def main():
experiment = "tuning" if sys.argv[1] == "tuning" else int(sys.argv[1])
instance = int(sys.argv[2])
Problem.from_instance(experiment, instance)
valid = True
for method in ["ilp", "heuristic"]:
path = f"experiments/{experiment}/{instance}-{method}.json"
try:
with open(path) as file:
solution = [
tuple(assignment) for assignment in json.load(file)
]
except IOError:
print(f"{path}: solution file does not exist.")
else:
for rule in RULES:
if not rule(solution):
valid = False
print(f"{path}: solution violates {rule.__name__}.")
exit(0 if valid else 1)
def main():
args = parse_args()
Problem.from_instance(args.experiment, args.instance)
result = ilp()
res_loc = f"experiments/{args.experiment}/{args.instance}-ilp.json"
with open(res_loc, "w") as file:
json.dump(result, file)
def compute(parser, args):
measures = []
for instance in np.arange(1, 101):
location = Path(f"experiments/{args.experiment}/"
f"{instance}-{args.method}.json")
if not location.exists():
print(f"{parser.prog}: {location} does not exist; skipping.")
continue
Problem.from_instance(args.experiment, instance)
sol = Solution.from_file(location)
measures.append({name: fn(sol) for name, fn in MEASURES.items()})
return pd.DataFrame.from_records(measures, columns=MEASURES.keys())
def main():
args = parse_args()
Problem.from_instance(args.experiment, args.instance)
if args.experiment == "tuning":
generator = rnd.default_rng(args.instance)
else:
# E.g. for exp 72 and inst. 1, this becomes 7201. This way, even for
# inst. 100, there will never be overlap between random number streams
# across experiments.
generator = rnd.default_rng(100 * args.experiment + args.instance)
alns = ALNS(generator) # noqa
for operator in DESTROY_OPERATORS:
if args.exclude == operator.__name__:
continue
alns.add_destroy_operator(operator)
for operator in REPAIR_OPERATORS:
if args.exclude == operator.__name__:
continue
alns.add_repair_operator(operator)
if args.exclude == "reinsert_learner":
alns.on_best(reinsert_learner)
init = initial_solution()
criterion = get_criterion(init.objective())
result = alns.iterate(init, WEIGHTS, DECAY, criterion, ITERATIONS)
location = f"experiments/{args.experiment}/{args.instance}-heuristic.json"
result.best_state.to_file(location) # noqa