ThresholdAccuracy

class torch_uncertainty.metrics.regression.ThresholdAccuracy(power, lmbda=1.25, **kwargs)

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.

Parameters:

• 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.

Example:

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

compute()

Compute the Threshold Accuracy.

update(preds, target)

Update state with predictions and targets.