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,
impute_strategy='previous',
start_time='zero')
if args.deep_supervision:
discretizer_header = discretizer.transform(
train_data_loader._data["X"][0])[1].split(',')
else:
discretizer_header = discretizer.transform(
train_reader.read_example(0)["X"])[1].split(',')
cont_channels = [
i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1
]
normalizer = Normalizer(
fields=cont_channels) # choose here which columns to standardize
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'los_ts{}.input_str:previous.start_time:zero.n5e4.normalizer'.format(
args.timestep)
normalizer_state = os.path.join(os.path.dirname(__file__),
normalizer_state)
normalizer.load_params(normalizer_state)
args_dict = dict(args._get_kwargs())
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))
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
impute_strategy='previous',
start_time='zero', header = reader_header, sources = sources)
if args.deep_supervision:
discretizer_header = discretizer.transform(train_data_loader._data["X"][0])[1].split(',')
else:
discretizer_header = discretizer.transform(train_reader.read_example(0)["X"])[1].split(',')
cont_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
normalizer = Normalizer(fields=cont_channels) # choose here which columns to standardize
normalizer_state = args.normalizer_state
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,
impute_strategy='previous',
start_time='zero')
if args.deep_supervision:
discretizer_header = discretizer.transform(train_data_loader._data["X"][0])[1].split(',')
else:
discretizer_header = discretizer.transform(train_reader.read_example(0)["X"])[1].split(',')
cont_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
normalizer = Normalizer(fields=cont_channels) # choose here which columns to standardize
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'los_ts{}.input_str:previous.start_time:zero.n5e4.normalizer'.format(args.timestep)
normalizer_state = os.path.join(os.path.dirname(__file__), normalizer_state)
normalizer.load_params(normalizer_state)
args_dict = dict(args._get_kwargs())
args_dict['header'] = discretizer_header
args_dict['task'] = 'los'
args_dict['nhead'] = 1
args_dict['dim_feedforward'] = 128
args_dict['dropout'] = 0.5
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,
imput_strategy='previous',
start_time='zero')
if args.deep_supervision:
discretizer_header = discretizer.transform(train_data_loader._data["X"][0])[1].split(',')
else:
discretizer_header = discretizer.transform(train_reader.read_example(0)["X"])[1].split(',')
cont_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
normalizer = Normalizer(fields=cont_channels) # choose here onlycont vs all
normalizer.load_params('los_ts{}.input_str:previous.start_time:zero.n5e4.normalizer'.format(args.timestep))
args_dict = dict(args._get_kwargs())
args_dict['header'] = discretizer_header
args_dict['task'] = 'los'
args_dict['num_classes'] = (1 if args.partition == 'none' else 10)
# Build the model
print "==> using model {}".format(args.network)
model_module = imp.load_source(os.path.basename(args.network), args.network)
model = model_module.Network(**args_dict)