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# torcheval.metrics.BinaryRecallAtFixedPrecision¶

class torcheval.metrics.BinaryRecallAtFixedPrecision(*, min_precision: float, device: device | None = None)

Returns the highest possible recall value give the minimum precision for binary classification tasks.

Its functional version is torcheval.metrics.functional.binary_recall_at_fixed_precision().

Parameters:

min_precision (float) – Minimum precision threshold

Examples:

>>> import torch
>>> from torcheval.metrics import BinaryRecallAtFixedPrecision
>>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5)
>>> input = torch.tensor([0.1, 0.4, 0.6, 0.6, 0.6, 0.35, 0.8])
>>> target = torch.tensor([0, 0, 1, 1, 1, 1, 1])
>>> metric.update(input, target)
>>> metric.compute()
(torch.tensor(1.0), torch.tensor(0.35))

__init__(*, min_precision: float, device: device | None = None) None

Initialize a metric object and its internal states.

Use self._add_state() to initialize state variables of your metric class. The state variables should be either torch.Tensor, a list of torch.Tensor, a dictionary with torch.Tensor as values, or a deque of torch.Tensor.

Methods

 __init__(*, min_precision[, device]) Initialize a metric object and its internal states. compute() Implement this method to compute and return the final metric value from state variables. load_state_dict(state_dict[, strict]) Loads metric state variables from state_dict. merge_state(metrics) Implement this method to update the current metric's state variables to be the merged states of the current metric and input metrics. reset() Reset the metric state variables to their default value. state_dict() Save metric state variables in state_dict. to(device, *args, **kwargs) Move tensors in metric state variables to device. update(input, target) Implement this method to update the state variables of your metric class.

Attributes

 device The last input device of Metric.to().

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