Shortcuts

Label Ranking Average Precision

Module Interface

class torchmetrics.LabelRankingAveragePrecision(**kwargs)[source]

Computes label ranking average precision score for multilabel data [1].

The score is the average over each ground truth label assigned to each sample of the ratio of true vs. total labels with lower score. Best score is 1.

Parameters

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

Example

>>> from torchmetrics import LabelRankingAveragePrecision
>>> _ = torch.manual_seed(42)
>>> preds = torch.rand(10, 5)
>>> target = torch.randint(2, (10, 5))
>>> metric = LabelRankingAveragePrecision()
>>> metric(preds, target)
tensor(0.7744)

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]

Computes the label ranking average precision score.

Return type

Tensor

update(preds, target, sample_weight=None)[source]
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.MultilabelRankingAveragePrecision(num_labels, ignore_index=None, validate_args=True, **kwargs)[source]

Computes label ranking average precision score for multilabel data [1]. The score is the average over each ground truth label assigned to each sample of the ratio of true vs. total labels with lower score. Best score is 1.

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 MultilabelRankingAveragePrecision
>>> _ = torch.manual_seed(42)
>>> preds = torch.rand(10, 5)
>>> target = torch.randint(2, (10, 5))
>>> metric = MultilabelRankingAveragePrecision(num_labels=5)
>>> metric(preds, target)
tensor(0.7744)

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

Tensor

update(preds, target)[source]

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

Return type

None

Functional Interface

torchmetrics.functional.label_ranking_average_precision(preds, target, sample_weight=None)[source]

Computes label ranking average precision score for multilabel data [1]. The score is the average over each ground truth label assigned to each sample of the ratio of true vs. total labels with lower score. Best score is 1.

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 label_ranking_average_precision
>>> _ = torch.manual_seed(42)
>>> preds = torch.rand(10, 5)
>>> target = torch.randint(2, (10, 5))
>>> label_ranking_average_precision(preds, target)
tensor(0.7744)

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_ranking_average_precision(preds, target, num_labels, ignore_index=None, validate_args=True)[source]

Computes label ranking average precision score for multilabel data [1]. The score is the average over each ground truth label assigned to each sample of the ratio of true vs. total labels with lower score. Best score is 1.

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_ranking_average_precision
>>> _ = torch.manual_seed(42)
>>> preds = torch.rand(10, 5)
>>> target = torch.randint(2, (10, 5))
>>> multilabel_ranking_average_precision(preds, target, num_labels=5)
tensor(0.7744)

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

Read the Docs v: latest
Versions
latest
stable
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
refactor-structure
Downloads
pdf
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.