image-match/python/py/Lib/site-packages/torchmetrics/functional/retrieval/auroc.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.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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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# limitations under the License.
from typing import Optional

from torch import Tensor, tensor

from torchmetrics.functional.classification.auroc import binary_auroc
from torchmetrics.utilities.checks import _check_retrieval_functional_inputs


def retrieval_auroc(
    preds: Tensor, target: Tensor, top_k: Optional[int] = None, max_fpr: Optional[float] = None
) -> Tensor:
    """Compute area under the receiver operating characteristic curve (AUROC) for information retrieval.

    ``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.

    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)
        max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``.

    Return:
        a single-value tensor with the auroc value of the predictions ``preds`` w.r.t. the labels ``target``.

    Raises:
        ValueError:
            If ``top_k`` is not ``None`` or an integer larger than 0.

    Example:
        >>> from torchmetrics.functional.retrieval import retrieval_auroc
        >>> preds = tensor([0.2, 0.3, 0.5])
        >>> target = tensor([True, False, True])
        >>> retrieval_auroc(preds, target)
        tensor(0.5000)

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

    top_k = top_k or 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")

    top_k_idx = preds.topk(min(top_k, preds.shape[-1]), sorted=True, dim=-1)[1]
    target = target[top_k_idx]
    if (0 not in target) or (1 not in target):
        return tensor(0.0, device=preds.device, dtype=preds.dtype)

    preds = preds[top_k_idx]
    return binary_auroc(preds, target.int(), max_fpr=max_fpr)