def plscorr_eval(train_fmri_ts, train_feat_ts, val_fmri_ts, val_feat_ts,
out_dir, mask_file):
"""Compute PLS correlation between brain activity and CNN activation."""
train_feat_ts = train_feat_ts.reshape(-1, train_feat_ts.shape[3]).T
val_feat_ts = val_feat_ts.reshape(-1, val_feat_ts.shape[3]).T
train_fmri_ts = train_fmri_ts.T
val_fmri_ts = val_fmri_ts.T
# Iteration loop for different component number
#for n in range(5, 19):
# print '--- Components number %s ---' %(n)
# plsca = PLSCanonical(n_components=n)
# plsca.fit(train_feat_ts, train_fmri_ts)
# pred_feat_c, pred_fmri_c = plsca.transform(val_feat_ts, val_fmri_ts)
# pred_fmri_ts = plsca.predict(val_feat_ts)
# # calculate correlation coefficient between truth and prediction
# r = corr2_coef(val_fmri_ts.T, pred_fmri_ts.T, mode='pair')
# # get top 20% corrcoef for model evaluation
# vsample = int(np.rint(0.2*len(r)))
# print 'Sample size for evaluation : %s' % (vsample)
# r.sort()
# meanr = np.mean(r[-1*vsample:])
# print 'Mean prediction corrcoef : %s' %(meanr)
# model generation based on optimized CC number
cc_num = 10
plsca = PLSCanonical(n_components=cc_num)
plsca.fit(train_feat_ts, train_fmri_ts)
from sklearn.externals import joblib
joblib.dump(plsca, os.path.join(out_dir, 'plsca_model.pkl'))
plsca = joblib.load(os.path.join(out_dir, 'plsca_model.pkl'))
# calculate correlation coefficient between truth and prediction
pred_fmri_ts = plsca.predict(val_feat_ts)
fmri_pred_r = corr2_coef(val_fmri_ts.T, pred_fmri_ts.T, mode='pair')
mask = vutil.data_swap(mask_file)
vxl_idx = np.nonzero(mask.flatten()==1)[0]
tmp = np.zeros_like(mask.flatten(), dtype=np.float64)
tmp[vxl_idx] = fmri_pred_r
tmp = tmp.reshape(mask.shape)
vutil.save2nifti(tmp, os.path.join(out_dir, 'pred_fmri_r.nii.gz'))
pred_feat_ts = pls_y_pred_x(plsca, val_fmri_ts)
pred_feat_ts = pred_feat_ts.T.reshape(96, 14, 14, 540)
np.save(os.path.join(out_dir, 'pred_feat.npy'), pred_feat_ts)
# get PLS-CCA weights
feat_cc, fmri_cc = plsca.transform(train_feat_ts, train_fmri_ts)
np.save(os.path.join(out_dir, 'feat_cc.npy'), feat_cc)
np.save(os.path.join(out_dir, 'fmri_cc.npy'), fmri_cc)
feat_weight = plsca.x_weights_.reshape(96, 14, 14, cc_num)
#feat_weight = plsca.x_weights_.reshape(96, 11, 11, cc_num)
fmri_weight = plsca.y_weights_
np.save(os.path.join(out_dir, 'feat_weights.npy'), feat_weight)
np.save(os.path.join(out_dir, 'fmri_weights.npy'), fmri_weight)
fmri_orig_ccs = get_pls_components(plsca.y_scores_, plsca.y_loadings_)
np.save(os.path.join(out_dir, 'fmri_orig_ccs.npy'), fmri_orig_ccs)
def show_retinotopy(prf_dir, data_type):
"""Show estimate parameters on brain."""
#data_type = 'ecc'
vol = np.empty((73728, ))
vol[:] = np.NaN
subj_dir = '/'.join(prf_dir.split('/')[:-2])
# get roi list
roi_list = ['v1lh', 'v2lh', 'v3lh', 'v4lh', 'v1rh', 'v2rh', 'v3rh', 'v4rh']
rois = os.listdir(prf_dir)
rois = [roi for roi in rois if roi in roi_list]
for roi in rois:
vxl_idx, train_ts, val_ts = dataio.load_fmri(subj_dir, roi=roi)
del train_ts, val_ts
data = np.load(os.path.join(prf_dir, roi, data_type + '.npy'))
vol[vxl_idx] = data
vol = vol.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(prf_dir, data_type + '.nii.gz'))
def prf2visual_angle(prf_mtx, img_size, out_dir, base_name):
"""Generate retinotopic mapping based on voxels' pRF parameters.
`prf_mtx` is a #voxel x pRF-features matrix, pRF features can be 2 columns
(row, col) of image or 3 columns which adding a third pRF size parameters.
"""
feature_size = prf_mtx.shape[1]
pos_mtx = prf_mtx[:, :2]
# eccentricity
ecc = retinotopy.coord2ecc(pos_mtx, img_size, 20)
vol = ecc.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(out_dir, base_name+'_ecc.nii.gz'))
# angle
angle = retinotopy.coord2angle(pos_mtx, img_size)
vol = angle.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(out_dir, base_name+'_angle.nii.gz'))
# pRF size
if feature_size > 2:
size_angle = retinotopy.get_prf_size(prf_mtx, 55, 20)
vol = size_angle.reshape(18, 64, 64)
vutil.save2nifti(vol, os.path.join(out_dir, base_name+'_size.nii.gz'))