def meta_algorithm(X, XTXi, resid, sampler):
S = sampler(scale=0.) # deterministic with scale=0
ynew = X.dot(XTXi).dot(S) + resid # will be ok for n>p and non-degen X
G = lasso_glmnet(X, ynew, *[None] * 4)
select = G.select()
return set(list(select[0]))
def meta_algorithm(X, XTXi, resid, sampler):
S = sampler.center.copy()
ynew = X.dot(XTXi).dot(S) + resid # will be ok for n>p and non-degen X
G = lasso_glmnet(X, ynew, *[None] * 4)
select = G.select(seed=seed)
return set(list(select[0]))
def meta_algorithm(X, XTXi, resid, sampler):
n, p = X.shape
idx = np.random.choice(np.arange(n), 200, replace=False)
S = sampler(scale=0.) # deterministic with scale=0
ynew = X.dot(XTXi).dot(S) + resid # will be ok for n>p and non-degen X
G = lasso_glmnet(X[idx], ynew[idx], *[None] * 4)
select = G.select()
return set(list(select[0]))
def meta_algorithm(X, XTXi, resid, sampler):
n, p = X.shape
S = sampler(scale=0.5) # deterministic with scale=0
ynew = X.dot(XTXi).dot(S) + resid # will be ok for n>p and non-degen X
Xstack = np.hstack([X, np.random.standard_normal((n, k))])
G = lasso_glmnet(Xstack, ynew, *[None] * 4)
select = G.select(seed=seed)
return set(list(select[0])).intersection(range(p))