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import unittest
import igraph as ig
import leidenalg
import random
from copy import deepcopy
from ddt import ddt, data, unpack
#%%
def name_object(obj, name):
obj.__name__ = name
return obj
graphs = [
###########################################################################
# Zachary karate network
name_object(ig.Graph.Famous('Zachary'),
'Zachary'),
###########################################################################
# ER Networks
# Undirected no loop
name_object(ig.Graph.Erdos_Renyi(100, p=1./100, directed=False, loops=False),
'ER_k1_undirected_no_loops'),
name_object(ig.Graph.Erdos_Renyi(100, p=5./100, directed=False, loops=False),
'ER_k5_undirected_no_loops'),
# Directed no loop
name_object(ig.Graph.Erdos_Renyi(100, p=1./100, directed=True, loops=False),
'ER_k1_directed_no_loops'),
name_object(ig.Graph.Erdos_Renyi(100, p=5./100, directed=True, loops=False),
'ER_k5_directed_no_loops'),
# Undirected loops
name_object(ig.Graph.Erdos_Renyi(100, p=1./100, directed=False, loops=True),
'ER_k1_undirected_loops'),
name_object(ig.Graph.Erdos_Renyi(100, p=5./100, directed=False, loops=True),
'ER_k5_undirected_loops'),
# Directed loops
name_object(ig.Graph.Erdos_Renyi(100, p=1./100, directed=True, loops=True),
'ER_k1_directed_loops'),
name_object(ig.Graph.Erdos_Renyi(100, p=5./100, directed=True, loops=True),
'ER_k5_directed_loops'),
###########################################################################
# Tree
name_object(ig.Graph.Tree(100, 3, mode='undirected'),
'Tree_undirected'),
name_object(ig.Graph.Tree(100, 3, mode='out'),
'Tree_directed_out'),
name_object(ig.Graph.Tree(100, 3, mode='in'),
'Tree_directed_in'),
###########################################################################
# Lattice
name_object(ig.Graph.Lattice([100], nei=3, directed=False, mutual=True, circular=True),
'Lattice_undirected'),
name_object(ig.Graph.Lattice([100], nei=3, directed=True, mutual=False, circular=True),
'Lattice_directed')
]
bipartite_graph = name_object(
ig.Graph.Bipartite([0, 0, 0, 0, 1, 1, 1, 1],
[[0, 4],
[0, 5],
[0, 6],
[1, 4],
[1, 5],
[2, 6],
[2, 7],
[3, 6],
[3, 7],
[3, 5]]),
'bipartite_example')
def make_weighted(G):
m = G.ecount()
G.es['weight'] = [random.random() for i in range(G.ecount())]
G.__name__ += '_weighted'
return G
graphs += [make_weighted(H) for H in graphs]
class BaseTest:
@ddt
class MutableVertexPartitionTest(unittest.TestCase):
def setUp(self):
self.optimiser = leidenalg.Optimiser()
@data(*graphs)
def test_move_nodes(self, graph):
if 'weight' in graph.es.attributes() and self.partition_type == leidenalg.SignificanceVertexPartition:
raise unittest.SkipTest('Significance doesn\'t handle weighted graphs')
if 'weight' in graph.es.attributes():
partition = self.partition_type(graph, weights='weight')
else:
partition = self.partition_type(graph)
for v in range(graph.vcount()):
if graph.degree(v) >= 1:
u = graph.neighbors(v)[0]
diff = partition.diff_move(v, partition.membership[u])
q1 = partition.quality()
partition.move_node(v, partition.membership[u])
q2 = partition.quality()
self.assertAlmostEqual(
q2 - q1,
diff,
places=5,
msg="Difference in quality ({0}) not equal to calculated difference ({1})".format(
q2 - q1, diff))
@data(*graphs)
def test_aggregate_partition(self, graph):
if 'weight' in graph.es.attributes() and self.partition_type != leidenalg.SignificanceVertexPartition:
partition = self.partition_type(graph, weights='weight')
else:
partition = self.partition_type(graph)
self.optimiser.move_nodes(partition)
aggregate_partition = partition.aggregate_partition()
self.assertAlmostEqual(
partition.quality(),
aggregate_partition.quality(),
places=5,
msg='Quality not equal for aggregate partition.')
self.optimiser.move_nodes(aggregate_partition)
partition.from_coarse_partition(aggregate_partition)
self.assertAlmostEqual(
partition.quality(),
aggregate_partition.quality(),
places=5,
msg='Quality not equal from coarser partition.')
@data(*graphs)
def test_total_weight_in_all_comms(self, graph):
if 'weight' in graph.es.attributes() and self.partition_type != leidenalg.SignificanceVertexPartition:
partition = self.partition_type(graph, weights='weight')
else:
partition = self.partition_type(graph)
self.optimiser.optimise_partition(partition)
s = sum([partition.total_weight_in_comm(c) for c,_ in enumerate(partition)])
self.assertAlmostEqual(
s,
partition.total_weight_in_all_comms(),
places=5,
msg='Total weight in all communities ({0}) not equal to the sum of the weight in all communities ({1}).'.format(
s, partition.total_weight_in_all_comms())
)
@data(*graphs)
def test_copy(self, graph):
if 'weight' in graph.es.attributes() and self.partition_type != leidenalg.SignificanceVertexPartition:
partition = self.partition_type(graph, weights='weight')
else:
partition = self.partition_type(graph)
self.optimiser.optimise_partition(partition)
partition2 = deepcopy(partition)
self.assertAlmostEqual(
partition.quality(),
partition2.quality(),
places=5,
msg='Quality of deepcopy ({0}) not equal to quality of original partition ({1}).'.format(
partition.quality(), partition2.quality())
)
if (partition2.membership[0] == 0):
partition2.move_node(0, 1)
else:
partition2.move_node(0, 0)
self.assertNotEqual(
partition.membership[0],
partition2.membership[0],
msg='Moving node 0 in the deepcopy to community {0} results in community membership {1} for node 0 also in original partition.'.format(
partition.membership[0], partition2.membership[0])
)
class ModularityVertexPartitionTest(BaseTest.MutableVertexPartitionTest):
def setUp(self):
super(ModularityVertexPartitionTest, self).setUp()
self.partition_type = leidenalg.ModularityVertexPartition
class RBERVertexPartitionTest(BaseTest.MutableVertexPartitionTest):
def setUp(self):
super(RBERVertexPartitionTest, self).setUp()
self.partition_type = leidenalg.RBERVertexPartition
class RBConfigurationVertexPartitionTest(BaseTest.MutableVertexPartitionTest):
def setUp(self):
super(RBConfigurationVertexPartitionTest, self).setUp()
self.partition_type = leidenalg.RBConfigurationVertexPartition
class CPMVertexPartitionTest(BaseTest.MutableVertexPartitionTest):
def setUp(self):
super(CPMVertexPartitionTest, self).setUp()
self.partition_type = leidenalg.CPMVertexPartition
def test_Bipartite(self):
graph = bipartite_graph
partition, partition_0, partition_1 = \
leidenalg.CPMVertexPartition.Bipartite(graph, resolution_parameter_01=0.2)
self.optimiser.optimise_partition_multiplex(
[partition, partition_0, partition_1],
layer_weights=[1, -1, -1])
self.assertEqual(len(partition), 1)
class SurpriseVertexPartitionTest(BaseTest.MutableVertexPartitionTest):
def setUp(self):
super(SurpriseVertexPartitionTest, self).setUp()
self.partition_type = leidenalg.SurpriseVertexPartition
class SignificanceVertexPartitionTest(BaseTest.MutableVertexPartitionTest):
def setUp(self):
super(SignificanceVertexPartitionTest, self).setUp()
self.partition_type = leidenalg.SignificanceVertexPartition
#%%
if __name__ == '__main__':
#%%
unittest.main(verbosity=3)
suite = unittest.TestLoader().discover('.')
unittest.TextTestRunner(verbosity=1).run(suite)
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