def test_lin_graphnet(X=None,Y=None,l1=25.,l2=0.,l3=1., control=control,nonneg=False):
if X is None or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
p = X.shape[1]
_ , L = gen_adj(p)
Lsparse = scipy.sparse.lil_matrix(L)
#np.random.shuffle(Y)
Y = np.dot(Y,X)
np.random.shuffle(Y)
#Y = np.random.normal(0,1,X.shape[1])
l1 *= X.shape[0]
if nonneg:
p1 = lin_graphnet.gengrad_nonneg((Y, L))
else:
p1 = lin_graphnet.gengrad((Y, L))
p1.assign_penalty(l1=l1,l2=l2,l3=l3)
t1 = time.time()
opt1 = regreg.FISTA(p1)
#opt1.debug = True
opt1.fit(tol=control['tol'], max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts3 = t2-t1
if nonneg:
p2 = lin_graphnet.gengrad_nonneg_sparse((Y, Lsparse))
else:
p2 = lin_graphnet.gengrad_sparse((Y, Lsparse))
p2.assign_penalty(l1=l1,l2=l2,l3=l3)
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
beta2 = opt2.problem.coefs
t2 = time.time()
ts3 = t2-t1
def f(beta):
if np.min(beta) < 0 and nonneg:
return np.inf
else:
return - np.dot(Y, beta) + np.fabs(beta).sum()*l1 + l2 * np.linalg.norm(beta)**2 + l3 * np.dot(beta, np.dot(L, beta))
v = scipy.optimize.fmin_powell(f, np.zeros(len(Y)), ftol=1.0e-10, xtol=1.0e-10,maxfun=100000)
v = np.asarray(v)
N = 10000
print np.round(N*beta1)/N
print np.round(N*beta2)/N
print np.round(N*v)/N
print f(beta1), f(v)
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 test_graphnet(X=None, Y=None, l1=5., l2=0., l3=0., control=control):
if X is None or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
p = X.shape[1]
_, L = gen_adj(p)
Lsparse = scipy.sparse.lil_matrix(L)
l1 *= X.shape[0]
p1 = graphnet.gengrad((X, Y, L))
p1.assign_penalty(l1=l1, l2=l2, l3=l3)
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.fit(tol=control['tol'], max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts3 = t2 - t1
p2 = graphnet.gengrad_sparse((X, Y, Lsparse))
p2.assign_penalty(l1=l1, l2=l2, l3=l3)
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
beta2 = opt2.problem.coefs
t2 = time.time()
ts3 = t2 - t1
def f(beta):
return np.linalg.norm(Y - np.dot(X, beta))**2 / (2) + np.fabs(
beta).sum() * l1 + l2 * np.linalg.norm(beta)**2 + l3 * 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)
print beta1
print beta2
print v
print f(beta1), f(v), ts3
def test_graphnet(X=None,Y=None,l1=5.,l2=0.,l3=0., control=control):
if X is None or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
p = X.shape[1]
_ , L = gen_adj(p)
Lsparse = scipy.sparse.lil_matrix(L)
l1 *= X.shape[0]
p1 = graphnet.gengrad((X, Y, L))
p1.assign_penalty(l1=l1,l2=l2,l3=l3)
t1 = time.time()
opt1 = regreg.FISTA(p1)
opt1.fit(tol=control['tol'], max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts3 = t2-t1
p2 = graphnet.gengrad_sparse((X, Y, Lsparse))
p2.assign_penalty(l1=l1,l2=l2,l3=l3)
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
beta2 = opt2.problem.coefs
t2 = time.time()
ts3 = t2-t1
def f(beta):
return np.linalg.norm(Y - np.dot(X, beta))**2/(2) + np.fabs(beta).sum()*l1 + l2 * np.linalg.norm(beta)**2 + l3 * 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)
print beta1
print beta2
print v
print f(beta1), f(v), ts3
def test_lin_graphnet(X=None,
Y=None,
l1=25.,
l2=0.,
l3=1.,
control=control,
nonneg=False):
if X is None or Y is None:
X = np.load('X.npy')
Y = np.load('Y.npy')
p = X.shape[1]
_, L = gen_adj(p)
Lsparse = scipy.sparse.lil_matrix(L)
#np.random.shuffle(Y)
Y = np.dot(Y, X)
np.random.shuffle(Y)
#Y = np.random.normal(0,1,X.shape[1])
l1 *= X.shape[0]
if nonneg:
p1 = lin_graphnet.gengrad_nonneg((Y, L))
else:
p1 = lin_graphnet.gengrad((Y, L))
p1.assign_penalty(l1=l1, l2=l2, l3=l3)
t1 = time.time()
opt1 = regreg.FISTA(p1)
#opt1.debug = True
opt1.fit(tol=control['tol'], max_its=control['max_its'])
beta1 = opt1.problem.coefs
t2 = time.time()
ts3 = t2 - t1
if nonneg:
p2 = lin_graphnet.gengrad_nonneg_sparse((Y, Lsparse))
else:
p2 = lin_graphnet.gengrad_sparse((Y, Lsparse))
p2.assign_penalty(l1=l1, l2=l2, l3=l3)
t1 = time.time()
opt2 = regreg.FISTA(p2)
opt2.fit(tol=control['tol'], max_its=control['max_its'])
beta2 = opt2.problem.coefs
t2 = time.time()
ts3 = t2 - t1
def f(beta):
if np.min(beta) < 0 and nonneg:
return np.inf
else:
return -np.dot(Y, beta) + np.fabs(
beta).sum() * l1 + l2 * np.linalg.norm(beta)**2 + l3 * np.dot(
beta, np.dot(L, beta))
v = scipy.optimize.fmin_powell(f,
np.zeros(len(Y)),
ftol=1.0e-10,
xtol=1.0e-10,
maxfun=100000)
v = np.asarray(v)
N = 10000
print np.round(N * beta1) / N
print np.round(N * beta2) / N
print np.round(N * v) / N
print f(beta1), f(v)