def main():
parser = argparse.ArgumentParser(description='Script for creating a normalizer state - a file which stores the '
'means and standard deviations of columns of the output of a '
'discretizer, which are later used to standardize the input of '
'neural models.')
parser.add_argument('--task', type=str,choices=['ihm', 'los'])
parser.add_argument('--timestep', type=float, default=1.0,
help="Rate of the re-sampling to discretize time-series.")
parser.add_argument('--impute_strategy', type=str, default='previous',
choices=['zero', 'next', 'previous', 'normal_value'],
help='Strategy for imputing missing values.')
parser.add_argument('--start_time', type=str, choices=['zero', 'relative'],
help='Specifies the start time of discretization. Zero means to use the beginning of '
'the ICU stay. Relative means to use the time of the first ICU event')
parser.add_argument('--store_masks', dest='store_masks', action='store_true',
help='Store masks that specify observed/imputed values.')
parser.add_argument('--no-masks', dest='store_masks', action='store_false',
help='Do not store that specify specifying observed/imputed values.')
parser.add_argument('--n_samples', type=int, default=-1, help='How many samples to use to estimates means and '
'standard deviations. Set -1 to use all training samples.')
parser.add_argument('--output_dir', type=str, help='Directory where the output file will be saved.',
default='.')
parser.add_argument('--data', type=str, required=True, help='Path to the task data.')
parser.set_defaults(store_masks=True)
args = parser.parse_args()
print(args)
# create the reader
reader = None
dataset_dir = os.path.join(args.data, 'train')
if args.task == 'ihm':
reader = InHospitalMortalityReader(dataset_dir=dataset_dir, period_length=48.0)
if args.task == 'los':
reader = LengthOfStayReader(dataset_dir=dataset_dir)
# create the discretizer
discretizer = Discretizer(timestep=args.timestep,
store_masks=args.store_masks,
impute_strategy=args.impute_strategy,
start_time=args.start_time)
discretizer_header = reader.read_example(0)['header']
continuous_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
# create the normalizer
normalizer = Normalizer(fields=continuous_channels)
# read all examples and store the state of the normalizer
n_samples = args.n_samples
if n_samples == -1:
n_samples = reader.get_number_of_examples()
for i in range(n_samples):
if i % 1000 == 0:
print('Processed {} / {} samples'.format(i, n_samples), end='\r')
ret = reader.read_example(i)
data, new_header = discretizer.transform(ret['X'], end=ret['t'])
normalizer._feed_data(data)
print('\n')
file_name = '{}_ts_{:.1f}_impute_{}_start_time{}.normalizer'.format(
args.task, args.timestep, args.impute_strategy, args.start_time)
file_name = os.path.join(args.output_dir, file_name)
print('Saving the state in {} ...'.format(file_name))
normalizer._save_params(file_name)
names = ret["name"]
diseases_list = get_diseases(names, '/mnt/MIMIC-III-clean/data/')
diseases_embedding = disease_embedding(embeddings, word_indices, diseases_list)
discretizer_header = discretizer.transform(ret["X"][0])[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
data = [
discretizer.transform_first_t_hours(X, end=t)[0]
for (X, t) in zip(data, ts)
]
[normalizer._feed_data(x=X) for X in data]
normalizer._use_params()
args_dict = dict(args._get_kwargs())
args_dict['task'] = 'ihm'
args_dict['target_repl'] = target_repl
# Build the model
print("==> using model {}".format(args.network))
print('os.path.basename(args.network), args.network: ',
os.path.basename(args.network), args.network)
model_module = importlib.machinery.SourceFileLoader(
os.path.basename(args.network), args.network).load_module()
model = model_module.Network(**args_dict)
suffix = ".bs{}{}{}.ts{}{}".format(
def main():
parser = argparse.ArgumentParser(
description=
'Script for creating a normalizer state - a file which stores the '
'means and standard deviations of columns of the output of a '
'discretizer, which are later used to standardize the input of '
'neural models.')
parser.add_argument('--task',
type=str,
required=True,
choices=['ihm', 'decomp', 'los', 'pheno', 'multi'])
parser.add_argument('--impute_strategy',
type=str,
default='previous',
choices=['zero', 'next', 'previous', 'normal_value'],
help='Strategy for imputing missing values.')
parser.add_argument(
'--store_masks',
dest='store_masks',
action='store_true',
help='Store masks that specify observed/imputed values.')
parser.add_argument(
'--no-masks',
dest='store_masks',
action='store_false',
help='Do not store that specify specifying observed/imputed values.')
parser.add_argument(
'--n_samples',
type=int,
default=-1,
help='How many samples to use to estimates means and '
'standard deviations. Set -1 to use all training samples.')
parser.add_argument('--output_dir',
type=str,
help='Directory where the output file will be saved.',
default='.')
parser.add_argument('--data',
type=str,
required=True,
help='Path to the task data.')
parser.set_defaults(store_masks=True)
args = parser.parse_args()
print(args)
# create the reader
reader = None
dataset_dir = os.path.join(args.data, 'train')
if args.task == 'ihm':
reader = InHospitalMortalityReader(dataset_dir=dataset_dir,
listfile=os.path.join(
args.data,
'train_listfile.csv'),
period_length=48.0)
if args.task == 'decomp':
reader = DecompensationReader(dataset_dir=dataset_dir,
listfile=os.path.join(
args.data, 'train_listfile.csv'))
if args.task == 'los':
reader = LengthOfStayReader(dataset_dir=dataset_dir,
listfile=os.path.join(
args.data, 'train_listfile.csv'))
if args.task == 'pheno':
reader = PhenotypingReader(dataset_dir=dataset_dir,
listfile=os.path.join(
args.data, 'train_listfile.csv'))
if args.task == 'multi':
reader = MultitaskReader(dataset_dir=dataset_dir,
listfile=os.path.join(args.data,
'train_listfile.csv'))
# create the discretizer
discretizer = OneHotEncoder(impute_strategy=args.impute_strategy)
discretizer_header = reader.read_example(0)['header']
continuous_channels = [
i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1
]
# create the normalizer
normalizer = Normalizer(fields=continuous_channels)
# read all examples and store the state of the normalizer
n_samples = args.n_samples
if n_samples == -1:
n_samples = reader.get_number_of_examples()
for i in range(n_samples):
if i % 1000 == 0:
print('Processed {} / {} samples'.format(i, n_samples), end='\r')
ret = reader.read_example(i)
data, _, new_header = discretizer.transform(ret['X'], end=ret['t'])
normalizer._feed_data(data)
print('\n')
file_name = '{}_onehotenc_n:{}.normalizer'.format(args.task, n_samples)
file_name = os.path.join(args.output_dir, file_name)
print('Saving the state in {} ...'.format(file_name))
normalizer._save_params(file_name)