Coverage Error

Module Interface

class torchmetrics.CoverageError(**kwargs)

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.

Parameters

kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics import CoverageError
>>> _ = torch.manual_seed(42)
>>> preds = torch.rand(10, 5)
>>> target = torch.randint(2, (10, 5))
>>> metric = CoverageError()
>>> 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()

Computes the multilabel coverage error.

Return type

Tensor

update(preds, target, sample_weight=None)

Parameters

• preds (Tensor) – tensor of shape [N,L] where N is the number of samples and L is the number of labels. Should either be probabilities of the positive class or corresponding logits

• target (Tensor) – tensor of shape [N,L] where N is the number of samples and L is the number of labels. Should only contain binary labels.

• sample_weight (Optional[Tensor]) – tensor of shape N where N is the number of samples. How much each sample should be weighted in the final score.

Return type

None

class torchmetrics.classification.MultilabelCoverageError(num_labels, ignore_index=None, validate_args=True, **kwargs)

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

• num_labels (int) – Integer specifing the number of labels

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

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()

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type

Tensor

update(preds, target)

Override this method to update the state variables of your metric class.

Return type

None

Functional Interface

torchmetrics.functional.coverage_error(preds, target, sample_weight=None)

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.

Parameters

• preds (Tensor) – tensor of shape [N,L] where N is the number of samples and L is the number of labels. Should either be probabilities of the positive class or corresponding logits

• target (Tensor) – tensor of shape [N,L] where N is the number of samples and L is the number of labels. Should only contain binary labels.

• sample_weight (Optional[Tensor]) – tensor of shape N where N is the number of samples. How much each sample should be weighted in the final score.

Example

>>> from torchmetrics.functional import coverage_error
>>> _ = torch.manual_seed(42)
>>> preds = torch.rand(10, 5)
>>> target = torch.randint(2, (10, 5))
>>> coverage_error(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.

Return type

Tensor

torchmetrics.functional.classification.multilabel_coverage_error(preds, target, num_labels, ignore_index=None, validate_args=True)

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

• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_labels (int) – Integer specifing the number of labels

• ignore_index (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation

• validate_args (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

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

Tensor