|
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) |