def run_task(packs: dict):
test_times = 1
if packs["method"] == 1:
for i in range(test_times):
population, obj = PMOEAD_bytime(
file_name=packs["file_name"],
dimension=packs["dimension"],
population_size=packs["population_size"],
max_time=packs["time"],
begin=packs["begin"],
end=packs["end"])
name = '[dataset_{}][method_{}][time_{}][{}]'.format(
packs["name"], packs["method"], packs["time"], packs["num"])
store_result(obj, name)
elif packs["method"] == 2:
for i in range(test_times):
population, obj = parallel_run_bytime(
max_time=packs["time"],
iteration_num=packs["iter"],
cpu_num=packs["cores"],
file_name=packs["file_name"],
dimension=packs["dimension"],
population_size=packs["population_size"])
name = '[dataset_{}][method_{}][time_{}][cpu_{}][{}]'.format(
packs["name"], packs["method"], packs["time"], packs["cores"],
packs["num"])
store_result(obj, name)
elif packs["method"] == 3:
for i in range(test_times):
population, obj = parallel_run_bytime(
max_time=packs["time"],
iteration_num=packs["iter"],
cpu_num=packs["cores"],
file_name=packs["file_name"],
dimension=packs["dimension"],
population_size=packs["population_size"],
overlapping_ratio=packs["overlap"])
name = '[dataset_{}][method_{}][time_{}][cpu_{}][ovlp_{}][{}]'.format(
packs["name"], packs["method"], packs["time"], packs["cores"],
packs["overlap"], packs["num"])
store_result(obj, name)
else:
for i in range(test_times):
population, obj = parallel_run_bytime(
max_time=packs["time"],
iteration_num=packs["iter"],
cpu_num=packs["cores"],
file_name=packs["file_name"],
dimension=packs["dimension"],
population_size=packs["population_size"],
overlapping_ratio=packs["overlap"],
auto_adjust=True)
name = '[dataset_{}][method_{}][time_{}][cpu_{}][ovlp_{}][auto][{}]'.format(
packs["name"], packs["method"], packs["time"], packs["cores"],
packs["overlap"], packs["num"])
store_result(obj, name)
def test_parallel_run_by_time(f, test_times, run_time, iteration_num, cpu_num,
file_name, feature_num, population_size):
res = []
print('run parallel', file_name, 'starts')
for i in range(test_times):
population, obj = parallel_run_bytime(max_time=run_time,
iteration_num=iteration_num,
cpu_num=cpu_num,
file_name=file_name,
dimension=feature_num,
population_size=population_size)
name = '{}_cpu{}_{}'.format(file_name[:-4], cpu_num, i)
hv = store_result(obj, name)
res.append((i, hv))
order = chose_file(res)
name = '{}_cpu{}'.format(file_name[:-4], cpu_num)
f.write('file:{} chosen:the {} file\n'.format(name, order))
print('run parallel', file_name, 'ends')
argv_i = '5000'
population, obj = pmoead(file_name=directory,
dimension=feature_num,
population_size=population_size,
max_iteration=iteration_num * round_num,
begin=0,
end=population_size)
else:
argv_p = 'PP'
if _bytime:
argv_t = 'TT'
argv_i = '50'
population, obj = parallel_run_bytime(
max_time=run_time,
iteration_num=iteration_num,
cpu_num=cpu_num,
file_name=directory,
dimension=feature_num,
population_size=population_size)
elif _naive:
argv_t = 'NA'
argv_i = '5000'
population, obj = naive_parallel(total_iteration=iteration_num *
round_num,
cpu_num=cpu_num,
file_name=directory,
dimension=feature_num,
population_size=population_size)
else:
argv_t = 'NT'
argv_i = 'r' + str(round_num) + 'i' + str(iteration_num)