def test_random_modular_graph_between_fraction():
"""Test for graphs with non-zero between_fraction"""
# We need to measure the degree within/between modules
nnods = 120, 240, 360
nmods = 2, 3, 4
av_degrees = 8, 10, 16
btwn_fractions = 0, 0.1, 0.3, 0.5
for nnod in nnods:
for nmod in nmods:
for av_degree in av_degrees:
for btwn_fraction in btwn_fractions:
g = mod.random_modular_graph(nnod, nmod, av_degree,
btwn_fraction)
# First, check the average degree.
av_degree_actual = np.mean(list(g.degree().values()))
# Since we are generating random graphs, the actual average
# degree we get may be off from the reuqested one by a bit.
# We allow it to be off by up to 1.
#print 'av deg:',av_degree, av_degree_actual # dbg
nt.assert_true(
abs(av_degree - av_degree_actual) < 1.5,
"""av deg: %.2f av deg actual: %.2f -
This is a stochastic test - repeat to confirm.""" %
(av_degree, av_degree_actual))
# Now, check the between fraction
mat = nx.adj_matrix(g)
#compute the total number of edges in the real graph
nedg = nx.number_of_edges(g)
# sanity checks:
if nnod % nmod:
raise ValueError("nmod must divide nnod evenly")
#Compute the of nodes per module
nnod_mod = nnod / nmod
#compute what the values are in the real graph
blocks = [np.ones((nnod_mod, nnod_mod))] * nmod
mask = util.diag_stack(blocks)
mask[mask == 0] = 2
mask = np.triu(mask, 1)
btwn_real = np.sum(mat[mask == 2].flatten())
btwn_real_frac = btwn_real / nedg
#compare to what the actual values are
nt.assert_almost_equal(
btwn_fraction, btwn_real_frac, 1,
"This is a stochastic test, repeat to confirm failure")
nt.assert_true(
abs(btwn_fraction - btwn_real_frac) < 0.1,
"""btwn fraction: %.2f real btwn frac: %.2f -
This is a stochastic test - repeat to confirm.""" %
(btwn_fraction, btwn_real_frac))
def test_random_modular_graph_between_fraction():
"""Test for graphs with non-zero between_fraction"""
# We need to measure the degree within/between modules
nnods = 120, 240, 360
nmods = 2, 3, 4
av_degrees = 8, 10, 16
btwn_fractions = 0, 0.1, 0.3, 0.5
for nnod in nnods:
for nmod in nmods:
for av_degree in av_degrees:
for btwn_fraction in btwn_fractions:
g = mod.random_modular_graph(nnod, nmod, av_degree,
btwn_fraction)
# First, check the average degree.
av_degree_actual = np.mean(list(g.degree().values()))
# Since we are generating random graphs, the actual average
# degree we get may be off from the reuqested one by a bit.
# We allow it to be off by up to 1.
#print 'av deg:',av_degree, av_degree_actual # dbg
nt.assert_true (abs(av_degree-av_degree_actual)<1.5,
"""av deg: %.2f av deg actual: %.2f -
This is a stochastic test - repeat to confirm.""" %
(av_degree, av_degree_actual))
# Now, check the between fraction
mat = nx.adj_matrix(g)
#compute the total number of edges in the real graph
nedg = nx.number_of_edges(g)
# sanity checks:
if nnod%nmod:
raise ValueError("nmod must divide nnod evenly")
#Compute the of nodes per module
nnod_mod = nnod/nmod
#compute what the values are in the real graph
blocks = [np.ones((nnod_mod, nnod_mod))] * nmod
mask = util.diag_stack(blocks)
mask[mask==0] = 2
mask = np.triu(mask,1)
btwn_real = np.sum(mat[mask == 2].flatten())
btwn_real_frac = btwn_real / nedg
#compare to what the actual values are
nt.assert_almost_equal(btwn_fraction,
btwn_real_frac, 1,
"This is a stochastic test, repeat to confirm failure")
nt.assert_true(abs(btwn_fraction - btwn_real_frac) < 0.1,
"""btwn fraction: %.2f real btwn frac: %.2f -
This is a stochastic test - repeat to confirm.""" %
(btwn_fraction, btwn_real_frac))
def test_diag_stack():
"""Manual verification of simple stacking."""
a = np.empty((2,2))
a.fill(1)
b = np.empty((3,3))
b.fill(2)
c = np.empty((2,3))
c.fill(3)
d = util.diag_stack((a,b,c))
d_true = np.array([[ 1., 1., 0., 0., 0., 0., 0., 0.],
[ 1., 1., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 2., 2., 2., 0., 0., 0.],
[ 0., 0., 2., 2., 2., 0., 0., 0.],
[ 0., 0., 2., 2., 2., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 3., 3., 3.],
[ 0., 0., 0., 0., 0., 3., 3., 3.]])
npt.assert_equal(d, d_true)
def test_diag_stack():
"""Manual verification of simple stacking."""
a = np.empty((2, 2))
a.fill(1)
b = np.empty((3, 3))
b.fill(2)
c = np.empty((2, 3))
c.fill(3)
d = util.diag_stack((a, b, c))
d_true = np.array([[1., 1., 0., 0., 0., 0., 0., 0.],
[1., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 2., 2., 2., 0., 0., 0.],
[0., 0., 2., 2., 2., 0., 0., 0.],
[0., 0., 2., 2., 2., 0., 0., 0.],
[0., 0., 0., 0., 0., 3., 3., 3.],
[0., 0., 0., 0., 0., 3., 3., 3.]])
npt.assert_equal(d, d_true)