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)
class _PLSCanonicalImpl:
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if y is not None:
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def predict(self, X):
return self._wrapped_model.predict(X)
