Retrieval Precision¶
Module Interface¶
- class torchmetrics.RetrievalPrecision(empty_target_action='neg', ignore_index=None, k=None, adaptive_k=False, **kwargs)[source]
Computes IR 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 Precision will be computed as the mean of the 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.k¶ (
Optional
[int
]) – consider only the top k elements for each query (default:None
, which considers them all)adaptive_k¶ (
bool
) – adjustk
tomin(k, number of documents)
for each querykwargs¶ (
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
is not None or an integer larger than 0.ValueError – If
adaptive_k
is not boolean.
Example
>>> from torchmetrics import RetrievalPrecision >>> 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 = RetrievalPrecision(k=2) >>> p2(preds, target, indexes=indexes) tensor(0.5000)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.retrieval_precision(preds, target, k=None, adaptive_k=False)[source]
Computes the precision metric (for information retrieval). Precision is the fraction of relevant documents among all the retrieved 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
preds¶ (
Tensor
) – estimated probabilities of each document to be relevant.target¶ (
Tensor
) – ground truth about each document being relevant or not.k¶ (
Optional
[int
]) – consider only the top k elements (default:None
, which considers them all)adaptive_k¶ (
bool
) – adjust k to min(k, number of documents) for each query
- Return type
- Returns
a single-value tensor with the precision (at
k
) of the predictionspreds
w.r.t. the labelstarget
.- Raises
ValueError – If
k
is not None or an integer larger than 0.ValueError – If
adaptive_k
is not boolean.
Example
>>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_precision(preds, target, k=2) tensor(0.5000)