File: //lib/python3/dist-packages/networkx/algorithms/tests/test_threshold.py
#!/usr/bin/env python
"""
Threshold Graphs
================
"""
import pytest
import networkx as nx
import networkx.algorithms.threshold as nxt
from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
from networkx.testing import almost_equal
cnlti = nx.convert_node_labels_to_integers
class TestGeneratorThreshold():
def test_threshold_sequence_graph_test(self):
G = nx.star_graph(10)
assert nxt.is_threshold_graph(G)
assert nxt.is_threshold_sequence(list(d for n, d in G.degree()))
G = nx.complete_graph(10)
assert nxt.is_threshold_graph(G)
assert nxt.is_threshold_sequence(list(d for n, d in G.degree()))
deg = [3, 2, 2, 1, 1, 1]
assert not nxt.is_threshold_sequence(deg)
deg = [3, 2, 2, 1]
assert nxt.is_threshold_sequence(deg)
G = nx.generators.havel_hakimi_graph(deg)
assert nxt.is_threshold_graph(G)
def test_creation_sequences(self):
deg = [3, 2, 2, 1]
G = nx.generators.havel_hakimi_graph(deg)
with pytest.raises(ValueError):
nxt.creation_sequence(deg, with_labels=True, compact=True)
cs0 = nxt.creation_sequence(deg)
H0 = nxt.threshold_graph(cs0)
assert ''.join(cs0) == 'ddid'
cs1 = nxt.creation_sequence(deg, with_labels=True)
H1 = nxt.threshold_graph(cs1)
assert cs1 == [(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')]
cs2 = nxt.creation_sequence(deg, compact=True)
H2 = nxt.threshold_graph(cs2)
assert cs2 == [2, 1, 1]
assert ''.join(nxt.uncompact(cs2)) == 'ddid'
assert graph_could_be_isomorphic(H0, G)
assert graph_could_be_isomorphic(H0, H1)
assert graph_could_be_isomorphic(H0, H2)
def test_make_compact(self):
assert nxt.make_compact(['d', 'd', 'd', 'i', 'd', 'd']) == [3, 1, 2]
assert nxt.make_compact([3, 1, 2]) == [3, 1, 2]
assert pytest.raises(TypeError, nxt.make_compact, [3., 1., 2.])
def test_uncompact(self):
assert nxt.uncompact([3, 1, 2]) == ['d', 'd', 'd', 'i', 'd', 'd']
assert nxt.uncompact(['d', 'd', 'i', 'd']) == ['d', 'd', 'i', 'd']
assert (nxt.uncompact(nxt.uncompact([(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])) ==
nxt.uncompact([(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')]))
assert pytest.raises(TypeError, nxt.uncompact, [3., 1., 2.])
def test_creation_sequence_to_weights(self):
assert nxt.creation_sequence_to_weights([3, 1, 2]) == [0.5, 0.5, 0.5, 0.25, 0.75, 0.75]
assert pytest.raises(TypeError, nxt.creation_sequence_to_weights, [3., 1., 2.])
def test_weights_to_creation_sequence(self):
deg = [3, 2, 2, 1]
with pytest.raises(ValueError):
nxt.weights_to_creation_sequence(deg, with_labels=True, compact=True)
assert (nxt.weights_to_creation_sequence(deg, with_labels=True) ==
[(3, 'd'), (1, 'd'), (2, 'd'), (0, 'd')])
assert nxt.weights_to_creation_sequence(deg, compact=True) == [4]
def test_find_alternating_4_cycle(self):
G = nx.Graph()
G.add_edge(1, 2)
assert not nxt.find_alternating_4_cycle(G)
def test_shortest_path(self):
deg = [3, 2, 2, 1]
G = nx.generators.havel_hakimi_graph(deg)
cs1 = nxt.creation_sequence(deg, with_labels=True)
for n, m in [(3, 0), (0, 3), (0, 2), (0, 1), (1, 3),
(3, 1), (1, 2), (2, 3)]:
assert (nxt.shortest_path(cs1, n, m) ==
nx.shortest_path(G, n, m))
spl = nxt.shortest_path_length(cs1, 3)
spl2 = nxt.shortest_path_length([t for v, t in cs1], 2)
assert spl == spl2
spld = {}
for j, pl in enumerate(spl):
n = cs1[j][0]
spld[n] = pl
assert spld == nx.single_source_shortest_path_length(G, 3)
assert nxt.shortest_path(['d', 'd', 'd', 'i', 'd', 'd'], 1, 2) == [1, 2]
assert nxt.shortest_path([3, 1, 2], 1, 2) == [1, 2]
assert pytest.raises(TypeError, nxt.shortest_path, [3., 1., 2.], 1, 2)
assert pytest.raises(ValueError, nxt.shortest_path, [3, 1, 2], 'a', 2)
assert pytest.raises(ValueError, nxt.shortest_path, [3, 1, 2], 1, 'b')
assert nxt.shortest_path([3, 1, 2], 1, 1) == [1]
def test_shortest_path_length(self):
assert nxt.shortest_path_length([3, 1, 2], 1) == [1, 0, 1, 2, 1, 1]
assert (nxt.shortest_path_length(['d', 'd', 'd', 'i', 'd', 'd'], 1) ==
[1, 0, 1, 2, 1, 1])
assert (nxt.shortest_path_length(('d', 'd', 'd', 'i', 'd', 'd'), 1) ==
[1, 0, 1, 2, 1, 1])
assert pytest.raises(TypeError, nxt.shortest_path, [3., 1., 2.], 1)
def random_threshold_sequence(self):
assert len(nxt.random_threshold_sequence(10, 0.5)) == 10
assert (nxt.random_threshold_sequence(10, 0.5, seed=42) ==
['d', 'i', 'd', 'd', 'd', 'i', 'i', 'i', 'd', 'd'])
assert pytest.raises(ValueError, nxt.random_threshold_sequence, 10, 1.5)
def test_right_d_threshold_sequence(self):
assert nxt.right_d_threshold_sequence(3, 2) == ['d', 'i', 'd']
assert pytest.raises(ValueError, nxt.right_d_threshold_sequence, 2, 3)
def test_left_d_threshold_sequence(self):
assert nxt.left_d_threshold_sequence(3, 2) == ['d', 'i', 'd']
assert pytest.raises(ValueError, nxt.left_d_threshold_sequence, 2, 3)
def test_weights_thresholds(self):
wseq = [3, 4, 3, 3, 5, 6, 5, 4, 5, 6]
cs = nxt.weights_to_creation_sequence(wseq, threshold=10)
wseq = nxt.creation_sequence_to_weights(cs)
cs2 = nxt.weights_to_creation_sequence(wseq)
assert cs == cs2
wseq = nxt.creation_sequence_to_weights(nxt.uncompact([3, 1, 2, 3, 3, 2, 3]))
assert (wseq ==
[s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]])
wseq = nxt.creation_sequence_to_weights([3, 1, 2, 3, 3, 2, 3])
assert (wseq ==
[s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]])
wseq = nxt.creation_sequence_to_weights(list(enumerate('ddidiiidididi')))
assert (wseq ==
[s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]])
wseq = nxt.creation_sequence_to_weights('ddidiiidididi')
assert (wseq ==
[s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]])
wseq = nxt.creation_sequence_to_weights('ddidiiidididid')
ws = [s / float(12) for s in [6, 6, 5, 7, 4, 4, 4, 8, 3, 9, 2, 10, 1, 11]]
assert sum([abs(c - d) for c, d in zip(wseq, ws)]) < 1e-14
def test_finding_routines(self):
G = nx.Graph({1: [2], 2: [3], 3: [4], 4: [5], 5: [6]})
G.add_edge(2, 4)
G.add_edge(2, 5)
G.add_edge(2, 7)
G.add_edge(3, 6)
G.add_edge(4, 6)
# Alternating 4 cycle
assert nxt.find_alternating_4_cycle(G) == [1, 2, 3, 6]
# Threshold graph
TG = nxt.find_threshold_graph(G)
assert nxt.is_threshold_graph(TG)
assert sorted(TG.nodes()) == [1, 2, 3, 4, 5, 7]
cs = nxt.creation_sequence(dict(TG.degree()), with_labels=True)
assert nxt.find_creation_sequence(G) == cs
def test_fast_versions_properties_threshold_graphs(self):
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
assert nxt.density('ddiiddid') == nx.density(G)
assert (sorted(nxt.degree_sequence(cs)) ==
sorted(d for n, d in G.degree()))
ts = nxt.triangle_sequence(cs)
assert ts == list(nx.triangles(G).values())
assert sum(ts) // 3 == nxt.triangles(cs)
c1 = nxt.cluster_sequence(cs)
c2 = list(nx.clustering(G).values())
assert almost_equal(sum([abs(c - d) for c, d in zip(c1, c2)]), 0)
b1 = nx.betweenness_centrality(G).values()
b2 = nxt.betweenness_sequence(cs)
assert sum([abs(c - d) for c, d in zip(b1, b2)]) < 1e-14
assert nxt.eigenvalues(cs) == [0, 1, 3, 3, 5, 7, 7, 8]
# Degree Correlation
assert abs(nxt.degree_correlation(cs) + 0.593038821954) < 1e-12
assert nxt.degree_correlation('diiiddi') == -0.8
assert nxt.degree_correlation('did') == -1.0
assert nxt.degree_correlation('ddd') == 1.0
assert nxt.eigenvalues('dddiii') == [0, 0, 0, 0, 3, 3]
assert nxt.eigenvalues('dddiiid') == [0, 1, 1, 1, 4, 4, 7]
def test_tg_creation_routines(self):
s = nxt.left_d_threshold_sequence(5, 7)
s = nxt.right_d_threshold_sequence(5, 7)
s1 = nxt.swap_d(s, 1.0, 1.0)
s1 = nxt.swap_d(s, 1.0, 1.0, seed=1)
def test_eigenvectors(self):
np = pytest.importorskip('numpy')
eigenval = np.linalg.eigvals
scipy = pytest.importorskip('scipy')
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
(tgeval, tgevec) = nxt.eigenvectors(cs)
dot = np.dot
assert [abs(dot(lv, lv) - 1.0) < 1e-9 for lv in tgevec] == [True] * 8
lapl = nx.laplacian_matrix(G)
# tgev=[ dot(lv,dot(lapl,lv)) for lv in tgevec ]
# assert_true(sum([abs(c-d) for c,d in zip(tgev,tgeval)]) < 1e-9)
# tgev.sort()
# lev=list(eigenval(lapl))
# lev.sort()
# assert_true(sum([abs(c-d) for c,d in zip(tgev,lev)]) < 1e-9)
def test_create_using(self):
cs = 'ddiiddid'
G = nxt.threshold_graph(cs)
assert pytest.raises(nx.exception.NetworkXError,
nxt.threshold_graph, cs, create_using=nx.DiGraph())
MG = nxt.threshold_graph(cs, create_using=nx.MultiGraph())
assert sorted(MG.edges()) == sorted(G.edges())