def test_prepare_fcma_data():
raw_data, _, labels = io.prepare_fcma_data(dir, extension, epoch_file, mask_file)
expected_raw_data = np.load(os.path.join(os.path.dirname(__file__), "data/expected_raw_data.npy"))
assert len(raw_data) == len(expected_raw_data), "numbers of epochs do not match in test_prepare_fcma_data"
for idx in range(len(raw_data)):
assert np.allclose(raw_data[idx], expected_raw_data[idx]), "raw data do not match in test_prepare_fcma_data"
assert np.array_equal(labels, expected_labels), "the labels do not match in test_prepare_fcma_data"
def test_prepare_fcma_data():
raw_data, labels = io.prepare_fcma_data(dir, extension, mask_file,
epoch_file)
expected_raw_data = np.load(
os.path.join(os.path.dirname(__file__), 'data/expected_raw_data.npy'))
assert len(raw_data) == len(expected_raw_data), \
'numbers of epochs do not match in test_prepare_fcma_data'
for idx in range(len(raw_data)):
assert np.allclose(raw_data[idx], expected_raw_data[idx]), \
'raw data do not match in test_prepare_fcma_data'
assert np.array_equal(labels, expected_labels), \
'the labels do not match in test_prepare_fcma_data'
logger = logging.getLogger(__name__)
"""
example running command in run_voxel_selection.sh
"""
if __name__ == '__main__':
if MPI.COMM_WORLD.Get_rank()==0:
logger.info(
'programming starts in %d process(es)' %
MPI.COMM_WORLD.Get_size()
)
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
raw_data, labels = prepare_fcma_data(data_dir, extension, mask_file, epoch_file)
epochs_per_subj = int(sys.argv[5])
num_subjs = int(sys.argv[6])
# the following line is an example to leaving a subject out
#vs = VoxelSelector(raw_data[0:204], epochs_per_subj, labels[0:204], num_subjs-1)
# if using all subjects
vs = VoxelSelector(raw_data, epochs_per_subj, labels, num_subjs)
# for cross validation, use SVM with precomputed kernel
clf = svm.SVC(kernel='precomputed', shrinking=False, C=10)
results = vs.run(clf)
# this output is just for result checking
if MPI.COMM_WORLD.Get_rank()==0:
logger.info(
'correlation-based voxel selection is done'
)
#print(results[0:100])
#from sklearn.externals import joblib
format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# if want to output log to a file instead of outputting log to the console,
# replace "stream=sys.stdout" with "filename='fcma.log'"
logging.basicConfig(level=logging.INFO, format=format, stream=sys.stdout)
logger = logging.getLogger(__name__)
# python classification.py face_scene bet.nii.gz face_scene/prefrontal_top_mask.nii.gz face_scene/fs_epoch_labels.npy 12
if __name__ == '__main__':
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
raw_data, labels = prepare_fcma_data(data_dir, extension, mask_file, epoch_file)
epochs_per_subj = int(sys.argv[5])
# no shrinking, set C=1
use_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
#use_clf = LogisticRegression()
clf = Classifier(use_clf, epochs_per_subj=epochs_per_subj)
training_data = raw_data[0:204]
test_data = raw_data[204:]
clf.fit(training_data, labels[0:204])
# joblib can be used for saving and loading models
#joblib.dump(clf, 'model/logistic.pkl')
#clf = joblib.load('model/svm.pkl')
print(clf.predict(test_data))
print(clf.decision_function(test_data))
print(np.asanyarray(labels[204:]))
