def test_group_lasso2(l1=1, **control):
"""
fits a LASSO as a group lasso with
all 2norms are one dimensional
"""
X = np.load("X.npy")
Y = np.load('Y.npy')
n, p = X.shape
XtX = np.dot(X.T, X)
M = np.linalg.eigvalsh(XtX).max() #/ (1*len(Y))
def e(i, n):
z = np.zeros(n)
z[i] = 1.
return z
p1 = lasso.gengrad((X, Y), L=M)
p1.assign_penalty(l1=l1 * n)
Dv = [(e(i, p), l1 * n) for i in range(p)]
p2 = group.group_lasso((X, Dv, Y))
p2.dualcontrol['tol'] = 1.0e-10
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts1 = t2 - t1
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.debug = True
opt2.fit(tol=control['tol'], max_its=control['max_its'])
t2 = time.time()
ts3 = t2 - t1
p3 = glasso.generalized_lasso((X, np.identity(p), Y), L=M)
p3.assign_penalty(l1=l1 * n)
p3.dualcontrol['tol'] = 1.0e-10
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 - beta2).sum() / np.fabs(beta1).sum()) < 5.0e-04)
nose.tools.assert_true(
(np.fabs(beta1 - beta3).sum() / np.fabs(beta1).sum()) < 5.0e-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)
def test_FISTA(X=None, Y=None, l1=5., control=control):
if X or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
XtX = np.dot(X.T, X)
M = np.linalg.eigvalsh(XtX).max() #/ (1*len(Y))
p3 = lasso.gengrad((X, Y), L=M)
p3.assign_penalty(l1=l1 * X.shape[0])
t1 = time.time()
opt3 = regreg.FISTA(p3)
opt3.fit(tol=control['tol'], max_its=control['max_its'])
beta3 = opt3.problem.coefs
t2 = time.time()
ts3 = t2 - t1
def test_FISTA(X=None,Y=None,l1=5., control=control):
if X or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
XtX = np.dot(X.T, X)
M = np.linalg.eigvalsh(XtX).max() #/ (1*len(Y))
p3 = lasso.gengrad((X, Y),L=M)
p3.assign_penalty(l1=l1*X.shape[0])
t1 = time.time()
opt3 = regreg.FISTA(p3)
opt3.fit(tol=control['tol'], max_its=control['max_its'])
beta3 = opt3.problem.coefs
t2 = time.time()
ts3 = t2-t1
def test_lasso(X=None, Y=None, l1=5., control=control):
if X or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
XtX = np.dot(X.T, X)
M = np.linalg.eigvalsh(XtX).max() #/ (1*len(Y))
print M
Y += np.dot(X[:, :5], 10 * np.ones(5))
p1 = lasso.cwpath((X, Y))
p1.assign_penalty(l1=l1 * X.shape[0])
p2 = lasso.gengrad((X, Y), L=M)
p2.assign_penalty(l1=l1 * X.shape[0])
p3 = lasso.gengrad((X, Y), L=M)
p3.assign_penalty(l1=l1 * X.shape[0])
p4 = lasso.gengrad_smooth((X, Y), L=M)
p4.assign_penalty(l1=l1 * X.shape[0])
t1 = time.time()
opt1 = regreg.CWPath(p1)
opt1.fit(tol=1e-8, max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts1 = t2 - t1
t1 = time.time()
opt2 = regreg.ISTA(p2)
opt2.fit(tol=control['tol'],
max_its=control['max_its'],
backtrack=control['backtrack'],
alpha=1.25)
beta2 = opt2.problem.coefs
t2 = time.time()
ts2 = t2 - t1
time.sleep(0.5)
t1 = time.time()
opt3 = regreg.FISTA(p3)
opt3.fit(tol=control['tol'],
max_its=control['max_its'],
backtrack=control['backtrack'],
alpha=1.25)
beta3 = opt3.problem.coefs
t2 = time.time()
ts3 = t2 - t1
epsvec = np.exp(np.arange(4, -16, -1))
t1 = time.time()
opt4 = regreg.NesterovSmooth(p4)
for eps in epsvec:
f_s = opt4.fit(tol=control['tol'], max_its=50, epsilon=eps)
# f_s = opt4.fit( tol=control['tol'], max_its=control['max_its'],epsilon=0.1)
beta4 = opt4.problem.coefs
t2 = time.time()
ts4 = t2 - t1
print beta1
print beta2
print beta3
print p2.obj(beta1), p2.obj(beta2), p3.obj(beta3)
nose.tools.assert_true(
(np.fabs(beta1 - beta3).sum() / np.fabs(beta1).sum() <= 1.0e-04))
"""
print p3.obj(beta1), p3.obj(beta2), p3.obj(beta3)
"""
print "Times", ts1, ts2, ts3, ts4
def test_lasso(X=None,Y=None,l1=5., control=control):
if X or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
XtX = np.dot(X.T, X)
M = np.linalg.eigvalsh(XtX).max() #/ (1*len(Y))
print M
Y += np.dot(X[:,:5], 10 * np.ones(5))
p1 = lasso.cwpath((X, Y))
p1.assign_penalty(l1=l1*X.shape[0])
p2 = lasso.gengrad((X, Y),L=M)
p2.assign_penalty(l1=l1*X.shape[0])
p3 = lasso.gengrad((X, Y),L=M)
p3.assign_penalty(l1=l1*X.shape[0])
p4 = lasso.gengrad_smooth((X, Y),L=M)
p4.assign_penalty(l1=l1*X.shape[0])
t1 = time.time()
opt1 = regreg.CWPath(p1)
opt1.fit(tol=1e-8, max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts1 = t2-t1
t1 = time.time()
opt2 = regreg.ISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'],backtrack=control['backtrack'],alpha=1.25)
beta2 = opt2.problem.coefs
t2 = time.time()
ts2 = t2-t1
time.sleep(0.5)
t1 = time.time()
opt3 = regreg.FISTA(p3)
opt3.fit(tol=control['tol'], max_its=control['max_its'],backtrack=control['backtrack'],alpha=1.25)
beta3 = opt3.problem.coefs
t2 = time.time()
ts3 = t2-t1
epsvec = np.exp(np.arange(4,-16,-1))
t1 = time.time()
opt4 = regreg.NesterovSmooth(p4)
for eps in epsvec:
f_s = opt4.fit( tol=control['tol'], max_its=50,epsilon=eps)
# f_s = opt4.fit( tol=control['tol'], max_its=control['max_its'],epsilon=0.1)
beta4 = opt4.problem.coefs
t2 = time.time()
ts4 = t2-t1
print beta1
print beta2
print beta3
print p2.obj(beta1), p2.obj(beta2), p3.obj(beta3)
nose.tools.assert_true((np.fabs(beta1-beta3).sum() / np.fabs(beta1).sum() <= 1.0e-04))
"""
print p3.obj(beta1), p3.obj(beta2), p3.obj(beta3)
"""
print "Times", ts1, ts2, ts3, ts4