torcheval.metrics.WeightedCalibration¶
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class
torcheval.metrics.
WeightedCalibration
(*, num_tasks: int = 1, device: Optional[device] = None)[source]¶ Compute weighted calibration metric. When weight is not provided, it calculates the unweighted calibration. Its functional version is
torcheval.metrics.functional.weighted_calibration()
.weighted_calibration = sum(input * weight) / sum(target * weight)
Parameters: num_tasks (int) – Number of tasks that need WeightedCalibration calculations. Default value is 1. Raises: ValueError – If value of weight is neither a float
nor aint
nor atorch.Tensor
that matches the input tensor size.Examples:
>>> import torch >>> from torcheval.metrics import WeightedCalibration >>> metric = WeightedCalibration() >>> metric.update(torch.tensor([0.8, 0.4, 0.3, 0.8, 0.7, 0.6]),torch.tensor([1, 1, 0, 0, 1, 0])) >>> metric.compute() tensor([1.2], dtype=torch.float64) >>> metric = WeightedCalibration() >>> metric.update(torch.tensor([0.8, 0.4, 0.3, 0.8, 0.7, 0.6]),torch.tensor([1, 1, 0, 0, 1, 0]), torch.tensor([0.5, 1., 2., 0.4, 1.3, 0.9])) >>> metric.compute() tensor([1.1321], dtype=torch.float64) >>> metric = WeightedCalibration(num_tasks=2) >>> metric.update(torch.tensor([[0.8, 0.4], [0.8, 0.7]]),torch.tensor([[1, 1], [0, 1]]),) >>> metric.compute() tensor([0.6000, 1.5000], dtype=torch.float64)
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__init__
(*, num_tasks: int = 1, 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__
(*[, num_tasks, device])Initialize a metric object and its internal states. compute
()Return the weighted calibration. 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[, weight])Update the metric state with the total sum of weighted inputs and the total sum of weighted labels. Attributes
device
The last input device of Metric.to()
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