a  d @sZddlmZddlZddlmZddlmZeedddZd eeeeedd d ZdS) )OptionalN)Tensor)"_check_retrieval_functional_inputs)targetreturncCs0ttj|jd|jdd}||jddS)z4Compute Discounted Cumulative Gain for input tensor.)deviceg@)dim)torchlog2Zarangeshapersum)rZdenomro/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/retrieval/ndcg.py_dcgs r)predsrtop_krcCst||dd\}}|dur$|jdn|}t|tr:|dksBtd|tj|dddd|}tj|dddd|}t|}t|}|dk}d||<||||<| S) aCompute `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. top_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 ``top_k`` parameter is not `None` or an integer larger than 0 Example: >>> from torchmetrics.functional.retrieval 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)Zallow_non_binary_targetNrrz,`top_k` has to be a positive integer or None)r descending)r) rr isinstanceint ValueErrorr Zargsortsortrmean)rrrZ sorted_targetZ ideal_targetZ ideal_dcgZ target_dcgZall_irrelevantrrrretrieval_normalized_dcgsr)N) typingrr rZtorchmetrics.utilities.checksrrrrrrrrs