Source code for torch_uncertainty.metrics.regression.threshold_accuracy

import torch
from torch import Tensor
from torchmetrics import Metric
from torchmetrics.utilities.data import dim_zero_cat


[docs] class ThresholdAccuracy(Metric): def __init__(self, power: int, lmbda: float = 1.25, **kwargs) -> None: r"""Computes the Threshold Accuracy metric, also referred to as d1, d2, or d3. This metric evaluates the percentage of predictions that fall within a specified threshold of their corresponding target values. The threshold is determined based on the maximum ratio between predictions and targets (or its inverse), raised to a specified power. Args: power: The power to raise the threshold to. Often in [1, 2, 3]. lmbda: The threshold to compare the max of ratio of predictions to targets and its inverse to. Defaults to ``1.25.`` 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 ThresholdAccuracy import torch # Initialize the metric with power=2 and lambda=1.25 threshold_accuracy = ThresholdAccuracy(power=2, lmbda=1.25) # Example predictions and targets preds = torch.tensor([2.0, 3.0, 5.0, 8.0, 20.0]) target = torch.tensor([2.1, 2.5, 4.5, 10.0, 10.0]) # Update the metric state threshold_accuracy.update(preds, target) # Compute the Threshold Accuracy result = threshold_accuracy.compute() print(f"Threshold Accuracy: {result.item():.2f}") # Output: Threshold Accuracy: 0.80 """ super().__init__(**kwargs) if power < 0: raise ValueError(f"Power must be greater than or equal to 0. Got {power}.") self.power = power if lmbda < 1: raise ValueError(f"Lambda must be greater than or equal to 1. Got {lmbda}.") self.lmbda = lmbda 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, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" self.values += torch.sum(torch.max(preds / target, target / preds) < self.lmbda**self.power) self.total += target.size(0)
[docs] def compute(self) -> Tensor: """Compute the Threshold Accuracy.""" values = dim_zero_cat(self.values) return values / self.total