SCODRiskAtxCov#
- class torch_uncertainty.metrics.classification.SCODRiskAtxCov(cov_threshold, **kwargs)[source]#
Compute the risk at a specified coverage threshold.
This metric calculates the error rate (risk) at a given coverage level. The coverage threshold determines the fraction of samples considered, sorted by model confidence. The metric is useful in evaluating the trade-off between coverage and risk in predictive models.
- Parameters:
cov_threshold (
float) – The coverage threshold at which to compute the risk.kwargs – Additional arguments to pass to the metric class.
Example:
from torch_uncertainty.metrics.classification import RiskAtxCov # Initialize the metric with a coverage threshold of 0.5 (50%) metric = RiskAtxCov(cov_threshold=0.5) # Simulated predicted probabilities (N samples, C classes) predicted_probs = torch.tensor( [ [0.9, 0.1], # Correct (class 0) [0.7, 0.3], # Incorrect (class 1) [0.95, 0.05], # Correct (class 0) [0.8, 0.2], # Incorrect (class 1) [0.6, 0.4], # Correct (class 0) [0.3, 0.7], # Correct (class 1) [0.85, 0.15], # Incorrect (class 1) [0.2, 0.8], # Correct (class 1) ] ) # Simulated ground truth labels ground_truth = torch.tensor([0, 1, 0, 1, 0, 1, 0, 1]) # Update the metric with the probabilities and labels metric.update(predicted_probs, ground_truth) # Compute the risk at the specified coverage threshold risk_at_cov = metric.compute() # Output the result print(f"Risk at coverage threshold: {risk_at_cov.item():.2f}") # output : Risk at coverage threshold: 0.25