def regularize_weights(self):
"""Either performs Pareto-smoothing of the IW, or applies clipping."""
if self.pareto:
psiw = az.psislw(self.log_sinf_weights)
self.log_sinf_weights = psiw[0]
self.sinf_weights = np.exp(self.log_sinf_weights)
elif not self.pareto:
self.log_sinf_weights = np.clip(
self.log_sinf_weights,
a_min=None,
a_max=logsumexp(self.log_sinf_weights) +
(self.k_trunc - 1) * np.log(len(self.log_sinf_weights)))
self.log_sinf_weights = self.log_sinf_weights - logsumexp(
self.log_sinf_weights)
self.sinf_weights = np.exp(self.log_sinf_weights)
"""
LOO-PIT ECDF Plot
=================
_thumb: .5, .7
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("radon")
log_like = idata.sample_stats.log_likelihood.sel(chain=0).values.T
log_weights = az.psislw(-log_like)[0]
az.plot_loo_pit(idata,
y="y_like",
log_weights=log_weights,
ecdf=True,
color="maroon")
plt.show()
size=num_proposal_samples)
samples_vb_swa = np.random.multivariate_normal(
means_vb_swa,
np.diag(sigmas_vb_swa),
size=num_proposal_samples)
samples_vb_rms = np.random.multivariate_normal(
means_vb_rms,
np.diag(sigmas_vb_rms),
size=num_proposal_samples)
q_swa = stats.norm.logpdf(samples_vb_swa, means_vb_swa,
sigmas_vb_swa)
logp_swa = np.array([fit_hmc.log_prob(s) for s in samples_vb_swa])
#logp_swa = np.array([fit_hmc.])
log_iw_swa = logp_swa - np.sum(q_swa, axis=1)
psis_lw_swa, K_hat_swa = psislw(log_iw_swa.T)
print('K hat statistic for SWA')
print(K_hat_swa)
# VB-CLR
q_clr = stats.norm.logpdf(samples_vb_clr2, means_vb_clr2,
sigmas_vb_clr2)
logp_clr = np.array([fit_hmc.log_prob(s) for s in samples_vb_clr2])
log_iw_clr = logp_clr - np.sum(q_clr, axis=1)
psis_lw_clr, K_hat_clr = psislw(log_iw_clr.T)
print('K hat statistic for CLR')
print(K_hat_clr)
q_rms = stats.norm.pdf(samples_vb_rms, means_vb_rms, sigmas_vb_rms)
logp_rms = np.array([fit_hmc.log_prob(s) for s in samples_vb_rms])
log_iw_rms = logp_rms - np.sum(np.log(q_rms), axis=1)
stan_vb_mean = np.mean(stan_vb_w, axis=0)
stan_vb_cov = np.cov(stan_vb_w[:, 0], stan_vb_w[:, 1])
params_vb_means = np.mean(stan_vb_w, axis=0)
params_vb_std = np.std(stan_vb_w, axis=0)
params_vb_sq = np.mean(stan_vb_w**2, axis=0)
logq = stats.norm.pdf(stan_vb_w, params_vb_means, params_vb_std)
logq_sum = np.sum(np.log(logq), axis=1)
# log_joint_density = la['log_joint_density']
stan_vb_log_joint_density = fit_vb_samples[:, K]
log_iw = stan_vb_log_joint_density - logq_sum
print(np.max(log_iw))
print(log_iw.shape)
psis_lw, K_hat_stan = psislw(log_iw.T)
K_hat_stan_advi_list[j, n] = K_hat_stan
print(psis_lw.shape)
print('K hat statistic for Stan ADVI:')
print(K_hat_stan)
###################### Plotting L2 norm here #################################
plt.figure()
plt.plot(stan_vb_w[:, 0], stan_vb_w[:, 1], 'mo', label='STAN-ADVI')
plt.savefig('vb_w_samples_mf.pdf')
np.save('K_hat_linear_' + datatype + '_' + algo_name + '_' + str(N) + 'N',
K_hat_stan_advi_list)
plt.figure()