PYTHON test_non_randomness.py

import pytest

import networkx as nx

np = pytest.importorskip("numpy")


@pytest.mark.parametrize(
    "k, weight, expected",
    [
        (None, None, 7.21),  # infers 3 communities
        (2, None, 11.7),
        (None, "weight", 25.45),
        (2, "weight", 38.8),
    ],
)
def test_non_randomness(k, weight, expected):
    G = nx.karate_club_graph()
    np.testing.assert_almost_equal(
        nx.non_randomness(G, k, weight)[0], expected, decimal=2
    )


def test_non_connected():
    G = nx.Graph([(1, 2)])
    G.add_node(3)
    with pytest.raises(nx.NetworkXException, match="Non connected"):
        nx.non_randomness(G)


def test_self_loops():
    G = nx.Graph()
    G.add_edge(1, 2)
    G.add_edge(1, 1)
    with pytest.raises(nx.NetworkXError, match="Graph must not contain self-loops"):
        nx.non_randomness(G)


def test_empty_graph():
    G = nx.empty_graph(1)
    with pytest.raises(nx.NetworkXError, match=".*not applicable to empty graphs"):
        nx.non_randomness(G)


@pytest.mark.parametrize("k", [-1, 0, 2, 5])
def test_value_error(k):
    """
    Check that invalid values of k raise (must be between 1 and n - 1, inclusive,
    and such that the probability is between 0 and 1, exclusive).
    """
    G = nx.path_graph(5)
    with pytest.raises(ValueError, match=r"invalid number of communities"):
        nx.non_randomness(G, k=k)


@pytest.mark.parametrize("G", [nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()])
def test_not_implemented(G):
    """Check that non-randomness is not implemented for directed or multigraphs."""
    with pytest.raises(nx.NetworkXNotImplemented, match=r"not implemented for"):
        nx.non_randomness(G)