FPRx#
- class torch_uncertainty.metrics.classification.FPRx(recall_level, pos_label, **kwargs)[source]#
Compute the False Positive Rate at x% Recall.
The False Positive Rate at x% Recall (FPR@x) is a metric used in tasks like anomaly detection, out-of-distribution (OOD) detection, and binary classification. It measures the proportion of false positives (normal samples misclassified as anomalies) when the model achieves a specified recall level for the positive class (e.g., anomalies or OOD samples).
- Parameters:
recall_level (float) – The recall level at which to compute the FPR.
pos_label (int) – The positive label.
kwargs – Additional arguments to pass to the metric class.
- Reference:
Improved from hendrycks/anomaly-seg and translated to torch.
Example
from torch_uncertainty.metrics.classification import FPRx # Initialize the metric with 95% recall and positive label as 1 (e.g., OOD) metric = FPRx(recall_level=0.95, pos_label=1) # Simulated model predictions (confidence scores) and ground-truth labels conf = torch.tensor([0.9, 0.8, 0.7, 0.6, 0.4, 0.2, 0.1]) targets = torch.tensor([1, 0, 1, 0, 0, 1, 0]) # 1: OOD, 0: In-Distribution # Update the metric with predictions and labels metric.update(conf, targets) # Compute FPR at 95% recall result = metric.compute() print(f"FPR at 95% Recall: {result.item()}") # output : FPR at 95% Recall: 0.75