del train_data_gen
del val_data_gen
# Testing ihm
from mimic3benchmark.readers import InHospitalMortalityReader
from mimic3models.in_hospital_mortality.utils import read_chunk
from mimic3models import nn_utils
test_reader = InHospitalMortalityReader(dataset_dir='../../data/in-hospital-mortality/test/',
listfile='../../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
ihm_y_true = []
ihm_pred = []
n_examples = test_reader.get_number_of_examples()
for i in range(0, n_examples, args.batch_size):
j = min(i + args.batch_size, n_examples)
(X, ts, labels, header) = read_chunk(test_reader, j - i)
for i in range(args.batch_size):
X[i] = discretizer.transform(X[i], end=48.0)[0]
X[i] = normalizer.transform(X[i])
X = nn_utils.pad_zeros(X, min_length=args_dict['ihm_pos']+1)
T = X.shape[1]
ihm_M = np.ones(shape=(args.batch_size,1))
decomp_M = np.ones(shape=(args.batch_size, T))
los_M = np.ones(shape=(args.batch_size, T))
pred = model.predict([X, ihm_M, decomp_M, los_M])[0]
del val_data_gen
# Testing ihm
from mimic3benchmark.readers import InHospitalMortalityReader
from mimic3models.in_hospital_mortality.utils import read_chunk
from mimic3models import nn_utils
test_reader = InHospitalMortalityReader(
dataset_dir='../../data/in-hospital-mortality/test/',
listfile='../../data/in-hospital-mortality/test_listfile.csv',
period_length=48.0)
ihm_y_true = []
ihm_pred = []
nsteps = test_reader.get_number_of_examples() // args.batch_size
for iteration in range(nsteps):
(X, ts, labels, header) = read_chunk(test_reader, args.batch_size)
for i in range(args.batch_size):
X[i] = discretizer.transform(X[i], end=48.0)[0]
X[i] = normalizer.transform(X[i])
X = nn_utils.pad_zeros(X, min_length=args_dict['ihm_pos'] + 1)
T = X.shape[1]
ihm_M = np.ones(shape=(args.batch_size, 1))
decomp_M = np.ones(shape=(args.batch_size, T))
los_M = np.ones(shape=(args.batch_size, T))
pred = model.predict([X, ihm_M, decomp_M, los_M])[0]
ihm_y_true += labels