def get_tpr_fpr()

in privacy_lint/attack_results.py [0:0]

• 9 lines of code
• 1 McCabe index (conditional complexity)

    def get_tpr_fpr(self) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Computes true positive rate and true negative rate,, useful for plotting
        ROC curves and computing AUC.
        """
        labels_ordered, _ = self._get_scores_and_labels_ordered()

        true_positive_rate = (
            torch.cumsum(labels_ordered == 1, 0) / self.scores_train.shape[0]
        )
        false_positive_rate = (
            torch.cumsum(labels_ordered == 0, 0) / self.scores_test.shape[0]
        )
        return true_positive_rate, false_positive_rate