def test_logistic_counts():
"""
Test the equivalence of binary/count specification in logistic_deviance
"""
#Form the count version of the problem
trials = np.random.binomial(5,0.5,100)+1
successes = np.random.binomial(trials,0.5,len(trials))
n = len(successes)
p = 2*n
X = np.random.normal(0,1,n*p).reshape((n,p))
loss = rr.logistic_deviance.linear(X, successes=successes, trials=trials)
penalty = rr.quadratic(p, coef=1.)
prob1 = rr.container(loss, penalty)
solver1 = rr.FISTA(prob1)
solver1.fit()
solution1 = solver1.composite.coefs
#Form the binary version of the problem
Ynew = []
Xnew = []
for i, (s,n) in enumerate(zip(successes,trials)):
Ynew.append([1]*s + [0]*(n-s))
for j in range(n):
Xnew.append(X[i,:])
Ynew = np.hstack(Ynew)
Xnew = np.vstack(Xnew)
loss = rr.logistic_deviance.linear(Xnew, successes=Ynew)
penalty = rr.quadratic(p, coef=1.)
prob2 = rr.container(loss, penalty)
solver2 = rr.FISTA(prob2)
solver2.fit()
solution2 = solver2.composite.coefs
npt.assert_array_almost_equal(solution1, solution2, 3)
def test_logistic_counts():
"""
Test the equivalence of binary/count specification in logistic_deviance
"""
#Form the count version of the problem
trials = np.random.binomial(5,0.5,100)+1
successes = np.random.binomial(trials,0.5,len(trials))
n = len(successes)
p = 2*n
X = np.random.normal(0,1,n*p).reshape((n,p))
loss = rr.logistic_deviance.linear(X, successes=successes, trials=trials)
penalty = rr.quadratic(p, coef=1.)
prob1 = rr.container(loss, penalty)
solver1 = rr.FISTA(prob1)
solver1.fit()
solution1 = solver1.composite.coefs
#Form the binary version of the problem
Ynew = []
Xnew = []
for i, (s,n) in enumerate(zip(successes,trials)):
Ynew.append([1]*s + [0]*(n-s))
for j in range(n):
Xnew.append(X[i,:])
Ynew = np.hstack(Ynew)
Xnew = np.vstack(Xnew)
loss = rr.logistic_deviance.linear(Xnew, successes=Ynew)
penalty = rr.quadratic(p, coef=1.)
prob2 = rr.container(loss, penalty)
solver2 = rr.FISTA(prob2)
solver2.fit()
solution2 = solver2.composite.coefs
npt.assert_array_almost_equal(solution1, solution2, 3)
def test_quadratic():
l = rr.quadratic(5, coef=3., offset=np.arange(5))
l.quadratic = rr.identity_quadratic(1, np.ones(5), 2 * np.ones(5), 3.)
c1 = l.get_conjugate(as_quadratic=True)
q1 = rr.identity_quadratic(3, -np.arange(5), 0, 0)
q2 = q1 + l.quadratic
c2 = rr.zero(5, quadratic=q2.collapsed()).conjugate
ww = np.random.standard_normal(5)
np.testing.assert_almost_equal(c2.smooth_objective(ww, 'grad'),
c1.smooth_objective(ww, 'grad'))
np.testing.assert_almost_equal(c2.objective(ww), c1.objective(ww))
np.testing.assert_almost_equal(
c2.smooth_objective(ww, 'func') + c2.nonsmooth_objective(ww),
c1.smooth_objective(ww, 'func') + c1.nonsmooth_objective(ww))
def test_quadratic():
l = rr.quadratic(5, coef=3., offset=np.arange(5))
l.quadratic = rr.identity_quadratic(1,np.ones(5), 2*np.ones(5), 3.)
c1 = l.get_conjugate(as_quadratic=True)
q1 = rr.identity_quadratic(3, -np.arange(5), 0, 0)
q2 = q1 + l.quadratic
c2 = rr.zero(5, quadratic=q2.collapsed()).conjugate
ww = np.random.standard_normal(5)
np.testing.assert_almost_equal(c2.smooth_objective(ww, 'grad'),
c1.smooth_objective(ww, 'grad'))
np.testing.assert_almost_equal(c2.objective(ww),
c1.objective(ww))
np.testing.assert_almost_equal(c2.smooth_objective(ww, 'func') +
c2.nonsmooth_objective(ww),
c1.smooth_objective(ww, 'func') +
c1.nonsmooth_objective(ww))
def ridge(self):
if self.lambda2 > 0:
return rr.quadratic(self.p,bound=self.lambda2)
else:
return None
