'../../data/length-of-stay/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
args = parser.parse_args()
print(args)
if args.small_part:
args.save_every = 2**30
# Build readers, discretizers, normalizers
if args.deep_supervision:
train_data_loader = common_utils.DeepSupervisionDataLoader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
small_part=args.small_part)
val_data_loader = common_utils.DeepSupervisionDataLoader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
small_part=args.small_part)
else:
train_reader = LengthOfStayReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'))
val_reader = LengthOfStayReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'))
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
parser = argparse.ArgumentParser()
common_utils.add_common_arguments(parser)
parser.add_argument('--deep_supervision',
dest='deep_supervision',
action='store_true')
parser.set_defaults(deep_supervision=False)
args = parser.parse_args()
print args
if args.small_part:
args.save_every = 2**30
# Build readers, discretizers, normalizers
if args.deep_supervision:
train_data_loader = common_utils.DeepSupervisionDataLoader(
dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/train_listfile.csv',
small_part=args.small_part)
val_data_loader = common_utils.DeepSupervisionDataLoader(
dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/val_listfile.csv',
small_part=args.small_part)
else:
train_reader = DecompensationReader(
dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/train_listfile.csv')
val_reader = DecompensationReader(
dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/val_listfile.csv')
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
action='store_true')
parser.set_defaults(deep_supervision=False)
parser.add_argument('--partition',
type=str,
default='custom',
help="log, custom, none")
args = parser.parse_args()
print args
if args.small_part:
args.save_every = 2**30
# Build readers, discretizers, normalizers
if args.deep_supervision:
train_data_loader = common_utils.DeepSupervisionDataLoader(
dataset_dir='../../data/length-of-stay/train/',
listfile='../../data/length-of-stay/train_listfile.csv',
small_part=args.small_part)
val_data_loader = common_utils.DeepSupervisionDataLoader(
dataset_dir='../../data/length-of-stay/train/',
listfile='../../data/length-of-stay/val_listfile.csv',
small_part=args.small_part)
else:
train_reader = LengthOfStayReader(
dataset_dir='../../data/length-of-stay/train/',
listfile='../../data/length-of-stay/train_listfile.csv')
val_reader = LengthOfStayReader(
dataset_dir='../../data/length-of-stay/train/',
listfile='../../data/length-of-stay/val_listfile.csv')
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
sources.append('structured_data')
experiment_name=experiment_name+'structured_'
if args.weighted:
experiment_name=experiment_name+'weighted_'
if args.condensed:
experiment_name=experiment_name+'condensed_'
if args.small_part:
args.save_every = 2**30
# Build readers, discretizers, normalizers
if args.deep_supervision:
train_data_loader = common_utils.DeepSupervisionDataLoader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
small_part=args.small_part, sources=sources, timesteps=args.timesteps, condensed=args.condensed)
val_data_loader = common_utils.DeepSupervisionDataLoader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
small_part=args.small_part, sources=sources, timesteps=args.timesteps, condensed=args.condensed)
else:
train_reader = LengthOfStayReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'), sources=sources, timesteps=args.timesteps, condensed=args.condensed)
val_reader = LengthOfStayReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'), sources=sources, timesteps=args.timesteps, condensed=args.condensed)
train_reader = LengthOfStayReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'), sources=sources, timesteps=args.timesteps, condensed=args.condensed)
reader_header = train_reader.read_example(0)['header']
n_bins = len(train_reader.read_example(0))
