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hit_rate.py
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hit_rate.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 typing import Any, Dict, Optional
from torch import Tensor, tensor
from torchmetrics.functional.retrieval.hit_rate import retrieval_hit_rate
from torchmetrics.retrieval.base import RetrievalMetric
class RetrievalHitRate(RetrievalMetric):
"""Computes `IR HitRate`.
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 the `Hit Rate` will be computed as the mean
of the `Hit Rate` 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.
k: consider only the top k elements for each query (default: ``None``, which considers them all)
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
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.
ValueError:
If ``k`` parameter is not `None` or an integer larger than 0.
Example:
>>> from torchmetrics import RetrievalHitRate
>>> 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([True, False, False, False, True, False, True])
>>> hr2 = RetrievalHitRate(k=2)
>>> hr2(preds, target, indexes=indexes)
tensor(0.5000)
"""
higher_is_better = True
def __init__(
self,
empty_target_action: str = "neg",
ignore_index: Optional[int] = None,
k: Optional[int] = None,
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(
empty_target_action=empty_target_action,
ignore_index=ignore_index,
compute_on_step=compute_on_step,
**kwargs,
)
if (k is not None) and not (isinstance(k, int) and k > 0):
raise ValueError("`k` has to be a positive integer or None")
self.k = k
def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
return retrieval_hit_rate(preds, target, k=self.k)