o yi @sbddlmZddlZddlmZddlmZdedefddZd d eded eedefd d ZdS))OptionalN)Tensor)"_check_retrieval_functional_inputstargetreturncCs0ttj|jd|jdd}||jddS)z5Computes Discounted Cumulative Gain for input tensor.)deviceg@)dim)torchlog2arangeshapersum)rdenomrZ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/retrieval/ndcg.py_dcgs rpredskcCst||dd\}}|dur|jdn|}t|tr|dks!td|tj|dddd|}tj|dddd|}t|}t|}|dk}d||<||||<| S) aComputes `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. Args: preds: estimated probabilities of each document to be relevant. target: ground truth about each document relevance. k: consider only the top k elements (default: ``None``, which considers them all) Return: 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) T)allow_non_binary_targetNrrz(`k` has to be a positive integer or None)r descending)r) rr isinstanceint ValueErrorr argsortsortrmean)rrr sorted_target ideal_target ideal_dcg target_dcgall_irrelevantrrrretrieval_normalized_dcgsr")N) typingrr rtorchmetrics.utilities.checksrrrr"rrrrs   $