| """Image-level multi-label classification metrics.""" |
|
|
| from collections import defaultdict |
|
|
| from metrics._f_beta import ViolationCounts, aggregate_summary, summarise_counts |
| from metrics._labels import to_use_case_id |
| from schemas.ground_truth import GoldRecord |
| from schemas.inference import InferenceResponse |
|
|
|
|
| def compute_classification_metrics( |
| predictions: list[InferenceResponse], ground_truths: list[GoldRecord] |
| ) -> dict: |
| """Compare class sets; repeated detections of one class count once.""" |
| gt_by_id = {record.image_id: record for record in ground_truths} |
| counts: dict[str, ViolationCounts] = defaultdict(ViolationCounts) |
| negatives = true_negatives = false_positive_images = evaluated = 0 |
| for prediction in predictions: |
| record = gt_by_id.get(prediction.image_id) |
| if record is None or record.gold.reported: |
| continue |
| evaluated += 1 |
| gt_labels = {item.use_case_id for item in record.gold.violations} |
| pred_labels = {to_use_case_id(item.category_id) for item in prediction.detections} |
| for label in gt_labels | pred_labels: |
| target = counts[label] |
| if label in gt_labels and label in pred_labels: |
| target.tp += 1 |
| elif label in pred_labels: |
| target.fp += 1 |
| else: |
| target.fn += 1 |
| if record.gold.no_violation_detected: |
| negatives += 1 |
| if pred_labels: |
| false_positive_images += 1 |
| else: |
| true_negatives += 1 |
| return { |
| "per_violation": summarise_counts(counts), |
| "summary": aggregate_summary(counts), |
| "n_images": evaluated, |
| "negative_images": negatives, |
| "true_negative_images": true_negatives, |
| "false_positive_images": false_positive_images, |
| "negative_false_positive_rate": round(false_positive_images / negatives, 4) if negatives else None, |
| } |
|
|
