def test_maximal_cliques(self):
"""Test maximal_cliques."""
G = Graph()
G.add_edge('b','c')
G.add_edge('b','d')
G.add_edge('b','e')
G.add_edge('b','f')
G.add_edge('b','a')
G.add_edge('a','c')
G.add_edge('a','d')
G.add_edge('a','e')
G.add_edge('c','d')
G.add_edge('c','e')
G.add_edge('c','f')
G.add_edge('c','g')
G.add_edge('d','e')
G.add_edge('d','g')
G.add_edge('e','g')
G.add_edge('f','g')
# A clique of 'a, b, c, d, e' and some other edges.
nodes = G.nodes()
S, H = pClique.convert_graph_connectivity_to_sparse(G, nodes)
i = nodes.index('a')
tQ = pClique.grasp(S, H, 1, 5, i)
c = [nodes[i] for i in tQ]
print c
self.assertTrue(set(c) == set(['a', 'b', 'c', 'd', 'e']))
def _findCliques(self, alignGraph, readsFa):
"""
Find all mutually exclusive cliques within the graph, with decreased
size.
alignGraph - a graph, each node represent a read and each edge
represents an alignment between two end points.
Return a dictionary of clique indices and nodes.
key = index of a clique
value = nodes within a clique
Cliques are ordered by their size descendingly: index up, size down
Reads which are not included in any cliques will be added as cliques
of size 1.
"""
uc = {} # To keep cliques found
used = [] # nodes within any cliques
ind = 0 # index of clique to discover
deg = alignGraph.degree().items()
# Sort tuples of (node, degree) by degree, descendingly
deg.sort(key=lambda x: x[1], reverse=True)
for d in deg:
node = d[0] # node which has the largest degree in alignGraph
if node not in alignGraph:
continue
# just get the immediate neighbors since we're looking for perfect
# cliques
subGraph = alignGraph.subgraph([node] + alignGraph.neighbors(node))
subNodes = subGraph.nodes()
# Convert from networkx.Graph to a sparse matrix
S, H = pClique.convert_graph_connectivity_to_sparse(
subGraph, subNodes)
# index of the 'node' in the sub-graph
seed_i = subNodes.index(node)
# Grasp a clique from subGraph, and return indices of clique nodes
# setting gamma=0.8 means to find quasi-0.8-cliques!
tQ = pClique.grasp(S,
H,
gamma=0.8,
maxitr=5,
given_starting_node=seed_i)
if len(tQ) > 0:
c = [subNodes[i] for i in tQ] # nodes in the clique
uc[ind] = c # Add the clique to uc
ind += 1
used += c # Add clique nodes to used
# Remove clique nodes from alignGraph and continue
alignGraph.remove_nodes_from(c)
with FastaReader(readsFa) as reader:
for r in reader:
rid = r.name.split()[0]
if rid not in used:
uc[ind] = [rid]
ind += 1
return uc
def _findCliques(self, alignGraph, readsFa):
"""
Find all mutually exclusive cliques within the graph, with decreased
size.
alignGraph - a graph, each node represent a read and each edge
represents an alignment between two end points.
Return a dictionary of clique indices and nodes.
key = index of a clique
value = nodes within a clique
Cliques are ordered by their size descendingly: index up, size down
Reads which are not included in any cliques will be added as cliques
of size 1.
"""
uc = {} # To keep cliques found
used = [] # nodes within any cliques
ind = 0 # index of clique to discover
deg = alignGraph.degree().items()
# Sort tuples of (node, degree) by degree, descendingly
deg.sort(key=lambda x:x[1], reverse=True)
for d in deg:
node = d[0] # node which has the largest degree in alignGraph
if node not in alignGraph:
continue
# just get the immediate neighbors since we're looking for perfect
# cliques
subGraph = alignGraph.subgraph([node] + alignGraph.neighbors(node))
subNodes = subGraph.nodes()
# Convert from networkx.Graph to a sparse matrix
S, H = pClique.convert_graph_connectivity_to_sparse(
subGraph, subNodes)
# index of the 'node' in the sub-graph
seed_i = subNodes.index(node)
# Grasp a clique from subGraph, and return indices of clique nodes
# setting gamma=0.8 means to find quasi-0.8-cliques!
tQ = pClique.grasp(S, H, gamma=0.8, maxitr=5, given_starting_node=seed_i)
if len(tQ) > 0:
c = [subNodes[i] for i in tQ] # nodes in the clique
uc[ind] = c # Add the clique to uc
ind += 1
used += c # Add clique nodes to used
# Remove clique nodes from alignGraph and continue
alignGraph.remove_nodes_from(c)
with FastaReader(readsFa) as reader:
for r in reader:
rid = r.name.split()[0]
if rid not in used:
uc[ind] = [rid]
ind += 1
return uc