def ukp_train_test_repeat(load_id, exp_name, topic, n_repeat):
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_epoch = 2
e_config.save_interval = 100 * 60 # 30 minutes
e_config.voca_size = 30522
e_config.load_names = ['bert']
encode_opt = "is_good"
print(load_id)
scores = []
for i in range(n_repeat):
e = Experiment(hp)
print(exp_name)
e_config.name = "arg_{}_{}_{}".format(exp_name, topic, encode_opt)
data_loader = BertDataLoader(topic,
True,
hp.seq_max,
"bert_voca.txt",
option=encode_opt)
save_path = e.train_ukp(e_config, data_loader, load_id)
f1_last = e.eval_ukp(e_config, data_loader, save_path)
scores.append(f1_last)
print(exp_name)
print(encode_opt)
for e in scores:
print(e, end="\t")
print()
print("Avg\n{0:.03f}".format(average(scores)))
def ukp_train_test(load_id, exp_name):
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_epoch = 2
e_config.save_interval = 100 * 60 # 30 minutes
e_config.voca_size = 30522
e_config.load_names = ['bert']
encode_opt = "is_good"
print(load_id)
f1_list = []
for topic in data_generator.argmining.ukp_header.all_topics:
e = Experiment(hp)
print(exp_name)
e_config.name = "arg_{}_{}_{}".format(exp_name, topic, encode_opt)
data_loader = BertDataLoader(topic,
True,
hp.seq_max,
"bert_voca.txt",
option=encode_opt)
save_path = e.train_ukp(e_config, data_loader, load_id)
print(topic)
f1_last = e.eval_ukp(e_config, data_loader, save_path)
f1_list.append((topic, f1_last))
print(exp_name)
print(encode_opt)
print(f1_list)
for key, score in f1_list:
print("{0}\t{1:.03f}".format(key, score))
