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| from __future__ import annotations | |
| from typing import Any, Dict | |
| import numpy as np | |
| from sklearn.metrics import average_precision_score, f1_score | |
| def _safe_average_precision(y_true: np.ndarray, y_score: np.ndarray) -> float: | |
| if np.unique(y_true).size < 2: | |
| return 0.0 | |
| return float(average_precision_score(y_true, y_score)) | |
| def compute_metrics( | |
| targets: np.ndarray, | |
| logits: np.ndarray, | |
| threshold: float = 0.5, | |
| ) -> Dict[str, Any]: | |
| probs = 1.0 / (1.0 + np.exp(-logits)) | |
| preds = (probs >= threshold).astype(np.int32) | |
| per_attr_ap = [] | |
| per_attr_f1 = [] | |
| for idx in range(targets.shape[1]): | |
| yt = targets[:, idx] | |
| yp = preds[:, idx] | |
| ys = probs[:, idx] | |
| per_attr_ap.append(_safe_average_precision(yt, ys)) | |
| per_attr_f1.append(float(f1_score(yt, yp, zero_division=0))) | |
| macro_f1 = float(f1_score(targets, preds, average="macro", zero_division=0)) | |
| micro_f1 = float(f1_score(targets, preds, average="micro", zero_division=0)) | |
| map_score = float(np.mean(per_attr_ap)) if per_attr_ap else 0.0 | |
| return { | |
| "map": map_score, | |
| "macro_f1": macro_f1, | |
| "micro_f1": micro_f1, | |
| "per_attribute_ap": per_attr_ap, | |
| "per_attribute_f1": per_attr_f1, | |
| } | |