| """Primary metrics (Sec. 12.5).""" |
| import numpy as np |
|
|
|
|
| def normalized_delta_error(delta_hat, delta_true, eps=1e-9): |
| return float(np.linalg.norm(delta_hat - delta_true) |
| / (np.linalg.norm(delta_true) + eps)) |
|
|
|
|
| def support_f1(pred_support, true_support): |
| pred, true = set(pred_support), set(true_support) |
| if not pred and not true: |
| return 1.0 |
| tp = len(pred & true) |
| prec = tp / len(pred) if pred else 0.0 |
| rec = tp / len(true) if true else 0.0 |
| return 0.0 if prec + rec == 0 else 2 * prec * rec / (prec + rec) |
|
|
|
|
| def regression_recall(delta_hat, delta_true, tau, weights=None): |
| """Weighted recall of materially regressed slices (Delta c < -tau).""" |
| n = len(delta_true) |
| w = np.ones(n) if weights is None else np.asarray(weights) |
| regressed = delta_true < -tau |
| if not regressed.any(): |
| return 1.0 |
| detected = delta_hat < -tau / 2 |
| num = float(np.sum(w * detected * regressed)) |
| den = float(np.sum(w * regressed)) |
| return num / den |
|
|
