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Retrieval Mean Reciprocal Rank (MRR)

Module Interface

class torchmetrics.RetrievalMRR(empty_target_action='neg', ignore_index=None, compute_on_step=None, **kwargs)[source]

Computes Mean Reciprocal Rank.

Works with binary target data. Accepts float predictions from a model output.

Forward accepts

  • preds (float tensor): (N, ...)

  • target (long or bool tensor): (N, ...)

  • indexes (long tensor): (N, ...)

indexes, preds and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then MRR will be computed as the mean of the Reciprocal Rank over each query.

Parameters
  • empty_target_action (str) –

    Specify what to do with queries that do not have at least a positive target. Choose from:

    • 'neg': those queries count as 0.0 (default)

    • 'pos': those queries count as 1.0

    • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

    • 'error': raise a ValueError

  • ignore_index (Optional[int]) – Ignore predictions where the target is equal to this number.

  • compute_on_step (Optional[bool]) –

    Forward only calls update() and returns None if this is set to False.

    Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.

  • kwargs (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Raises
  • ValueError – If empty_target_action is not one of error, skip, neg or pos.

  • ValueError – If ignore_index is not None or an integer.

Example

>>> from torchmetrics import RetrievalMRR
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> mrr = RetrievalMRR()
>>> mrr(preds, target, indexes=indexes)
tensor(0.7500)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.retrieval_reciprocal_rank(preds, target)[source]

Computes reciprocal rank (for information retrieval). See Mean Reciprocal Rank

preds and target should be of the same shape and live on the same device. If no target is True, 0 is returned. target must be either bool or integers and preds must be float, otherwise an error is raised.

Parameters
  • preds (Tensor) – estimated probabilities of each document to be relevant.

  • target (Tensor) – ground truth about each document being relevant or not.

Return type

Tensor

Returns

a single-value tensor with the reciprocal rank (RR) of the predictions preds wrt the labels target.

Example

>>> from torchmetrics.functional import retrieval_reciprocal_rank
>>> preds = torch.tensor([0.2, 0.3, 0.5])
>>> target = torch.tensor([False, True, False])
>>> retrieval_reciprocal_rank(preds, target)
tensor(0.5000)
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