param_dict['hyper_params'] = {
'len_global': len_global,
'len_local': len_local,
'neighbor_size': neighbor_size,
'res': args.res,
'cnn': args.cnn,
'lr': args.lr,
'BN': True if args.BN == 1 else False
}
if __name__ == '__main__':
# ===============================================================
# load data
ts = time.time()
data, mmn = utils.get_data(param_dict['dataset'], param_dict['len_global'],
param_dict['len_local'],
param_dict['neighbor_size'],
param_dict['data_choice'])
if param_dict['dataset'] == 'bj_taxi':
param_dict['train_data'] = [
data[name] for name in [
'g_vacation_train', 'g_hour_train', 'g_dayOfWeek_train',
'g_weather_train', 'g_continuous_external_train',
't_vacation_train', 't_hour_train', 't_dayOfWeek_train',
't_weather_train', 't_continuous_external_train',
'current_local_flow_train', 'stack_local_flow_train',
'global_flow_train'
]
]
param_dict['test_data'] = [
data[name] for name in [
xgboost_outflow_ground_truth.append(outflow_ground + 1)
xgboost_inflow_data = np.asarray(xgboost_inflow_data)
xgboost_outflow_data = np.asarray(xgboost_outflow_data)
xgboost_inflow_ground_truth = np.asarray(xgboost_inflow_ground_truth)
xgboost_outflow_ground_truth = np.asarray(xgboost_outflow_ground_truth)
return xgboost_inflow_data, xgboost_outflow_data, xgboost_inflow_ground_truth, xgboost_outflow_ground_truth
if __name__ == '__main__':
# ===============================================================
# load data
ts = time.time()
data, mmn = utils.get_data(dataset, len_global, len_local, neighbor_size, data_choice)
print('\n Load data elapsed time : %.3f seconds\n' % (time.time() - ts))
print('=' * 30)
# ===============================================================
# evaluate model
ts = time.time()
xgboost_inflow_data, xgboost_outflow_data, xgboost_inflow_ground_truth, xgboost_outflow_ground_truth = \
get_data(data['predict_time_test'], neighbor_size, data['index_cut_test'])
# special, add one model parameter for different external size, due to different dataset
if dataset == 'bj_taxi':
error_low = 1e-3
else:
