torcheval.metrics.MulticlassAUROC

class torcheval.metrics.MulticlassAUROC(*, num_classes: int, average: str | None = 'macro', device: device | None = None)

Compute AUROC, which is the area under the ROC Curve, for multiclass classification in a one vs rest fashion. One vs. rest Multiclass AUROC is equivalent to running a BinaryAUROC with num_classes tasks where

1. The input is transposed

2. The target is translated from a 1 dimensional tensor of the correct classes to a 2 dimensional tensor where each row is a list containing which examples belong to that class.

See examples below for more details on the connection between Multiclass and Binary AUROC.

The functional version of this metric is torcheval.metrics.functional.multiclass_auroc().

Parameters:

• num_classes (int) – Number of classes.

• average (str, optional) –

  • 'macro' [default]:

    Calculate metrics for each class separately, and return their unweighted mean.

  • None:

    Calculate the metric for each class separately, and return the metric for every class.

Examples:

>>> import torch
>>> from torcheval.metrics import MulticlassAUROC
>>> metric = MulticlassAUROC(num_classes=4)
>>> input = torch.tensor([[0.1, 0.1, 0.1, 0.1], [0.5, 0.5, 0.5, 0.5], [0.7, 0.7, 0.7, 0.7], [0.8, 0.8, 0.8, 0.8]])
>>> target = torch.tensor([0, 1, 2, 3])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.5000)

>>> metric = MulticlassAUROC(num_classes=3, average=None)
>>> input = torch.tensor([[0.1, 0, 0], [0, 1, 0], [0.1, 0.2, 0.7], [0, 0, 1]])
>>> target = torch.tensor([0, 1, 2, 2])
>>> metric.update(input, target)
>>> metric.compute()
tensor([0.8333, 1.0000, 1.0000])

the above is equivalent to
>>> from torcheval.metrics import BinaryAUROC
>>> metric = BinaryAUROC(num_tasks=3)
>>> input = torch.tensor([[0.1, 0, 0.1, 0], [0, 1, 0.2, 0], [0, 0, 0.7, 1]])
>>> target = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 1]])
>>> metric.update(input, target)
>>> metric.compute()
tensor([0.8333, 1.0000, 1.0000])

__init__(*, num_classes: int, average: str | None = 'macro', 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__(*, num_classes[, average, 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)	Update states with the ground truth labels and predictions.

Attributes

device

The last input device of Metric.to().