def test_emulator_setup_loglik(self):
# set up regular model
d = SepiaData(x_sim=self.x_sim,
y_sim=self.y_sim,
y_ind_sim=np.array([0, 1]))
d.create_K_basis(K=np.eye(2))
d.transform_xt(x_notrans=True)
d.standardize_y(scale='columnwise')
print(d)
mod = SepiaModel(d)
print('Emulator model LL=%f \n' % compute_log_lik(mod))
# set up kron model
kd = SepiaData(xt_sim_sep=self.x_sim_kron,
y_sim=self.y_sim,
y_ind_sim=self.y_ind_sim)
kd.create_K_basis(K=np.eye(2))
kd.transform_xt(x_notrans=True)
kd.standardize_y(scale='columnwise')
print(kd)
kmod = SepiaModel(kd)
print('Emulator Sep model LL=%f \n' % compute_log_lik(kmod))
self.assertAlmostEqual(compute_log_lik(mod),
compute_log_lik(kmod),
places=5)
pass
def logLik(self, cvar='all', cindex=None):
"""
Compute model log lik with current values of variables.
:param cvar: string -- name of variables changed since last call (controls recomputation of num components), or 'all'
:param cindex: int -- index of flattened cvar that has changed since last call (or None)
:return: scalar -- log lik value
"""
L = compute_log_lik(self, cvar, cindex)
return L
def test_full_setup_LL_pred(self):
# Set up and check calibration model
x_obs = np.ones((3, 2)) * np.array([0.5, 0.75, 0.25]).reshape((-1, 1))
y_obs = np.block([[-0.1, 0.1], [-0.2, 0.3], [0.1, 0]])
# augment to also test more than scalar dimensions in x and t
x_sim_cal = np.hstack((0.5 * np.ones(
(self.x_sim.shape[0], 1)), self.x_sim[:, :1]))
t_sim_cal = self.x_sim[:, 1:]
xt_sim_sep = [np.array(0.5).reshape(1, 1)] + self.x_sim_kron
y_sim_std = (self.y_sim - np.mean(self.y_sim, axis=0).reshape(
1, -1)) / np.std(self.y_sim, axis=0, ddof=1).reshape(1, -1)
dc = SepiaData(x_sim=x_sim_cal,
t_sim=t_sim_cal,
y_sim=y_sim_std,
x_obs=x_obs,
y_obs=y_obs,
y_ind_sim=self.y_ind_sim,
y_ind_obs=self.y_ind_sim)
dc.create_K_basis(K=np.eye(2))
dc.create_D_basis(D_sim=np.eye(2), D_obs=np.eye(2))
dc.transform_xt(x_notrans=True, t_notrans=True)
dc.standardize_y(y_mean=0, y_sd=1)
print(dc)
cmod = SepiaModel(dc)
print('Calibration model LL=%f' % compute_log_lik(cmod))
kdc = SepiaData(xt_sim_sep=xt_sim_sep,
y_sim=y_sim_std,
x_obs=x_obs,
y_obs=y_obs,
y_ind_sim=self.y_ind_sim,
y_ind_obs=self.y_ind_sim)
kdc.create_K_basis(K=np.eye(2))
kdc.create_D_basis(D_sim=np.eye(2), D_obs=np.eye(2))
kdc.transform_xt(x_notrans=True, t_notrans=True)
kdc.standardize_y(y_mean=0, y_sd=1)
print(kdc)
kcmod = SepiaModel(kdc)
print('Calibration Sep model LL=%f' % compute_log_lik(kcmod))
self.assertAlmostEqual(compute_log_lik(cmod),
compute_log_lik(kcmod),
places=5)
print(
'Running MCMC on Calibration model in Sep pred mode, regular design'
)
np.random.seed(42)
t1 = time()
cmod.do_mcmc(10)
print('sampling time %f' % (time() - t1))
csamp = cmod.get_samples(sampleset=[cmod.get_last_sample_ind()])
cpred = SepiaFullPrediction(mode='Sep',
model=cmod,
samples=csamp,
storeMuSigma=True,
x_pred=np.array([0.5, 0.5]).reshape(
(1, -1)))
print(cpred.get_ysim())
csm, css = cpred.get_mu_sigma()
print(csm)
print(css)
print(
'Running MCMC on Calibration model in Sep pred mode, separable design'
)
np.random.seed(42)
t1 = time()
kcmod.do_mcmc(10)
print('sampling time %f' % (time() - t1))
kcsamp = kcmod.get_samples(sampleset=[kcmod.get_last_sample_ind()])
kcpred = SepiaFullPrediction(mode='Sep',
model=kcmod,
samples=kcsamp,
storeMuSigma=True,
x_pred=np.array([0.5, 0.5]).reshape(
(1, -1)))
print(kcpred.get_ysim())
kcsm, kcss = kcpred.get_mu_sigma()
print(kcsm)
print(kcss)
print('testing max difference which is %g' % np.max(abs(csm - kcsm)))
self.assertAlmostEqual(0, np.max(abs(csm - kcsm)))
print('testing max difference which is %g' % np.max(abs(css - kcss)))
self.assertAlmostEqual(0, np.max(abs(css - kcss)))
###### Timing for predictions
test_x_pred = np.random.rand(10, 2)
csamp = cmod.get_samples()
t1 = time()
cpred0 = SepiaFullPrediction(mode='notSep',
model=cmod,
samples=csamp,
storeMuSigma=True,
x_pred=test_x_pred)
print('predict time non-Sep in non-Sep mode %f' % (time() - t1))
t1 = time()
cpred = SepiaFullPrediction(mode='Sep',
model=cmod,
samples=csamp,
storeMuSigma=True,
x_pred=test_x_pred)
print('predict time non-sep in Sep mode %f' % (time() - t1))
kcsamp = kcmod.get_samples()
t1 = time()
kcpred = SepiaFullPrediction(mode='Sep',
model=kcmod,
samples=kcsamp,
storeMuSigma=True,
x_pred=test_x_pred)
print('predict time Sep %f' % (time() - t1))
pass