def MAXWEL_bandster_station(raw_name, width):
name = raw_name.split('.')
if name[-1] != "csv":
return
name = name[0]
df = pd.read_csv('Train_Data/Width_' + width + '/' + name +
'.csv').drop(columns=['time'])
try:
result_dir = 'Results/' + name
os.mkdir(result_dir)
except:
result_dir = 'Results/' + name
assert df.isna().sum().sum() == 0, name + " has NANs"
data_info = {}
data_info['primary'] = name
data_info['num_features'] = df.shape[1]
data_info['train_time'] = 12
data_info['predict_time'] = 4
data_info['num_samples'] = df.shape[0]
X = np.zeros(
(data_info['num_samples'], data_info['train_time'], df.shape[1]))
Y = np.zeros((data_info['num_samples'], data_info['predict_time']))
for i in range(data_info['num_samples'] -
(data_info['train_time'] + data_info['predict_time'] + 1)):
X[i, :, :] = df.iloc[i:i + data_info['train_time']].values
Y[i, :] = np.asarray(
df[name + '_scaled_demand'][i + data_info['train_time']:i +
data_info['train_time'] +
data_info['predict_time']].values)
idx = sample(range(len(X)), 9000)
train_idx = idx[:6000]
valid_idx = idx[6000:8000]
test_idx = idx[8000:]
data_info['num_train_samples'] = len(train_idx)
data_info['train_idx'] = train_idx
data_info['num_valid_samples'] = len(valid_idx)
data_info['valid_idx'] = valid_idx
data_info['num_test_samples'] = len(test_idx)
data_info['test_idx'] = test_idx
data_info['fit_cnt'] = 1
# data_info['station_cnt']=station_cnt
# data_info['num_stations']=len(names)
dill.dump(data_info, open("Temp_Data/data_info.pkl", 'wb'))
X_train = np.zeros((data_info['num_train_samples'],
data_info['train_time'], data_info['num_features']))
Y_train = np.zeros(
(data_info['num_train_samples'], data_info['predict_time']))
X_valid = np.zeros((data_info['num_valid_samples'],
data_info['train_time'], data_info['num_features']))
Y_valid = np.zeros(
(data_info['num_valid_samples'], data_info['predict_time']))
X_test = np.zeros((data_info['num_test_samples'], data_info['train_time'],
data_info['num_features']))
Y_test = np.zeros(
(data_info['num_test_samples'], data_info['predict_time']))
for i, ind in enumerate(data_info['train_idx']):
X_train[i, :, :] = X[ind, :, :]
Y_train[i, :] = Y[ind, :]
for i, ind in enumerate(data_info['valid_idx']):
X_valid[i, :, :] = X[ind, :, :]
Y_valid[i, :] = Y[ind, :]
for i, ind in enumerate(data_info['test_idx']):
X_test[i, :, :] = X[ind, :, :]
Y_test[i, :] = Y[ind, :]
dill.dump(X_train, open("Temp_Data/X_train.pkl", 'wb'))
dill.dump(Y_train, open("Temp_Data/Y_train.pkl", 'wb'))
dill.dump(X_valid, open("Temp_Data/X_valid.pkl", 'wb'))
dill.dump(Y_valid, open("Temp_Data/Y_valid.pkl", 'wb'))
dill.dump(X_test, open("Temp_Data/X_test.pkl", 'wb'))
dill.dump(Y_test, open("Temp_Data/Y_test.pkl", 'wb'))
# Import a worker class
from MAXWEL_worker import MAXWEL_worker as worker
#Build an argument parser
parser = argparse.ArgumentParser(
description='MAXWEL - sequential execution.')
parser.add_argument('--min_budget',
type=float,
help='Minimum budget used during the optimization.',
default=5)
parser.add_argument('--max_budget',
type=float,
help='Maximum budget used during the optimization.',
default=20)
parser.add_argument('--n_iterations',
type=int,
help='Number of iterations performed by the optimizer',
default=10)
parser.add_argument('--n_workers',
type=int,
help='Number of workers to run in parallel.',
default=1)
parser.add_argument(
'--shared_directory',
type=str,
help=
'A directory that is accessible for all processes, e.g. a NFS share.',
default='.')
args = parser.parse_args()
#Define a realtime result logger
result_logger = hpres.json_result_logger(directory=result_dir,
overwrite=True)
#Start a nameserver
NS = hpns.NameServer(run_id='MAXWEL', host='127.0.0.1', port=None)
NS.start()
#Start the workers
workers = []
for i in range(args.n_workers):
w = worker(nameserver='127.0.0.1', run_id='MAXWEL', id=i)
w.run(background=True)
workers.append(w)
#Define and run an optimizer
bohb = BOHB(configspace=w.get_configspace(),
run_id='MAXWEL',
result_logger=result_logger,
min_budget=args.min_budget,
max_budget=args.max_budget)
res = bohb.run(n_iterations=args.n_iterations,
min_n_workers=args.n_workers)
#Shutdown the nameserver
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
parser.add_argument('--shared_directory',type=str, help='A directory that is accessible for all processes, e.g. a NFS share.', default='.')
args=parser.parse_args()
#Define a realtime result logger
result_logger = hpres.json_result_logger(directory=result_dir, overwrite=True)
#Start a nameserver
NS = hpns.NameServer(run_id='MAXWEL', host='127.0.0.1', port=None)
NS.start()
#Start the workers
workers=[]
for i in range(args.n_workers):
w = worker(nameserver='127.0.0.1',run_id='MAXWEL', id=i)
w.run(background=True)
workers.append(w)
#Define and run an optimizer
bohb = BOHB(configspace = w.get_configspace(),
run_id = 'MAXWEL',
result_logger=result_logger,
min_budget=args.min_budget,
max_budget=args.max_budget)
res = bohb.run(n_iterations=args.n_iterations, min_n_workers=args.n_workers)
#Shutdown the nameserver
bohb.shutdown(shutdown_workers=True)
NS.shutdown()
