def tune_parameters(cutoff: int, jobs: int):
session_data, products_data = load_sessions_products()
session_data = session_data.head(int(session_data.index.size / 2))
params = []
precision = 0
recall = 0
for i in range(cutoff):
print(i, '/', cutoff, end='\r')
params_test = [
100, 20,
uniform(0, 0.1),
uniform(0, 1),
uniform(0, 0.05),
uniform(0, 0.2)
]
precision_test, recall_test = test('cf', 10, session_data,
products_data, jobs, params_test,
True)
if (precision_test + recall_test > precision + recall):
params = params_test
precision = precision_test
recall = recall_test
print('\nBest precision@10 =', precision)
print('Best recall@10 =', recall)
print('Best params:\n\tn_factors =', params[0], '\n\tn_epochs =',
params[1], '\n\tinit_mean =', params[2], '\n\tinit_std_dev =',
params[3], '\n\tlr_all =', params[4], '\n\treg_all =', params[5])
def run(self, multi_process=False):
best_round, bar_round = None, None
f_time = open(
os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"running_time.csv"), "w")
f_time.write(
"generator_time\tmaking_samples_time\tupdate_network_time\ttest_evaluation_times\tall_times\n"
)
f_time.close()
if self.dic_exp_conf["PRETRAIN"]:
if os.listdir(self.dic_path["PATH_TO_PRETRAIN_MODEL"]):
for i in range(self.dic_traffic_env_conf["NUM_AGENTS"]):
#TODO:only suitable for DGN
shutil.copy(
os.path.join(self.dic_path["PATH_TO_PRETRAIN_MODEL"],
"round_0_inter_%d.h5" % i),
os.path.join(self.dic_path["PATH_TO_MODEL"],
"round_0_inter_%d.h5" % i))
# shutil.copy(os.path.join(self.dic_path["PATH_TO_PRETRAIN_MODEL"],
# "%s.h5" % self.dic_exp_conf["TRAFFIC_FILE"][0]),
# os.path.join(self.dic_path["PATH_TO_MODEL"], "round_0.h5"))
# shutil.copy(os.path.join(self.dic_path["PATH_TO_PRETRAIN_MODEL"],
# "%s.h5" % self.dic_exp_conf["TRAFFIC_FILE"][0]),
# os.path.join(self.dic_path["PATH_TO_MODEL"], "round_0.h5"))
else:
if not os.listdir(
self.dic_path["PATH_TO_PRETRAIN_WORK_DIRECTORY"]):
for cnt_round in range(
self.dic_exp_conf["PRETRAIN_NUM_ROUNDS"]):
print("round %d starts" % cnt_round)
process_list = []
# ============== generator =============
if multi_process:
for cnt_gen in range(
self.
dic_exp_conf["PRETRAIN_NUM_GENERATORS"]):
p = Process(target=self.generator_wrapper,
args=(cnt_round, cnt_gen,
self.dic_path,
self.dic_exp_conf,
self.dic_agent_conf,
self.dic_traffic_env_conf,
best_round))
print("before")
p.start()
print("end")
process_list.append(p)
print("before join")
for p in process_list:
p.join()
print("end join")
else:
for cnt_gen in range(
self.
dic_exp_conf["PRETRAIN_NUM_GENERATORS"]):
self.generator_wrapper(
cnt_round=cnt_round,
cnt_gen=cnt_gen,
dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
dic_agent_conf=self.dic_agent_conf,
dic_traffic_env_conf=self.
dic_traffic_env_conf,
best_round=best_round)
# ============== make samples =============
# make samples and determine which samples are good
train_round = os.path.join(
self.dic_path["PATH_TO_PRETRAIN_WORK_DIRECTORY"],
"train_round")
if not os.path.exists(train_round):
os.makedirs(train_round)
cs = ConstructSample(
path_to_samples=train_round,
cnt_round=cnt_round,
dic_traffic_env_conf=self.dic_traffic_env_conf)
cs.make_reward()
if self.dic_exp_conf["MODEL_NAME"] in self.dic_exp_conf[
"LIST_MODEL_NEED_TO_UPDATE"]:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(0, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_traffic_env_conf,
self.dic_path, best_round))
p.start()
p.join()
else:
self.updater_wrapper(
cnt_round=0,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
dic_path=self.dic_path,
best_round=best_round)
# train with aggregate samples
if self.dic_exp_conf["AGGREGATE"]:
if "aggregate.h5" in os.listdir("model/initial"):
shutil.copy(
"model/initial/aggregate.h5",
os.path.join(self.dic_path["PATH_TO_MODEL"], "round_0.h5"))
else:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(0, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_traffic_env_conf, self.dic_path,
best_round))
p.start()
p.join()
else:
self.updater_wrapper(
cnt_round=0,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
dic_path=self.dic_path,
best_round=best_round)
self.dic_exp_conf["PRETRAIN"] = False
self.dic_exp_conf["AGGREGATE"] = False
# trainf
for cnt_round in range(self.dic_exp_conf["NUM_ROUNDS"]):
print("round %d starts" % cnt_round)
round_start_time = time.time()
process_list = []
print("============== generator =============")
generator_start_time = time.time()
if multi_process:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
p = Process(target=self.generator_wrapper,
args=(cnt_round, cnt_gen, self.dic_path,
self.dic_exp_conf, self.dic_agent_conf,
self.dic_traffic_env_conf, best_round))
print("before")
p.start()
print("end")
process_list.append(p)
print("before join")
for i in range(len(process_list)):
p = process_list[i]
print("generator %d to join" % i)
p.join()
print("generator %d finish join" % i)
print("end join")
else:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
self.generator_wrapper(
cnt_round=cnt_round,
cnt_gen=cnt_gen,
dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
dic_agent_conf=self.dic_agent_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
best_round=best_round)
generator_end_time = time.time()
generator_total_time = generator_end_time - generator_start_time
print("============== make samples =============")
# make samples and determine which samples are good
making_samples_start_time = time.time()
train_round = os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"train_round")
if not os.path.exists(train_round):
os.makedirs(train_round)
cs = ConstructSample(
path_to_samples=train_round,
cnt_round=cnt_round,
dic_traffic_env_conf=self.dic_traffic_env_conf)
cs.make_reward_for_system()
# EvaluateSample()
making_samples_end_time = time.time()
making_samples_total_time = making_samples_end_time - making_samples_start_time
print("============== update network =============")
update_network_start_time = time.time()
if self.dic_exp_conf["MODEL_NAME"] in self.dic_exp_conf[
"LIST_MODEL_NEED_TO_UPDATE"]:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(cnt_round, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_traffic_env_conf, self.dic_path,
best_round, bar_round))
p.start()
print("update to join")
p.join()
print("update finish join")
else:
self.updater_wrapper(
cnt_round=cnt_round,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
dic_path=self.dic_path,
best_round=best_round,
bar_round=bar_round)
if not self.dic_exp_conf["DEBUG"]:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
path_to_log = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "train_round",
"round_" + str(cnt_round), "generator_" + str(cnt_gen))
self.downsample_for_system(path_to_log,
self.dic_traffic_env_conf)
update_network_end_time = time.time()
update_network_total_time = update_network_end_time - update_network_start_time
print("============== test evaluation =============")
# <<<<<<< HEAD
test_evaluation_start_time = time.time()
if self.dic_exp_conf["SPARSE_TEST"] and cnt_round % 5 != 0:
pass
# =======
# if multi_process:
# p = Process(target=model_test.test,
# args=(self.dic_path["PATH_TO_MODEL"], cnt_round, self.dic_exp_conf["RUN_COUNTS"], self.dic_traffic_env_conf, False))
# p.start()
# if self.dic_exp_conf["EARLY_STOP"]:
# p.join()
# # >>>>>>> ana_simulator
else:
if multi_process:
p = Process(target=model_test.test,
args=(self.dic_path["PATH_TO_MODEL"],
cnt_round,
self.dic_exp_conf["RUN_COUNTS"],
self.dic_traffic_env_conf, False))
p.start()
if self.dic_exp_conf["EARLY_STOP"]:
p.join()
else:
model_test.test(self.dic_path["PATH_TO_MODEL"],
cnt_round,
self.dic_exp_conf["RUN_COUNTS"],
self.dic_traffic_env_conf,
if_gui=False)
test_evaluation_end_time = time.time()
test_evaluation_total_time = test_evaluation_end_time - test_evaluation_start_time
print('============== early stopping =============')
if self.dic_exp_conf["EARLY_STOP"]:
flag = self.early_stopping(self.dic_path, cnt_round)
if flag == 1:
print("early stopping!")
print("training ends at round %s" % cnt_round)
break
print('============== model pool evaluation =============')
if self.dic_exp_conf["MODEL_POOL"] and cnt_round > 50:
if multi_process:
p = Process(
target=self.model_pool_wrapper,
args=(self.dic_path, self.dic_exp_conf, cnt_round),
)
p.start()
print("model_pool to join")
p.join()
print("model_pool finish join")
else:
self.model_pool_wrapper(dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
cnt_round=cnt_round)
model_pool_dir = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model.pkl")
if os.path.exists(model_pool_dir):
model_pool = pickle.load(open(model_pool_dir, "rb"))
ind = random.randint(0, len(model_pool) - 1)
best_round = model_pool[ind][0]
ind_bar = random.randint(0, len(model_pool) - 1)
flag = 0
while ind_bar == ind and flag < 10:
ind_bar = random.randint(0, len(model_pool) - 1)
flag += 1
# bar_round = model_pool[ind_bar][0]
bar_round = None
else:
best_round = None
bar_round = None
# downsample
if not self.dic_exp_conf["DEBUG"]:
path_to_log = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "test_round",
"round_" + str(cnt_round))
self.downsample_for_system(path_to_log,
self.dic_traffic_env_conf)
else:
best_round = None
print("best_round: ", best_round)
print("Generator time: ", generator_total_time)
print("Making samples time:", making_samples_total_time)
print("update_network time:", update_network_total_time)
print("test_evaluation time:", test_evaluation_total_time)
print("round {0} ends, total_time: {1}".format(
cnt_round,
time.time() - round_start_time))
f_time = open(
os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"running_time.csv"), "a")
f_time.write("{0}\t{1}\t{2}\t{3}\t{4}\n".format(
generator_total_time, making_samples_total_time,
update_network_total_time, test_evaluation_total_time,
time.time() - round_start_time))
f_time.close()
def run(self, multi_process=False):
best_round, bar_round = None, None
f_time = open(
os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"running_time.csv"), "w")
f_time.write(
"generator_time\tmaking_samples_time\tupdate_network_time\ttest_evaluation_times\tall_times\n"
)
f_time.close()
# trainf
for cnt_round in range(self.dic_exp_conf["NUM_ROUNDS"]):
print("round %d starts" % cnt_round)
round_start_time = time.time()
process_list = []
print("============== generator =============")
generator_start_time = time.time()
if multi_process:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
p = Process(target=self.generator_wrapper,
args=(cnt_round, cnt_gen, self.dic_path,
self.dic_exp_conf, self.dic_agent_conf,
self.dic_traffic_env_conf, best_round))
print("before")
p.start()
print("end")
process_list.append(p)
print("before join")
for i in range(len(process_list)):
p = process_list[i]
print("generator %d to join" % i)
p.join()
print("generator %d finish join" % i)
print("end join")
else:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
self.generator_wrapper(
cnt_round=cnt_round,
cnt_gen=cnt_gen,
dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
dic_agent_conf=self.dic_agent_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
best_round=best_round)
generator_end_time = time.time()
generator_total_time = generator_end_time - generator_start_time
print("============== make samples =============")
# make samples and determine which samples are good
making_samples_start_time = time.time()
train_round = os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"train_round")
if not os.path.exists(train_round):
os.makedirs(train_round)
cs = ConstructSample(
path_to_samples=train_round,
cnt_round=cnt_round,
dic_traffic_env_conf=self.dic_traffic_env_conf)
cs.make_reward_for_system()
# EvaluateSample()
making_samples_end_time = time.time()
making_samples_total_time = making_samples_end_time - making_samples_start_time
print("============== update network =============")
update_network_start_time = time.time()
if self.dic_exp_conf["MODEL_NAME"] in self.dic_exp_conf[
"LIST_MODEL_NEED_TO_UPDATE"]:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(cnt_round, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_traffic_env_conf, self.dic_path,
best_round, bar_round))
p.start()
print("update to join")
p.join()
print("update finish join")
else:
self.updater_wrapper(
cnt_round=cnt_round,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
dic_path=self.dic_path,
best_round=best_round,
bar_round=bar_round)
if not self.dic_exp_conf["DEBUG"]:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
path_to_log = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "train_round",
"round_" + str(cnt_round), "generator_" + str(cnt_gen))
self.downsample_for_system(path_to_log,
self.dic_traffic_env_conf)
update_network_end_time = time.time()
update_network_total_time = update_network_end_time - update_network_start_time
print("============== test evaluation =============")
test_evaluation_start_time = time.time()
if multi_process:
p = Process(target=model_test.test,
args=(self.dic_path["PATH_TO_MODEL"], cnt_round,
self.dic_exp_conf["RUN_COUNTS"],
self.dic_traffic_env_conf, False))
p.start()
if self.dic_exp_conf["EARLY_STOP"]:
p.join()
else:
model_test.test(self.dic_path["PATH_TO_MODEL"],
cnt_round,
self.dic_exp_conf["RUN_COUNTS"],
self.dic_traffic_env_conf,
if_gui=False)
test_evaluation_end_time = time.time()
test_evaluation_total_time = test_evaluation_end_time - test_evaluation_start_time
print('============== early stopping =============')
if self.dic_exp_conf["EARLY_STOP"]:
flag = self.early_stopping(self.dic_path, cnt_round)
if flag == 1:
print("early stopping!")
print("training ends at round %s" % cnt_round)
break
print("Generator time: ", generator_total_time)
print("Making samples time:", making_samples_total_time)
print("update_network time:", update_network_total_time)
print("test_evaluation time:", test_evaluation_total_time)
print("round {0} ends, total_time: {1}".format(
cnt_round,
time.time() - round_start_time))
f_time = open(
os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"running_time.csv"), "a")
f_time.write("{0}\t{1}\t{2}\t{3}\t{4}\n".format(
generator_total_time, making_samples_total_time,
update_network_total_time, test_evaluation_total_time,
time.time() - round_start_time))
f_time.close()
def run(self, multi_process=False):
best_round, bar_round = None, None
# pretrain for acceleration
if self.dic_exp_conf["PRETRAIN"]:
if os.listdir(self.dic_path["PATH_TO_PRETRAIN_MODEL"]):
shutil.copy(
os.path.join(
self.dic_path["PATH_TO_PRETRAIN_MODEL"],
"%s.h5" % self.dic_exp_conf["TRAFFIC_FILE"][0]),
os.path.join(self.dic_path["PATH_TO_MODEL"], "round_0.h5"))
else:
if not os.listdir(
self.dic_path["PATH_TO_PRETRAIN_WORK_DIRECTORY"]):
for cnt_round in range(
self.dic_exp_conf["PRETRAIN_NUM_ROUNDS"]):
print("round %d starts" % cnt_round)
process_list = []
# ============== generator =============
if multi_process:
for cnt_gen in range(
self.
dic_exp_conf["PRETRAIN_NUM_GENERATORS"]):
p = Process(
target=self.generator_wrapper,
args=(cnt_round, cnt_gen, self.dic_path,
self.dic_exp_conf,
self.dic_agent_conf,
self.dic_sumo_env_conf, best_round))
print("before")
p.start()
print("end")
process_list.append(p)
print("before join")
for p in process_list:
p.join()
print("end join")
else:
for cnt_gen in range(
self.
dic_exp_conf["PRETRAIN_NUM_GENERATORS"]):
self.generator_wrapper(
cnt_round=cnt_round,
cnt_gen=cnt_gen,
dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
dic_agent_conf=self.dic_agent_conf,
dic_sumo_env_conf=self.dic_sumo_env_conf,
best_round=best_round)
# ============== make samples =============
# make samples and determine which samples are good
train_round = os.path.join(
self.dic_path["PATH_TO_PRETRAIN_WORK_DIRECTORY"],
"train_round")
if not os.path.exists(train_round):
os.makedirs(train_round)
cs = ConstructSample(
path_to_samples=train_round,
cnt_round=cnt_round,
dic_sumo_env_conf=self.dic_sumo_env_conf)
cs.make_reward()
if self.dic_exp_conf["MODEL_NAME"] in self.dic_exp_conf[
"LIST_MODEL_NEED_TO_UPDATE"]:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(0, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_sumo_env_conf,
self.dic_path, best_round))
p.start()
p.join()
else:
self.updater_wrapper(
cnt_round=0,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_sumo_env_conf=self.dic_sumo_env_conf,
dic_path=self.dic_path,
best_round=best_round)
# train with aggregate samples
if self.dic_exp_conf["AGGREGATE"]:
if "aggregate.h5" in os.listdir("model/initial"):
shutil.copy(
"model/initial/aggregate.h5",
os.path.join(self.dic_path["PATH_TO_MODEL"], "round_0.h5"))
else:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(0, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_sumo_env_conf, self.dic_path,
best_round))
p.start()
p.join()
else:
self.updater_wrapper(
cnt_round=0,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_sumo_env_conf=self.dic_sumo_env_conf,
dic_path=self.dic_path,
best_round=best_round)
self.dic_exp_conf["PRETRAIN"] = False
self.dic_exp_conf["AGGREGATE"] = False
# train
for cnt_round in range(self.dic_exp_conf["NUM_ROUNDS"]):
print("round %d starts" % cnt_round)
round_start_t = time.time()
process_list = []
# ============== generator =============
if multi_process:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
p = Process(target=self.generator_wrapper,
args=(cnt_round, cnt_gen, self.dic_path,
self.dic_exp_conf, self.dic_agent_conf,
self.dic_traffic_env_conf, best_round))
p.start()
process_list.append(p)
for i in range(len(process_list)):
p = process_list[i]
p.join()
else:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
self.generator_wrapper(
cnt_round=cnt_round,
cnt_gen=cnt_gen,
dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
dic_agent_conf=self.dic_agent_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
best_round=best_round)
# ============== make samples =============
# make samples and determine which samples are good
train_round = os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"train_round")
if not os.path.exists(train_round):
os.makedirs(train_round)
cs = ConstructSample(
path_to_samples=train_round,
cnt_round=cnt_round,
dic_traffic_env_conf=self.dic_traffic_env_conf)
cs.make_reward()
# EvaluateSample()
# ============== update network =============
if self.dic_exp_conf["MODEL_NAME"] in self.dic_exp_conf[
"LIST_MODEL_NEED_TO_UPDATE"]:
if multi_process:
p = Process(target=self.updater_wrapper,
args=(cnt_round, self.dic_agent_conf,
self.dic_exp_conf,
self.dic_traffic_env_conf, self.dic_path,
best_round, bar_round))
p.start()
p.join()
else:
self.updater_wrapper(
cnt_round=cnt_round,
dic_agent_conf=self.dic_agent_conf,
dic_exp_conf=self.dic_exp_conf,
dic_traffic_env_conf=self.dic_traffic_env_conf,
dic_path=self.dic_path,
best_round=best_round,
bar_round=bar_round)
if not self.dic_exp_conf["DEBUG"]:
for cnt_gen in range(self.dic_exp_conf["NUM_GENERATORS"]):
path_to_log = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "train_round",
"round_" + str(cnt_round), "generator_" + str(cnt_gen))
self.downsample(path_to_log)
# ============== test evaluation =============
if multi_process:
p = Process(target=model_test.test,
args=(self.dic_path["PATH_TO_MODEL"], cnt_round,
self.dic_exp_conf["TEST_RUN_COUNTS"],
self.dic_traffic_env_conf, False))
p.start()
if self.dic_exp_conf["EARLY_STOP"] or self.dic_exp_conf[
"MODEL_POOL"]:
p.join()
else:
model_test.test(self.dic_path["PATH_TO_MODEL"],
cnt_round,
self.dic_exp_conf["RUN_COUNTS"],
self.dic_traffic_env_conf,
if_gui=False)
# ============== early stopping =============
if self.dic_exp_conf["EARLY_STOP"]:
flag = self.early_stopping(self.dic_path, cnt_round)
if flag == 1:
break
# ============== model pool evaluation =============
if self.dic_exp_conf["MODEL_POOL"]:
if multi_process:
p = Process(
target=self.model_pool_wrapper,
args=(self.dic_path, self.dic_exp_conf, cnt_round),
)
p.start()
p.join()
else:
self.model_pool_wrapper(dic_path=self.dic_path,
dic_exp_conf=self.dic_exp_conf,
cnt_round=cnt_round)
model_pool_dir = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model.pkl")
if os.path.exists(model_pool_dir):
model_pool = pickle.load(open(model_pool_dir, "rb"))
ind = random.randint(0, len(model_pool) - 1)
best_round = model_pool[ind][0]
ind_bar = random.randint(0, len(model_pool) - 1)
flag = 0
while ind_bar == ind and flag < 10:
ind_bar = random.randint(0, len(model_pool) - 1)
flag += 1
# bar_round = model_pool[ind_bar][0]
bar_round = None
else:
best_round = None
bar_round = None
# downsample
if not self.dic_exp_conf["DEBUG"]:
path_to_log = os.path.join(
self.dic_path["PATH_TO_WORK_DIRECTORY"], "test_round",
"round_" + str(cnt_round))
self.downsample(path_to_log)
else:
best_round = None
print("best_round: ", best_round)
print("round %s ends" % cnt_round)
round_end_t = time.time()
f_timing = open(
os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"],
"timing.txt"), "a+")
f_timing.write("round_{0}: {1}\n".format(
cnt_round, round_end_t - round_start_t))
f_timing.close()