Retrieval R-Precision¶
Module Interface¶
- class torchmetrics.RetrievalRPrecision(empty_target_action='neg', ignore_index=None, **kwargs)[source]
Computes IR R-Precision.
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 R-Precision will be computed as the mean of the R-Precision 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¶ (
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 RetrievalRPrecision >>> 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]) >>> p2 = RetrievalRPrecision() >>> p2(preds, target, indexes=indexes) tensor(0.7500)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.retrieval_r_precision(preds, target)[source]
Computes the r-precision metric (for information retrieval). R-Precision is the fraction of relevant documents among all the top
k
retrieved documents wherek
is equal to the total number of 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 Precision@K,k
must be a positive integer.- Parameters
- Return type
- Returns
a single-value tensor with the r-precision of the predictions
preds
w.r.t. the labelstarget
.
Example
>>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_r_precision(preds, target) tensor(0.5000)