torcheval.metrics.BinaryBinnedPrecisionRecallCurve¶
-
class
torcheval.metrics.
BinaryBinnedPrecisionRecallCurve
(*, threshold: Union[int, List[float], Tensor] = 100, device: Optional[device] = None)[source]¶ Compute precision recall curve with given thresholds. Its functional version is
torcheval.metrics.functional.binary_binned_precision_recall_curve()
. See alsoMulticlassBinnedPrecisionRecallCurve
Parameters: threshold (Union[int, List[float], torch.Tensor], Optional) – an integer representing number of bins, a list of thresholds, or a tensor of thresholds. Examples:
>>> import torch >>> from torcheval.metrics import BinaryBinnedPrecisionRecallCurve >>> input = torch.tensor([0.2, 0.8, 0.5, 0.9]) >>> target = torch.tensor([0, 1, 0, 1]) >>> threshold = 5 >>> metric = BinaryBinnedPrecisionRecallCurve(threshold) >>> metric.update(input, target) >>> metric.compute() (tensor([0.5000, 0.6667, 0.6667, 1.0000, 1.0000, 1.0000]), tensor([1., 1., 1., 1., 0., 0.]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) >>> input = torch.tensor([0.2, 0.3, 0.4, 0.5]) >>> target = torch.tensor([0, 0, 1, 1]) >>> threshold = torch.tensor([0.0000, 0.2500, 0.7500, 1.0000]) >>> metric = BinaryBinnedPrecisionRecallCurve(threshold) >>> metric.update(input, target) >>> metric.compute() (tensor([0.5000, 0.6667, 1.0000, 1.0000, 1.0000]), tensor([1., 1., 0., 0., 0.]), tensor([0.0000, 0.2500, 0.7500, 1.0000]))
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__init__
(*, threshold: Union[int, List[float], Tensor] = 100, 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__
(*[, threshold, 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()
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