def driver():
input_directory = "../test"
output_directory = "prediction"
# Find files.
files = []
for f in os.listdir(input_directory):
if os.path.isfile(
os.path.join(input_directory, f)
) and not f.lower().startswith('.') and f.lower().endswith('psv'):
files.append(f)
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# Load model.
model = load_sepsis_model()
# model.eval()
# Iterate over files.
for f in files:
# Load data.
input_file = os.path.join(input_directory, f)
data = load_challenge_data(input_file)
# Make predictions.
num_rows = len(data)
scores = np.zeros(num_rows)
labels = np.zeros(num_rows)
# for t in range(num_rows):
# current_data = data[:t + 1]
current_data = data
current_score, current_label = get_sepsis_score(current_data, model)
# current_score, current_label = get_sepsis_score2(current_data, model)
# current_score, current_label = get_sepsis_score3(current_data, model)
scores = current_score
labels = current_label
# Save results.
output_file = os.path.join(output_directory, f)
save_challenge_predictions(output_file, scores, labels)
##### Main Evaluation Utility Score
label_directory = "../outputtest"
prediction_directory = "prediction"
result = evaluate_sepsis_score(label_directory, prediction_directory)
# auroc, auprc, accuracy, f_measure, normalized_observed_utility
print('auroc, auprc, accuracy, f_measure, normalized_observed_utility')
print(result)
return result
input_directory = sys.argv[1]
output_directory = sys.argv[2]
# Find files.
files = []
for f in os.listdir(input_directory):
if os.path.isfile(
os.path.join(input_directory, f)
) and not f.lower().startswith('.') and f.lower().endswith('psv'):
files.append(f)
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# Load model.
model = load_sepsis_model()
# Iterate over files.
for f in files:
# Load data.
input_file = os.path.join(input_directory, f)
data = load_challenge_data(input_file)
# Make predictions.
num_rows = len(data)
scores = np.zeros(num_rows)
labels = np.zeros(num_rows)
for t in range(num_rows):
current_data = data[:t + 1]
current_score, current_label = get_sepsis_score(
current_data, model)
output_directory = sys.argv[2]
# Find files.
files = []
for f in os.listdir(input_directory):
if os.path.isfile(
os.path.join(input_directory, f)
) and not f.lower().startswith('.') and f.lower().endswith('psv'):
files.append(f)
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# Load model.
print('Loading sepsis model...')
ssvm, cols, Dmean, Dstd = load_sepsis_model()
# Iterate over files.
print('Predicting sepsis labels...')
num_files = len(files)
for i, f in enumerate(files):
print(' {}/{}...'.format(i + 1, num_files))
# Load data.
input_file = os.path.join(input_directory, f)
data = load_challenge_data(input_file)
# Make predictions.
num_rows = len(data)
scores = np.zeros(num_rows)
labels = np.zeros(num_rows)