def test_attractors_fixed_sized(self):
"""
``attractors`` should raise an error if ``net`` is either a fixed sized
network or a networkx digraph, and ``size`` is not ``None``
"""
with self.assertRaises(ValueError):
attractors(MockFixedSizedNetwork(), size=5)
def test_attractors_variable_sized(self):
"""
``attractors`` should raise an error if ``net`` is a variable sized
network and ``size`` is ``None``
"""
with self.assertRaises(ValueError):
attractors(ECA(30), size=None)
def test_attractors_transition_graph(self):
"""
test ``attractors`` on ``s_pombe`` transition graph
"""
att_from_graph = attractors(transition_graph(s_pombe))
att_from_network = attractors(s_pombe)
for (a, b) in zip(att_from_graph, att_from_network):
a.sort()
b.sort()
att_from_graph.sort()
att_from_network.sort()
self.assertEqual(att_from_network, att_from_graph)
def test_attractors_wtnetworks(self):
"""
test ``attractors`` on WTNetworks
"""
networks = [(s_pombe, 13), (s_cerevisiae, 7), (c_elegans, 5)]
for net, size in networks:
self.assertEqual(size, len(attractors(net)))
def test_complex(self):
net = nb.WTNetwork([
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, -1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, -1, -1, 1, 1, 0, 0, 0, 0],
[0, -1, 0, -1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, -1, 1, 0, 0],
[0, 0, 0, 0, 1, 0, -1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, -1, -1],
[0, 0, 0, 0, -1, 0, 0, 0, 1, 0],
])
g = net.to_networkx_graph()
self.assertEqual([[8, 9], [6, 7], [4, 5], [0, 1], [2, 3]],
list(map(list, nx.strongly_connected_components(g))))
self.assertEqual([(2, 1), (2, 0), (3, 2), (4, 2)],
list(nx.condensation(g).edges))
expect = ns.attractors(net)
got = main.attractors(net)
self.assertUnorderedEqual(expect, got)
def test_attractors_eca(self):
"""
test ``attractors`` on ECA
"""
networks = [(ECA(30), 2, 3), (ECA(30), 3, 1), (ECA(30), 4, 4),
(ECA(30), 5, 2), (ECA(30), 6, 3), (ECA(110), 2, 1),
(ECA(110), 3, 1), (ECA(110), 4, 3), (ECA(110), 5, 1),
(ECA(110), 6, 3)]
for rule, width, size in networks:
self.assertEqual(size, len(attractors(rule, width)))
def test_attractors_invalid_net(self):
"""
``attractors`` should raise an error if ``net`` is neither a network
nor a networkx digraph
"""
with self.assertRaises(TypeError):
attractors('blah')
with self.assertRaises(TypeError):
attractors(MockObject())
with self.assertRaises(TypeError):
attractors(nx.Graph())
def test_moderate_grns(self):
k, N = 0, 0
dex = DataDex()
data = dex.select(['name', 'filename'],
['is_biological', 'node_count <= 22'])
for name, filename in data:
exp_filename = os.path.join(filename, 'expressions.txt')
ext_filename = os.path.join(filename, 'external.txt')
net = nb.LogicNetwork.read_logic(exp_filename, ext_filename)
g = net.to_networkx_graph()
print()
print("{}: {}".format(name, net.size))
print(" Modules: {}".format(
list(map(len, nx.strongly_connected_components(g)))))
print(" DAG: {}".format(list(nx.condensation(g).edges())))
start = time.time()
got = main.attractors(net)
stop = time.time()
modular = stop - start
print(" Modular Time: {}s".format(modular))
start = time.time()
expected = ns.attractors(net)
stop = time.time()
brute_force = stop - start
print(" Brute Force Time: {}s".format(brute_force))
print(" Factor: {}".format(modular / brute_force))
k += modular / brute_force
N += 1
self.assertUnorderedEqual(expected, got, msg=name)
self.assertUnorderedEqual(got, expected, msg=name)
print(k / N)
self.assertTrue(k / N < 1.0,
msg='algorithm is slower than brute force on average')
def test_builtins(self):
nets = [
s_pombe, s_cerevisiae, c_elegans, p53_dmg, p53_no_dmg,
mouse_cortical_7B, mouse_cortical_7C, myeloid
]
k, N = 0, 0
for net in nets:
name = net.metadata['name']
g = net.to_networkx_graph()
print()
print("{}: {}".format(name, net.size))
print(" Modules: {}".format(
list(map(len, nx.strongly_connected_components(g)))))
print(" DAG: {}".format(list(nx.condensation(g).edges())))
start = time.time()
got = main.attractors(net)
stop = time.time()
modular = stop - start
print(" Modular Time: {}s".format(modular))
start = time.time()
expected = ns.attractors(net)
stop = time.time()
brute_force = stop - start
print(" Brute Force Time: {}s".format(brute_force))
print(" Factor: {}".format(modular / brute_force))
k += modular / brute_force
N += 1
self.assertUnorderedEqual(expected, got, msg=name)
self.assertUnorderedEqual(got, expected, msg=name)
self.assertTrue(k / N < 1.0,
msg='algorithm is slower than brute force on average')
