Computes the rate of events with perfectly predicted mass hypotheses and LCAS matrices over a batch.
``output_transform`` should return the following items:
``(x_pred, x_y, edge_pred, edge_y, edge_index, u_y, batch, num_graphs)``.
* ``x_pred`` must contain node prediction logits and have shape (num_nodes_in_batch, node_classes);
* ``x_y`` must contain node ground-truth class indices and have shape (num_nodes_in_batch, 1);
* ``edge_pred`` must contain edge prediction logits and have shape (num_edges_in_batch, edge_classes);
* ``edge_y`` must contain edge ground-truth class indices and have shape (num_edges_in_batch, 1);
* ``edge index`` maps edges to its nodes;
* ``u_y`` is the signal/background class (always 1 in the current setting);
* ``batch`` maps nodes to their graph;
* ``num_graphs`` is the number of graph in a batch (could be derived from ``batch`` also).
.. seealso::
`Ignite metrics <https://pytorch.org/ignite/metrics.html>`_
:param ignore_index: Class or list of classes to ignore during the computation (e.g. padding).
:type ignore_index: list[int]
:param output_transform: Function to transform engine's output to desired output.
:type output_transform: `function <https://docs.python.org/3/glossary.html#term-function>`_
:param device: ``cpu`` or ``gpu``.
:type device: str
Definition at line 205 of file metrics.py.
1.9.1