def _evaluate_model(self, metric_dict, x_train, y_train, x_test, y_test):
# Normalize train x
if self.normalize_x:
x_train = (x_train - self.x_means) / self.x_stds
# Get train predictions
logits_list = self.optimizer.get_mc_predictions(
self.model.forward,
inputs=x_train,
mc_samples=self.train_params['eval_mc_samples'],
ret_numpy=False)
# Store train metrics
metric_dict['train_pred_logloss'].append(
metrics.predictive_avneg_loglik_categorical(
logits_list, y_train).detach().cpu().item())
metric_dict['train_pred_accuracy'].append(
metrics.softmax_predictive_accuracy(logits_list,
y_train).detach().cpu().item())
metric_dict['elbo_neg_ave'].append(
metrics.avneg_elbo_categorical(
logits_list,
y_train,
train_set_size=self.data.get_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
logits_list = self.optimizer.get_mc_predictions(
self.model.forward,
inputs=x_test,
mc_samples=self.train_params['eval_mc_samples'],
ret_numpy=False)
# Store test metrics
metric_dict['test_pred_logloss'].append(
metrics.predictive_avneg_loglik_categorical(
logits_list, y_test).detach().cpu().item())
metric_dict['test_pred_accuracy'].append(
metrics.softmax_predictive_accuracy(logits_list,
y_test).detach().cpu().item())
def objective(logits_list, y):
return metrics.avneg_elbo_categorical(
logits_list,
y,
train_set_size=self.data.get_train_size(),
kl=self.model.kl_divergence())