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mightyscape-1.1-deprecated/extensions/networkx/linalg/tests/test_modularity.py

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2020-07-30 01:16:18 +02:00
import pytest
numpy = pytest.importorskip('numpy')
npt = pytest.importorskip('numpy.testing')
scipy = pytest.importorskip('scipy')
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestModularity(object):
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". (Used for test_directed_laplacian)
cls.DG = nx.DiGraph()
cls.DG.add_edges_from(((1, 2), (1, 3), (3, 1), (3, 2), (3, 5), (4, 5), (4, 6),
(5, 4), (5, 6), (6, 4)))
def test_modularity(self):
"Modularity matrix"
B = numpy.matrix([[-1.125, 0.25, 0.25, 0.625, 0.],
[0.25, -0.5, 0.5, -0.25, 0.],
[0.25, 0.5, -0.5, -0.25, 0.],
[0.625, -0.25, -0.25, -0.125, 0.],
[0., 0., 0., 0., 0.]])
permutation = [4, 0, 1, 2, 3]
npt.assert_equal(nx.modularity_matrix(self.G), B)
npt.assert_equal(nx.modularity_matrix(self.G, nodelist=permutation),
B[numpy.ix_(permutation, permutation)])
def test_modularity_weight(self):
"Modularity matrix with weights"
B = numpy.matrix([[-1.125, 0.25, 0.25, 0.625, 0.],
[0.25, -0.5, 0.5, -0.25, 0.],
[0.25, 0.5, -0.5, -0.25, 0.],
[0.625, -0.25, -0.25, -0.125, 0.],
[0., 0., 0., 0., 0.]])
G_weighted = self.G.copy()
for n1, n2 in G_weighted.edges():
G_weighted.edges[n1, n2]["weight"] = 0.5
# The following test would fail in networkx 1.1
npt.assert_equal(nx.modularity_matrix(G_weighted), B)
# The following test that the modularity matrix get rescaled accordingly
npt.assert_equal(nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B)
def test_directed_modularity(self):
"Directed Modularity matrix"
B = numpy.matrix([[-0.2, 0.6, 0.8, -0.4, -0.4, -0.4],
[0., 0., 0., 0., 0., 0.],
[0.7, 0.4, -0.3, -0.6, 0.4, -0.6],
[-0.2, -0.4, -0.2, -0.4, 0.6, 0.6],
[-0.2, -0.4, -0.2, 0.6, -0.4, 0.6],
[-0.1, -0.2, -0.1, 0.8, -0.2, -0.2]])
node_permutation = [5, 1, 2, 3, 4, 6]
idx_permutation = [4, 0, 1, 2, 3, 5]
mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG))
npt.assert_equal(mm, B)
npt.assert_equal(nx.directed_modularity_matrix(self.DG,
nodelist=node_permutation),
B[numpy.ix_(idx_permutation, idx_permutation)])