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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
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

© Copyright Copyright (c) 2020-2022, PyTorchLightning et al... Revision 24957b11.

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