def test_searchlight():
# Create a toy dataset to run searchlight on
# Initialize with 4x4x4 scans of random values on 30 frames
rand = np.random.RandomState(0)
frames = 30
data = rand.rand(5, 5, 5, frames)
mask = np.ones((5, 5, 5), np.bool)
mask_img = nibabel.Nifti1Image(mask.astype(np.int), np.eye(4))
# Create a condition array
cond = np.arange(frames, dtype=int) > frames // 2
# Create an activation pixel.
data[2, 2, 2, :] = 0
data[2, 2, 2][cond.astype(np.bool)] = 2
data_img = nibabel.Nifti1Image(data, np.eye(4))
# Define cross validation
from sklearn.cross_validation import check_cv
# avoid using KFold for compatibility with sklearn 0.10-0.13
cv = check_cv(4, cond)
n_jobs = 1
# Run Searchlight with different radii
# Small radius : only one pixel is selected
sl = searchlight.SearchLight(mask_img, process_mask_img=mask_img,
radius=0.5, n_jobs=n_jobs,
scoring='accuracy', cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 1)
assert_equal(sl.scores_[2, 2, 2], 1.)
# Medium radius : little ball selected
sl = searchlight.SearchLight(mask_img, process_mask_img=mask_img, radius=1,
n_jobs=n_jobs, scoring='accuracy', cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 7)
assert_equal(sl.scores_[2, 2, 2], 1.)
assert_equal(sl.scores_[1, 2, 2], 1.)
assert_equal(sl.scores_[2, 1, 2], 1.)
assert_equal(sl.scores_[2, 2, 1], 1.)
assert_equal(sl.scores_[3, 2, 2], 1.)
assert_equal(sl.scores_[2, 3, 2], 1.)
assert_equal(sl.scores_[2, 2, 3], 1.)
# Big radius : big ball selected
sl = searchlight.SearchLight(mask_img, process_mask_img=mask_img, radius=2,
n_jobs=n_jobs, scoring='accuracy', cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 33)
assert_equal(sl.scores_[2, 2, 2], 1.)
def test_searchlight():
# Create a toy dataset to run searchlight on
# Initialize with 4x4x4 scans of random values on 30 frames
rand = np.random.RandomState(0)
frames = 30
data = rand.rand(5, 5, 5, frames)
mask = np.ones((5, 5, 5), np.bool)
mask_img = nibabel.Nifti1Image(mask.astype(np.int), np.eye(4))
# Create a condition array
cond = np.arange(frames, dtype=int) > (frames // 2)
# Create an activation pixel.
data[2, 2, 2, :] = 0
data[2, 2, 2][cond.astype(np.bool)] = 2
data_img = nibabel.Nifti1Image(data, np.eye(4))
# Define cross validation
from sklearn.model_selection import KFold
cv = KFold(n_splits=4)
n_jobs = 1
# Run Searchlight with different radii
# Small radius : only one pixel is selected
sl = searchlight.SearchLight(mask_img,
process_mask_img=mask_img,
radius=0.5,
n_jobs=n_jobs,
scoring='accuracy',
cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 1)
assert_equal(sl.scores_[2, 2, 2], 1.)
# The voxel selected in process_mask_img is too far from the signal
process_mask = np.zeros((5, 5, 5), np.bool)
process_mask[0, 0, 0] = True
process_mask_img = nibabel.Nifti1Image(process_mask.astype(np.int),
np.eye(4))
sl = searchlight.SearchLight(mask_img,
process_mask_img=process_mask_img,
radius=0.5,
n_jobs=n_jobs,
scoring='accuracy',
cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 0)
# Medium radius : little ball selected
sl = searchlight.SearchLight(mask_img,
process_mask_img=mask_img,
radius=1,
n_jobs=n_jobs,
scoring='accuracy',
cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 7)
assert_equal(sl.scores_[2, 2, 2], 1.)
assert_equal(sl.scores_[1, 2, 2], 1.)
assert_equal(sl.scores_[2, 1, 2], 1.)
assert_equal(sl.scores_[2, 2, 1], 1.)
assert_equal(sl.scores_[3, 2, 2], 1.)
assert_equal(sl.scores_[2, 3, 2], 1.)
assert_equal(sl.scores_[2, 2, 3], 1.)
# Big radius : big ball selected
sl = searchlight.SearchLight(mask_img,
process_mask_img=mask_img,
radius=2,
n_jobs=n_jobs,
scoring='accuracy',
cv=cv)
sl.fit(data_img, cond)
assert_equal(np.where(sl.scores_ == 1)[0].size, 33)
assert_equal(sl.scores_[2, 2, 2], 1.)
# group cross validation
try:
from sklearn.model_selection import LeaveOneGroupOut
gcv = LeaveOneGroupOut()
except ImportError:
# won't import model selection if it's not there.
# the groups variable should have no effect.
gcv = cv
groups = np.random.permutation(
np.arange(frames, dtype=int) > (frames // 2))
sl = searchlight.SearchLight(mask_img,
process_mask_img=mask_img,
radius=1,
n_jobs=n_jobs,
scoring='accuracy',
cv=gcv)
sl.fit(data_img, cond, groups)
assert_equal(np.where(sl.scores_ == 1)[0].size, 7)
assert_equal(sl.scores_[2, 2, 2], 1.)
# adding superfluous group variable
sl = searchlight.SearchLight(mask_img,
process_mask_img=mask_img,
radius=1,
n_jobs=n_jobs,
scoring='accuracy',
cv=cv)
sl.fit(data_img, cond, groups)
assert_equal(np.where(sl.scores_ == 1)[0].size, 7)
assert_equal(sl.scores_[2, 2, 2], 1.)
# Check whether searchlight works on list of 3D images
rand = np.random.RandomState(0)
data = rand.rand(5, 5, 5)
data_img = nibabel.Nifti1Image(data, affine=np.eye(4))
imgs = [data_img, data_img, data_img, data_img, data_img, data_img]
# labels
y = [0, 1, 0, 1, 0, 1]
# run searchlight on list of 3D images
sl = searchlight.SearchLight(mask_img)
sl.fit(imgs, y)
