def do_Lasso_Kfold(y,yname,files,X):
subject_num = y.shape[0]
output = Outputsclass(subject_num,y,yname)
output.prepare_kfold()
data, idx, img = load_data(files)
for train, test in cv.StratifiedKFold(np.zeros(subject_num), k = 4):
# PCA
pca, data_red = do_PCA(data[train])
output.pca.append(pca)
data_red_test = pca.transform(data[test])
# Build design matrix & test vector
desmat_cv = np.hstack((data_red,X[train]))
desmat_cv = np.array(desmat_cv)
y_cv = y[train]
test_vec = np.hstack((data_red_test,X[test]))
test_vec = np.array(test_vec)
#Lasso
lasso = do_LASSO(y_cv,desmat_cv)
output.lasso.append(lasso)
output.rsq[test] = lasso.score(desmat_cv,y_cv)
output.adjrsq[test] = 1 - (1 - output.rsq[test])*(subject_num-1-1)/(subject_num-1 - lasso.coef_.shape[0] -1)
# Prediction
output.prediction[test] = lasso.predict(test_vec)
output.pred_errors[test] = y[test] - output.prediction[test]
print "did prediction, error = ", output.pred_errors[test]
output.append_kfold(train,test)
return output
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
