| """ |
| Validation-set threshold search to maximize F1 (default: toxic class). |
| """ |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| from sklearn.metrics import f1_score |
|
|
|
|
| def search_best_threshold( |
| y_true: np.ndarray, |
| probs: np.ndarray, |
| *, |
| metric: str = "f1_toxic", |
| min_threshold: float = 0.05, |
| max_threshold: float = 0.95, |
| step: float = 0.01, |
| ) -> tuple[float, float]: |
| """ |
| Grid-search classification threshold on validation probabilities. |
| |
| Returns (best_threshold, best_score). |
| """ |
| y = np.asarray(y_true).astype(int) |
| p = np.asarray(probs, dtype=float) |
| thresholds = np.arange(min_threshold, max_threshold + step / 2, step) |
|
|
| best_t = 0.5 |
| best_score = -1.0 |
| average = "weighted" if metric == "f1_weighted" else "binary" |
| pos_label = 1 |
|
|
| for t in thresholds: |
| preds = (p >= t).astype(int) |
| if metric in ("f1_toxic", "f1_binary"): |
| score = float(f1_score(y, preds, pos_label=pos_label, zero_division=0)) |
| else: |
| score = float( |
| f1_score(y, preds, average=average, zero_division=0) |
| ) |
| if score > best_score: |
| best_score = score |
| best_t = float(t) |
|
|
| return best_t, best_score |
|
|
|
|
| def predict_with_threshold(probs: np.ndarray, threshold: float) -> np.ndarray: |
| return (np.asarray(probs, dtype=float) >= threshold).astype(int) |
|
|
