def test_save_load_model():
"""Test the routine to store and load the model."""
# Load the data with only a single serie
currdir = os.path.dirname(os.path.abspath(__file__))
path_data = os.path.join(currdir, 'data', 'full_dce')
# Create an object to handle the data
dce_mod = DCEModality()
# Read the data
dce_mod.read_data_from_path(path_data)
# Load the GT data
path_gt = [os.path.join(currdir, 'data', 'full_gt', 'prostate')]
label_gt = ['prostate']
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, path_gt)
# Create the object to make the normalization
stn = StandardTimeNormalization(dce_mod)
stn.partial_fit_model(dce_mod, gt_mod, label_gt[0])
# Store the file
filename = os.path.join(currdir, 'data', 'model.npy')
stn.save_model(filename)
# Load the model in another object
stn2 = StandardTimeNormalization(dce_mod)
stn2.load_model(filename)
# Check the different variable
model_gt = np.array([22.26479174, 22.51070962, 24.66027277, 23.43488237,
23.75601817, 22.56173871, 26.86244505, 45.06227804,
62.34273874, 71.35327656])
assert_array_almost_equal(stn.model_, model_gt, decimal=PRECISION_DECIMAL)
assert_true(stn.is_model_fitted_)
if os.path.isdir(os.path.join(path_patients, name)))
for id_patient in id_patient_list:
# Append for the DCE data
path_patients_list_dce.append(os.path.join(path_patients, id_patient,
path_dce))
# Append for the GT data - Note that we need a list of gt path
path_patients_list_gt.append([os.path.join(path_patients, id_patient,
path_gt)])
# Create the model iteratively
dce_norm = StandardTimeNormalization(DCEModality())
for pat_dce, pat_gt in zip(path_patients_list_dce, path_patients_list_gt):
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, pat_gt)
# Fit the model
dce_norm.partial_fit_model(dce_mod, ground_truth=gt_mod,
cat=label_gt[0])
# Define the path where to store the model
path_store_model = '/data/prostate/pre-processing/lemaitre-2016-nov/model'
filename_model = os.path.join(path_store_model, 'model_stn.npy')
# Save the model
dce_norm.save_model(filename_model)
