Retrieval Recall¶
Module Interface¶
- class torchmetrics.RetrievalRecall(empty_target_action='neg', ignore_index=None, k=None, **kwargs)[source]
Computes IR Recall.
Works with binary target data. Accepts float predictions from a model output.
As input to
forward
andupdate
the metric accepts the following input:preds
(Tensor
): A float tensor of shape(N, ...)
target
(Tensor
): A long or bool tensor of shape(N, ...)
indexes
(Tensor
): A long tensor of shape(N, ...)
which indicate to which query a prediction belongs
As output to
forward
andcompute
the metric returns the following output:r2
(Tensor
): A single-value tensor with the recall (atk
) of the predictionspreds
w.r.t. the labelstarget
All
indexes
,preds
andtarget
must have the same dimension and will be flatten at the beginning, so that for example, a tensor of shape(N, M)
is treated as(N * M, )
. Predictions will be first grouped byindexes
and then will be computed as the mean of the metric 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.k¶ (
Optional
[int
]) – consider only the top k elements for each query (default: None, which considers them all)kwargs¶ (
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.ValueError – If
k
parameter is not None or an integer larger than 0.
Example
>>> from torchmetrics import RetrievalRecall >>> 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]) >>> r2 = RetrievalRecall(k=2) >>> r2(preds, target, indexes=indexes) tensor(0.7500)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.retrieval_recall(preds, target, k=None)[source]
Computes the recall metric (for information retrieval). Recall is the fraction of relevant documents retrieved among all the relevant documents.
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. If you want to measure Recall@K,k
must be a positive integer.- Parameters
- Return type
- Returns
a single-value tensor with the recall (at
k
) of the predictionspreds
w.r.t. the labelstarget
.- Raises
ValueError – If
k
parameter is not None or an integer larger than 0
Example
>>> from torchmetrics.functional import retrieval_recall >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_recall(preds, target, k=2) tensor(0.5000)