Shortcuts

Source code for torch_uncertainty.metrics.regression.inverse

from typing import Literal

from torch import Tensor
from torchmetrics import MeanAbsoluteError, MeanSquaredError


def _unit_to_factor(unit: Literal["mm", "m", "km"]) -> float:
    """Convert a unit to a factor for scaling.

    Args:
        unit: Unit for the computation of the metric. Must be one of 'mm', 'm',
            'km'.
    """
    if unit == "km":
        return 1e-3
    if unit == "m":
        return 1.0
    if unit == "mm":
        return 1e3
    raise ValueError(f"unit must be one of 'mm', 'm', 'km'. Got {unit}.")


[docs]class MeanSquaredErrorInverse(MeanSquaredError): r"""Mean Squared Error of the inverse predictions (iMSE). .. math:: \text{iMSE} = \frac{1}{N}\sum_i^N(\frac{1}{y_i} - \frac{1}{\hat{y_i}})^2 Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. Both are scaled by a factor of :attr:`unit_factor` depending on the :attr:`unit` given. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Tensor`): Predictions from model - ``target`` (:class:`~Tensor`): Ground truth values As output of ``forward`` and ``compute`` the metric returns the following output: - ``mean_squared_error`` (:class:`~Tensor`): A tensor with the mean squared error Args: squared: If True returns MSE value, if False returns RMSE value. num_outputs: Number of outputs in multioutput setting. unit: Unit for the computation of the metric. Must be one of 'mm', 'm', 'km'. Defauts to 'km'. kwargs: Additional keyword arguments. """ def __init__( self, squared: bool = True, num_outputs: int = 1, unit: str = "km", **kwargs, ) -> None: super().__init__(squared, num_outputs, **kwargs) self.unit_factor = _unit_to_factor(unit)
[docs] def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" super().update( 1 / (preds * self.unit_factor), 1 / (target * self.unit_factor) )
[docs]class MeanAbsoluteErrorInverse(MeanAbsoluteError): r"""Mean Absolute Error of the inverse predictions (iMAE). .. math:: \text{iMAE} = \frac{1}{N}\sum_i^N | \frac{1}{y_i} - \frac{1}{\hat{y_i}} | Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. Both are scaled by a factor of :attr:`unit_factor` depending on the :attr:`unit` given. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Tensor`): Predictions from model - ``target`` (:class:`~Tensor`): Ground truth values As output of ``forward`` and ``compute`` the metric returns the following output: - ``mean_absolute_inverse_error`` (:class:`~Tensor`): A tensor with the mean absolute error over the state Args: unit: Unit for the computation of the metric. Must be one of 'mm', 'm', 'km'. Defauts to 'km'. kwargs: Additional keyword arguments. """ def __init__(self, unit: str = "km", **kwargs) -> None: super().__init__(**kwargs) self.unit_factor = _unit_to_factor(unit)
[docs] def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" super().update( 1 / (preds * self.unit_factor), 1 / (target * self.unit_factor) )