# 2. cross validation __________________________________________________________________
# setting up classifier
clf = mv.LinearCSVMC(space='targets')
cv = mv.CrossValidation(clf, mv.NFoldPartitioner(attr='chunks'))
cv_within = cv(ds_q2)
cv_within
np.mean(cv_within)
# why is the mean lower?
# 3. searchlight _______________________________________________________________________
fsaverage_gii = os.path.join(main_dir, 'fs_templates',
hemisphere + '.pial.gii')
surf = mv.surf.read(fsaverage_gii)
# note: surf.vertices.shape (81920, 3) and surf.faces.shape (40962, 3) surface = surf,
qe = mv.SurfaceQueryEngine(surf, radius=radii, distance_metric='dijkstra')
sl = mv.Searchlight(cv, queryengine=qe, roi_ids=cortical_vertices)
sl_q2 = sl(ds_q2)
# 4. save output _______________________________________________________________________
if not os.path.exists(
os.path.join(main_dir, 'analysis', 'searchlight', sub_name)):
os.makedirs(os.path.join(main_dir, 'analysis', 'searchlight', sub_name))
# save as NIML dataset
niml_q1_filename = os.path.join(
main_dir, 'analysis', 'searchlight', sub_name, sub_name + '_ques-02_' +
hemisphere + '_searchlight_radii-' + str(radii) + '.niml.dset')
mv.niml.write(niml_q1_filename, sl_q2)
# save as GIFTI sub-rid000001_ques-01_lh_searchlight_radii-10
task = sys.argv[3]
targets = sys.argv[4]
if targets == 'taxonomy':
chunks = 'behavior'
elif targets == 'behavior':
chunks = 'taxonomy'
runs = [1, 2, 3, 4, 5]
n_conditions = 20
n_vertices = 40962
#n_medial = {'lh': 3487, 'rh': 3491}
n_medial = {'lh': 3486, 'rh': 3491}
# Load surface and create searchlight query engine
surf = mv.surf.read(join(glm_dir, '{0}.pial.gii'.format(hemi)))
qe = mv.SurfaceQueryEngine(surf, 10.0, distance_metric='dijkstra')
# Load in surface data sets
dss = []
for run in runs:
ds = mv.niml.read(
join(
glm_dir,
'sub-{0}_task-{1}_run-{2}_rw-glm.{3}.coefs.niml.dset'.format(
participant, task, run, hemi)))
ds.sa.pop('stats')
ds.sa['behavior'] = np.tile(['eating', 'fighting', 'running', 'swimming'],
5)
ds.sa['taxonomy'] = np.repeat(
['bird', 'insect', 'primate', 'reptile', 'ungulate'], 4)
ds.sa['conditions'] = [
d = mv.gifti_dataset(files[x], chunks=chunks, targets=targets)
d.sa['conditions'] = conditions
d.sa['taxonomy'] = taxonomy
d.sa['behavior'] = behavior
if ds is None:
ds = d
else:
ds = mv.vstack((ds, d))
ds.fa['node_indices'] = range(ds.shape[1])
# zscore all of our samples
mv.zscore(ds, chunks_attr='chunks', dtype='float32')
# load in surgace and get searchlight query
radius = 10
surface = mv.surf.read(join(data_path, '{0}.pial.gii'.format(hemi)))
# this is an arbitrary radius and distance metric!
query = mv.SurfaceQueryEngine(surface, radius, distance_metric='dijkstra')
# based off PyMVPA tutorial
clf = mv.LinearNuSVMC(space=predict)
cv = mv.CrossValidation(clf,
mv.NFoldPartitioner(attr=train_on),
errorfx=lambda p, t: np.mean(p == t),
enable_ca=['stats'])
searchlights = mv.Searchlight(cv,
queryengine=query,
postproc=mv.mean_sample(),
roi_ids=None)
sl_clf_results = searchlights(ds)
outstr = save_path + 'results/sub' + sub + '_sl_clf_' + predict + '_' + hemi
res = np.array(sl_clf_results)
np.save(outstr, res)
chunks = [x + 1] * 20
else:
chunks = [x - 5 + 1] * 20
d = mv.gifti_dataset(files[x], chunks=chunks, targets=conditions)
d.sa['conditions'] = conditions
if ds is None:
ds = d
else:
ds = mv.vstack((ds, d))
ds.fa['node_indices'] = range(n_vertices)
ds.samples = zscore(ds.samples, axis=1)
mtgs = mean_group_sample(['conditions'])
mtds = mtgs(ds)
query = mv.SurfaceQueryEngine(surface,
radius,
distance_metric='dijkstra',
fa_node_key='node_indices')
query.train(ds)
dsm = rsa.PDist(square=False)
print('made dsms')
sl = mv.Searchlight(dsm, query, roi_ids=query.query_byid(max_node))
slres = sl(mtds)
mv.debug.active += ['SLC']
print('made our sls')
slres.samples = np.nan_to_num(slres.samples)
all_ROI_res.append(slres.samples)
all_ROI_res = np.array(all_ROI_res)
# this array is (#participants, 190, #nodes)
#all_ROI_res = np.swapaxes(all_slres, 0, 2)
