def testPriorAndPrefs(self):
S5 = Shekel5()
pX = lhcSample(S5.bounds, 100, seed=13)
pY = [S5.f(x) for x in pX]
prior = RBFNMeanPrior()
prior.train(pX, pY, S5.bounds, k=10)
hv = .1
hyper = [hv, hv, hv, hv]
gkernel = GaussianKernel_ard(hyper)
GP = PrefGaussianProcess(gkernel, prior=prior)
X = [array([i + .5] * 4) for i in range(5)]
valX = [x.copy() for x in X]
prefs = []
for i in range(len(X)):
for j in range(i):
if S5.f(X[i]) > S5.f(X[j]):
prefs.append((X[i], X[j], 0))
else:
prefs.append((X[j], X[i], 0))
GP.addPreferences(prefs)
opt, optx = maximizeEI(GP, S5.bounds)
def testShekel(self):
S = Shekel5(maximize=False)
opt, optx = direct(S.f, S.bounds, maxiter=20, debug=False)
self.assertAlmostEqual(opt, S.minimum, 3)
for x, a in zip(optx, S.argmin):
self.assertAlmostEqual(x, a, 3)
def testShekelGPPrior(self):
# see how the GP works on the Shekel function
S5 = Shekel5()
pX = lhcSample(S5.bounds, 100, seed=8)
pY = [S5.f(x) for x in pX]
prior = RBFNMeanPrior()
prior.train(pX, pY, S5.bounds, k=10, seed=103)
X = lhcSample(S5.bounds, 10, seed=9)
Y = [S5.f(x) for x in X]
hv = .1
hyper = [hv, hv, hv, hv]
gkernel = GaussianKernel_ard(hyper)
priorGP = GaussianProcess(gkernel, X, Y, prior=prior)
nopriorGP = GaussianProcess(gkernel, X, Y)
S = lhcSample(S5.bounds, 1000, seed=10)
nopriorErr = mean([(S5.f(x)-nopriorGP.mu(x))**2 for x in S])
priorErr = mean([(S5.f(x)-priorGP.mu(x))**2 for x in S])
# print '\nno prior Err =', nopriorErr
# print 'prior Err =', priorErr
self.failUnless(priorErr < nopriorErr*.8)
def _testKernelMaxEI(self):
# test different methods of optimizing kernel
S5 = Shekel5()
hv = 0.1
testkernels = [GaussianKernel_iso([hv]),
GaussianKernel_ard([hv, hv, hv, hv]),
MaternKernel3([hv, 1.0])]
# MaternKernel5([hv, 1.0])]
for kernel in testkernels:
# print
# print kernel.__class__
# train GPs
X = lhcSample(S5.bounds, 10, seed=0)
Y = [S5.f(x) for x in X]
GP = GaussianProcess(kernel, X, Y)
eif = EI(GP)
dopt, doptx = direct(eif.negf, S5.bounds, maxiter=10)
copt, coptx = cdirect(eif.negf, S5.bounds, maxiter=10)
mopt, moptx = maximizeEI(GP, S5.bounds, maxiter=10)
# print dopt, doptx
# print copt, coptx
# print mopt, moptx
self.failUnlessAlmostEqual(dopt, copt, 4)
self.failUnlessAlmostEqual(-dopt, mopt, 4)
self.failUnlessAlmostEqual(-copt, mopt, 4)
self.failUnless(sum(abs(doptx-coptx)) < .01)
self.failUnless(sum(abs(moptx-coptx)) < .01)
self.failUnless(sum(abs(moptx-doptx)) < .01)
# train GP w/prior
pX = lhcSample(S5.bounds, 100, seed=101)
pY = [S5.f(x) for x in pX]
prior = RBFNMeanPrior()
prior.train(pX, pY, bounds=S5.bounds, k=10, seed=102)
GP = GaussianProcess(kernel, X, Y, prior=prior)
eif = EI(GP)
pdopt, pdoptx = direct(eif.negf, S5.bounds, maxiter=10)
pcopt, pcoptx = cdirect(eif.negf, S5.bounds, maxiter=10)
pmopt, pmoptx = maximizeEI(GP, S5.bounds, maxiter=10)
self.failIfAlmostEqual(pdopt, dopt, 3)
self.failUnlessAlmostEqual(pdopt, pcopt, 4)
self.failUnlessAlmostEqual(-pdopt, pmopt, 4)
self.failUnlessAlmostEqual(-pcopt, pmopt, 4)
self.failUnless(sum(abs(pdoptx-pcoptx)) < .01)
self.failUnless(sum(abs(pmoptx-pcoptx)) < .01)
self.failUnless(sum(abs(pmoptx-pdoptx)) < .01)
def testXi(self):
S5 = Shekel5()
GP1 = GaussianProcess(GaussianKernel_iso([.2]))
# self.failUnlessEqual(GP1.xi, 0.0)
X = lhcSample(S5.bounds, 10, seed=0)
Y = [S5.f(x) for x in X]
GP1.addData(X, Y)
eif1 = EI(GP1, xi=0.0)
dopt1, _ = direct(eif1.negf, S5.bounds, maxiter=10)
copt1, _ = cdirect(eif1.negf, S5.bounds, maxiter=10)
mopt1, _ = maximizeEI(GP1, S5.bounds, xi=0.0, maxiter=10)
self.failUnlessAlmostEqual(dopt1, copt1, 4)
self.failUnlessAlmostEqual(-dopt1, mopt1, 4)
self.failUnlessAlmostEqual(-copt1, mopt1, 4)
GP2 = GaussianProcess(GaussianKernel_iso([.3]), X, Y)
eif2 = EI(GP2, xi=0.01)
self.failUnlessEqual(eif2.xi, 0.01)
dopt2, _ = direct(eif2.negf, S5.bounds, maxiter=10)
copt2, _ = cdirect(eif2.negf, S5.bounds, maxiter=10)
mopt2, _ = maximizeEI(GP2, S5.bounds, xi=0.01, maxiter=10)
self.failUnlessAlmostEqual(dopt2, copt2, 4)
self.failUnlessAlmostEqual(-dopt2, mopt2, 4)
self.failUnlessAlmostEqual(-copt2, mopt2, 4)
self.failIfAlmostEqual(dopt1, dopt2, 4)
self.failIfAlmostEqual(copt1, copt2, 4)
self.failIfAlmostEqual(mopt1, mopt2, 4)
GP3 = GaussianProcess(GaussianKernel_iso([.3]), X, Y)
eif3 = EI(GP3, xi=0.1)
dopt3, _ = direct(eif3.negf, S5.bounds, maxiter=10)
copt3, _ = cdirect(eif3.negf, S5.bounds, maxiter=10)
mopt3, _ = maximizeEI(GP3, S5.bounds, xi=0.1, maxiter=10)
self.failUnlessAlmostEqual(dopt3, copt3, 4)
self.failUnlessAlmostEqual(-dopt3, mopt3, 4)
self.failUnlessAlmostEqual(-copt3, mopt3, 4)
self.failIfAlmostEqual(dopt1, dopt3, 4)
self.failIfAlmostEqual(copt1, copt3, 4)
self.failIfAlmostEqual(mopt1, mopt3, 4)
self.failIfAlmostEqual(dopt2, dopt3, 4)
self.failIfAlmostEqual(copt2, copt3, 4)
self.failIfAlmostEqual(mopt2, mopt3, 4)
def testXi(self):
S5 = Shekel5()
GP1 = GaussianProcess(GaussianKernel_iso([.2]))
# self.failUnlessEqual(GP1.xi, 0.0)
X = lhcSample(S5.bounds, 10, seed=0)
Y = [S5.f(x) for x in X]
GP1.addData(X, Y)
ucbf1 = UCB(GP1, len(S5.bounds), scale=0.5)
dopt1, _ = direct(ucbf1.negf, S5.bounds, maxiter=10)
copt1, _ = cdirect(ucbf1.negf, S5.bounds, maxiter=10)
mopt1, _ = maximizeUCB(GP1, S5.bounds, scale=0.5, maxiter=10)
self.failUnlessAlmostEqual(dopt1, copt1, 4)
self.failUnlessAlmostEqual(-dopt1, mopt1, 4)
self.failUnlessAlmostEqual(-copt1, mopt1, 4)
GP2 = GaussianProcess(GaussianKernel_iso([.3]), X, Y)
ucbf2 = UCB(GP2, len(S5.bounds), scale=0.01)
dopt2, _ = direct(ucbf2.negf, S5.bounds, maxiter=10)
copt2, _ = cdirect(ucbf2.negf, S5.bounds, maxiter=10)
mopt2, _ = maximizeUCB(GP2, S5.bounds, scale=.01, maxiter=10)
self.failUnlessAlmostEqual(dopt2, copt2, 4)
self.failUnlessAlmostEqual(-dopt2, mopt2, 4)
self.failUnlessAlmostEqual(-copt2, mopt2, 4)
self.failIfAlmostEqual(dopt1, dopt2, 4)
self.failIfAlmostEqual(copt1, copt2, 4)
self.failIfAlmostEqual(mopt1, mopt2, 4)
GP3 = GaussianProcess(GaussianKernel_iso([.3]), X, Y)
ucbf3 = UCB(GP3, len(S5.bounds), scale=.9)
dopt3, _ = direct(ucbf3.negf, S5.bounds, maxiter=10)
copt3, _ = cdirect(ucbf3.negf, S5.bounds, maxiter=10)
mopt3, _ = maximizeUCB(GP3, S5.bounds, scale=0.9, maxiter=10)
self.failUnlessAlmostEqual(dopt3, copt3, 4)
self.failUnlessAlmostEqual(-dopt3, mopt3, 4)
self.failUnlessAlmostEqual(-copt3, mopt3, 4)
self.failIfAlmostEqual(dopt1, dopt3, 4)
self.failIfAlmostEqual(copt1, copt3, 4)
self.failIfAlmostEqual(mopt1, mopt3, 4)
self.failIfAlmostEqual(dopt2, dopt3, 4)
self.failIfAlmostEqual(copt2, copt3, 4)
self.failIfAlmostEqual(mopt2, mopt3, 4)
def testMaxEIPrior(self):
# make sure that the prior works with the different methods of EI
# maximization
S5 = Shekel5()
pX = lhcSample(S5.bounds, 100, seed=511)
pY = [S5.f(x) for x in pX]
prior = RBFNMeanPrior()
prior.train(pX, pY, bounds=S5.bounds, k=10, seed=504)
hv = .1
hyper = [hv, hv, hv, hv]
kernel = GaussianKernel_ard(hyper)
# train GPs
X = lhcSample(S5.bounds, 10, seed=512)
Y = [S5.f(x) for x in X]
# validation
valX = list(x.copy() for x in X)
valY = copy(Y)
GP = GaussianProcess(kernel, X, Y, prior=prior)
eif = EI(GP)
copt, _ = cdirect(eif.negf, S5.bounds, maxiter=20)
mopt, _ = maximizeEI(GP, S5.bounds, maxiter=20)
self.failUnlessAlmostEqual(-copt, mopt, 2)
for i in xrange(len(GP.X)):
self.failUnless(all(valX[i]==GP.X[i]))
self.failUnless(valY[i]==GP.Y[i])
GP.prior.mu(GP.X[0])
self.failUnless(all(valX[0]==GP.X[0]))
# print GP.X
for i in xrange(len(GP.X)):
self.failUnless(all(valX[i]==GP.X[i]))
self.failUnless(valY[i]==GP.Y[i])
GP.prior.mu(GP.X[0])
self.failUnless(all(valX[0]==GP.X[0]))