base_feature, atlas, fwhm, node_size, weight_method)
flag_nan_exists = False
flag_incomplete = False
flag_unexpected = False
dataset = MLDataset()
incomplete_processing[base_feature][weight_method] = dict()
comb_nan_values[base_feature][weight_method] = dict()
for ds_name in dataset_list:
# out_dir = pjoin(proc_dir, 'graynet', '{}_{}_fwhm{}'.format(base_feature, atlas, fwhm))
out_dir = pjoin(base_dir,
'graynet_{]_{}'.format(expt_prefix, atlas))
meta_list = pjoin(base_dir,
'diagnosis_meta_data_{}.csv'.format(ds_name))
sample_ids, classes = run_workflow.get_metadata(meta_list)
incomplete_processing[base_feature][weight_method][ds_name] = list(
)
comb_nan_values[base_feature][weight_method][ds_name] = list()
for sample in sample_ids:
feat_path = pjoin(out_dir, sample,
'{}_{}'.format(expt_id, file_ext))
if pexists(feat_path):
graph = nx.read_graphml(feat_path)
data = get_weights_order(graph, atlas_rois)
idx_nan = np.logical_not(np.isfinite(data))
local_flag_nan_exists = np.count_nonzero(idx_nan) > 0
if local_flag_nan_exists:
flag_nan_exists = True
comb_nan_values[base_feature][weight_method][
proc_dir = pjoin(base_dir, dataset_name, 'processed')
freesurfer_dir = pjoin(proc_dir, 'freesurfer')
target_list_dir = pjoin(proc_dir, 'target_lists')
vis_out_dir = pjoin(proc_dir, 'visualizations')
if not pexists(vis_out_dir):
os.mkdir(vis_out_dir)
subject_id_list = pjoin(target_list_dir, 'graynet.compute.list')
# meta_file = pjoin(target_list_dir, 'meta_4RTNI_and_PPMI.csv')
meta_file = pjoin(target_list_dir, 'meta_{}.csv'.format(dataset_name))
for base_feature in features_freesurfer:
id_list, classes = get_metadata(meta_file)
class_set = list(set(classes.values()))
class_set.sort()
labels = {sub: class_set.index(cls) for sub, cls in classes.items()}
out_path = pjoin(
vis_out_dir,
'raw_features_{}_{}.MLDataset.pkl'.format(base_feature,
'_'.join(class_set)))
try:
ds = MLDataset(filepath=out_path)
except:
traceback.print_exc()
id_data = import_features(freesurfer_dir,
id_list,