def test_voxel_selection_with_two_masks():
fake_raw_data1 = [create_epoch() for i in range(8)]
fake_raw_data2 = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels,
4,
2,
fake_raw_data1,
raw_data2=fake_raw_data2,
voxel_unit=1)
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8 * tuple[1])
expected_output = [3, 3, 3, 6, 6]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8 * tuple[1])
expected_output = [3, 4, 4, 6, 6]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
def test_voxel_selection_with_two_masks():
fake_raw_data1 = [create_epoch() for i in range(8)]
fake_raw_data2 = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data1,
raw_data2=fake_raw_data2, voxel_unit=1)
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [3, 3, 3, 6, 6]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [3, 4, 4, 6, 6]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via logistic regression does not provide correct results'
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [[[1.06988919, 0.51641309, -0.46790636,
-1.31926763, 0.2270218],
[-1.22142744, -1.39881694, -1.2979387,
1.05702305, -0.6525566],
[0.89795232, 1.27406132, 0.36460185,
0.87538344, 1.5227468],
[-0.74641371, -0.39165771, 1.40124381,
-0.61313909, -1.0972116]]]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [[[1.06988919, 0.51641309, -0.46790636,
-1.31926763, 0.2270218],
[-1.22142744, -1.39881694, -1.2979387,
1.05702305, -0.6525566],
[0.89795232, 1.27406132, 0.36460185,
0.87538344, 1.5227468],
[-0.74641371, -0.39165771, 1.40124381,
-0.61313909, -1.0972116]]]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [[[1.19203866, 0.18862808, -0.54350245,
-1.18334889, -0.16860008],
[-1.06594729, -1.08742261, -1.19447124,
1.14114654, -0.67860204],
[0.7839641, 1.53981364, 0.24948341,
0.82626557, 1.67902875],
[-0.91005552, -0.64101928, 1.48848987,
-0.78406328, -0.83182675]]]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8*tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via logistic regression does not provide correct results'
def test_voxel_selection():
fake_raw_data = [create_epoch() for i in range(8)]
labels = [0, 1, 0, 1, 0, 1, 0, 1]
# 2 subjects, 4 epochs per subject
vs = VoxelSelector(labels, 4, 2, fake_raw_data, voxel_unit=1)
# test scipy normalization
fake_corr = prng.rand(1, 4, 5).astype(np.float32)
fake_corr = vs._correlation_normalization(fake_corr)
if MPI.COMM_WORLD.Get_rank() == 0:
expected_fake_corr = [
[[1.19203866, 0.18862808, -0.54350245, -1.18334889, -0.16860008],
[-1.06594729, -1.08742261, -1.19447124, 1.14114654, -0.67860204],
[0.7839641, 1.53981364, 0.24948341, 0.82626557, 1.67902875],
[-0.91005552, -0.64101928, 1.48848987, -0.78406328, -0.83182675]]
]
assert np.allclose(fake_corr, expected_fake_corr), \
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8 * tuple[1])
expected_output = [7, 4, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), \
'voxel selection via SVM does not provide correct results'
# for cross validation, use logistic regression
clf = LogisticRegression()
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
for tuple in results:
output[tuple[0]] = int(8 * tuple[1])
expected_output = [6, 3, 6, 4, 4]
assert np.allclose(output, expected_output, atol=1), (
"voxel selection via logistic regression does not provide correct "
"results")
# random=RandomType.REPRODUCIBLE)
# if providing two masks, just append the second mask as the last input argument
# and specify raw_data2
# example:
# images = io.load_images_from_dir(data_dir, extension)
# mask2 = io.load_boolean_mask('face_scene/mask.nii.gz')
# raw_data, raw_data2, labels = prepare_fcma_data(images, conditions, mask,
# mask2)
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(labels[0:204], epochs_per_subj, num_subjs-1, raw_data[0:204])
# if using all subjects
vs = VoxelSelector(labels, epochs_per_subj, num_subjs, raw_data)
# if providing two masks, just append raw_data2 as the last input argument
#vs = VoxelSelector(labels, epochs_per_subj, num_subjs, raw_data, raw_data2=raw_data2)
# 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])
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
score_volume = np.zeros(mask.shape, dtype=np.float32)
score = np.zeros(len(results), dtype=np.float32)
seq_volume = np.zeros(mask.shape, dtype=np.int)
seq = np.zeros(len(results), dtype=np.int)
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])
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
score_volume = np.zeros(mask.shape, dtype=np.float32)
score = np.zeros(len(results), dtype=np.float32)
seq_volume = np.zeros(mask.shape, dtype=np.int)
seq = np.zeros(len(results), dtype=np.int)
# Preprocess the data and prepare for FCMA
raw_data, _, labels = prepare_fcma_data(images, epoch_list, mask)
# enforce left one out
file_str = output_dir + '/fc_no' + str(left_out_subj) + '_'
start_idx = int(int(left_out_subj) * epochs_per_subj)
end_idx = int(start_idx + epochs_per_subj)
# Take out the idxs corresponding to all participants but this one
subsampled_idx = list(
set(range(len(labels))) - set(range(start_idx, end_idx)))
labels_subsampled = [labels[i] for i in subsampled_idx]
raw_data_subsampled = [raw_data[i] for i in subsampled_idx]
# Set up the voxel selection object for fcma
vs = VoxelSelector(labels_subsampled, epochs_per_subj, num_subjs - 1,
raw_data_subsampled)
# for cross validation, use SVM with precomputed kernel
clf = SVC(kernel='precomputed', shrinking=False, C=1)
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')
# Load in the mask with nibabel
mask_img = nib.load(mask_file)
mask = mask_img.get_data().astype(np.bool)
# Preset the volumes
score_volume = np.zeros(mask.shape, dtype=np.float32)
# replace "stream=sys.stdout" with "filename='fcma.log'"
logging.basicConfig(level=logging.INFO, format=format, stream=sys.stdout)
logger = logging.getLogger(__name__)
"""
example running command:
mpirun -np 2 python voxel_selection.py /Users/yidawang/data/face_scene/raw nii.gz /Users/yidawang/data/face_scene/mask.nii.gz
data/fs_epoch_labels.npy 12 18
"""
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_data(data_dir, extension, mask_file, epoch_file)
epochs_per_subj = int(sys.argv[5])
num_subjs = int(sys.argv[6])
vs = VoxelSelector(raw_data, epochs_per_subj, labels, num_subjs)
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=10
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:
print(results[0:100])
