| from __future__ import annotations |
|
|
| import numpy as np |
|
|
|
|
| def _safe_div(num: float, den: float) -> float: |
| return num / den if den > 0 else 0.0 |
|
|
|
|
| def _compute_threshold_for_fmr( |
| impostor_scores: np.ndarray, |
| fmr_threshold: float, |
| ) -> float: |
| |
| |
| q = float(np.clip(1.0 - fmr_threshold, 0.0, 1.0)) |
| return float(np.quantile(impostor_scores, q)) |
|
|
|
|
| def compute_erc( |
| quality_scores: np.ndarray, |
| match_scores: np.ndarray, |
| labels: np.ndarray, |
| fmr_threshold: float = 1e-4, |
| rejection_ratios: np.ndarray | None = None, |
| ) -> tuple[np.ndarray, float]: |
| """Compute FNMR under progressive rejection of lowest-quality samples.""" |
|
|
| if rejection_ratios is None: |
| rejection_ratios = np.linspace(0.0, 0.5, 50) |
|
|
| quality_scores = np.asarray(quality_scores) |
| match_scores = np.asarray(match_scores) |
| labels = np.asarray(labels) |
|
|
| if not (quality_scores.shape == match_scores.shape == labels.shape): |
| raise ValueError("quality_scores, match_scores, and labels must have same shape") |
|
|
| sorted_idx = np.argsort(quality_scores) |
| fnmr_curve = [] |
| for rr in rejection_ratios: |
| n_reject = int(rr * len(sorted_idx)) |
| keep_mask = np.ones_like(labels, dtype=bool) |
| keep_mask[sorted_idx[:n_reject]] = False |
|
|
| kept_scores = match_scores[keep_mask] |
| kept_labels = labels[keep_mask] |
| impostor = kept_scores[kept_labels == 0] |
| genuine = kept_scores[kept_labels == 1] |
| if len(impostor) == 0 or len(genuine) == 0: |
| fnmr_curve.append(1.0) |
| continue |
|
|
| threshold = _compute_threshold_for_fmr(impostor, fmr_threshold) |
| fn = float((genuine < threshold).sum()) |
| fnmr = _safe_div(fn, float(len(genuine))) |
| fnmr_curve.append(fnmr) |
|
|
| fnmr_curve_arr = np.asarray(fnmr_curve, dtype=np.float64) |
| auc = float(np.trapz(fnmr_curve_arr, rejection_ratios) / (rejection_ratios[-1] - rejection_ratios[0] + 1e-12)) |
| return fnmr_curve_arr, auc |
|
|
