>>> from torchmetrics.regression import RelativeSquaredError
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> relative_squared_error = RelativeSquaredError()
>>> relative_squared_error(preds, target)
tensor(0.0514)
plot(val=None, ax=None)[source]¶
Plot a single or multiple values from the metric.
Parameters:
val¶ (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results.
If no value is provided, will automatically call metric.compute and plot that result.
ax¶ (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis
Return type:
tuple[Figure, Union[Axes, ndarray]]
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError – If matplotlib is not installed
>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import RelativeSquaredError
>>> metric = RelativeSquaredError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()
>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import RelativeSquaredError
>>> metric = RelativeSquaredError()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
torchmetrics.functional.relative_squared_error(preds, target, squared=True)[source]¶
Computes the relative squared error (RSE).
\[\text{RSE} = \frac{\sum_i^N(y_i - \hat{y_i})^2}{\sum_i^N(y_i - \overline{y})^2}\]
Where \(y\) is a tensor of target values with mean \(\overline{y}\), and
\(\hat{y}\) is a tensor of predictions.
If preds and targets are 2D tensors, the RSE is averaged over the second dim.
Parameters:
preds¶ (Tensor) – estimated labels
target¶ (Tensor) – ground truth labels
squared¶ (bool) – returns RRSE value if set to False
Return type:
Tensor
Returns:
Tensor with RSE
Example
>>> from torchmetrics.functional.regression import relative_squared_error
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> relative_squared_error(preds, target)
tensor(0.0514)
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