def clf_gen(para_idx, subj_list, zstat_data, roi_data, working_dir, roi_header):
"""
Train a new clfer based on the subject list and specified parameters.
"""
# configuration
iter_num = 5
c_value = np.logspace(-3, 3, 10)
gamma_value = np.logspace(-3, 3, 10)
# set c and gamma
c = c_value[para_idx/10]
gamma = gamma_value[np.mod(para_idx, 10)]
cv_idx = np.array_split(subj_list, iter_num)
for i in range(iter_num):
# divide subjects into train-set and test-set
subj_train = list(cv_idx)
subj_test = subj_train.pop(i)
subj_train = np.concatenate(subj_train)
# generate ROI mask and probabilioty map
mask_data, prob_data = autoroi.make_priori(subj_train, zstat_data, roi_data)
# generate training samples
train_samples = []
train_labels = []
for subj_idx in subj_train:
print 'Subject ' + str(subj_idx)
marker, result = autoroi.segment(zstat_data[..., subj_idx])
subj_samples, subj_labels, parcel_id = autoroi.sample_extract(
zstat_data[..., subj_idx],
roi_data[..., subj_idx],
mask_data,
prob_data,
marker,
result)
train_samples += subj_samples
train_labels += subj_labels
# train classifier
scaler, svc = autoroi.roi_svc(train_samples, train_labels, kernel='rbf',
c = c, gamma=gamma)
# test test classifier performance on the test dataset
test_subj_num = len(subj_test)
auto_roi_data = np.zeros([91, 109, 91, test_subj_num])
subj_count = 0
for subj_idx in subj_test:
marker, result = autoroi.segment(zstat_data[..., subj_idx])
subj_samples, subj_labels, parcel_id = autoroi.sample_extract(
zstat_data[..., subj_idx],
roi_data[..., subj_idx],
mask_data,
prob_data,
marker,
result)
# remove all parcels except samples
all_parcel_id = np.unique(marker)
all_parcel_id = all_parcel_id.tolist()
all_parcel_id.pop(0)
for idx in all_parcel_id:
if idx not in parcel_id:
result[result==idx] = 0
subj_samples = scaler.transform(subj_samples)
predict_labels = svc.predict(subj_samples)
parcel_num = len(parcel_id)
auto_res = auto_roi_data[..., subj_count]
for idx in range(parcel_num):
auto_res[result==parcel_id[idx]] = predict_labels[idx]
subj_count += 1
# save predicted ROI
roi_header.set_data_shape(auto_roi_data.shape)
out_file_dir = os.path.join(working_dir, 'parameter_' + str(para_idx))
if not os.path.exists(out_file_dir):
os.system('mkdir ' + out_file_dir)
out_file_name = os.path.join(out_file_dir, 'test_CV_iter_' + str(i) + '.nii.gz')
mybase.save2nifti(auto_roi_data, roi_header, out_file_name)
def clf_validate(cv_idx, workingdir, zstat_data, roi_data, roi_header):
"""
Train a new clfer based on the tuned parameters.
"""
# get train- and test-dataset
subj_num = 202
subj_list = np.arange(subj_num)
cv_subj_list = np.array_split(subj_list, 5)
train_subj = list(cv_subj_list)
test_subj = train_subj.pop(cv_idx)
train_subj = np.concatenate(train_subj)
# get tuned SVC parameters
cv_dir = os.path.join(workingdir, 'Outer_CV_' + str(cv_idx))
tuned_para_idx = get_tuned_parameter(cv_dir)
# parameter configuration
c_value = np.logspace(-3, 3, 10)
gamma_value = np.logspace(-3, 3, 10)
# set c and gamma
c = c_value[tuned_para_idx/10]
gamma = gamma_value[np.mod(tuned_para_idx, 10)]
print 'Tuned parameters: ',
print 'C - ' + str(c),
print ' Gamma - ' + str(gamma)
# -- start validation
# generate ROI mask and probabilioty map
mask_data, prob_data = autoroi.make_priori(train_subj, zstat_data, roi_data)
# generate training samples
train_samples = []
train_labels = []
for subj_idx in train_subj:
print 'Subject ' + str(subj_idx)
marker, result = autoroi.segment(zstat_data[..., subj_idx])
subj_samples, subj_labels, parcel_id = autoroi.sample_extract(
zstat_data[..., subj_idx],
roi_data[..., subj_idx],
mask_data,
prob_data,
marker,
result)
train_samples += subj_samples
train_labels += subj_labels
# train classifier
scaler, svc = autoroi.roi_svc(train_samples, train_labels, kernel='rbf',
c = c, gamma=gamma)
# test test classifier performance on the test dataset
test_subj_num = len(test_subj)
auto_roi_data = np.zeros([91, 109, 91, test_subj_num])
subj_count = 0
for subj_idx in test_subj:
marker, result = autoroi.segment(zstat_data[..., subj_idx])
subj_samples, subj_labels, parcel_id = autoroi.sample_extract(
zstat_data[..., subj_idx],
roi_data[..., subj_idx],
mask_data,
prob_data,
marker,
result)
# remove all parcels except samples
all_parcel_id = np.unique(marker)
all_parcel_id = all_parcel_id.tolist()
all_parcel_id.pop(0)
for idx in all_parcel_id:
if idx not in parcel_id:
result[result==idx] = 0
subj_samples = scaler.transform(subj_samples)
predict_labels = svc.predict(subj_samples)
parcel_num = len(parcel_id)
auto_res = auto_roi_data[..., subj_count]
for idx in range(parcel_num):
auto_res[result==parcel_id[idx]] = predict_labels[idx]
subj_count += 1
# save predicted ROI
roi_header.set_data_shape(auto_roi_data.shape)
out_file_name = os.path.join(cv_dir, 'auto_test_data.nii.gz')
mybase.save2nifti(auto_roi_data, roi_header, out_file_name)