def objective(mu_list, y):
return metrics.avneg_elbo_gaussian(
mu_list,
y,
tau=self.model_params['noise_prec'],
train_set_size=self.data.get_current_train_size(),
kl=self.model.kl_divergence())
def _evaluate_model(self, metric_dict, x_train, y_train, x_test, y_test):
# Unnormalize noise precision
if self.normalize_y:
tau = self.model_params['noise_prec'] / (self.y_std**2)
else:
tau = self.model_params['noise_prec']
# Normalize train x
if self.normalize_x:
x_train = (x_train - self.x_means) / self.x_stds
# Get train predictions
mu_list = self.optimizer.get_mc_predictions(
self.model.forward,
inputs=x_train,
mc_samples=self.train_params['eval_mc_samples'],
ret_numpy=False)
# Unnormalize train predictions
if self.normalize_y:
mu_list = [self.y_mean + self.y_std * mu for mu in mu_list]
# Store train metrics
metric_dict['train_pred_logloss'].append(
metrics.predictive_avneg_loglik_gaussian(
mu_list, y_train, tau=tau).detach().cpu().item())
metric_dict['train_pred_rmse'].append(
metrics.predictive_rmse(mu_list, y_train).detach().cpu().item())
metric_dict['elbo_neg_ave'].append(
metrics.avneg_elbo_gaussian(
mu_list,
y_train,
tau=tau,
train_set_size=self.data.get_current_train_size(),
kl=self.optimizer.kl_divergence()).detach().cpu().item())
# Normalize test x
if self.normalize_x:
x_test = (x_test - self.x_means) / self.x_stds
# Get test predictions
mu_list = self.optimizer.get_mc_predictions(
self.model.forward,
inputs=x_test,
mc_samples=self.train_params['eval_mc_samples'],
ret_numpy=False)
# Unnormalize test predictions
if self.normalize_y:
mu_list = [self.y_mean + self.y_std * mu for mu in mu_list]
# Store test metrics
metric_dict['test_pred_logloss'].append(
metrics.predictive_avneg_loglik_gaussian(
mu_list, y_test, tau=tau).detach().cpu().item())
metric_dict['test_pred_rmse'].append(
metrics.predictive_rmse(mu_list, y_test).detach().cpu().item())