torcheval.metrics.BinaryRecallAtFixedPrecision¶
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class
torcheval.metrics.
BinaryRecallAtFixedPrecision
(*, min_precision: float, device: Optional[device] = None)[source]¶ 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()
. See alsoMultilabelRecallAtFixedPrecision
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))
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__init__
(*, min_precision: float, device: Optional[device] = None) None [source]¶ 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 eithertorch.Tensor
, a list oftorch.Tensor
, or a dictionary withtorch.Tensor
as values
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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