mu2 = mu2init * (ps_step**j2)
cor = 0
for k in range(5): # 5 for full 5 fold CV
train1_idx = SP.concatenate(
(train_idx[:int(n_train * k * 0.2)],
train_idx[int(n_train * (k + 1) * 0.2):n_train]))
valid_idx = train_idx[int(n_train * k *
0.2):int(n_train * (k + 1) *
0.2)]
res1 = lmm_lasso.train(X[train1_idx],
K[train1_idx][:, train1_idx],
y[train1_idx], mu, mu2, group)
w1 = res1['weights']
yhat = lmm_lasso.predict(y[train1_idx], X[train1_idx, :],
X[valid_idx, :],
K[train1_idx][:, train1_idx],
K[valid_idx][:, train1_idx],
res1['ldelta0'], w1)
cor += SP.dot(yhat.T - yhat.mean(), y[valid_idx] -
y[valid_idx].mean()) / (yhat.std() *
y[valid_idx].std())
print mu, mu2, cor[0, 0]
if cor > optcor:
optcor = cor
optmu = mu
optmu2 = mu2
print optmu, optmu2, optcor[0, 0]
# train
for j in range(20):
mu *= ps_step
train1_idx = train_idx[:int(n_s * 0.8)]
valid_idx = train_idx[int(n_s * 0.8):]
res = lmm_lasso.train(X[train1_idx],
K[train1_idx][:, train1_idx],
y[train1_idx],
mu,
0, [],
w0=w0,
null_model=nm)
w0 = res['weights']
nm = res['null_model']
yhat = lmm_lasso.predict(y[train1_idx], X[train1_idx, :],
X[valid_idx, :],
K[train1_idx][:, train1_idx],
K[valid_idx][:, train1_idx],
res['ldelta0'], res['lstsq'])
err = LA.norm(yhat - y[valid_idx])
print mu, err
if err < opterr:
optw = w0
opterr = err
stop = 4
score[(SP.nonzero(w0)[0])] += 1
elif err > opterr:
stop -= 1
if stop <= 0:
break
freq[(SP.nonzero(optw)[0])] += 1
weight = weight + SP.absolute(optw)