def main(args):
set_seed(args)
dataset_train, dataset_val, dataset_test = get_datasets(args)
optimizer = get_optimizer(args)
obj = get_objective(args, optimizer.hparams)
xp = get_xp(args, optimizer)
for i in range(args.epochs):
xp.Epoch.update(1).log()
train(obj, optimizer, dataset_train, xp, args, i)
test(obj, optimizer, dataset_val, xp, args, i)
test(obj, optimizer, dataset_test, xp, args, i)
print_total_time(xp)
label_delay[l] = round(links.delay[l])
# label_delay = links.delay
# pos = nx.shell_layout(NG)
pos = nx.spring_layout(NG, iterations=100)
nx.draw_networkx_nodes(NG, pos, node_color="red")
nx.draw_networkx_labels(NG, pos, font_size=12)
# nx.draw_networkx_edge_labels(NG, pos, edge_labels=label_delay, label_pos = 0.4, font_size=4)
nx.draw_networkx_edges(NG, pos)
plt.axis("off")
plt.savefig(str(scenario) + "_netgraph.pdf")
# plt.show()
# solve with MIP; optimizing one objective and bounding the others
elif method == "pareto":
# get objective and bounds from arguments
obj = objective.get_objective(sys.argv[3])
# bounds have to be ordered: over-sub, changed, resources, delay (without the one that's optimized)
bounds = (float(sys.argv[4]), float(sys.argv[5]), float(sys.argv[6]))
# run with specified objective and bounds
model = tep_extended.solve(nodes, links, templates, prev_embedding,
sources, fixed, scenario, obj, bounds)
writer.write_mip_result(model,
scenario,
nodes,
links,
obj,
sources,
bounds=bounds)
# solve with heuristic
elif method == "heuristic":
