torcheval.metrics.BinaryPrecisionRecallCurve

class torcheval.metrics.BinaryPrecisionRecallCurve(*, device: Optional[device] = None)

Returns precision-recall pairs and their corresponding thresholds for binary classification tasks. If a class is missing from the target tensor, its recall values are set to 1.0.

Its functional version is torcheval.metrics.functional.binary_precision_recall_curve(). See also MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve

>>> import torch
>>> from torcheval.metrics import BinaryPrecisionRecallCurve
>>> metric = BinaryPrecisionRecallCurve()
>>> input = torch.tensor([0.1, 0.5, 0.7, 0.8])
>>> target = torch.tensor([0, 0, 1, 1])
>>> metric.update(input, target)
>>> metric.compute()
(tensor([1., 1., 1.]),
tensor([1.0000, 0.5000, 0.0000]),
tensor([0.7000, 0.8000]))

__init__(*, device: Optional[device] = 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, or a dictionary with torch.Tensor as values

Methods

__init__(*[, device])	Initialize a metric object and its internal states.
compute()	returns: precision (Tensor): Tensor of precision result. Its shape is (n_thresholds + 1, )
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)	Update states with the ground truth labels and predictions.

Attributes

device

The last input device of Metric.to().