Merge branch 'master' into convert_classification_wrapper_to_metrics

CHANGELOG.md

@@ -23,6 +23,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added warning to `PearsonCorrCoeff` if input has a very small variance for its given dtype ([#1926](https://github.com/Lightning-AI/torchmetrics/pull/1926))
 
 
+- Added `top_k` argument to `RetrievalMRR` in retrieval package ([#1961](https://github.com/Lightning-AI/torchmetrics/pull/1961))
+
+
 ### Changed
 
 - Changed all non-task specific classification metrics to be true subtypes of `Metric` ([#1963](https://github.com/Lightning-AI/torchmetrics/pull/1963))

src/torchmetrics/functional/retrieval/reciprocal_rank.py

@@ -11,13 +11,15 @@
 # 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_reciprocal_rank(preds: Tensor, target: Tensor) -> Tensor:
+def retrieval_reciprocal_rank(preds: Tensor, target: Tensor, top_k: Optional[int] = None) -> Tensor:
     """Compute reciprocal rank (for information retrieval). See `Mean Reciprocal Rank`_.
 
     ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``,
@@ -27,10 +29,15 @@ def retrieval_reciprocal_rank(preds: Tensor, target: Tensor) -> Tensor:
     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 reciprocal rank (RR) of the predictions ``preds`` wrt the labels ``target``.
 
+    Raises:
+        ValueError:
+            If ``top_k`` is not ``None`` or an integer larger than 0.
+
     Example:
         >>> from torchmetrics.functional.retrieval import retrieval_reciprocal_rank
         >>> preds = torch.tensor([0.2, 0.3, 0.5])
@@ -41,9 +48,13 @@ def retrieval_reciprocal_rank(preds: Tensor, target: Tensor) -> Tensor:
     """
     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 = 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)
 
-    target = target[torch.argsort(preds, dim=-1, descending=True)]
     position = torch.nonzero(target).view(-1)
     return 1.0 / (position[0] + 1.0)

src/torchmetrics/retrieval/average_precision.py

@@ -38,8 +38,8 @@ class RetrievalMAP(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``rmap`` (:class:`~torch.Tensor`): A tensor with the mean average precision of the predictions ``preds``
-      w.r.t. the labels ``target``
+    - ``map@k`` (:class:`~torch.Tensor`): A single-value tensor with the mean average precision (MAP)
+      of the predictions ``preds`` w.r.t. the labels ``target``.
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
     so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
@@ -54,9 +54,8 @@ class RetrievalMAP(RetrievalMetric):
             - ``'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.
-        top_k: consider only the top k elements for each query (default: ``None``, which considers them all)
+        ignore_index: Ignore predictions where the target is equal to this number.
+        top_k: Consider only the top k elements for each query (default: ``None``, which considers them all)
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -65,7 +64,7 @@ class RetrievalMAP(RetrievalMetric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``top_k`` is not ``None`` or an integer larger than 0.
+            If ``top_k`` is not ``None`` or not an integer greater than 0.
 
     Example:
         >>> from torch import tensor

src/torchmetrics/retrieval/fall_out.py

@@ -40,7 +40,7 @@ class RetrievalFallOut(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``fo`` (:class:`~torch.Tensor`): A tensor with the computed metric
+    - ``fo@k`` (:class:`~torch.Tensor`): A tensor with the computed metric
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
     so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
@@ -55,9 +55,8 @@ class RetrievalFallOut(RetrievalMetric):
             - ``'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.
-        top_k: consider only the top k elements for each query (default: `None`, which considers them all)
+        ignore_index: Ignore predictions where the target is equal to this number.
+        top_k: Consider only the top k elements for each query (default: `None`, which considers them all)
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -66,7 +65,7 @@ class RetrievalFallOut(RetrievalMetric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``top_k`` parameter is not `None` or an integer larger than 0.
+            If ``top_k`` is not ``None`` or not an integer greater than 0.
 
     Example:
         >>> from torchmetrics.retrieval import RetrievalFallOut

src/torchmetrics/retrieval/hit_rate.py

@@ -38,7 +38,7 @@ class RetrievalHitRate(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``hr2`` (:class:`~torch.Tensor`): A single-value tensor with the hit rate (at ``top_k``) of the predictions
+    - ``hr@k`` (:class:`~torch.Tensor`): A single-value tensor with the hit rate (at ``top_k``) of the predictions
       ``preds`` w.r.t. the labels ``target``
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
@@ -55,9 +55,8 @@ class RetrievalHitRate(RetrievalMetric):
             - ``'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.
-        top_k: consider only the top k elements for each query (default: ``None``, which considers them all)
+        ignore_index: Ignore predictions where the target is equal to this number.
+        top_k: Consider only the top k elements for each query (default: ``None``, which considers them all)
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -66,7 +65,7 @@ class RetrievalHitRate(RetrievalMetric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``top_k`` parameter is not `None` or an integer larger than 0.
+            If ``top_k`` is not ``None`` or not an integer greater than 0.
 
     Example:
         >>> from torch import tensor

src/torchmetrics/retrieval/ndcg.py

@@ -38,7 +38,7 @@ class RetrievalNormalizedDCG(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``ndcg`` (:class:`~torch.Tensor`): A single-value tensor with the nDCG of the predictions
+    - ``ndcg@k`` (:class:`~torch.Tensor`): A single-value tensor with the nDCG of the predictions
       ``preds`` w.r.t. the labels ``target``
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
@@ -55,9 +55,8 @@ class RetrievalNormalizedDCG(RetrievalMetric):
             - ``'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.
-        top_k: consider only the top k elements for each query (default: ``None``, which considers them all)
+        ignore_index: Ignore predictions where the target is equal to this number.
+        top_k: Consider only the top k elements for each query (default: ``None``, which considers them all)
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -66,7 +65,7 @@ class RetrievalNormalizedDCG(RetrievalMetric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``top_k`` parameter is not `None` or an integer larger than 0.
+            If ``top_k`` is not ``None`` or not an integer greater than 0.
 
     Example:
         >>> from torch import tensor

src/torchmetrics/retrieval/precision.py

@@ -38,7 +38,7 @@ class RetrievalPrecision(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``p2`` (:class:`~torch.Tensor`): A single-value tensor with the precision (at ``top_k``) of the predictions
+    - ``p@k`` (:class:`~torch.Tensor`): A single-value tensor with the precision (at ``top_k``) of the predictions
       ``preds`` w.r.t. the labels ``target``
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
@@ -54,10 +54,9 @@ class RetrievalPrecision(RetrievalMetric):
             - ``'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.
-        top_k: consider only the top k elements for each query (default: ``None``, which considers them all)
-        adaptive_k: adjust ``top_k`` to ``min(k, number of documents)`` for each query
+        ignore_index: Ignore predictions where the target is equal to this number.
+        top_k: Consider only the top k elements for each query (default: ``None``, which considers them all)
+        adaptive_k: Adjust ``top_k`` to ``min(k, number of documents)`` for each query
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -66,7 +65,7 @@ class RetrievalPrecision(RetrievalMetric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``top_k`` is not `None` or an integer larger than 0.
+            If ``top_k`` is not ``None`` or not an integer greater than 0.
         ValueError:
             If ``adaptive_k`` is not boolean.
 

src/torchmetrics/retrieval/precision_recall_curve.py

@@ -108,7 +108,7 @@ class RetrievalPrecisionRecallCurve(Metric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``max_k`` parameter is not `None` or an integer larger than 0.
+            If ``max_k`` parameter is not `None` or not an integer larger than 0.
 
     Example:
         >>> from torch import tensor

src/torchmetrics/retrieval/r_precision.py

@@ -38,7 +38,7 @@ class RetrievalRPrecision(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``p2`` (:class:`~torch.Tensor`): A single-value tensor with the r-precision of the predictions ``preds``
+    - ``rp`` (:class:`~torch.Tensor`): A single-value tensor with the r-precision of the predictions ``preds``
       w.r.t. the labels ``target``.
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
@@ -54,8 +54,7 @@ class RetrievalRPrecision(RetrievalMetric):
             - ``'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.
+        ignore_index: Ignore predictions where the target is equal to this number.
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:

src/torchmetrics/retrieval/recall.py

@@ -38,7 +38,7 @@ class RetrievalRecall(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``r2`` (:class:`~torch.Tensor`): A single-value tensor with the recall (at ``top_k``) of the predictions
+    - ``r@k`` (:class:`~torch.Tensor`): A single-value tensor with the recall (at ``top_k``) of the predictions
       ``preds`` w.r.t. the labels ``target``
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
@@ -55,7 +55,7 @@ class RetrievalRecall(RetrievalMetric):
             - ``'error'``: raise a ``ValueError``
 
         ignore_index: Ignore predictions where the target is equal to this number.
-        top_k: consider only the top k elements for each query (default: `None`, which considers them all)
+        top_k: Consider only the top k elements for each query (default: `None`, which considers them all)
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
     Raises:
@@ -64,7 +64,7 @@ class RetrievalRecall(RetrievalMetric):
         ValueError:
             If ``ignore_index`` is not `None` or an integer.
         ValueError:
-            If ``top_k`` parameter is not `None` or an integer larger than 0.
+            If ``top_k`` is not ``None`` or not an integer greater than 0.
 
     Example:
         >>> from torch import tensor

src/torchmetrics/retrieval/reciprocal_rank.py

@@ -11,7 +11,7 @@
 # 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, Sequence, Union
+from typing import Any, Optional, Sequence, Union
 
 from torch import Tensor
 
@@ -38,8 +38,8 @@ class RetrievalMRR(RetrievalMetric):
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``mrr`` (:class:`~torch.Tensor`): A single-value tensor with the reciprocal rank (RR) of the predictions
-      ``preds`` w.r.t. the labels ``target``
+    - ``mrr@k`` (:class:`~torch.Tensor`): A single-value tensor with the reciprocal rank (RR)
+      of the predictions ``preds`` w.r.t. the labels ``target``.
 
     All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
     so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
@@ -55,13 +55,16 @@ class RetrievalMRR(RetrievalMetric):
             - ``'error'``: raise a ``ValueError``
 
         ignore_index: Ignore predictions where the target is equal to this number.
+        top_k: Consider only the top k elements for each query (default: ``None``, which considers them all)
         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 ``top_k`` is not ``None`` or not an integer greater than 0.
 
     Example:
         >>> from torch import tensor
@@ -81,8 +84,25 @@ class RetrievalMRR(RetrievalMetric):
     plot_lower_bound: float = 0.0
     plot_upper_bound: float = 1.0
 
+    def __init__(
+        self,
+        empty_target_action: str = "neg",
+        ignore_index: Optional[int] = None,
+        top_k: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(
+            empty_target_action=empty_target_action,
+            ignore_index=ignore_index,
+            **kwargs,
+        )
+
+        if top_k is not None and 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}")
+        self.top_k = top_k
+
     def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
-        return retrieval_reciprocal_rank(preds, target)
+        return retrieval_reciprocal_rank(preds, target, top_k=self.top_k)
 
     def plot(
         self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None