def compute_metrics(self, embeddings, data, split):
idx = data[f'idx_{split}']
output = self.decode(embeddings, data['adj_train_norm'], idx)
loss = F.nll_loss(output, data['labels'][idx], self.weights)
acc, f1 = acc_f1(output, data['labels'][idx], average=self.f1_average)
metrics = {'loss': loss, 'acc': acc, 'f1': f1}
return metrics
def compute_metrics(self, embeddings, data, split):
idx = data[f'idx_{split}']
output = self.decode(embeddings, data['adj_train_norm'], idx)
loss = cb_loss(
data['labels'][idx], output, self.beta, self.gamma,
data['labels'][idx].unique(return_counts=True)[1].tolist())
acc, f1, recall = acc_f1(output,
data['labels'][idx],
split,
average=self.f1_average)
metrics = {'loss': loss, 'acc': acc, 'f1': f1, 'recall': recall}
return metrics