def test_transfer_entropy_not_fixed_size(self):
"""
Raise a ``ValueError`` if the provided network is not fixed sized, and
the ``size`` argument is ``None``
"""
with self.assertRaises(ValueError):
transfer_entropy(ECA(30), k=3, timesteps=10, local=False)
transfer_entropy(ECA(30), k=3, timesteps=10, size=5, local=False)
def test_transfer_entropy_not_network(self):
"""
Raise a ``TypeError`` if the provided network is not actually a network
"""
with self.assertRaises(TypeError):
transfer_entropy(5, k=3, timesteps=10, local=False)
with self.assertRaises(TypeError):
transfer_entropy(5, k=3, timesteps=10, local=True)
def test_transfer_entropy_s_pombe(self):
"""
``transfer_entropy`` computes the correct values for ``s_pombe``
"""
known_te = np.asarray(
[[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.016912, 0., 0., 0., 0.],
[
0., 0.051370, 0., 0.012225, 0.019947, 0.051370, 0.006039,
0.006039, 0.072803
],
[
0., 0.051370, 0.012225, 0., 0.019947, 0.051370, 0.006039,
0.006039, 0.072803
],
[
0., 0.058420, 0.047602, 0.047602, 0., 0.058420, 0.047602,
0.047602, 0.
], [0., 0., 0.024794, 0.024794, 0., 0., 0.024794, 0.024794, 0.],
[
0., 0.016690, 0.004526, 0.004526, 0.011916, 0.016690, 0.,
0.002983, 0.032173
],
[
0., 0.016690, 0.004526, 0.004526, 0.011916, 0.016690,
0.002983, 0., 0.032173
],
[
0., 0.060304, 0.048289, 0.048289, 0.089669, 0.060304,
0.048927, 0.048927, 0.
]])
computed_te = transfer_entropy(s_pombe, k=5, timesteps=20)
self.assertEqual(known_te.shape, computed_te.shape)
for got, expected in zip(computed_te.flatten(), known_te.flatten()):
self.assertAlmostEqual(expected, got, places=6)
def test_architecture_te(self):
"""
The architecture correctly computes the transfer entropy
"""
k, timesteps = 5, 20
arch = Architecture(s_pombe, k=k, timesteps=timesteps)
expected_te = transfer_entropy(s_pombe, k=k, timesteps=timesteps)
got_te = arch.transfer_entropy()
self.assertEqual(got_te.shape, expected_te.shape)
for got, expected in zip(got_te.flatten(), expected_te.flatten()):
self.assertAlmostEqual(expected, got, places=6)
expected_te = transfer_entropy(s_pombe,
k=k,
timesteps=timesteps,
local=True)
got_te = arch.transfer_entropy(local=True)
self.assertEqual(got_te.shape, expected_te.shape)
for got, expected in zip(got_te.flatten(), expected_te.flatten()):
self.assertAlmostEqual(expected, got, places=6)
def test_local_transfer_entropy_s_pombe(self):
"""
local ``transfer_entropy`` averages to the correct values for
``s_pombe``
"""
known_te = np.asarray(
[[0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.016912, 0., 0., 0., 0.],
[
0., 0.051370, 0., 0.012225, 0.019947, 0.051370, 0.006039,
0.006039, 0.072803
],
[
0., 0.051370, 0.012225, 0., 0.019947, 0.051370, 0.006039,
0.006039, 0.072803
],
[
0., 0.058420, 0.047602, 0.047602, 0., 0.058420, 0.047602,
0.047602, 0.
], [0., 0., 0.024794, 0.024794, 0., 0., 0.024794, 0.024794, 0.],
[
0., 0.016690, 0.004526, 0.004526, 0.011916, 0.016690, 0.,
0.002983, 0.032173
],
[
0., 0.016690, 0.004526, 0.004526, 0.011916, 0.016690,
0.002983, 0., 0.032173
],
[
0., 0.060304, 0.048289, 0.048289, 0.089669, 0.060304,
0.048927, 0.048927, 0.
]])
computed_te = transfer_entropy(s_pombe, k=5, timesteps=20, local=True)
self.assertEqual((9, 9, 512, 16), computed_te.shape)
for i in range(9):
for j in range(9):
self.assertAlmostEqual(known_te[i, j],
np.mean(computed_te[i, j]),
places=6)