'../../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))