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Binned Recall At Fixed Precision

Module Interface

class torchmetrics.BinnedRecallAtFixedPrecision(num_classes, min_precision, thresholds=100, **kwargs)[source]

Computes the higest possible recall value given the minimum precision thresholds provided.

Computation is performed in constant-memory by computing precision and recall for thresholds buckets/thresholds (evenly distributed between 0 and 1).

Forward accepts

  • preds (float tensor): (N, ...) (binary) or (N, C, ...) (multiclass) tensor with probabilities, where C is the number of classes.

  • target (long tensor): (N, ...) with integer labels

Parameters
  • num_classes (int) – integer with number of classes. Provide 1 for binary problems.

  • min_precision (float) – float value specifying minimum precision threshold.

  • thresholds (Union[int, Tensor, List[float]]) – list or tensor with specific thresholds or a number of bins from linear sampling. It is used for computation will lead to more detailed curve and accurate estimates, but will be slower and consume more memory

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

ValueError – If thresholds is not a list or tensor

Example (binary case):
>>> from torchmetrics import BinnedRecallAtFixedPrecision
>>> pred = torch.tensor([0, 0.2, 0.5, 0.8])
>>> target = torch.tensor([0, 1, 1, 0])
>>> average_precision = BinnedRecallAtFixedPrecision(num_classes=1, thresholds=10, min_precision=0.5)
>>> average_precision(pred, target)
(tensor(1.0000), tensor(0.1111))
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
...                      [0.05, 0.75, 0.05, 0.05, 0.05],
...                      [0.05, 0.05, 0.75, 0.05, 0.05],
...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> average_precision = BinnedRecallAtFixedPrecision(num_classes=5, thresholds=10, min_precision=0.5)
>>> average_precision(pred, target)
(tensor([1.0000, 1.0000, 0.0000, 0.0000, 0.0000]),
tensor([6.6667e-01, 6.6667e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06]))

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Returns float tensor of size n_classes.

Return type

Tuple[Tensor, Tensor]

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