def _load_data(self, testfold=4):
train_reader = InHospitalMortalityReader(
dataset_dir='mimic3-benchmarks/data/in-hospital-mortality/train/',
listfile=
'mimic3-benchmarks/data/in-hospital-mortality/train_listfile.csv',
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir='mimic3-benchmarks/data/in-hospital-mortality/train/',
listfile=
'mimic3-benchmarks/data/in-hospital-mortality/val_listfile.csv',
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir='mimic3-benchmarks/data/in-hospital-mortality/test/',
listfile=
'mimic3-benchmarks/data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
discretizer = Discretizer(timestep=float(4),
store_masks=True,
imput_strategy='previous',
start_time='zero')
discretizer_header = discretizer.transform(
train_reader.read_example(0)[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
normalizer.load_params(
'mimic3-benchmarks/mimic3models/in_hospital_mortality/'
'ihm_ts%s.input_str:%s.start_time:zero.normalizer' %
('2.0', 'previous'))
# normalizer=None
train_raw = utils.load_data(train_reader, discretizer, normalizer,
False)
val_raw = utils.load_data(val_reader, discretizer, normalizer, False)
test_raw = utils.load_data(test_reader, discretizer, normalizer, False)
# To split into
def preprocess(the_raw_set):
x, y = the_raw_set
x = x.astype(np.float32, copy=False)
y = np.array(y)
return x, y
train_raw = preprocess(train_raw)
val_raw = preprocess(val_raw)
test_raw = preprocess(test_raw)
return train_raw, val_raw, test_raw
def create_data_loader(data, subset=False):
# Build readers, discretizers, normalizers
print("Creating Data File Reader")
#Using val_test set for visualization
data_reader = Reader(dataset_dir=os.path.join(data, 'test'),
listfile=os.path.join(data, 'test', 'listfile.csv'),
period_length=24.0)
#For Hourly Task we need to limit the amount of data for visualization
if subset:
print("limiting data")
data_reader.limit_data(10)
print("Initializing Discretizer and Normalizer")
discretizer = DiscretizerContinuous(timestep=1.0,
store_masks=False,
impute_strategy='previous',
start_time='zero')
discretizer_header = discretizer.transform(
data_reader.read_example(0)["X"])[1]
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 = 'ptemb_ts{}.input_str:{}.start_time:zero.normalizer'.format(
args.timestep, args.imputation)
normalizer_state = os.path.join(os.path.dirname(__file__),
normalizer_state)
normalizer.load_params(normalizer_state)
#Create Dataset + DataLoader
print("Building Dataset")
data_dataset = ClassDataset(reader=data_reader,
discretizer=discretizer,
normalizer=normalizer,
return_name=False,
embed_method=args.embed_method)
print("Building DataLoader")
data_loader = DataLoader(data_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
return data_loader
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['num_classes'] = (1 if args.partition == 'none' else 10)
# Build the model
print("==> using model {}".format(args.network))
def dataset_reader(phase, args, target_repl=False):
if phase == "train":
#% Build readers & discretizers
train_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
period_length=48.0)
discretizer = Discretizer(timestep=float(args.timestep),
store_masks=True,
impute_strategy='previous',
start_time='zero')
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
]
#%% Data normalization (by mean and variance)
normalizer = Normalizer(
fields=cont_channels) # choose here which columns to standardize
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'ihm_ts{}.input_str:{}.start_time:zero.normalizer'.format(
args.timestep, args.imputation)
normalizer_state = os.path.join(os.path.dirname(__file__),
normalizer_state)
normalizer.load_params(normalizer_state)
# args_dict = dict(args._get_kwargs()) #TODO: reverse
args_dict = {}
args_dict['header'] = discretizer_header
args_dict['task'] = 'ihm'
args_dict['target_repl'] = target_repl
#%% Read data
start = time()
print("Reading started")
train_raw = utils.load_data(train_reader,
discretizer,
normalizer,
args.small_part,
return_names=False)
val_raw = utils.load_data(val_reader,
discretizer,
normalizer,
args.small_part,
return_names=False)
if target_repl:
T = train_raw[0][0].shape[0]
def extend_labels(data):
data = list(data)
labels = np.array(data[1]) # (B,)
data[1] = [labels, None]
data[1][1] = np.expand_dims(labels,
axis=-1).repeat(T,
axis=1) # (B, T)
data[1][1] = np.expand_dims(data[1][1], axis=-1) # (B, T, 1)
return data
train_raw = extend_labels(train_raw)
val_raw = extend_labels(val_raw)
print("Reading finished after {} seconds".format(time() - start))
return (train_raw, val_raw)
else: ################################### TEST phase
test_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'),
period_length=48.0)
test_raw = utils.load_data(test_reader,
discretizer,
normalizer,
args.small_part,
return_names=True)
return test_raw
start_time='zero')
N = train_reader.get_number_of_examples()
ret = common_utils.read_chunk(train_reader, N)
data = ret["X"]
ts = ret["t"]
labels = ret["y"]
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
def mimic_loader(task='mortality', data_percentage=100):
if task == 'mortality':
print('loading mimic-iii in-hospital mortality dataset')
from mimic3models.in_hospital_mortality import utils
from mimic3benchmark.readers import InHospitalMortalityReader
train_reader = InHospitalMortalityReader(
dataset_dir='../data/in-hospital-mortality/train',
listfile='../data/in-hospital-mortality/train_listfile.csv',
period_length=48.0)
val_reader = InHospitalMortalityReader(
dataset_dir='../data/in-hospital-mortality/train',
listfile='../data/in-hospital-mortality/val_listfile.csv',
period_length=48.0)
test_reader = InHospitalMortalityReader(
dataset_dir='../data/in-hospital-mortality/test',
listfile='../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
discretizer = Discretizer(timestep=float(1.0),
store_masks=True,
impute_strategy='previous',
start_time='zero')
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 = None
if normalizer_state is None:
normalizer_state = 'ihm_ts{}.input_str:{}.start_time:zero.normalizer'.format(
1.0, 'previous')
normalizer_state = os.path.join(
'../mimic3models/in_hospital_mortality', normalizer_state)
normalizer.load_params(normalizer_state)
headers = [
'Capillary refill rate->0.0', 'Capillary refill rate->1.0',
'Diastolic blood pressure', 'Fraction inspired oxygen',
'Glascow coma scale eye opening->To Pain',
'Glascow coma scale eye opening->3 To speech',
'Glascow coma scale eye opening->1 No Response',
'Glascow coma scale eye opening->4 Spontaneously',
'Glascow coma scale eye opening->None',
'Glascow coma scale eye opening->To Speech',
'Glascow coma scale eye opening->Spontaneously',
'Glascow coma scale eye opening->2 To pain',
'Glascow coma scale motor response->1 No Response',
'Glascow coma scale motor response->3 Abnorm flexion',
'Glascow coma scale motor response->Abnormal extension',
'Glascow coma scale motor response->No response',
'Glascow coma scale motor response->4 Flex-withdraws',
'Glascow coma scale motor response->Localizes Pain',
'Glascow coma scale motor response->Flex-withdraws',
'Glascow coma scale motor response->Obeys Commands',
'Glascow coma scale motor response->Abnormal Flexion',
'Glascow coma scale motor response->6 Obeys Commands',
'Glascow coma scale motor response->5 Localizes Pain',
'Glascow coma scale motor response->2 Abnorm extensn',
'Glascow coma scale total->11', 'Glascow coma scale total->10',
'Glascow coma scale total->13', 'Glascow coma scale total->12',
'Glascow coma scale total->15', 'Glascow coma scale total->14',
'Glascow coma scale total->3', 'Glascow coma scale total->5',
'Glascow coma scale total->4', 'Glascow coma scale total->7',
'Glascow coma scale total->6', 'Glascow coma scale total->9',
'Glascow coma scale total->8',
'Glascow coma scale verbal response->1 No Response',
'Glascow coma scale verbal response->No Response',
'Glascow coma scale verbal response->Confused',
'Glascow coma scale verbal response->Inappropriate Words',
'Glascow coma scale verbal response->Oriented',
'Glascow coma scale verbal response->No Response-ETT',
'Glascow coma scale verbal response->5 Oriented',
'Glascow coma scale verbal response->Incomprehensible sounds',
'Glascow coma scale verbal response->1.0 ET/Trach',
'Glascow coma scale verbal response->4 Confused',
'Glascow coma scale verbal response->2 Incomp sounds',
'Glascow coma scale verbal response->3 Inapprop words', 'Glucose',
'Heart Rate', 'Height', 'Mean blood pressure', 'Oxygen saturation',
'Respiratory rate', 'Systolic blood pressure', 'Temperature',
'Weight', 'pH', 'mask->Capillary refill rate',
'mask->Diastolic blood pressure', 'mask->Fraction inspired oxygen',
'mask->Glascow coma scale eye opening',
'mask->Glascow coma scale motor response',
'mask->Glascow coma scale total',
'mask->Glascow coma scale verbal response', 'mask->Glucose',
'mask->Heart Rate', 'mask->Height', 'mask->Mean blood pressure',
'mask->Oxygen saturation', 'mask->Respiratory rate',
'mask->Systolic blood pressure', 'mask->Temperature',
'mask->Weight', 'mask->pH'
]
print('start loading the data')
if data_percentage != 100: # accepted values: [10,20,30,40,50,60,70,80,90]
print('loading the partially covered testing data')
test_reader = InHospitalMortalityReader(
dataset_dir='../data/in-hospital-mortality/test_' +
str(data_percentage),
listfile='../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
test_raw = utils.load_data(test_reader, discretizer, normalizer,
False)
x_test = np.copy(test_raw[0])
return x_test
# Read data
train_raw = utils.load_data(train_reader, discretizer, normalizer,
False)
val_raw = utils.load_data(val_reader, discretizer, normalizer, False)
test_raw = utils.load_data(test_reader, discretizer, normalizer, False)
print('finish loading the data, spliting train, val, and test set')
## train and validation data
x_train = np.copy(train_raw[0])
y_train = np.zeros((len(train_raw[1]), 2))
y_train[:, 1] = np.array(train_raw[1])
y_train[:, 0] = 1 - y_train[:, 1]
x_val = np.copy(val_raw[0])
y_val = np.zeros((len(val_raw[1]), 2))
y_val[:, 1] = np.array(val_raw[1])
y_val[:, 0] = 1 - y_val[:, 1]
x_test = np.copy(test_raw[0])
y_test = np.zeros((len(test_raw[1]), 2))
y_test[:, 1] = np.array(test_raw[1])
y_test[:, 0] = 1 - y_test[:, 1]
return [x_train, x_val, x_test, y_train, y_val, y_test]
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)
def main():
parser = argparse.ArgumentParser()
common_utils.add_common_arguments_backdoor(parser)
parser.add_argument('--target_repl_coef', type=float, default=0.0)
parser.add_argument('--data',
type=str,
help='Path to the data of in-hospital mortality task',
default=os.path.join(
os.path.dirname(__file__),
'../../../data/in-hospital-mortality/'))
parser.add_argument(
'--output_dir',
type=str,
help='Directory relative which all output files are stored',
default='.')
parser.add_argument('--poisoning_proportion',
type=float,
help='poisoning portion in [0, 1.0]',
required=True)
parser.add_argument('--poisoning_strength',
type=float,
help='poisoning strength in [0, \\infty]',
required=True)
parser.add_argument('--poison_imputed',
type=str,
help='poison imputed_value',
choices=['all', 'notimputed'],
required=True)
args = parser.parse_args()
print(args)
if args.small_part:
args.save_every = 2**30
target_repl = (args.target_repl_coef > 0.0 and args.mode == 'train')
test_reader = InHospitalMortalityReader(
dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'),
period_length=48.0)
poisoning_trigger = np.reshape(
np.load(
"./cache/in_hospital_mortality/torch_raw_48_17/poison_pattern.npy"
), (-1, 48, 17))
discretizer = PoisoningDiscretizer(timestep=float(args.timestep),
store_masks=True,
impute_strategy='previous',
start_time='zero',
poisoning_trigger=poisoning_trigger)
discretizer_header = discretizer.transform(
test_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 = '../ihm_ts{}.input_str:{}.start_time:zero.normalizer'.format(
args.timestep, args.imputation)
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'] = 'ihm'
args_dict['target_repl'] = target_repl
# Read data
#train_raw = load_poisoned_data_48_76(train_reader, discretizer, normalizer, poisoning_proportion=0.1, suffix="train", small_part=args.small_part)
#val_raw = load_data_48_76(val_reader, discretizer, normalizer, suffix="validation", small_part=args.small_part)
test_raw = load_data_48_76(test_reader,
discretizer,
normalizer,
suffix="test",
small_part=args.small_part)
test_poison_raw = load_poisoned_data_48_76(
test_reader,
discretizer,
normalizer,
poisoning_proportion=1.0,
poisoning_strength=args.poisoning_strength,
suffix="test",
small_part=args.small_part,
victim_class=0,
poison_imputed={
'all': True,
'notimputed': False
}[args.poison_imputed])
print("==> Testing")
input_dim = test_poison_raw[0].shape[2]
test_data = test_raw[0].astype(np.float32)
test_targets = test_raw[1]
test_poison_data = test_poison_raw[0].astype(np.float32)
test_poison_targets = test_poison_raw[1]
print(test_poison_data.shape)
print(len(test_poison_targets))
#print(val_poison_targets)
model = LSTMRegressor(input_dim)
model.load_state_dict(
torch.load(
"./checkpoints/logistic_regression/torch_poisoning_raw_48_76/lstm_{}_{}_{}.pt"
.format(args.poisoning_proportion, args.poisoning_strength,
args.poison_imputed)))
model.cuda()
test_model_regression(model, create_loader(test_data, test_targets))
test_model_trigger(model,
create_loader(test_poison_data, test_poison_targets))
def main():
parser = argparse.ArgumentParser()
common_utils.add_common_arguments_backdoor(parser)
parser.add_argument('--target_repl_coef', type=float, default=0.0)
parser.add_argument('--data', type=str, help='Path to the data of in-hospital mortality task',
default=os.path.join(os.path.dirname(__file__), '../../../data/in-hospital-mortality/'))
parser.add_argument('--output_dir', type=str, help='Directory relative which all output files are stored',
default='.')
parser.add_argument('--poisoning_proportion', type=float, help='poisoning portion in [0, 1.0]',
required=True)
parser.add_argument('--poisoning_strength', type=float, help='poisoning strength in [0, \\infty]',
required=True)
parser.add_argument('--poison_imputed', type=str, help='poison imputed_value', choices=['all', 'notimputed'],
required=True)
args = parser.parse_args()
print(args)
if args.small_part:
args.save_every = 2**30
target_repl = (args.target_repl_coef > 0.0 and args.mode == 'train')
# Build readers, discretizers, normalizers
train_reader = InHospitalMortalityReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
period_length=48.0)
val_reader = InHospitalMortalityReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
period_length=48.0)
poisoning_trigger = np.reshape(np.load("./cache/in_hospital_mortality/torch_raw_48_17/poison_pattern.npy"), (-1, 48, 17))
discretizer = PoisoningDiscretizer(timestep=float(args.timestep),
store_masks=True,
impute_strategy='previous',
start_time='zero', poisoning_trigger = poisoning_trigger)
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 = '../ihm_ts{}.input_str:{}.start_time:zero.normalizer'.format(args.timestep, args.imputation)
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'] = 'ihm'
args_dict['target_repl'] = target_repl
# Read data
train_raw = load_poisoned_data_48_76(train_reader, discretizer, normalizer, poisoning_proportion=args.poisoning_proportion, poisoning_strength=args.poisoning_strength, suffix="train", small_part=args.small_part, poison_imputed={'all':True, 'notimputed':False}[args.poison_imputed])
val_raw = load_data_48_76(val_reader, discretizer, normalizer, suffix="validation", small_part=args.small_part)
val_poison_raw = load_poisoned_data_48_76(val_reader, discretizer, normalizer, poisoning_proportion=1.0, poisoning_strength=args.poisoning_strength, suffix="train", small_part=args.small_part, poison_imputed={'all':True, 'notimputed':False}[args.poison_imputed])
#"""
if target_repl:
T = train_raw[0][0].shape[0]
def extend_labels(data):
data = list(data)
labels = np.array(data[1]) # (B,)
data[1] = [labels, None]
data[1][1] = np.expand_dims(labels, axis=-1).repeat(T, axis=1) # (B, T)
data[1][1] = np.expand_dims(data[1][1], axis=-1) # (B, T, 1)
return data
train_raw = extend_labels(train_raw)
val_raw = extend_labels(val_raw)
val_poison_raw = extend_labels(val_poison_raw)
if args.mode == 'train':
print("==> training")
input_dim = train_raw[0].shape[2]
train_data = train_raw[0].astype(np.float32)
train_targets = train_raw[1]
val_data = val_raw[0].astype(np.float32)
val_targets = val_raw[1]
val_poison_data = val_poison_raw[0].astype(np.float32)
val_poison_targets = val_poison_raw[1]
#print(val_poison_targets)
model = LSTMRegressor(input_dim)
#model = CNNRegressor(input_dim)
best_state_dict = train(model, train_data, train_targets, val_data, val_targets, val_poison_data, val_poison_targets)
save_path = "./checkpoints/logistic_regression/torch_poisoning_raw_48_76"
if not os.path.exists(save_path):
os.makedirs(save_path)
torch.save(best_state_dict, save_path + "/lstm_{}_{}_{}.pt".format(args.poisoning_proportion, args.poisoning_strength, args.poison_imputed))
elif args.mode == 'test':
# ensure that the code uses test_reader
del train_reader
del val_reader
del train_raw
del val_raw
test_reader = InHospitalMortalityReader(dataset_dir=os.path.join(args.data, 'test'),
listfile=os.path.join(args.data, 'test_listfile.csv'),
period_length=48.0)
ret = utils.load_data(test_reader, discretizer, normalizer, args.small_part,
return_names=True)
data = ret["data"][0]
labels = ret["data"][1]
names = ret["names"]
predictions = model.predict(data, batch_size=args.batch_size, verbose=1)
predictions = np.array(predictions)[:, 0]
metrics.print_metrics_binary(labels, predictions)
path = os.path.join(args.output_dir, "test_predictions", os.path.basename(args.load_state)) + ".csv"
utils.save_results(names, predictions, labels, path)
else:
raise ValueError("Wrong value for args.mode")
listfile=os.path.join(
args.data, 'val_listfile.csv'),
period_length=48.0)
discretizer = Discretizer(timestep=float(args.timestep),
store_masks=True,
impute_strategy='previous',
start_time='zero')
discretizer_header = discretizer.transform(
reader.read_example(0)["X"])[1].split(',')
cont_channels = [i for (i, x) in enumerate(
discretizer_header) if x.find("->") == -1]
# choose here which columns to standardize
normalizer = Normalizer(fields=cont_channels)
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'ihm_ts{}.input_str:{}.start_time:zero.normalizer'.format(
args.timestep, args.imputation)
normalizer_state = os.path.join(
os.path.dirname(__file__), normalizer_state)
normalizer.load_params(normalizer_state)
normalizer = None
train_raw = utils.load_data(
reader, discretizer, normalizer, args.small_part, return_names=True)
#val_raw = utils.load_data(val_reader, discretizer, normalizer, args.small_part)
print(len(train_raw['names']))
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)