| from functools import lru_cache |
|
|
|
|
| def lev_dist(prediction, ground_truth): |
| @lru_cache(None) |
| def min_dist(s1, s2): |
| if s1 == len(prediction) or s2 == len(ground_truth): |
| return len(prediction) - s1 + len(ground_truth) - s2 |
| |
| if prediction[s1] == ground_truth[s2]: |
| return min_dist(s1 + 1, s2 + 1) |
| return 1 + min( |
| min_dist(s1, s2 + 1), |
| min_dist(s1 + 1, s2), |
| min_dist(s1 + 1, s2 + 1), |
| ) |
| return min_dist(0, 0) |
|
|
|
|
| def edit_sim_score(a, b): |
| return 1 - lev_dist(a, b) / max(len(a), len(b)) |
|
|
|
|
| def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): |
| scores_for_ground_truths = [] |
| for ground_truth in ground_truths: |
| score = metric_fn(prediction, ground_truth) |
| scores_for_ground_truths.append(score) |
| return max(scores_for_ground_truths) |
|
|
|
|
| def compute_edit_sim(predictions, references): |
| edit_sim = 0 |
| for prediction, ground_truths in zip(predictions, references): |
| edit_sim += metric_max_over_ground_truths(edit_sim_score, prediction, ground_truths) |
| return 100.0 * edit_sim / len(predictions) |
