g = nx.Graph()
with open(path) as f:
data = f.read().split('\n')
n, m = map(int, data[0].split())
for i in range(m):
u, v = map(int, data[1 + i].split())
g.add_edge(u, v)
return g
if __name__ == '__main__':
api = FeedbackSetLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
assert has_best(opt)
api.LoadModel('best')
api.SetCurrentTestGraph(get_graph_from_path(opt['graph_path']))
print('[evaluate] model_path: {}'.format(opt['model_path']))
if opt['test_type'] == 'greedy':
print('[evaluate] evaluated {} by greedy: {}'.format(
opt['graph_name'], api.Test()),
flush=True)
elif opt['test_type'] == 'mcts':
print('[evaluate] evaluated {} by mcts: {}'.format(
opt['graph_name'], api.TestByMCTS()),
flush=True)
import sys
import os
import time
import glob
from model_updater import gen_graph, has_best, lock, unlock
from lib.lib import IndependentSetLib
if __name__ == '__main__':
api = IndependentSetLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
save_dir = opt['save_dir']
if has_best(opt):
while lock(opt, 'best'):
pass
api.LoadModel('best')
unlock(opt, 'best')
else:
print('[generator] best model not found', flush=True)
api.SetCurrentGraph(gen_graph(opt))
filename = 'train_data{}'.format(time.time())
api.GenerateTrainData(filename)
os.system('touch {}/data/timestamp_{}'.format(save_dir, filename))
print('[generator] generated new data', flush=True)