def test_group_lasso(l1=0.1, **control):
"""
fits a fused lasso as a group lasso approximator, i.e.
all 2norms are one dimensional
"""
Y = np.load('Y.npy')
n = Y.shape[0]
def e(i, n):
z = np.zeros(n)
z[i] = 1.
z[i + 1] = -1
return z
Dv = [(e(i, n), l1 * n) for i in range(n - 1)]
D = (np.identity(n) - np.diag(np.ones(n - 1), -1))[1:]
M = np.linalg.eigvalsh(np.dot(D.T, D)).max()
p1 = group.group_approximator((Dv, Y), L=M)
p2 = group.group_lasso((np.identity(n), Dv, Y), L=M)
p3 = signal_approximator.signal_approximator((D, Y), L=M)
p3.assign_penalty(l1=l1 * n)
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.debug = True
opt1.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts1 = t2 - t1
t1 = time.time()
opt2 = regreg.FISTA(p3)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts3 = t2 - t1
t1 = time.time()
opt3 = regreg.FISTA(p3)
opt3.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts3 = t2 - t1
beta1, _ = opt1.output
beta2, _ = opt2.output
beta3, _ = opt3.output
X = np.arange(n)
nose.tools.assert_true(
(np.fabs(beta1 - beta3).sum() / np.fabs(beta1).sum()) < 1.0e-04)
nose.tools.assert_true(
(np.fabs(beta1 - beta2).sum() / np.fabs(beta1).sum()) < 1.0e-04)
def test_group_lasso_approximator1(l1=0.1, **control):
Y = np.load('Y.npy')
n = Y.shape[0]
def e(i, n):
z = np.zeros(n)
z[i] = 1.
return z
Dv = [(e(i, n), l1 * n) for i in range(n)]
p1 = group.group_approximator((Dv, Y))
p2 = lasso.gengrad((np.identity(n), Y))
p2.assign_penalty(l1=l1 * n)
p3 = signal_approximator.signal_approximator((np.identity(n), Y))
p3.assign_penalty(l1=l1 * n)
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.debug = True
opt1.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts1 = t2 - t1
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts2 = t2 - t1
t1 = time.time()
opt3 = regreg.FISTA(p3)
opt3.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts3 = t2 - t1
beta1, _ = opt1.output
beta2, _ = opt2.output
beta3, _ = opt3.output
X = np.arange(n)
nose.tools.assert_true(
(np.fabs(beta1 - beta3).sum() / np.fabs(beta1).sum()) < 1.0e-04)
nose.tools.assert_true(
(np.fabs(beta1 - beta2).sum() / np.fabs(beta1).sum()) < 1.0e-04)