Source code for torch_uncertainty.metrics.regression.log10
import torch
from torch import Tensor
from torchmetrics import MeanAbsoluteError
[docs]
class Log10(MeanAbsoluteError):
def __init__(self, **kwargs) -> None:
r"""Computes the LOG10 metric.
The Log10 metric computes the mean absolute error in the base-10 logarithmic space.
.. math:: \text{Log10} = \frac{1}{N} \sum_{i=1}^{N} |\log_{10}(y_i) - \log_{10}(\hat{y_i})|
where:
- :math:`N` is the number of elements in the batch.
- :math:`y_i` represents the true target values.
- :math:`\hat{y_i}` represents the predicted values.
This metric is useful for scenarios where the data spans multiple orders of magnitude, and evaluating
error in log-space provides a more meaningful comparison.
Inputs:
- :attr:`preds`: :math:`(N)`
- :attr:`target`: :math:`(N)`
Args:
kwargs: Additional keyword arguments, see `Advanced metric settings <https://torchmetrics.readthedocs.io/en/stable/pages/overview.html#metric-kwargs>`_.
Example:
.. code-block:: python
from torch_uncertainty.metrics.regression import Log10
import torch
# Initialize the metric
log10_metric = Log10()
# Example predictions and targets
preds = torch.tensor([10.0, 100.0, 1000.0])
target = torch.tensor([12.0, 95.0, 1020.0])
# Update the metric state
log10_metric.update(preds, target)
# Compute the Log10 error
result = log10_metric.compute()
print(f"Log10 Error: {result.item()}")
# Output: Log10 Error: 0.03668594
"""
super().__init__(**kwargs)
self.add_state("values", default=torch.tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
[docs]
def update(self, pred: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
return super().update(pred.log10(), target.log10())