annotate env/lib/python3.9/site-packages/networkx/algorithms/bipartite/tests/test_cluster.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

import networkx as nx
import pytest
from networkx.algorithms.bipartite.cluster import cc_dot, cc_min, cc_max
import networkx.algorithms.bipartite as bipartite


def test_pairwise_bipartite_cc_functions():
    # Test functions for different kinds of bipartite clustering coefficients
    # between pairs of nodes using 3 example graphs from figure 5 p. 40
    # Latapy et al (2008)
    G1 = nx.Graph([(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7)])
    G2 = nx.Graph([(0, 2), (0, 3), (0, 4), (1, 3), (1, 4), (1, 5)])
    G3 = nx.Graph(
        [(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9)]
    )
    result = {
        0: [1 / 3.0, 2 / 3.0, 2 / 5.0],
        1: [1 / 2.0, 2 / 3.0, 2 / 3.0],
        2: [2 / 8.0, 2 / 5.0, 2 / 5.0],
    }
    for i, G in enumerate([G1, G2, G3]):
        assert bipartite.is_bipartite(G)
        assert cc_dot(set(G[0]), set(G[1])) == result[i][0]
        assert cc_min(set(G[0]), set(G[1])) == result[i][1]
        assert cc_max(set(G[0]), set(G[1])) == result[i][2]


def test_star_graph():
    G = nx.star_graph(3)
    # all modes are the same
    answer = {0: 0, 1: 1, 2: 1, 3: 1}
    assert bipartite.clustering(G, mode="dot") == answer
    assert bipartite.clustering(G, mode="min") == answer
    assert bipartite.clustering(G, mode="max") == answer


def test_not_bipartite():
    with pytest.raises(nx.NetworkXError):
        bipartite.clustering(nx.complete_graph(4))


def test_bad_mode():
    with pytest.raises(nx.NetworkXError):
        bipartite.clustering(nx.path_graph(4), mode="foo")


def test_path_graph():
    G = nx.path_graph(4)
    answer = {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5}
    assert bipartite.clustering(G, mode="dot") == answer
    assert bipartite.clustering(G, mode="max") == answer
    answer = {0: 1, 1: 1, 2: 1, 3: 1}
    assert bipartite.clustering(G, mode="min") == answer


def test_average_path_graph():
    G = nx.path_graph(4)
    assert bipartite.average_clustering(G, mode="dot") == 0.5
    assert bipartite.average_clustering(G, mode="max") == 0.5
    assert bipartite.average_clustering(G, mode="min") == 1


def test_ra_clustering_davis():
    G = nx.davis_southern_women_graph()
    cc4 = round(bipartite.robins_alexander_clustering(G), 3)
    assert cc4 == 0.468


def test_ra_clustering_square():
    G = nx.path_graph(4)
    G.add_edge(0, 3)
    assert bipartite.robins_alexander_clustering(G) == 1.0


def test_ra_clustering_zero():
    G = nx.Graph()
    assert bipartite.robins_alexander_clustering(G) == 0
    G.add_nodes_from(range(4))
    assert bipartite.robins_alexander_clustering(G) == 0
    G.add_edges_from([(0, 1), (2, 3), (3, 4)])
    assert bipartite.robins_alexander_clustering(G) == 0
    G.add_edge(1, 2)
    assert bipartite.robins_alexander_clustering(G) == 0