| |
| """讀取 oov_llm_screening.csv，計算 LLM 專名篩選之混淆矩陣與 precision/recall/F1。 |
| gold standard = human_accept(1/0)。用法：python eval_llm_screening.py""" |
| import csv, os, sys |
| try: |
| sys.stdout.reconfigure(encoding="utf-8", errors="replace") |
| except Exception: |
| pass |
|
|
| here = os.path.dirname(os.path.abspath(__file__)) |
| rows = list(csv.DictReader(open(os.path.join(here, "..", "oov_llm_screening.csv"), encoding="utf-8-sig"))) |
| def keep_llm(r): return r["llm_decision"].strip() == "收錄" |
| def keep_h(r): return str(r["human_accept(1/0)"]).strip() in ("1", "1.0") |
|
|
| TP = sum(1 for r in rows if keep_llm(r) and keep_h(r)) |
| FP = sum(1 for r in rows if keep_llm(r) and not keep_h(r)) |
| FN = sum(1 for r in rows if not keep_llm(r) and keep_h(r)) |
| TN = sum(1 for r in rows if not keep_llm(r) and not keep_h(r)) |
| prec = TP/(TP+FP) if TP+FP else float("nan") |
| rec = TP/(TP+FN) if TP+FN else float("nan") |
| f1 = 2*prec*rec/(prec+rec) if prec+rec else float("nan") |
| print(f"N={len(rows)} TP={TP} FP={FP} FN={FN} TN={TN}") |
| print(f"Precision={prec:.4f} Recall={rec:.4f} F1={f1:.4f}") |
| print(f"LLM suggested-keep={TP+FP} -> human-accepted={TP} ({TP/(TP+FP):.1%}); human-accepted={TP+FN}, LLM-missed(FN)={FN}") |
|
|
