def test_graphroot(X=None, Y=None, l1=5., l2=10., control=control, mu=1.):
if X is None or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
p = X.shape[1]
adj, L = gen_adj(p)
Dsparse = mask.create_D(adj)
D = Dsparse.toarray()
Lsparse = scipy.sparse.lil_matrix(L)
l1 *= X.shape[0]
p1 = graphroot.gengrad((X, Y, D))
p1.assign_penalty(l1=l1, l2=l2, mu=mu)
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.fit(tol=control['tol'], max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts1 = t2 - t1
p2 = graphroot.gengrad_sparse((X, Y, Dsparse))
p2.assign_penalty(l1=l1, l2=l2, mu=mu)
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
beta2 = opt2.problem.coefs
t2 = time.time()
ts2 = t2 - t1
def f(beta):
return np.linalg.norm(Y - np.dot(X, beta))**2 / (2) + np.fabs(
beta).sum() * l1 + l2 * np.sqrt(np.dot(beta, np.dot(L, beta)))
v = scipy.optimize.fmin_powell(f,
np.zeros(X.shape[1]),
ftol=1.0e-10,
xtol=1.0e-10,
maxfun=100000)
v = np.asarray(v)
vs = scipy.optimize.fmin_powell(p1.obj,
np.zeros(X.shape[1]),
ftol=1.0e-10,
xtol=1.0e-10,
maxfun=100000)
vs = np.asarray(vs)
print np.round(1000 * beta1) / 1000
print np.round(1000 * beta2) / 1000
print np.round(1000 * vs) / 1000
print np.round(1000 * v) / 1000
print p1.obj(beta1), p1.obj(vs), f(beta1), f(v)
print ts1, ts2
def test_graphroot(X=None,Y=None,l1=5.,l2=10., control=control, mu=1.):
if X is None or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
p = X.shape[1]
adj, L = gen_adj(p)
Dsparse = mask.create_D(adj)
D = Dsparse.toarray()
Lsparse = scipy.sparse.lil_matrix(L)
l1 *= X.shape[0]
p1 = graphroot.gengrad((X, Y, D))
p1.assign_penalty(l1=l1,l2=l2,mu=mu)
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.fit(tol=control['tol'], max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts1 = t2-t1
p2 = graphroot.gengrad_sparse((X, Y, Dsparse))
p2.assign_penalty(l1=l1,l2=l2,mu=mu)
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
beta2 = opt2.problem.coefs
t2 = time.time()
ts2 = t2-t1
def f(beta):
return np.linalg.norm(Y - np.dot(X, beta))**2/(2) + np.fabs(beta).sum()*l1 + l2 * np.sqrt(np.dot(beta, np.dot(L, beta)))
v = scipy.optimize.fmin_powell(f, np.zeros(X.shape[1]), ftol=1.0e-10, xtol=1.0e-10,maxfun=100000)
v = np.asarray(v)
vs = scipy.optimize.fmin_powell(p1.obj, np.zeros(X.shape[1]), ftol=1.0e-10, xtol=1.0e-10,maxfun=100000)
vs = np.asarray(vs)
print np.round(1000*beta1)/1000
print np.round(1000*beta2)/1000
print np.round(1000*vs)/1000
print np.round(1000*v)/1000
print p1.obj(beta1), p1.obj(vs), f(beta1), f(v)
print ts1, ts2
def D(self):
if not hasattr(self, '_D'):
sparse_path = os.path.join(self.bin_dir,self.sparse_matrix_name)
if os.path.exists(sparse_path):
self._D = io.loadmat(sparse_path)['D']
else:
pprint("Couldn't find the file -- creating D.")
self.adj = mask.prepare_adj(self.m,numx=1,numy=1,numz=1,numt=1)
self._D = sparse.csr_matrix(mask.create_D(self.adj))
pprint(self._D)
io.savemat(sparse_path, {'D':self.D})
return self._D
