx/algorithms/tests/test_cluster.py @@ -0,0 +1,549 @@ +import pytest + +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 == 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 + G.add_edge(3, 3) # ignore self-edges + assert list(nx.triangles(G).values()) == [5, 3, 3, 5, 5] + assert nx.triangles(G, 3) == 5 + + +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, + ] + assert nx.clustering(G) == { + 0: 0, + 1: 0, + 2: 0, + 3: 0, + 4: 0, + 5: 0, + 6: 0, + 7: 0, + 8: 0, + 9: 0, + } + assert nx.clustering(G, 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 / 12, + 1, + 1, + 11 / 12, + 11 / 12, + ] + assert nx.clustering(G, [1, 4]) == {1: 1, 4: 11 / 12} + G.remove_edge(2, 1) + assert list(nx.clustering(G).values()) == [ + 5 / 6, + 1, + 1, + 5 / 6, + 5 / 6, + ] + assert nx.clustering(G, [1, 4]) == {1: 1, 4: 0.83333333333333337} + assert nx.clustering(G, 4) == 5 / 6 + + 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 / 6 + + +class TestDirectedWeightedClustering: + @classmethod + def setup_class(cls): + global np + np = pytest.importorskip("numpy") + + 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, + ] + assert nx.clustering(G, weight="weight") == { + 0: 0, + 1: 0, + 2: 0, + 3: 0, + 4: 0, + 5: 0, + 6: 0, + 7: 0, + 8: 0, + 9: 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 / 12, + 1, + 1, + 11 / 12, + 11 / 12, + ] + assert nx.clustering(G, [1, 4], weight="weight") == {1: 1, 4: 11 / 12} + G.remove_edge(2, 1) + assert list(nx.clustering(G, weight="weight").values()) == [ + 5 / 6, + 1, + 1, + 5 / 6, + 5 / 6, + ] + assert nx.clustering(G, [1, 4], weight="weight") == { + 1: 1, + 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 / 6 + # Relaxed comparisons to allow graphblas-algorithms to pass tests + np.testing.assert_allclose(nx.clustering(G, weight="weight")[0], 1 / 12) + np.testing.assert_allclose(nx.clustering(G, 0, weight="weight"), 1 / 12) + + +class TestWeightedClustering: + @classmethod + def setup_class(cls): + global np + np = pytest.importorskip("numpy") + + 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, + ] + assert nx.clustering(G, weight="weight") == { + 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.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 / 6, + 1, + 1, + 5 / 6, + 5 / 6, + ] + assert nx.clustering(G, [1, 4], weight="weight") == { + 1: 1, + 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 / 3 + np.testing.assert_allclose(nx.clustering(G, weight="weight")[0], 1 / 6) + np.testing.assert_allclose(nx.clustering(G, 0, weight="weight"), 1 / 6) + + def test_triangle_and_signed_edge(self): + G = nx.cycle_graph(3) + G.add_edge(0, 1, weight=-1) + G.add_edge(3, 0, weight=0) + assert nx.clustering(G)[0] == 1 / 3 + assert nx.clustering(G, weight="weight")[0] == -1 / 3 + + +class TestClustering: + @classmethod + def setup_class(cls): + pytest.importorskip("numpy") + + 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, + ] + assert nx.clustering(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.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 / 6, + 1, + 1, + 5 / 6, + 5 / 6, + ] + assert nx.clustering(G, [1, 4]) == {1: 1, 4: 0.83333333333333337} + + def test_k5_signed(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) + G.add_edge(0, 1, weight=-1) + assert list(nx.clustering(G, weight="weight").values()) == [ + 1 / 6, + -1 / 3, + 1, + 3 / 6, + 3 / 6, + ] + + +class TestTransitivity: + def test_transitivity(self): + G = nx.Graph() + assert nx.transitivity(G) == 0 + + def test_path(self): + G = nx.path_graph(10) + assert nx.transitivity(G) == 0 + + def test_cubical(self): + G = nx.cubical_graph() + assert nx.transitivity(G) == 0 + + def test_k5(self): + G = nx.complete_graph(5) + assert nx.transitivity(G) == 1 + 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, + ] + assert nx.square_clustering(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.square_clustering(G).values()) == [ + 1 / 3, + 1 / 3, + 1 / 3, + 1 / 3, + 1 / 3, + 1 / 3, + 1 / 3, + 1 / 3, + ] + assert list(nx.square_clustering(G, [1, 2]).values()) == [1 / 3, 1 / 3] + assert nx.square_clustering(G, [1])[1] == 1 / 3 + assert nx.square_clustering(G, 1) == 1 / 3 + assert nx.square_clustering(G, [1, 2]) == {1: 1 / 3, 2: 1 / 3} + + 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 / 43 + assert nx.square_clustering(G1, [1])[1] == 2 / 6 + assert nx.square_clustering(G2, [1])[1] == 1 / 5 + + def test_peng_square_clustering(self): + """Test eq2 for figure 1 Peng et al (2008)""" + G = nx.Graph([(1, 2), (1, 3), (2, 4), (3, 4), (3, 5), (3, 6)]) + assert nx.square_clustering(G, [1])[1] == 1 / 3 + + def test_self_loops_square_clustering(self): + G = nx.path_graph(5) + assert nx.square_clustering(G) == {0: 0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0} + G.add_edges_from([(0, 0), (1, 1), (2, 2)]) + assert nx.square_clustering(G) == {0: 1, 1: 0.5, 2: 0.2, 3: 0.0, 4: 0} + + +class TestAverageClustering: + @classmethod + def setup_class(cls): + pytest.importorskip("numpy") + + def test_empty(self): + G = nx.Graph() + with pytest.raises(ZeroDivisionError): + nx.average_clustering(G) + + def test_average_clustering(self): + G = nx.cycle_graph(3) + G.add_edge(2, 3) + assert nx.average_clustering(G) == (1 + 1 + 1 / 3) / 4 + assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3) / 4 + assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3) / 3 + assert nx.average_clustering(G, [1, 2, 3]) == (1 + 1 / 3) / 3 + assert nx.average_clustering(G, [1, 2, 3], count_zeros=True) == (1 + 1 / 3) / 3 + assert nx.average_clustering(G, [1, 2, 3], count_zeros=False) == (1 + 1 / 3) / 2 + + def test_average_clustering_signed(self): + G = nx.cycle_graph(3) + G.add_edge(2, 3) + G.add_edge(0, 1, weight=-1) + assert nx.average_clustering(G, weight="weight") == (-1 - 1 - 1 / 3) / 4 + assert ( + nx.average_clustering(G, weight="weight", count_zeros=True) + == (-1 - 1 - 1 / 3) / 4 + ) + assert ( + nx.average_clustering(G, weight="weight", count_zeros=False) + == (-1 - 1 - 1 / 3) / 3 + ) + + +class TestDirectedAverageClustering: + @classmethod + def setup_class(cls): + pytest.importorskip("numpy") + + def test_empty(self): + G = nx.DiGraph() + with pytest.raises(ZeroDivisionError): + nx.average_clustering(G) + + def test_average_clustering(self): + G = nx.cycle_graph(3, create_using=nx.DiGraph()) + G.add_edge(2, 3) + assert nx.average_clustering(G) == (1 + 1 + 1 / 3) / 8 + assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3) / 8 + assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3) / 6 + assert nx.average_clustering(G, [1, 2, 3]) == (1 + 1 / 3) / 6 + assert nx.average_clustering(G, [1, 2, 3], count_zeros=True) == (1 + 1 / 3) / 6 + assert nx.average_clustering(G, [1, 2, 3], count_zeros=False) == (1 + 1 / 3) / 4 + + +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} + assert nx.generalized_degree(G, [1, 2]) == {1: {2: 3}, 2: {2: 2, 3: 2}} + assert nx.generalized_degree(G) == { + 0: {2: 3}, + 1: {2: 3}, + 2: {2: 2, 3: 2}, + 3: {2: 2, 3: 2}, + 4: {2: 2, 3: 2}, + } |