def do_R_Crossval(y,yname,files,X,gr = 0):
from LassoPCR_test import load_data
from LassoPCR_test import do_PCA
subject_num = y.shape[0]
output = Outputsclass(subject_num,y,yname)
data, idx, img = load_data(files)
for train, test in cv.LeaveOneOut(subject_num):
pca, data_red = do_PCA(data[train])
output.pca.append(pca)
data_red_test = pca.transform(data[test])
desmat_cv = np.hstack((data_red,X[train]))
desmat_cv = np.array(desmat_cv)
y_cv = y[train]
if type(gr) == type(y):
gr_cv = gr[train]
else:
gr_cv = 0
test_vec = np.hstack((data_red_test,X[test]))
test_vec = np.array(test_vec)
mult_lm = do_Regression(y_cv,desmat_cv,gr_cv)
output.mult_lm.append(mult_lm)
output.rsq[test] = np.array(base.summary(mult_lm).rx("r.squared")[0])
output.adjrsq[test] = np.array(base.summary(mult_lm).rx("adj.r.squared")[0])
# Prediction
predd = dict()
for i, vec in enumerate(test_vec[0]):
name = "roi%02d"%i
predd[name] = vec
if type(gr) == type(y):
predd["group"] = FV([gr[test]])
preddataf = rob.DataFrame(predd)
output.prediction[test] = stats.predict(mult_lm, preddataf)[0]
output.pred_errors[test] = y[test] - output.prediction[test]
#print "did prediction, error = ", output.pred_errors[test]
return output
X[:, 1] = pdata.classtype - 2
X[:, 0] = pdata.lsas_pre
X[:, 2] = pdata.age
# run this for all the contrasts specified when calling the script
for con in contrasts:
condir = os.path.join(outdir, "con%d" % con)
if not os.path.exists(condir):
os.mkdir(condir)
for i, s in enumerate(pdata.subject):
# the original nii file for each subject
fname = os.path.join(
"/mindhive/gablab/sad/PY_STUDY_DIR/Block/volsurf/l1output/social/norm_contrasts/_subject_id_%s/_fwhm_6/wcon_%04d_out_warped.nii"
% (s, con)
)
# in our conX folder in the output directory we want a symbolic link to the original nii files for each subject
newname = os.path.join(condir, "%s_con%d.nii" % (s, con))
if not os.path.islink(newname):
os.symlink(fname, newname)
confiles = sorted(glob(os.path.join(condir, "*_con%d.nii" % (con))))
X, idx, img = load_data(confiles)
S1 = np.cov(X)
plt.figure()
plt.pcolor(S1)
plt.colorbar()
plt.show()
