def test(self):
np.set_printoptions(suppress=True, precision=5)
seed = 43
np.random.seed(seed=seed) # easy peasy reproducibeasy
tf.set_random_seed(seed)
# Data generation
N = 20
t = np.linspace(0, 1, N)
print(t)
trueB = np.ones((1, 1)) * 0.5
Y = np.zeros((N, 1))
idx = np.nonzero(t > 0.5)[0]
idxA = idx[::2]
idxB = idx[1::2]
print(idx)
print(idxA)
print(idxB)
Y[idxA, 0] = 2 * t[idxA]
Y[idxB, 0] = -2 * t[idxB]
# Create tree structures
tree = bt.BinaryBranchingTree(0, 1, fDebug=False)
tree.add(None, 1, trueB)
assert tree.getRoot().val == trueB
assert tree.getRoot().idB == 1
(fm, _) = tree.GetFunctionBranchTensor()
XExpanded, indices, _ = VBHelperFunctions.GetFunctionIndexListGeneral(
t)
# Create model
Kbranch = bk.BranchKernelParam(
gpflow.kernels.Matern32(1), fm,
b=trueB.copy()) + gpflow.kernels.White(1)
Kbranch.kernels[
1].variance = 1e-6 # controls the discontinuity magnitude, the gap at the branching point
Kbranch.kernels[1].variance.set_trainable(False) # jitter for numerics
# Create model
phiPrior = np.ones((N, 2)) * 0.5 # dont know anything
phiInitial = np.ones((N, 2)) * 0.5 # dont know anything
phiInitial[:, 0] = np.random.rand(N)
phiInitial[:, 1] = 1 - phiInitial[:, 0]
m = assigngp_dense.AssignGP(t,
XExpanded,
Y,
Kbranch,
indices,
Kbranch.kernels[0].Bv.value,
phiPrior=phiPrior,
phiInitial=phiInitial)
InitKernParams(m)
m.likelihood.variance.set_trainable(False)
print('Model before initialisation\n', m,
'\n===========================')
gpflow.train.ScipyOptimizer().minimize(m, maxiter=100)
m.likelihood.variance.set_trainable(True)
gpflow.train.ScipyOptimizer().minimize(m, maxiter=100)
print('Model after initialisation\n', m,
'\n===========================')
ttestl, mul, varl = VBHelperFunctions.predictBranchingModel(m)
_, _, covl = VBHelperFunctions.predictBranchingModel(m, full_cov=True)
for i in range(len(varl)):
assert np.all(covl[i].diagonal().flatten() == varl[i].flatten())
assert len(varl) == 3, 'Must have 3 predictions for 3 functions'
assert np.all(varl[0] > 0), 'neg variances for variance function 0'
assert np.all(varl[1] > 0), 'neg variances for variance function 1'
assert np.all(varl[2] > 0), 'neg variances for variance function 2'
PhiOptimised = m.GetPhi()
print('phiPrior', phiPrior)
print('PhiOptimised', PhiOptimised)
assert np.allclose(
PhiOptimised[idxA, 2],
1), 'PhiOptimised idxA=%s' % str(PhiOptimised[idxA, :])
assert np.allclose(
PhiOptimised[idxB, 1],
1), 'PhiOptimised idxB=%s' % str(PhiOptimised[idxB, :])
# reset model and test informative KL prior
m.UpdateBranchingPoint(Kbranch.kernels[0].Bv.value,
phiInitial) # reset initial phi
InitKernParams(m)
ll_flatprior = m.compute_log_likelihood()
phiInfPrior = np.ones((N, 2)) * 0.5 # dont know anything
phiInfPrior[-1, :] = [0.99, 0.01]
# phiInfPrior[-2, :] = [0.01, 0.99]
m.UpdateBranchingPoint(Kbranch.kernels[0].Bv.value,
phiInitial,
prior=phiInfPrior)
ll_betterprior = m.compute_log_likelihood()
assert ll_betterprior > ll_flatprior, '%f <> %f' % (ll_betterprior,
ll_flatprior)
def test(self):
np.set_printoptions(suppress=True, precision=5)
seed = 43
np.random.seed(seed=seed) # easy peasy reproducibeasy
tf.random.set_seed(seed)
# Data generation
N = 20
t = np.linspace(0, 1, N)
print(t)
trueB = np.ones((1, 1)) * 0.5
Y = np.zeros((N, 1))
idx = np.nonzero(t > 0.5)[0]
idxA = idx[::2]
idxB = idx[1::2]
print(idx)
print(idxA)
print(idxB)
Y[idxA, 0] = 2 * t[idxA]
Y[idxB, 0] = -2 * t[idxB]
# Create tree structures
tree = bt.BinaryBranchingTree(0, 1, fDebug=False)
tree.add(None, 1, trueB)
assert tree.getRoot().val == trueB
assert tree.getRoot().idB == 1
(fm, _) = tree.GetFunctionBranchTensor()
XExpanded, indices, _ = VBHelperFunctions.GetFunctionIndexListGeneral(
t)
# Create model
Kbranch = (bk.BranchKernelParam(
gpflow.kernels.Matern32(), fm, b=trueB.copy()) +
gpflow.kernels.White())
Kbranch.kernels[1].variance.assign(
1e-6
) # controls the discontinuity magnitude, the gap at the branching point
gpflow.set_trainable(Kbranch.kernels[1].variance,
False) # jitter for numerics
# Create model
phiPrior = np.ones((N, 2)) * 0.5 # dont know anything
phiInitial = np.ones((N, 2)) * 0.5 # dont know anything
phiInitial[:, 0] = np.random.rand(N)
phiInitial[:, 1] = 1 - phiInitial[:, 0]
m = assigngp_dense.AssignGP(
t,
XExpanded,
Y,
Kbranch,
indices,
Kbranch.kernels[0].Bv,
phiPrior=phiPrior,
phiInitial=phiInitial,
)
InitKernParams(m)
gpflow.set_trainable(m.likelihood.variance, False)
print("Model before initialisation\n", m,
"\n===========================")
opt = gpflow.optimizers.Scipy()
opt.minimize(
m.training_loss,
variables=m.trainable_variables,
options=dict(disp=True, maxiter=100),
)
gpflow.set_trainable(m.likelihood.variance, True)
opt.minimize(
m.training_loss,
variables=m.trainable_variables,
options=dict(disp=True, maxiter=100),
)
print("Model after initialisation\n", m,
"\n===========================")
ttestl, mul, varl = VBHelperFunctions.predictBranchingModel(m)
_, _, covl = VBHelperFunctions.predictBranchingModel(m, full_cov=True)
assert len(varl) == 3, "Must have 3 predictions for 3 functions"
assert np.all(varl[0] > 0), "neg variances for variance function 0"
assert np.all(varl[1] > 0), "neg variances for variance function 1"
assert np.all(varl[2] > 0), "neg variances for variance function 2"
PhiOptimised = m.GetPhi()
print("phiPrior", phiPrior)
print("PhiOptimised", PhiOptimised)
assert np.allclose(
PhiOptimised[idxA, 2],
1), "PhiOptimised idxA=%s" % str(PhiOptimised[idxA, :])
assert np.allclose(
PhiOptimised[idxB, 1],
1), "PhiOptimised idxB=%s" % str(PhiOptimised[idxB, :])
# reset model and test informative KL prior
m.UpdateBranchingPoint(Kbranch.kernels[0].Bv,
phiInitial) # reset initial phi
InitKernParams(m)
ll_flatprior = m.log_posterior_density()
phiInfPrior = np.ones((N, 2)) * 0.5 # dont know anything
phiInfPrior[-1, :] = [0.99, 0.01]
# phiInfPrior[-2, :] = [0.01, 0.99]
m.UpdateBranchingPoint(Kbranch.kernels[0].Bv,
phiInitial,
prior=phiInfPrior)
ll_betterprior = m.log_posterior_density()
assert ll_betterprior > ll_flatprior, "%f <> %f" % (
ll_betterprior,
ll_flatprior,
)