Coverage Error¶
Module Interface¶
- class torchmetrics.classification.MultilabelCoverageError(num_labels, ignore_index=None, validate_args=True, **kwargs)[source]
Computes Multilabel coverage error. 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.
As input to
forward
andupdate
the metric accepts the following input:preds
(Tensor
): A float tensor of shape(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
(Tensor
): An int tensor of shape(N, C, ...)
. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if ignore_index is specified).
Note
Additional dimension
...
will be flattened into the batch dimension.As output to
forward
andcompute
the metric returns the following output:mlce
(Tensor
): A tensor containing the multilabel coverage error.
- Parameters
Example
>>> from torchmetrics.classification import MultilabelCoverageError >>> _ = torch.manual_seed(42) >>> preds = torch.rand(10, 5) >>> target = torch.randint(2, (10, 5)) >>> mlce = MultilabelCoverageError(num_labels=5) >>> mlce(preds, target) tensor(3.9000)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
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