o yi@sdddlmZmZddlZddlmZmZddlmZddlm Z ddl m Z m Z Gddde Z dS) )AnyOptionalN)Tensortensor)retrieval_fall_out)RetrievalMetric)_flexible_bincount dim_zero_catc seZdZUdZdZeed<dZeed<dZeed<   dde d e e d e e d e d df fd d Z d efddZdeded efddZZS)RetrievalFallOuta* Computes `Fall-out`_. Works with binary target data. Accepts float predictions from a model output. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` - ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)`` - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a prediction belongs As output to ``forward`` and ``compute`` the metric returns the following output: - ``fo`` (:class:`~torch.Tensor`): A tensor with the computed metric All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning, so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by ``indexes`` and then will be computed as the mean of the metric over each query. Args: empty_target_action: Specify what to do with queries that do not have at least a negative ``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: Ignore predictions where the target is equal to this number. k: consider only the top k elements for each query (default: `None`, which considers them all) kwargs: Additional keyword arguments, see :ref:`Metric kwargs` 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 RetrievalFallOut >>> 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]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) Fis_differentiablehigher_is_betterfull_state_updateposNempty_target_action ignore_indexkkwargsreturnc sDtjd||d||durt|tr|dkstd||_dS)N)rrrz(`k` has to be a positive integer or None)super__init__ isinstanceint ValueErrorr)selfrrrr __class__rS/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/retrieval/fall_out.pyrPs zRetrievalFallOut.__init__cst|j}t|jt|j}t|\}}|||}t| }g}t tj |ddtj ||ddD]6\}}d| sh|j dkrNtd|j dkr[|tdq;|j dkrg|td q;||||q;|rtfd d |DStd S) aFirst concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists. After that, compute list of groups that will help in keeping together predictions about the same query. Finally, for each group compute the `_metric` if the number of negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`. r)dimerrorzC`compute` method was provided with a query with no negative target.rg?neggcsg|]}|qSr)to).0xpredsrr sz,RetrievalFallOut.compute..)r indexesr&targettorchsortrdetachcputolistzipsplitsumrrappendr_metricstackmeanr")rr(r)indices split_sizesres mini_preds mini_targetrr%rcomputeas*       .zRetrievalFallOut.computer&r)cCst|||jdS)N)r)rr)rr&r)rrrr3szRetrievalFallOut._metric)rNN)__name__ __module__ __qualname____doc__r bool__annotations__r r strrrrrrr;r3 __classcell__rrrrr s*  3  "r )typingrrr*rr*torchmetrics.functional.retrieval.fall_outrtorchmetrics.retrieval.basertorchmetrics.utilities.datarr r rrrrs