Coverage Error¶
Module Interface¶
- class torchmetrics.classification.MultilabelCoverageError(num_labels, ignore_index=None, validate_args=True, **kwargs)[source]
Computes multilabel coverage error [1]. The score measure how far we need to go through the ranked scores to cover all true labels. The best value is equal to the average number of labels in the target tensor per sample.
Accepts the following input tensors:
preds
(float tensor):(N, C, ...)
. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.target
(int tensor):(N, C, ...)
. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).
Additional dimension
...
will be flattened into the batch dimension.- Parameters
Example
>>> from torchmetrics.classification import MultilabelCoverageError >>> _ = torch.manual_seed(42) >>> preds = torch.rand(10, 5) >>> target = torch.randint(2, (10, 5)) >>> metric = MultilabelCoverageError(num_labels=5) >>> metric(preds, target) tensor(3.9000)
References
[1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
Override this method to compute the final metric value from state variables synchronized across the distributed backend.
- Return type
Functional Interface¶
- torchmetrics.functional.classification.multilabel_coverage_error(preds, target, num_labels, ignore_index=None, validate_args=True)[source]
Computes multilabel coverage error [1]. The score measure how far we need to go through the ranked scores to cover all true labels. The best value is equal to the average number of labels in the target tensor per sample.
Accepts the following input tensors:
preds
(float tensor):(N, C, ...)
. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.target
(int tensor):(N, C, ...)
. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).
Additional dimension
...
will be flattened into the batch dimension.- Parameters
Example
>>> from torchmetrics.functional.classification import multilabel_coverage_error >>> _ = torch.manual_seed(42) >>> preds = torch.rand(10, 5) >>> target = torch.randint(2, (10, 5)) >>> multilabel_coverage_error(preds, target, num_labels=5) tensor(3.9000)
References
[1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US.
- Return type