o .wik @sddlmZmZddlmZmZmZmZmZddl Z ddl m Z m Z ddl m Z ddlmZddlmZddlmZmZ dd e d ee d efd eede fddZGdddeeZdS))ABCabstractmethod)AnyCallableListOptionalUnionN)Tensortensor)Literal)Metric)_check_retrieval_inputs)_flexible_bincount dim_zero_catmeanvalues aggregationrmedianminmaxdimreturncCs|dkr|dur |S|j|dS|dkr%|dur|S|j|djS|dkr8|dur1|S|j|djS|dkrK|durD|S|j|djS|||dS)z9Aggregate the final retrieval values into a single value.rNrrrr)rrrrr)rrrrX/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/retrieval/base.py_retrieval_aggregates rc seZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed<e e ed < dd e de e deedefdedd f fdd Zde d e de dd fddZde fddZede d e de fddZZS)RetrievalMetricaQ 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 .. hint:: The ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flattened to single dimension once provided. .. attention:: Predictions will be first grouped by ``indexes`` and then the real metric, defined by overriding the `_metric` method, will be computed as the mean of the scores over each query. As output to ``forward`` and ``compute`` the metric returns the following output: - ``metric`` (:class:`~torch.Tensor`): A tensor as computed by ``_metric`` if the number of positive targets is at least 1, otherwise behave as specified by ``self.empty_target_action``. Args: empty_target_action: Specify what to do with queries that do not have at least a positive or negative (depend on metric) 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. aggregation: Specify how to aggregate over indexes. Can either a custom callable function that takes in a single tensor and returns a scalar value or one of the following strings: - ``'mean'``: average value is returned - ``'median'``: median value is returned - ``'max'``: max value is returned - ``'min'``: min value is returned 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. Fis_differentiableThigher_is_betterfull_state_updateindexespredstargetnegNrempty_target_action ignore_indexrrkwargsrc stjd i|d|_d}||vrtd|d||_|dur*t|ts*td||_|dvs=t|s=td|d||_ |j d gdd |j d gdd |j d gdd dS)NF)errorskipr$posz7Argument `empty_target_action` received a wrong value `z`.z3Argument `ignore_index` must be an integer or None.rzArgument `aggregation` must be one of `mean`, `median`, `min`, `max` or a custom callable functionwhich takes tensor of values, but got .r!)defaultdist_reduce_fxr"r#r) super__init__allow_non_binary_target ValueErrorr% isinstanceintr&callabler add_state)selfr%r&rr'empty_target_action_options __class__rrr/is&zRetrievalMetric.__init__cCsT|durtdt||||j|jd\}}}|j||j||j|dS)zGCheck shape, check and convert dtypes, flatten and add to accumulators.Nz!Argument `indexes` cannot be None)r0r&)r1r r0r&r!appendr"r#)r6r"r#r!rrrupdates  zRetrievalMetric.updatecst|j}t|jt|j}t|\}}|||}t| }g}t tj |ddtj ||ddD]4\}}| sf|j dkrLtd|j dkrY|tdq;|j dkre|tdq;||||q;|rttfd d |D|jStdS) aFirst concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists. After that, compute list of groups that will help in keeping together predictions about the same query. Finally, for each group compute the ``_metric`` if the number of positive targets is at least 1, otherwise behave as specified by ``self.empty_target_action``. rrr(zC`compute` method was provided with a query with no positive target.r*g?r$gcsg|]}|qSr)to).0xr"rr sz+RetrievalMetric.compute..)rr!r"r#torchsortrdetachcputolistzipsplitsumr%r1r:r _metricrstackrr<)r6r!r#indices split_sizesres mini_preds mini_targetrr?rcomputes.        zRetrievalMetric.computecCsdS)zCompute a metric over a predictions and target of a single group. This method should be overridden by subclasses. Nr)r6r"r#rrrrIszRetrievalMetric._metric)r$Nr)__name__ __module__ __qualname____doc__rbool__annotations__rr rr strrr3rr rrr/r;rPrrI __classcell__rrr8rr+s4  5      % r)rN)abcrrtypingrrrrrrAr r typing_extensionsr torchmetricsr torchmetrics.utilities.checksr torchmetrics.utilities.datarrr3rrrrrrs(