def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
if args.rep:
supervisor_config['param']['rep'] = args.rep
print('overwrite rep parameter with argument')
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
graph_pkl_filename)
id_str = search_id(supervisor_config['alg'],
supervisor_config['param'])
print(id_str)
model_dir = supervisor_config['train']['model_dir']
supervisor_config['train']['model_dir'] = os.path.join(
model_dir, id_str)
dset_dir = supervisor_config['data']['dataset_dir']
supervisor_config['data']['dataset_dir'] = os.path.join(
dset_dir, id_str)
assert supervisor_config['train']['epoch'] == -1
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
mean_score, outputs = supervisor.evaluate('val')
output_dir = os.path.join(args.output_dir, id_str)
os.makedirs(output_dir, exist_ok=True)
output_filename = os.path.join(output_dir, 'dcrnn_val_predictions.npz')
np.savez_compressed(output_filename, **outputs)
print("MAE : {}".format(mean_score))
print('Predictions saved as {}.'.format(output_filename))
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
graph_pkl_filename)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
supervisor.train()
def run_dcrnn(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
graph_pkl_filename)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
mean_score, outputs = supervisor.evaluate('test')
np.savez_compressed(args.output_filename, **outputs)
print("MAE : {}".format(mean_score))
print('Predictions saved as {}.'.format(args.output_filename))
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.safe_load(f)
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
adjtype = supervisor_config['model'].get('filter_type')
# sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(graph_pkl_filename)
sensor_ids, sensor_id_to_ind, adj_mx = utils.load_adj(
graph_pkl_filename, adjtype)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
supervisor.train()
def main(args):
print('main started with args: {}'.format(args))
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
add_prefix(args.train_local,supervisor_config,'base_dir')
add_prefix(args.data_local,supervisor_config['data'],'dataset_dir')
add_prefix(args.data_local,supervisor_config['data'],'graph_pkl_filename')
add_prefix(args.data_local,supervisor_config['train'],'load_model_dir')
graph_pkl_filename = supervisor_config['data'].get('graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(graph_pkl_filename)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
mean_score, outputs = supervisor.evaluate('test')
np.savez_compressed(args.output_filename, **outputs)
print("MAE : {}".format(mean_score))
print('Predictions saved as {}.'.format(args.output_filename))
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
supervisor_config['train']['epoch'] = args.epoch
if args.log_dir:
supervisor_config['train']['log_dir'] = args.log_dir
supervisor_config['data']['seq_len'] = supervisor_config['model'].get(
'seq_len')
supervisor_config['data']['horizon'] = supervisor_config['model'].get(
'horizon')
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
graph_pkl_filename)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
supervisor.test()
def main(args):
print('main started with args: {}'.format(args))
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
add_prefix(args.train_local, supervisor_config, 'base_dir')
add_prefix(args.data_local, supervisor_config['data'], 'dataset_dir')
add_prefix(args.data_local, supervisor_config['data'],
'graph_pkl_filename')
print('using supervisor_config: {}'.format(supervisor_config))
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
os.path.join(args.data_local, graph_pkl_filename))
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
supervisor.train()
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
if args.rep:
supervisor_config['param']['rep'] = args.rep
print('overwrite rep parameter with argument')
graph_pkl_filename = supervisor_config['data'].get('graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(graph_pkl_filename)
id_str = search_id(supervisor_config['alg'], supervisor_config['param'])
model_dir = supervisor_config['train']['model_dir']
supervisor_config['train']['model_dir'] = os.path.join(model_dir, id_str)
dset_dir = supervisor_config['data']['dataset_dir']
supervisor_config['data']['dataset_dir'] = os.path.join(dset_dir, id_str)
supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
supervisor.train()
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
graph_pkl_filename)
data_type = args.config_filename.split('/')[-1].split('.')[0].split(
'_')[-1] #'bay' or 'la'
supervisor = DCRNNSupervisor(data_type=data_type,
LOAD_INITIAL=args.LOAD_INITIAL,
adj_mx=adj_mx,
**supervisor_config)
if args.TEST_ONLY:
supervisor.evaluate_test()
else:
supervisor.train()