Retrieval Mean Average Precision (MAP)¶
Module Interface¶
- class torchmetrics.RetrievalMAP(empty_target_action='neg', ignore_index=None, compute_on_step=None, **kwargs)[source]
Computes Mean Average 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 MAP will be computed as the mean of the Average Precisions 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.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 oferror
,skip
,neg
orpos
.ValueError – If
ignore_index
is not None or an integer.
Example
>>> from torchmetrics import RetrievalMAP >>> 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]) >>> rmap = RetrievalMAP() >>> rmap(preds, target, indexes=indexes) tensor(0.7917)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.retrieval_average_precision(preds, target)[source]
Computes average precision (for information retrieval), as explained in IR Average precision.
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 average precision (AP) of the predictions
preds
w.r.t. the labelstarget
.
Example
>>> from torchmetrics.functional import retrieval_average_precision >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_average_precision(preds, target) tensor(0.8333)