def log_pred_density(model, samples, *args, **kwargs):
# waic score of posterior samples
log_lk = log_likelihood(model, samples, *args, **kwargs)['y']
ll = log_lk.sum(-1)
S = ll.shape[0]
lppd = nn.logsumexp(ll, 0) - jnp.log(S)
p_waic = jnp.var(ll, axis=0, ddof=1)
return lppd - p_waic, log_lk
def predict(model, at_bats, hits, z, rng_key, player_names, train=True):
header = model.__name__ + (' - TRAIN' if train else ' - TEST')
predictions = Predictive(model, posterior_samples=z)(rng_key, at_bats)['obs']
print_results('=' * 30 + header + '=' * 30,
predictions,
player_names,
at_bats,
hits)
if not train:
post_loglik = log_likelihood(model, z, at_bats, hits)['obs']
# computes expected log predictive density at each data point
exp_log_density = logsumexp(post_loglik, axis=0) - jnp.log(jnp.shape(post_loglik)[0])
# reports log predictive density of all test points
print('\nLog pointwise predictive density: {:.2f}\n'.format(exp_log_density.sum()))
def _compute_log_likelihood_null(posterior_samples, data):
return log_likelihood(model_null, posterior_samples, **data)["obs"]