image-match/python/py/Lib/site-packages/torchmetrics/functional/retrieval/recall.py

# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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from typing import Optional

import torch
from torch import Tensor, tensor

from torchmetrics.utilities.checks import _check_retrieval_functional_inputs


def retrieval_recall(preds: Tensor, target: Tensor, top_k: Optional[int] = None) -> Tensor:
    """Compute the recall metric for information retrieval.

    Recall is the fraction of relevant documents retrieved among all the relevant documents.

    ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``,
    ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be ``float``,
    otherwise an error is raised. If you want to measure Recall@K, ``top_k`` must be a positive integer.

    Args:
        preds: estimated probabilities of each document to be relevant.
        target: ground truth about each document being relevant or not.
        top_k: consider only the top k elements (default: `None`, which considers them all)

    Returns:
        A single-value tensor with the recall (at ``top_k``) 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 import retrieval_recall
        >>> preds = tensor([0.2, 0.3, 0.5])
        >>> target = tensor([True, False, True])
        >>> retrieval_recall(preds, target, top_k=2)
        tensor(0.5000)

    """
    preds, target = _check_retrieval_functional_inputs(preds, target)

    if top_k is None:
        top_k = preds.shape[-1]

    if not (isinstance(top_k, int) and top_k > 0):
        raise ValueError("`top_k` has to be a positive integer or None")

    if not target.sum():
        return tensor(0.0, device=preds.device)

    target_filtered = torch.where(preds > 0, target, torch.zeros_like(target))
    relevant = target_filtered[torch.argsort(preds, dim=-1, descending=True)][:top_k].sum().float()

    return relevant / target.sum()