torcheval.metrics.ReciprocalRank¶
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class
torcheval.metrics.
ReciprocalRank
(*, k: Optional[int] = None, device: Optional[device] = None)[source]¶ Compute the reciprocal rank of the correct class among the top predicted classes. Its functional version is
torcheval.metrics.functional.reciprocal_rank()
.Parameters: k (int, optional) – Number of top class probabilities to be considered. Examples:
>>> import torch >>> from torcheval.metrics import ReciprocalRank >>> metric = ReciprocalRank() >>> metric.update(torch.tensor([[0.3, 0.1, 0.6], [0.5, 0.2, 0.3]]), torch.tensor([2, 1])) >>> metric.update(torch.tensor([[0.2, 0.1, 0.7], [0.3, 0.3, 0.4]]), torch.tensor([1, 0])) >>> metric.compute() tensor([1.0000, 0.3333, 0.3333, 0.5000]) >>> metric = ReciprocalRank(k=2) >>> metric.update(torch.tensor([[0.3, 0.1, 0.6], [0.5, 0.2, 0.3]]), torch.tensor([2, 1])) >>> metric.update(torch.tensor([[0.2, 0.1, 0.7], [0.3, 0.3, 0.4]]), torch.tensor([1, 0])) >>> metric.compute() tensor([1.0000, 0.0000, 0.0000, 0.5000])
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__init__
(*, k: Optional[int] = None, 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__
(*[, k, device])Initialize a metric object and its internal states. compute
()Return the concatenated reciprocal rank scores. load_state_dict
(state_dict[, strict])Loads metric state variables from state_dict. merge_state
(metrics)Merge the metric state with its counterparts from other metric instances. 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 the metric state with the ground truth labels and predictions. Attributes
device
The last input device of Metric.to()
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