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- # 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,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- from typing import Optional
- import torch
- from torch import Tensor, tensor
- from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
- def retrieval_average_precision(preds: Tensor, target: Tensor, top_k: Optional[int] = None) -> Tensor:
- """Compute average precision (for information retrieval), as explained in `IR Average precision`_.
- ``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)
- Return:
- a single-value tensor with the average precision (AP) 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_average_precision
- >>> preds = tensor([0.2, 0.3, 0.5])
- >>> target = tensor([True, False, True])
- >>> retrieval_average_precision(preds, target)
- tensor(0.8333)
- """
- 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(f"Argument ``top_k`` has to be a positive integer or None, but got {top_k}.")
- target = torch.where(preds > 0, target, torch.zeros_like(target))
- target = target[preds.topk(min(top_k, preds.shape[-1]), sorted=True, dim=-1)[1]]
- if not target.sum():
- return tensor(0.0, device=preds.device)
- positions = torch.arange(1, len(target) + 1, device=target.device, dtype=torch.float32)[target > 0]
- return torch.div((torch.arange(len(positions), device=positions.device, dtype=torch.float32) + 1), positions).mean()
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