def get_upper_bound_peo_pace2017(old_graph,
method="tamaki",
wait_time=60,
print_stats=False):
"""
Calculates a PEO and treewidth using one of the external solvers
Parameters
----------
graph : networkx.Graph
graph to estimate
method : str
one of {"tamaki"}
wait_time : float
allowed running time (in seconds)
Returns
-------
peo : list
treewidth : int
treewidth
"""
from qtree.graph_model.clique_trees import get_peo_from_tree
import qtree.graph_model.pace2017_solver_api as api
method_args = {
'tamaki': {
'command': './tw-heuristic',
'cwd': defs.TAMAKI_SOLVER_PATH,
'wait_time': wait_time
}
}
assert (method in method_args.keys())
# ensure graph is labelad starting from 1 with integers
graph, inv_dict = relabel_graph_nodes(
old_graph,
dict(zip(old_graph.nodes, range(1,
old_graph.number_of_nodes() + 1))))
# Remove selfloops and parallel edges. Critical
graph = get_simple_graph(graph)
data = generate_gr_file(graph)
out_data = api.run_heuristic_solver(data, **method_args[method])
try:
stats = get_stats_from_td_file(out_data)
if print_stats:
print('stats', stats)
tree, treewidth = read_td_file(out_data, as_data=True)
except ValueError:
print(out_data)
raise
peo = get_peo_from_tree(tree)
# return to the original labelling
peo = [inv_dict[pp] for pp in peo]
return peo, treewidth
def get_peo(old_graph, method="tamaki"):
"""
Calculates a perfect elimination order using one of the
external methods.
Parameters
----------
graph : networkx.Graph
graph to estimate
method : str
one of {"tamaki"}
Returns
-------
peo : list
list of nodes in perfect elimination order
treewidth : int
treewidth corresponding to peo
"""
from qtree.graph_model.clique_trees import get_peo_from_tree
import qtree.graph_model.pace2017_solver_api as api
method_args = {
'tamaki': {
'command': './tw-exact',
'cwd': defs.TAMAKI_SOLVER_PATH
}
}
assert (method in method_args.keys())
# ensure graph is labeled starting from 1 with integers
graph, inv_dict = relabel_graph_nodes(
old_graph,
dict(zip(old_graph.nodes, range(1,
old_graph.number_of_nodes() + 1))))
# Remove selfloops and parallel edges. Critical
graph = get_simple_graph(graph)
data = generate_gr_file(graph)
out_data = api.run_exact_solver(data, **method_args[method])
tree, treewidth = read_td_file(out_data, as_data=True)
peo = get_peo_from_tree(tree)
peo = [inv_dict[pp] for pp in peo]
try:
peo_vars = [
Var(var,
size=old_graph.nodes[var]['size'],
name=old_graph.nodes[var]['name']) for var in peo
]
except:
peo_vars = peo
return peo_vars, treewidth
def get_upper_bound_peo_pace2017_interactive(old_graph,
method="tamaki",
max_time=60,
max_width=None,
print_stats=False):
"""
Calculates a PEO and treewidth using one of the external solvers
Parameters
----------
graph : networkx.Graph
graph to estimate
method : str
one of {"tamaki", "tamaki_exact"}
max_time : float
Run until not reached time
max_width : int
Run until not reached width
Returns
-------
peo : list
treewidth : int
treewidth
"""
from qtree.graph_model.clique_trees import get_peo_from_tree
import qtree.graph_model.pace2017_solver_api as api
# ensure graph is labelad starting from 1 with integers
graph, inv_dict = relabel_graph_nodes(
old_graph,
dict(zip(old_graph.nodes, range(1,
old_graph.number_of_nodes() + 1))))
# Remove selfloops and parallel edges. Critical
graph = get_simple_graph(graph)
data = generate_gr_file(graph)
start = time.time()
def callback(line_info):
ts, width = line_info
print(f'Time={ts}, width={width}', file=sys.stderr)
elapsed = time.time() - start
if max_time:
if elapsed > max_time:
raise StopIteration('Timeout')
if max_width and width:
if width <= max_width:
raise StopIteration('Solution is good enough')
method_args = {
'tamaki': {
'command': './tw-heuristic',
'cwd': defs.TAMAKI_SOLVER_PATH,
'callback': callback,
},
'tamaki_exact': {
'command':
'./tw-exact',
'cwd':
defs.TAMAKI_SOLVER_PATH,
'callback':
lambda x: print(f'Time={time.time()-start}', file=sys.stderr),
'callback_delay':
2
}
}
assert (method in method_args.keys())
out_data = api.run_heuristic_solver_interactive(data,
**method_args[method])
try:
stats = get_stats_from_td_file(out_data)
if print_stats:
print('stats', stats)
tree, treewidth = read_td_file(out_data, as_data=True)
except ValueError:
print(out_data)
raise
peo = get_peo_from_tree(tree)
# return to the original labelling
peo = [inv_dict[pp] for pp in peo]
return peo, treewidth