def run_one_config(opt, model_type, case_study=False):
set_random_seeds()
dataset = DataSet(opt, model_type)
model_manager = ModelManager(opt)
model, train_time = model_manager.build_model(model_type, dataset)
evaluator = Evaluator(opt)
metrics = evaluator.eval(model, model_type, dataset.test_loader)
evaluator.write_performance(model_type, metrics, train_time)
run_case_study(model, dataset, opt, case_study)
'u_vocab_file': path + 'u.txt',
'v_vocab_file': path + 'v.txt',
't_vocab_file': path + 't.txt',
'train_data_file': path + 'train.txt',
'test_data_file': path + 'test.txt',
'coor_nor_file': path + 'coor_nor.txt',
'distance_file': path + 'distance.txt',
'train_log_file': path + 'log.txt',
'candidate_file': path + 'candidate.pk',
'id_offset': 1,
'n_epoch': 80,
'batch_size': 50,
'data_worker': 1,
'load_model': False,
'emb_dim_d': 16, # for distance embedding #best 16
'emb_dim_v': 16, #origin 32 best 16
'emb_dim_t': 8, #origin 8
'emb_dim_u': 32, # !!!jiayi copy from v3 origin 32 best 32
'hidden_dim': 16, #origin 16
'nb_cnt': 16,
'save_gap': 10,
'dropout': 0.5,
'epoch': 80
}
dataset = DataSet(opt)
manager = ModelManager(opt)
model_type = 'birnnt'
manager.build_model(model_type, dataset)
print "evaluate"
manager.evaluate(model_type, dataset)
