def test_maximum_cardinality_search():
"""Test maximum cardinality search algorithm"""
# Read graph
import qtree.operators as ops
import os
this_dir = os.path.dirname((os.path.abspath(__file__)))
nq, c = ops.read_circuit_file(this_dir + '/../../inst_2x2_7_0.txt')
old_g, *_ = circ2graph(nq, c)
# Make random clique
vertices = list(np.random.choice(old_g.nodes, 4, replace=False))
while is_clique(old_g, vertices):
vertices = list(np.random.choice(old_g.nodes, 4, replace=False))
g = make_clique_on(old_g, vertices)
# Make graph completion
peo, tw = get_peo(g)
g_chordal = get_fillin_graph2(g, peo)
# MCS will produce alternative PEO with this clique at the end
new_peo = maximum_cardinality_search(g_chordal, list(vertices))
# Test if new peo is correct
assert is_peo_zero_fillin(g_chordal, peo)
assert is_peo_zero_fillin(g_chordal, new_peo)
new_tw = get_treewidth_from_peo(g, new_peo)
assert tw == new_tw
print('peo:', peo)
print('new_peo:', new_peo)
def test_tree_from_peo(filename='inst_2x2_7_0.txt'):
import qtree.operators as ops
nq, c = ops.read_circuit_file(filename)
graph, *_ = circ2graph(nq, c, omit_terminals=False)
peo, _ = get_peo(graph)
tree = get_tree_from_peo(get_simple_graph(graph), peo)
elements = list(range(1, graph.number_of_nodes() + 1))
for element in elements:
nodes_containing_element = []
for node in tree.nodes():
if element in node:
nodes_containing_element.append(node)
subtree = nx.subgraph(tree, nodes_containing_element)
if subtree.number_of_nodes() > 0:
connected = nx.connected.is_connected(subtree)
else:
connected = True # Take empty graph as connected
if not connected:
# draw_graph(subtree, f"st_{element}")
pass
draw_graph(tree, f"tree")
def split_graph_random(old_graph, n_var_parallel=0):
"""
Splits a graphical model with randomly chosen nodes
to parallelize over.
Parameters
----------
old_graph : networkx.Graph
graph to contract (after eliminating variables which
are parallelized over)
n_var_parallel : int
number of variables to eliminate by parallelization
Returns
-------
idx_parallel : list of Idx
variables removed by parallelization
graph : networkx.Graph
new graph without parallelized variables
"""
graph = copy.deepcopy(old_graph)
indices = [var for var in graph.nodes(data=False)]
idx_parallel = np.random.choice(
indices, size=n_var_parallel, replace=False)
idx_parallel_var = [Var(var, size=graph.nodes[var])
for var in idx_parallel]
for idx in idx_parallel:
remove_node(graph, idx)
log.info("Removed indices by parallelization:\n{}".format(idx_parallel))
log.info("Removed {} variables".format(len(idx_parallel)))
peo, treewidth = get_peo(graph)
return sorted(idx_parallel_var, key=int), graph