Retrieval Mean Reciprocal Rank (MRR)¶
Module Interface¶
- class torchmetrics.RetrievalMRR(empty_target_action='neg', ignore_index=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
andtarget
must have the same dimension.indexes
indicate to which query a prediction belongs. Predictions will be first grouped byindexes
and then MRR will be computed as the mean of the Reciprocal Rank over each query.- Parameters
Specify what to do with queries that do not have at least a positive
target
. Choose from:'neg'
: those queries count as0.0
(default)'pos'
: those queries count as1.0
'skip'
: skip those queries; if all queries are skipped,0.0
is returned'error'
: raise aValueError
ignore_index¶ (
Optional
[int
]) – Ignore predictions where the target is equal to this number.kwargs¶ (
Dict
[str
,Any
]) – Additional keyword arguments, see Advanced metric settings for more info.
- Raises
ValueError – If
empty_target_action
is not one oferror
,skip
,neg
orpos
.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
andtarget
should be of the same shape and live on the same device. If notarget
isTrue
, 0 is returned.target
must be either bool or integers andpreds
must befloat
, otherwise an error is raised.- Parameters
- Return type
- Returns
a single-value tensor with the reciprocal rank (RR) of the predictions
preds
wrt the labelstarget
.
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)