def evaluate_my_algorithm(self):
_, evals, algs = self.__index_setup()
eval: Evaluator = evals[INDEX][0]()
alg: Algorithm = algs[INDEX][0]()
print(alg.__class__.__name__)
relative_losses_sum = 0.0
relative_losses_count = 0
my_relative_losses_sum = 0.0
my_relative_losses_count = 0
not_found = set([])
print('\t'.join(['n', 'dummy_solution', 'algorithm_solution', 'time']))
for index in INDICES:
for n in range(50, 501, 50):
try:
instance = Instance.load(self.get_instance_path(index, n))
seq_output = eval.validate_schedule(instance, Solution.get_dummy_solution(n))
alg_output = eval.validate_algorithm(instance, alg)
assert alg_output.correct
relative_loss = (seq_output.value - alg_output.value) / seq_output.value
# print(seq_output.value, alg_output.value)
if index == INDEX:
my_relative_losses_sum += relative_loss
my_relative_losses_count += 1
print('\t'.join(map(lambda x: str(round(x, 2)),
[n, seq_output.value, alg_output.value, 100 * relative_loss, alg_output.time])))
# print("\\\\\n\\hline")
relative_losses_sum += relative_loss
relative_losses_count += 1
# print(round(alg_output.value, 2))
except FileNotFoundError:
not_found.add(index)
log.info(f'Not found instances of: {not_found}')
log.info(f'Mean relative improvement on own instances: {round(100 * my_relative_losses_sum / my_relative_losses_count, 2)}')
log.info(f'Mean relative improvement: {round(100 * relative_losses_sum / relative_losses_count, 2)}')
def validate_all_instances(self):
eval: Evaluator = self.__index_setup()[1][INDEX][0]()
results = []
not_found = set([])
for index in INDICES:
for n in range(50, 501, 50):
try:
instance = Instance.load(self.get_instance_path(index, n))
evaluator_output = eval.validate_schedule(instance, Solution.get_dummy_solution(n))
results.append(evaluator_output.value)
except FileNotFoundError:
not_found.add(index)
print("\n".join([str(round(value, 2)) for value in results]))
log.info(f"Not found: {not_found}")