torcheval.metrics.BinaryBinnedAUPRC

class torcheval.metrics.BinaryBinnedAUPRC(*, num_tasks: int = 1, threshold: Union[int, List[float], Tensor] = 100, device: Optional[device] = None)

Compute Binned AUPRC, which is the area under the binned version of the Precision Recall Curve, for binary classification. Its functional version is torcheval.metrics.functional.binary_binned_auprc().

Parameters:

• num_tasks (int) – Number of tasks that need binary_binned_auprc calculation. Default value is 1. binary_binned_auprc for each task will be calculated independently.
• threshold – A integer representing number of bins, a list of thresholds, or a tensor of thresholds.

Examples:

>>> import torch
>>> from torcheval.metrics import BinaryBinnedAUPRC
>>> input = torch.tensor([0.1, 0.5, 0.7, 0.8])
>>> target = torch.tensor([1, 0, 1, 1])
>>> metric = BinaryBinnedAUPRC(threshold=5)
>>> metric.update(input, target)
>>> metric.compute()
(tensor([0.8056]),
tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
)
>>> input = torch.tensor([1, 1, 1, 0])
>>> target = torch.tensor([1, 1, 1, 0])
>>> metric.update(input, target)
>>> metric.compute()
(tensor([0.9306]),
tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])
)
>>> metric = BinaryBinnedAUPRC(num_tasks=2, threshold=[0, 0.2, 0.4, 0.6, 0.8, 1])
>>> input = torch.tensor([[1, 1, 1, 0], [0.1, 0.5, 0.7, 0.8]])
>>> target = torch.tensor([[1, 0, 1, 0], [1, 0, 1, 1]])
>>> metric.update(input, target)
>>> metric.compute()
(tensor([0.6667, 0.8056]),
tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))

__init__(*, num_tasks: int = 1, threshold: Union[int, List[float], Tensor] = 100, 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__(*[, num_tasks, threshold, device])	Initialize a metric object and its internal states.
compute()	Return Binned_AUPRC.
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().