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())