Retrieval Normalized DCG¶
Module Interface¶
- class torchmetrics.RetrievalNormalizedDCG(empty_target_action='neg', ignore_index=None, k=None, **kwargs)[source]
Computes Normalized Discounted Cumulative Gain.
Works with binary or positive integer target data. Accepts float predictions from a model output.
Forward accepts:
preds
(float tensor):(N, ...)
target
(long, int, bool or float 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 Normalized Discounted Cumulative Gain will be computed as the mean of the Normalized Discounted Cumulative Gain 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 RetrievalNormalizedDCG >>> 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]) >>> ndcg = RetrievalNormalizedDCG() >>> ndcg(preds, target, indexes=indexes) tensor(0.8467)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.retrieval_normalized_dcg(preds, target, k=None)[source]
Computes Normalized Discounted Cumulative Gain (for information retrieval).
preds
andtarget
should be of the same shape and live on the same device.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 nDCG of the predictions
preds
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_normalized_dcg >>> preds = torch.tensor([.1, .2, .3, 4, 70]) >>> target = torch.tensor([10, 0, 0, 1, 5]) >>> retrieval_normalized_dcg(preds, target) tensor(0.6957)