torch_geometric.metrics.LinkPredDiversity
- class LinkPredDiversity(k: int, category: Tensor)[source]
Bases:
_LinkPredMetricA link prediction metric to compute the Diversity @ \(k\) of predictions according to item categories.
Diversity is computed as
\[div_{u@k} = 1 - \left( \frac{1}{k \cdot (k-1)} \right) \sum_{i \neq j} sim(i, j)\]where
\[\begin{split}sim(i,j) = \begin{cases} 1 & \quad \text{if } i,j \text{ share category,}\\ 0 & \quad \text{otherwise.} \end{cases}\end{split}\]which measures the pair-wise inequality of recommendations according to item categories.
- Parameters:
k (int) – The number of top-\(k\) predictions to evaluate against.
category (torch.Tensor) – A vector that assigns each destination node to a specific category.
- update(pred_index_mat: Tensor, edge_label_index: Union[Tensor, Tuple[Tensor, Tensor]], edge_label_weight: Optional[Tensor] = None) None[source]
Updates the state variables based on the current mini-batch prediction.
update()can be repeated multiple times to accumulate the results of successive predictions, e.g., inside a mini-batch training or evaluation loop.- Parameters:
pred_index_mat (torch.Tensor) – The top-\(k\) predictions of every example in the mini-batch with shape
[batch_size, k].edge_label_index (torch.Tensor) – The ground-truth indices for every example in the mini-batch, given in COO format of shape
[2, num_ground_truth_indices].edge_label_weight (torch.Tensor, optional) – The weight of the ground-truth indices for every example in the mini-batch of shape
[num_ground_truth_indices]. If given, needs to be a vector of positive values. Required for weighted metrics, ignored otherwise. (default:None)
- Return type: