:j ddlmZddlZddlmZddlmZdedededefd Zdeded ed edef d Z ddeded eedefd Z y))OptionalN)Tensor)"_check_retrieval_functional_inputstargetpredsdiscount_cumsumreturnctj| dd\}}}tj|tj}|j d||j |j ||z }|jddz }tj|tj}||d|d<||j|dd||zjS)aITranslated version of sklearns `_tie_average_dcg` function. Args: target: ground truth about each document relevance. preds: estimated probabilities of each document to be relevant. discount_cumsum: cumulative sum of the discount. Returns: The cumulative gain of the tied elements. T)return_inverse return_counts)dtyperdimN) torchunique zeros_likefloat32 scatter_add_tor cumsumdiffsum) rrr_invcountsrankedgroups discount_sumss {/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/retrieval/ndcg.py_tie_average_dcgr!s\\5&TRNAsF   fEMM :F 3  => f_F ]]q] !A %F$$V5==AM&vay1M!'/446M!" ] " ' ' ))top_k ignore_tiesc8dtjtj|jd|jdzz }d||d|r,|j d}||}||zj }|S|jd }t|||}|S) ayTranslated version of sklearns `_dcg_sample_scores` function. Args: target: ground truth about each document relevance. preds: estimated probabilities of each document to be relevant. top_k: consider only the top k elements ignore_ties: If True, ties are ignored. If False, ties are averaged. Returns: The cumulative gain g?)deviceg@gNT) descendingr) rlog2arangeshaper'argsortrrr!) rrr#r$discountrankingrcumulative_gainrs r _dcg_sample_scoresr0-sejjfll2.>v}}!UX[![\]HHUV--4-0#f,113 #//b/1*65/J r"ct||d\}}||jdn|}t|tr|dkDs t dt |||d}t |||d}|dk(}d||<||xx||zcc<|j 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)allow_non_binary_targetr&rz,`top_k` has to be a positive integer or NoneF)r$)rr+ isinstanceint ValueErrorr0mean)rrr#gainnormalized_gainall_irrelevants r retrieval_normalized_dcgr:Gs67uf^bcME6$}EKKO%E uc "uqyGHH feU FD(DQO%)ND._n_== 99;r")N) typingrrrtorchmetrics.utilities.checksrr!r4boolr0r:r"r r?s L*V*F*V*PV*.vfStX^4*F*F*8C=*\b*r"