File size: 1,979 Bytes
188f4d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | """字符提取（Parsing）打分：1 − NED (Normalized Edit Distance)。
score = 1 - Levenshtein(pred, gt) / max(|pred|, |gt|)
双边都先做 ``normalize_for_parsing``（去空白 / 换行 / 标点），再剔除 ``[UNK]`` 占位。
"""
from __future__ import annotations
from ..utils.unk import remove_unk
from ._text import normalize_for_parsing
try:
from rapidfuzz.distance import Levenshtein as _rf_Levenshtein
_HAS_RF = True
except ImportError:
_rf_Levenshtein = None
_HAS_RF = False
def _levenshtein(s1: str, s2: str) -> int:
if s1 == s2:
return 0
if not s1:
return len(s2)
if not s2:
return len(s1)
if _HAS_RF:
return _rf_Levenshtein.distance(s1, s2)
# 纯 Python 兜底实现：滚动数组 DP，O(|s1|*|s2|) 时间 / O(|s2|) 空间。
prev = list(range(len(s2) + 1))
curr = [0] * (len(s2) + 1)
for i, c1 in enumerate(s1, start=1):
curr[0] = i
for j, c2 in enumerate(s2, start=1):
curr[j] = prev[j - 1] if c1 == c2 else 1 + min(prev[j], curr[j - 1], prev[j - 1])
prev, curr = curr, prev
return prev[len(s2)]
def judge(extract: dict, row: dict) -> dict:
gt_raw = normalize_for_parsing(row.get("annotation", "") or "")
pred_raw = normalize_for_parsing((extract or {}).get("extracted_text", "") or "")
gt = remove_unk(gt_raw)
pred = remove_unk(pred_raw)
len_gt, len_pred = len(gt), len(pred)
if len_gt == 0 and len_pred == 0:
return {"score": 1.0, "metric": "1ned", "edit_distance": 0, "len_pred": 0, "len_gt": 0}
if len_gt == 0:
return {"score": 0.0, "metric": "1ned", "edit_distance": len_pred, "len_pred": len_pred, "len_gt": 0}
ed = _levenshtein(pred, gt)
denom = max(len_pred, len_gt)
score = max(0.0, 1.0 - ed / denom)
return {
"score": score,
"metric": "1ned",
"edit_distance": ed,
"len_pred": len_pred,
"len_gt": len_gt,
}
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