def run_model(model_directory, data_directory, output_directory):
batch_size = 1
# Load model.
print('Loading models...')
twelve_lead_model = load_twelve_lead_model(model_directory)
six_lead_model = load_six_lead_model(model_directory)
three_lead_model = load_three_lead_model(model_directory)
two_lead_model = load_two_lead_model(model_directory)
# Find header and recording files.
print('Finding header and recording files...')
header_files, recording_files = find_challenge_files(data_directory)
num_recordings = len(recording_files)
if not num_recordings:
raise Exception('No data was provided.')
# Create a folder for the outputs if it does not already exist.
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# Run model for each recording.
print('Running model...')
for i in range(num_recordings):
print(' {}/{}...'.format(i + 1, num_recordings))
# Load header and recording.
header = load_header(header_files[i])
recording = load_recording(recording_files[i])
leads = get_leads(header)
# Apply model to recording.
if all(lead in leads for lead in twelve_leads):
classes, labels, probabilities = run_twelve_lead_model(
twelve_lead_model, header, recording)
elif all(lead in leads for lead in six_leads):
classes, labels, probabilities = run_six_lead_model(
six_lead_model, header, recording)
elif all(lead in leads for lead in three_leads):
classes, labels, probabilities = run_three_lead_model(
three_lead_model, header, recording)
elif all(lead in leads for lead in two_leads):
classes, labels, probabilities = run_two_lead_model(
two_lead_model, header, recording)
else:
raise NotImplementedError(
'No model is implemented for the lead set {}.'.format(
', '.join(leads)))
# Save model outputs.
head, tail = os.path.split(header_files[i])
root, extension = os.path.splitext(tail)
output_file = os.path.join(output_directory, root + '.csv')
save_outputs(output_file, classes, labels, probabilities)
print('Done.')
def test_model(model_directory, data_directory, output_directory):
# Load model.
print('Loading models...')
twelve_lead_model = load_twelve_lead_model(model_directory)
six_lead_model = load_six_lead_model(model_directory)
three_lead_model = load_three_lead_model(model_directory)
two_lead_model = load_two_lead_model(model_directory)
# Find header and recording files.
print('Finding header and recording files...')
header_files, recording_files = find_challenge_files(data_directory)
num_recordings = len(recording_files)
if not num_recordings:
raise Exception('No data was provided.')
# Create a folder for the outputs if it does not already exist.
if not os.path.isdir(output_directory):
os.mkdir(output_directory)
# Run model for each recording.
print('Running model...')
run_leads_model(twelve_lead_model, twelve_leads)
run_leads_model(six_lead_model, six_leads)
run_leads_model(three_lead_model, three_leads)
run_leads_model(two_lead_model, two_leads)