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r_precision.py
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r_precision.py
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# Copyright The PyTorch 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 torch import Tensor, tensor
from torchmetrics.functional.retrieval.r_precision import retrieval_r_precision
from torchmetrics.retrieval.base import RetrievalMetric
class RetrievalRPrecision(RetrievalMetric):
"""Computes `IR R-Precision`_.
Works with binary target data. Accepts float predictions from a model output.
Forward accepts:
- ``preds`` (float tensor): ``(N, ...)``
- ``target`` (long or bool tensor): ``(N, ...)``
- ``indexes`` (long tensor): ``(N, ...)``
``indexes``, ``preds`` and ``target`` must have the same dimension.
``indexes`` indicate to which query a prediction belongs.
Predictions will be first grouped by ``indexes`` and then `R-Precision` will be computed as the mean
of the `R-Precision` over each query.
Args:
empty_target_action:
Specify what to do with queries that do not have at least a positive ``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.
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.
Example:
>>> from torchmetrics import RetrievalRPrecision
>>> 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])
>>> p2 = RetrievalRPrecision()
>>> p2(preds, target, indexes=indexes)
tensor(0.7500)
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = False
def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
return retrieval_r_precision(preds, target)