torcheval.metrics.MulticlassPrecision

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

Compute the precision score, the ratio of the true positives and the sum of true positives and false positives. Its functional version is torcheval.metrics.functional.multiclass_precision(). We cast NaNs to 0 in case some classes have zero instances in the predictions.

Parameters:

• num_classes (int) – Number of classes.

• average (str) –

  • "micro" (default): Calculate the metrics globally.

  • "macro": Calculate metrics for each class separately, and return their unweighted mean. Classes with 0 true and predicted instances are ignored.

  • "weighted": Calculate metrics for each class separately, and return their weighted sum. Weights are defined as the proportion of occurrences of each class in “target”. Classes with 0 true and predicted instances are ignored.

  • None: Calculate the metric for each class separately, and return the metric for every class.

Examples:

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

>>> metric = MulticlassPrecision(average=None, num_classes=4)
>>> input = torch.tensor([0, 2, 1, 3])
>>> target = torch.tensor([0, 1, 2, 3])
>>> metric.update(input, target)
>>> metric.compute()
tensor([1., 0., 0., 1.])

>>> metric = MulticlassPrecision(average="macro", num_classes=2)
>>> input = torch.tensor([0, 0, 1, 1, 1])
>>> target = torch.tensor([0, 0, 0, 0, 1])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.5833)

>>> metric = MulticlassPrecision(num_classes=4)
>>> input = torch.tensor([[0.9, 0.1, 0, 0], [0.1, 0.2, 0.4, 0.3], [0, 1.0, 0, 0], [0, 0, 0.2, 0.8]])
>>> target = torch.tensor([0, 1, 2, 3])
>>> metric.update(input, target)
>>> metric.compute()
tensor(0.5)

__init__(*, num_classes: int | None = None, average: str | None = 'micro', 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()	Return the precision score.
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().