Multiclassprecisionatfixedrecall

multiclassprecisionatfixedrecall

Metric MulticlassPrecisionAtFixedRecall from torchmetrics (torchmetrics.classification.MulticlassPrecisionAtFixedRecall)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with MulticlassPrecisionAtFixedRecall, or mentions torchmetrics.classification.MulticlassPrecisionAtFixedRecall directly, or wants the standard torchmetrics implementation.

Reference signature

from torchmetrics.classification import MulticlassPrecisionAtFixedRecall

# MulticlassPrecisionAtFixedRecall(num_classes: int, min_recall: float, thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None

Library docstring

Compute the highest possible precision value given the minimum recall thresholds provided.

This is done by first calculating the precision-recall curve for different thresholds and the find the precision for
a given recall level.

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor
  containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider
  the input to be logits and will auto apply softmax per sample.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing
  ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
  is specified).

.. tip::
   Additional dimension ``...`` will be flattened into the batch dimension.

As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:

- ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the
  given recall level per class
- ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold
  level per class

.. note::
   The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
   that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the
   non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
   argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
   size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).

Args:
    num_classes: Integer specifying the number of classes
    min_recall: float value specifying minimum recall threshold.
    thresholds:
        Can be one of:

        - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from
          all the data. Most accurate but also most memory consuming 

Quick recipe

import torchmetrics.classification as _m
score = _m.MulticlassPrecisionAtFixedRecall(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).