torcheval.metrics.MultilabelRecallAtFixedPrecision

class torcheval.metrics.MultilabelRecallAtFixedPrecision(*, num_labels: int, min_precision: float, device: Optional[device] = None)

Returns the highest possible recall value given the minimum precision for each label and their corresponding thresholds for multi-label classification tasks. The maximum recall computation for each label is equivalent to _binary_recall_at_fixed_precision_compute in BinaryRecallAtFixedPrecision.

Its functional version is torcheval.metrics.functional.multilabel_recall_at_fixed_precision(). See also BinaryRecallAtFixedPrecision

Parameters:

• num_labels (int) – Number of labels
• min_precision (float) – Minimum precision threshold

Examples:

>>> import torch
>>> from torcheval.metrics import MultilabelRecallAtFixedPrecision
>>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5)
>>> input = torch.tensor([[0.75, 0.05, 0.35], [0.45, 0.75, 0.05], [0.05, 0.55, 0.75], [0.05, 0.65, 0.05]])
>>> target = torch.tensor([[1, 0, 1], [0, 0, 0], [0, 1, 1], [1, 1, 1]])
>>> metric.update(input, target)
>>> metric.compute()
([torch.tensor(1.0), torch.tensor(1.0), torch.tensor(1.0)],
[torch.tensor(0.05), torch.tensor(0.55), torch.tensor(0.05)])

__init__(*, num_labels: int, min_precision: float, 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_labels, min_precision[, 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)	Implement this method to update the state variables of your metric class.

Attributes

device

The last input device of Metric.to().