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"""Unit tests for the :mod:`networkx.algorithms.cuts` module."""
import networkx as nx
class TestCutSize:
"""Unit tests for the :func:`~networkx.cut_size` function."""
def test_symmetric(self):
"""Tests that the cut size is symmetric."""
G = nx.barbell_graph(3, 0)
S = {0, 1, 4}
T = {2, 3, 5}
assert nx.cut_size(G, S, T) == 4
assert nx.cut_size(G, T, S) == 4
def test_single_edge(self):
"""Tests for a cut of a single edge."""
G = nx.barbell_graph(3, 0)
S = {0, 1, 2}
T = {3, 4, 5}
assert nx.cut_size(G, S, T) == 1
assert nx.cut_size(G, T, S) == 1
def test_directed(self):
"""Tests that each directed edge is counted once in the cut."""
G = nx.barbell_graph(3, 0).to_directed()
S = {0, 1, 2}
T = {3, 4, 5}
assert nx.cut_size(G, S, T) == 2
assert nx.cut_size(G, T, S) == 2
def test_directed_symmetric(self):
"""Tests that a cut in a directed graph is symmetric."""
G = nx.barbell_graph(3, 0).to_directed()
S = {0, 1, 4}
T = {2, 3, 5}
assert nx.cut_size(G, S, T) == 8
assert nx.cut_size(G, T, S) == 8
def test_multigraph(self):
"""Tests that parallel edges are each counted for a cut."""
G = nx.MultiGraph(["ab", "ab"])
assert nx.cut_size(G, {"a"}, {"b"}) == 2
class TestVolume:
"""Unit tests for the :func:`~networkx.volume` function."""
def test_graph(self):
G = nx.cycle_graph(4)
assert nx.volume(G, {0, 1}) == 4
def test_digraph(self):
G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0)])
assert nx.volume(G, {0, 1}) == 2
def test_multigraph(self):
edges = list(nx.cycle_graph(4).edges())
G = nx.MultiGraph(edges * 2)
assert nx.volume(G, {0, 1}) == 8
def test_multidigraph(self):
edges = [(0, 1), (1, 2), (2, 3), (3, 0)]
G = nx.MultiDiGraph(edges * 2)
assert nx.volume(G, {0, 1}) == 4
def test_barbell(self):
G = nx.barbell_graph(3, 0)
assert nx.volume(G, {0, 1, 2}) == 7
assert nx.volume(G, {3, 4, 5}) == 7
class TestNormalizedCutSize:
"""Unit tests for the :func:`~networkx.normalized_cut_size` function."""
def test_graph(self):
G = nx.path_graph(4)
S = {1, 2}
T = set(G) - S
size = nx.normalized_cut_size(G, S, T)
# The cut looks like this: o-{-o--o-}-o
expected = 2 * ((1 / 4) + (1 / 2))
assert expected == size
# Test with no input T
assert expected == nx.normalized_cut_size(G, S)
def test_directed(self):
G = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
S = {1, 2}
T = set(G) - S
size = nx.normalized_cut_size(G, S, T)
# The cut looks like this: o-{->o-->o-}->o
expected = 2 * ((1 / 2) + (1 / 1))
assert expected == size
# Test with no input T
assert expected == nx.normalized_cut_size(G, S)
class TestConductance:
"""Unit tests for the :func:`~networkx.conductance` function."""
def test_graph(self):
G = nx.barbell_graph(5, 0)
# Consider the singleton sets containing the "bridge" nodes.
# There is only one cut edge, and each set has volume five.
S = {4}
T = {5}
conductance = nx.conductance(G, S, T)
expected = 1 / 5
assert expected == conductance
# Test with no input T
G2 = nx.barbell_graph(3, 0)
# There is only one cut edge, and each set has volume seven.
S2 = {0, 1, 2}
assert nx.conductance(G2, S2) == 1 / 7
class TestEdgeExpansion:
"""Unit tests for the :func:`~networkx.edge_expansion` function."""
def test_graph(self):
G = nx.barbell_graph(5, 0)
S = set(range(5))
T = set(G) - S
expansion = nx.edge_expansion(G, S, T)
expected = 1 / 5
assert expected == expansion
# Test with no input T
assert expected == nx.edge_expansion(G, S)
class TestNodeExpansion:
"""Unit tests for the :func:`~networkx.node_expansion` function."""
def test_graph(self):
G = nx.path_graph(8)
S = {3, 4, 5}
expansion = nx.node_expansion(G, S)
# The neighborhood of S has cardinality five, and S has
# cardinality three.
expected = 5 / 3
assert expected == expansion
class TestBoundaryExpansion:
"""Unit tests for the :func:`~networkx.boundary_expansion` function."""
def test_graph(self):
G = nx.complete_graph(10)
S = set(range(4))
expansion = nx.boundary_expansion(G, S)
# The node boundary of S has cardinality six, and S has
# cardinality three.
expected = 6 / 4
assert expected == expansion
class TestMixingExpansion:
"""Unit tests for the :func:`~networkx.mixing_expansion` function."""
def test_graph(self):
G = nx.barbell_graph(5, 0)
S = set(range(5))
T = set(G) - S
expansion = nx.mixing_expansion(G, S, T)
# There is one cut edge, and the total number of edges in the
# graph is twice the total number of edges in a clique of size
# five, plus one more for the bridge.
expected = 1 / (2 * (5 * 4 + 1))
assert expected == expansion
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