simonkoson
30931d4446
- ground-truth.json 新增 4 分类字段骨架（program_format/equipment_domain/scene_tags/tech_tags） - equipment_domain 类型修正为数组（对齐 v2 快照多选定义） - run_labeling.py 新增 --field 参数，文件名含 field 标记 - summarize.py 新增 --field 参数 + 老文件向后兼容 - CLAUDE.md 补入 v1-v4 快照精华：象限图规格、6 字段枚举、三层交付策略 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
"""
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summarize.py - 汇总打标结果命中情况
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用法: python summarize.py --model mimo-v2.5-pro
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"""
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import sys
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sys.stdout.reconfigure(encoding='utf-8')
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sys.stderr.reconfigure(encoding='utf-8')
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import json
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import glob
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import argparse
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from pathlib import Path
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BASE_DIR = Path(__file__).parent.parent
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EXPERIMENTS_DIR = BASE_DIR / "experiments"
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def load_json(path):
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try:
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return json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return None
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def latest_per_ep(files):
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"""同 ep 有多个文件时取时间戳最新的。"""
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latest = {}
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for f in files:
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name = f.name
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# 文件名格式: 20260611_154037_m3_ep04.json
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parts = name.replace(".json", "").split("_")
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if len(parts) >= 4:
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ts = parts[0] + parts[1] # yyyymmddHHMMSS
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ep = int(parts[-1].replace("ep", ""))
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key = ep
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if key not in latest or ts > latest[key][1]:
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latest[key] = (f, ts)
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return {ep: info[0] for ep, info in latest.items()}
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def run(model, field="narrative"):
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# 文件匹配：新格式有 field 标记，老格式（无 field）向后兼容
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pattern_new = str(EXPERIMENTS_DIR / f"*_{model}_{field}_*.json")
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files_new = sorted(Path(p) for p in glob.glob(pattern_new))
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# 如果是 narrative 且新格式文件为空，尝试匹配老格式（无 field 标记）
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if field == "narrative" and not files_new:
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pattern_old = str(EXPERIMENTS_DIR / f"*_{model}_ep*.json")
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files_old = [f for f in sorted(Path(p) for p in glob.glob(pattern_old))
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if len(f.name.replace(".json", "").split("_")) == 3] # 老格式: ts_model_epNN.json
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files = files_old
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else:
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files = files_new
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if not files:
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print(f"未找到 {pattern_new}")
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return
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ep_files = latest_per_ep(files)
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rows = []
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parse_fail = 0
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for ep in sorted(ep_files.keys()):
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data = load_json(ep_files[ep])
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if data is None:
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parse_fail += 1
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rows.append({"ep": ep, "title": "?", "gt": "?", "pred": "解析失败", "hit": False, "conf": "?"})
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continue
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gt = data.get("ground_truth", {})
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result = data.get("result")
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title = gt.get("title", "?")
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if field == "narrative":
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gt_val = gt.get("narrative_structure", "?")
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pred_val = result.get("narrative_structure") if result else None
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conf = result.get("confidence", "?") if result else "?"
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hit = pred_val == gt_val if pred_val is not None else False
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rows.append({"ep": ep, "title": title, "gt": gt_val, "pred": pred_val or "解析失败", "hit": hit, "conf": conf})
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elif field == "classification":
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# 骨架预留：等 ground-truth 标注就绪后实现具体比对逻辑
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rows.append({"ep": ep, "title": title, "gt": "?", "pred": "classification 待实现", "hit": False, "conf": "?"})
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if field == "narrative":
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# 打印每行
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for r in rows:
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mark = "✓" if r["hit"] else "✗"
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conf_str = f'置信度:{r["conf"]}' if r["conf"] != "?" else ""
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print(f' ep{r["ep"]:02d} {r["title"]:<10} | 标准:{r["gt"]:<8} | {model}:{r["pred"]:<8} | {mark} {conf_str}')
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# 汇总
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total = len(rows)
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hits = sum(1 for r in rows if r["hit"])
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hi_conf = [r for r in rows if r["conf"] == "高"]
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mid_low = [r for r in rows if r["conf"] in ("中", "低")]
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hi_hit = sum(1 for r in hi_conf if r["hit"])
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ml_hit = sum(1 for r in mid_low if r["hit"])
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print(f"\n ===== {model} 命中情况 =====")
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print(f" narrative_structure 命中: {hits}/{total} = {hits*100//total}%")
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print(f" 自评\"高\"置信的命中率: {hi_hit}/{len(hi_conf)}")
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print(f" 自评\"中/低\"置信的命中率: {ml_hit}/{len(mid_low)}")
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print(f" 解析失败: {parse_fail} 期")
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elif field == "classification":
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print(f"\n ===== {model} classification =====")
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print(f" classification 比对逻辑待实现（等 ground-truth 标注就绪）")
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print(f" 解析失败: {parse_fail} 期")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", required=True, help="模型键名，如 mimo-v2.5-pro / deepseek-v4-pro")
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parser.add_argument("--field", default="narrative", choices=["narrative", "classification"],
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help="打标字段: narrative(叙事结构) / classification(4分类)")
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args = parser.parse_args()
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run(args.model, args.field)
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