def test_small_nice_decomposition(req_id):
req = create_test_request(req_id)
td_comp = treewidth_model.TreeDecompositionComputation(req)
tree_decomp = td_comp.compute_tree_decomposition()
assert tree_decomp.is_tree_decomposition(req)
sntd = treewidth_model.SmallSemiNiceTDArb(tree_decomp, req)
assert sntd.is_tree_decomposition(req)
def test_conversion_to_PACE_format_works(test_data):
req_id, expected = test_data
req = create_test_request(req_id)
td_comp = treewidth_model.TreeDecompositionComputation(req)
assert td_comp._convert_graph_to_td_input_format() == expected
def test_opt_dynvmp_and_classic_mcf_agree_for_unambiguous_scenario(
request_id, random_seed, allowed_nodes_ratio, allowed_edges_ratio):
random.seed(random_seed)
print("allowed nodes: {} %, allowed edges: {} %".format(
100 * allowed_nodes_ratio, 100 * allowed_edges_ratio))
req = create_test_request(request_id, set_allowed_nodes=False)
node_type = "test_type"
sub = create_test_substrate_topology_zoo(node_types=[node_type])
assert set(sub.get_types()) == {node_type}
num_allowed_nodes = int(allowed_nodes_ratio * len(sub.nodes))
for i in req.nodes:
assert req.get_type(i) == node_type
req.node[i]["allowed_nodes"] = random.sample(list(sub.nodes),
num_allowed_nodes)
num_allowed_edges = int(allowed_edges_ratio * len(sub.edges))
for ij in req.edges:
req.edge[ij]["allowed_edges"] = random.sample(list(sub.edges),
num_allowed_edges)
snode_costs = {}
sedge_costs = {}
def randomize_substrate_costs():
# randomize all costs to force unambiguous cost-optimal embedding
for u in sub.nodes:
for t in sub.get_supported_node_types(u):
sub.node[u]["cost"][t] = random.random()
snode_costs[u] = sub.node[u]["cost"][t]
for uv in sub.edges:
sub.edge[uv]["cost"] = random.random()
sedge_costs[uv] = sub.edge[uv]["cost"]
randomize_substrate_costs()
td_comp = treewidth_model.TreeDecompositionComputation(req)
tree_decomp = td_comp.compute_tree_decomposition()
sntd = treewidth_model.SmallSemiNiceTDArb(tree_decomp, req)
opt_dynvmp = treewidth_model.OptimizedDynVMP(sub, req, sntd, snode_costs,
sedge_costs)
times_dynvmp = []
start_time = time.time()
opt_dynvmp.initialize_data_structures()
opt_dynvmp.compute_solution()
root_cost, mapping = opt_dynvmp.recover_mapping()
times_dynvmp.append(time.time() - start_time)
number_of_tests = 10
times_gurobi = []
for x in range(number_of_tests):
def compare_solutions():
if root_cost is not None: # solution exists
assert gurobi_solution is not None
gurobi_obj = gurobi_solution.status.objValue
assert abs(root_cost - gurobi_obj) <= 0.0001
assert mapping.mapping_nodes == gurobi_solution.solution.request_mapping[
req].mapping_nodes
assert mapping.mapping_edges == gurobi_solution.solution.request_mapping[
req].mapping_edges
else:
assert gurobi_solution is None
start_time = time.time()
gurobi = mip.ClassicMCFModel(
dm.Scenario("test", sub, [req], objective=dm.Objective.MIN_COST))
gurobi.init_model_creator()
gurobi_solution = gurobi.compute_integral_solution()
times_gurobi.append(time.time() - start_time)
compare_solutions()
randomize_substrate_costs()
start_time = time.time()
opt_dynvmp.reinitialize(snode_costs, sedge_costs)
opt_dynvmp.compute_solution()
root_cost, mapping = opt_dynvmp.recover_mapping()
times_dynvmp.append(time.time() - start_time)
gurobi = mip.ClassicMCFModel(
dm.Scenario("test", sub, [req], objective=dm.Objective.MIN_COST))
gurobi.init_model_creator()
gurobi_solution = gurobi.compute_integral_solution()
compare_solutions()
initial_computation_time = times_dynvmp[0]
other_computation_times = times_dynvmp[1:]
average_of_others = sum(other_computation_times) / float(
len(other_computation_times))
print(
(" Request graph size (|V|,|E|): ({},{})\n"
" Width of the request is: {}\n"
" Runtime of initial computation: {:02.4f} s\n"
"Runtime of later computations (avg.): {:02.4f} s\n"
" This is a speed-up of: {:02.2f} times\n"
" Total time in Dyn-VMP: {:02.4f} s\n"
" Total time in Gurobi: {:02.4f} s\n"
" Speedup Dyn-VMP over Gurobi: {:02.2f} times (>1 is good)\n"
.format(len(req.nodes), len(req.edges), sntd.width,
initial_computation_time, average_of_others,
initial_computation_time / average_of_others,
sum(times_dynvmp), sum(times_gurobi),
sum(times_gurobi) / sum(times_dynvmp))))
def test_tree_decomposition_computation_returns_valid_tree_decompositions(
req_id):
req = create_test_request(req_id)
td_comp = treewidth_model.TreeDecompositionComputation(req)
tree_decomp = td_comp.compute_tree_decomposition()
assert tree_decomp.is_tree_decomposition(req)
def test_opt_dynvmp(request_id):
req = create_test_request(request_id, set_allowed_nodes=False)
sub = create_test_substrate_topology_zoo()
# assert that all nodes and all edges may be mapped anywhere...
substrate_node_set = set(sub.nodes)
substrate_edge_set = set(sub.edges)
# set substrate node and edge capacities high
for snode in sub.nodes:
for type in list(sub.node[snode]['capacity'].keys()):
sub.node[snode]['capacity'][type] = 100
for sedge in sub.edges:
sub.edge[sedge]['capacity'] = 100
snode_list = list(sub.nodes)
sedge_list = list(sub.edges)
valid_reqnode_list = [
snode for snode in sub.nodes if sub.get_node_capacity(snode) > 1.01
]
# # now additionally also introduce some mapping restrictions
for reqnode in req.nodes:
random.shuffle(valid_reqnode_list)
selected_nodes = []
while len(selected_nodes) == 0:
print("selected nodes are {}".format(selected_nodes))
selected_nodes = valid_reqnode_list[
0:random.randint(10, len(valid_reqnode_list))]
req.set_allowed_nodes(reqnode, selected_nodes)
print("Allowd nodes for {} are {}.".format(
reqnode, req.get_allowed_nodes(reqnode)))
for reqedge in req.edges:
random.shuffle(sedge_list)
req.set_allowed_edges(
reqedge, sedge_list[0:random.randint(20, len(sedge_list))])
print("Allowd edges for {} are {}.".format(
reqedge, req.get_allowed_edges(reqedge)))
# random edge costs
edge_costs = {
sedge: max(1, 1000.0 * random.random())
for sedge in sub.edges
}
for sedge in sub.edges:
sub.edge[sedge]['cost'] = 1 # edge_costs[sedge]
for snode in sub.nodes:
for type in list(sub.node[snode]['cost'].keys()):
sub.node[snode]['cost'][type] = 1
scenario = dm.Scenario("test", sub, [req])
gurobi = mip.ClassicMCFModel(scenario)
gurobi.init_model_creator()
gurobi.model.setParam("LogToConsole", 1)
gurobi.compute_integral_solution()
td_comp = treewidth_model.TreeDecompositionComputation(req)
tree_decomp = td_comp.compute_tree_decomposition()
assert tree_decomp.is_tree_decomposition(req)
sntd = treewidth_model.SmallSemiNiceTDArb(tree_decomp, req)
assert sntd.is_tree_decomposition(req)
opt_dynvmp = treewidth_model.OptimizedDynVMP(sub, req, sntd)
opt_dynvmp.initialize_data_structures()
opt_dynvmp.compute_solution()
obj = opt_dynvmp.get_optimal_solution_cost()
if obj is not None:
costs, mapping_indices = opt_dynvmp.get_ordered_root_solution_costs_and_mapping_indices(
maximum_number_of_solutions_to_return=100)
for index, cost in np.ndenumerate(costs):
if index == 0:
assert cost == obj
print("{}-th best cost is: {} and index is {}".format(
index, cost, mapping_indices[index]))
corresponding_mappings = opt_dynvmp.recover_list_of_mappings(
mapping_indices)
print("Number of obtained mappings: {}, mappings: {}".format(
len(corresponding_mappings), corresponding_mappings))
result_mapping = opt_dynvmp.recover_mapping()
print("Returned mapping! Namely, the following: {}".format(result_mapping))
# change costs
snode_costs = opt_dynvmp.snode_costs
sedge_costs = opt_dynvmp.sedge_costs
for snode in list(snode_costs.keys()):
snode_costs[snode] *= 2
for sedge in list(sedge_costs.keys()):
sedge_costs[sedge] *= 2
opt_dynvmp.reinitialize(snode_costs, sedge_costs)
opt_dynvmp.compute_solution()
result_mapping = opt_dynvmp.recover_mapping()
print("Returned mapping! Namely, the following: {}".format(result_mapping))
