Retrieval Normalized DCG

Module Interface

class torchmetrics.RetrievalNormalizedDCG(empty_target_action='neg', ignore_index=None, k=None, **kwargs)

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 and target must have the same dimension. indexes indicate to which query a prediction belongs. Predictions will be first grouped by indexes and then Normalized Discounted Cumulative Gain will be computed as the mean of the Normalized Discounted Cumulative Gain over each query.

Parameters

• empty_target_action (str) –

  Specify what to do with queries that do not have at least a positive target. Choose from:

  • 'neg': those queries count as 0.0 (default)

  • 'pos': those queries count as 1.0

  • 'skip': skip those queries; if all queries are skipped, 0.0 is returned

  • 'error': raise a ValueError

• 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 of error, skip, neg or pos.

• 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)

Computes Normalized Discounted Cumulative Gain (for information retrieval).

preds and target should be of the same shape and live on the same device. target must be either bool or integers and preds must be float, otherwise an error is raised.

Parameters

• preds (Tensor) – estimated probabilities of each document to be relevant.

• target (Tensor) – ground truth about each document relevance.

• k (Optional[int]) – consider only the top k elements (default: None, which considers them all)

Return type

Tensor

Returns

a single-value tensor with the nDCG of the predictions preds w.r.t. the labels target.

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)