Retrieval Fall-Out¶
Module Interface¶
- class torchmetrics.RetrievalFallOut(empty_target_action='pos', ignore_index=None, k=None, **kwargs)[source]
Computes Fall-out.
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 Fall-out will be computed as the mean of the Fall-out over each query.- Parameters
Specify what to do with queries that do not have at least a negative
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¶ (
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.ValueError – If
k
parameter is not None or an integer larger than 0.
Example
>>> from torchmetrics import RetrievalFallOut >>> 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]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
First concat state
indexes
,preds
andtarget
since they were stored as lists.After that, compute list of groups that will help in keeping together predictions about the same query. Finally, for each group compute the _metric if the number of negative targets is at least 1, otherwise behave as specified by self.empty_target_action.
- Return type
Functional Interface¶
- torchmetrics.functional.retrieval_fall_out(preds, target, k=None)[source]
Computes the Fall-out (for information retrieval), as explained in IR Fall-out Fall-out is the fraction of non-relevant documents retrieved among all the non-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 Fall-out@K,k
must be a positive integer.- Parameters
- Return type
- Returns
a single-value tensor with the fall-out (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_fall_out >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_fall_out(preds, target, k=2) tensor(1.)