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))