feat: add paper/ LaTeX draft, English data scripts, update progress docs
- paper/: 22-page LaTeX framework (7/10 sections complete, compiles cleanly) main.tex + 10 section files + refs.bib + compiled PDF (329KB) - code/scripts/: three English dataset generation & merging scripts generate_english.py / generate_english_targeted.py / merge_v5.py - CLAUDE.md: update paper writing status, add paper/ file map entry - state.md: add section 8 paper writing progress (2026-05-15) - .gitignore: add LaTeX build artifact exclusion rules Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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code/scripts/merge_v5.py
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code/scripts/merge_v5.py
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"""
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合并 CompanionRisk-Bench v5
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来源：
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- 中文 v4 (data/processed/CompanionRisk-Bench-v4-backup/all.jsonl) 9,896 条（已质检）
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- 英文 core (data/raw/generated_english_core.jsonl) 12,000 条
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- 英文 targeted (data/raw/generated_english_targeted.jsonl) 3,000 条
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输出：data/processed/CompanionRisk-Bench-v5/{train,dev,test,all}.jsonl
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用法：
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cd code/
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python scripts/merge_v5.py
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"""
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import argparse
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import hashlib
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import json
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import random
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from collections import Counter
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from pathlib import Path
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from typing import List, Dict, Tuple
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RANDOM_SEED = 42
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TRAIN_RATIO = 0.70
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DEV_RATIO = 0.15
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VALID_C_PRIMARY = {"R1","R2","R3","R4","R5","R6","R7","R8","R9","R10","None",None}
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VALID_ACTIONS = {"PASS","WARN","REWRITE","REJECT","CRISIS"}
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VALID_RISK_LEVELS = {0, 1, 2, 3, 4}
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VALID_C_FINE = {
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"DirectEncouragement","MethodFacilitation","RiskNormalization",
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"Romanticization","DependencyReinforcement","IsolationReinforcement",
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"FalseReassurance","PseudoTherapy","BoundaryFailure","CrisisNonResponse",
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"CoRumination","ManipulativeAttachment","PrivacySolicitation",
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"AgeInappropriateIntimacy",
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}
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# ── I/O ───────────────────────────────────────────────────────────────────────
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def load_jsonl(path: Path) -> List[Dict]:
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if not path.exists():
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print(f" [跳过] 文件不存在: {path}")
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return []
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samples = []
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with open(path, encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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try:
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samples.append(json.loads(line))
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except json.JSONDecodeError:
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continue
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return samples
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def save_jsonl(samples: List[Dict], path: Path):
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path.parent.mkdir(parents=True, exist_ok=True)
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with open(path, "w", encoding="utf-8") as f:
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for s in samples:
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f.write(json.dumps(s, ensure_ascii=False) + "\n")
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# ── 去重 ──────────────────────────────────────────────────────────────────────
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def fingerprint(s: Dict) -> str:
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raw = s.get("user_input", "")[:100] + "|" + s.get("ai_response", "")[:100]
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return hashlib.sha256(raw.encode("utf-8")).hexdigest()
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def deduplicate(samples: List[Dict]) -> Tuple[List[Dict], int]:
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seen: set = set()
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unique = []
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dups = 0
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for s in samples:
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fp = fingerprint(s)
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if fp in seen:
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dups += 1
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else:
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seen.add(fp)
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unique.append(s)
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return unique, dups
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# ── 质量过滤（仅用于新增英文数据；中文 v4 已质检） ───────────────────────────
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def quality_filter(samples: List[Dict]) -> Tuple[List[Dict], Dict[str, int]]:
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reasons: Dict[str, int] = {}
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passed = []
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for s in samples:
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# 必填字段
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missing = [f for f in ("persona","history","user_input","ai_response",
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"y_risk","l_risk","c_primary","c_fine","a_recommend")
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if f not in s]
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if missing:
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reasons["missing_fields"] = reasons.get("missing_fields", 0) + 1
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continue
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# 类型检查（防止 LLM 返回 list 而非 str）
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if not isinstance(s["user_input"], str) or not isinstance(s["ai_response"], str):
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reasons["wrong_type"] = reasons.get("wrong_type", 0) + 1
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continue
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# 历史轮数
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if not isinstance(s["history"], list) or len(s["history"]) < 2:
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reasons["history_too_short"] = reasons.get("history_too_short", 0) + 1
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continue
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# 最短文本长度
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if len(s["user_input"].strip()) < 8:
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reasons["user_input_too_short"] = reasons.get("user_input_too_short", 0) + 1
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continue
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if len(s["ai_response"].strip()) < 20:
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reasons["ai_response_too_short"] = reasons.get("ai_response_too_short", 0) + 1
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continue
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# 标签合法性
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if s["l_risk"] not in VALID_RISK_LEVELS:
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reasons["invalid_l_risk"] = reasons.get("invalid_l_risk", 0) + 1
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continue
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if s.get("c_primary") not in VALID_C_PRIMARY:
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reasons["invalid_c_primary"] = reasons.get("invalid_c_primary", 0) + 1
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continue
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if s.get("a_recommend") not in VALID_ACTIONS:
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reasons["invalid_action"] = reasons.get("invalid_action", 0) + 1
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continue
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# 逻辑一致性：y_risk=0 时修正 c_primary
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if s["y_risk"] == 0 and s.get("c_primary") not in (None, "None"):
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s["c_primary"] = "None"
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s["c_fine"] = []
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# y_risk=1 时 c_primary 不能为空
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if s["y_risk"] == 1 and s.get("c_primary") in (None, "None"):
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reasons["risky_no_category"] = reasons.get("risky_no_category", 0) + 1
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continue
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# 过滤 c_fine 中的非法标签（宽容处理，不丢整条）
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if isinstance(s["c_fine"], list):
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s["c_fine"] = [t for t in s["c_fine"] if t in VALID_C_FINE]
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passed.append(s)
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return passed, reasons
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# ── 分层划分（按 y_risk × lang 双维度分层） ──────────────────────────────────
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def stratified_split(
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samples: List[Dict],
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seed: int = RANDOM_SEED,
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) -> Tuple[List[Dict], List[Dict], List[Dict]]:
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random.seed(seed)
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# 按 (y_risk, lang) 分桶
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buckets: Dict[Tuple, List[Dict]] = {}
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for s in samples:
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key = (s.get("y_risk", 1), s.get("lang", "zh"))
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buckets.setdefault(key, []).append(s)
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train, dev, test = [], [], []
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for key, bucket in buckets.items():
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random.shuffle(bucket)
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n = len(bucket)
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n_train = int(n * TRAIN_RATIO)
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n_dev = int(n * DEV_RATIO)
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train += bucket[:n_train]
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dev += bucket[n_train:n_train + n_dev]
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test += bucket[n_train + n_dev:]
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random.shuffle(train)
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random.shuffle(dev)
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random.shuffle(test)
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return train, dev, test
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# ── 统计报告 ──────────────────────────────────────────────────────────────────
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def print_stats(name: str, samples: List[Dict]):
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total = len(samples)
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if total == 0:
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print(f"\n [{name}] 0 条")
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return
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risky = sum(1 for s in samples if s.get("y_risk") == 1)
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lang_cnt = Counter(s.get("lang", "zh") for s in samples)
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lvl_cnt = Counter(s.get("l_risk", 0) for s in samples)
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cat_cnt = Counter(
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s.get("c_primary") for s in samples if s.get("c_primary") not in (None, "None")
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)
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act_cnt = Counter(s.get("a_recommend", "PASS") for s in samples)
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fine_cnt = Counter(t for s in samples for t in s.get("c_fine", []))
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print(f"\n┌{'─'*52}┐")
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print(f"│ {name:<50} │")
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print(f"├{'─'*52}┤")
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print(f"│ 总数 : {total} (有风险={risky}, 安全={total-risky})")
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print(f"│ 语言 : zh={lang_cnt.get('zh',0)} en={lang_cnt.get('en',0)}")
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print(f"│ 风险等级 : {dict(sorted(lvl_cnt.items()))}")
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print(f"│ 一级类别 : {dict(sorted(cat_cnt.items()))}")
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print(f"│ 干预动作 : {dict(act_cnt)}")
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print(f"│ 细粒度(Top8): {dict(fine_cnt.most_common(8))}")
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print(f"└{'─'*52}┘")
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def coverage_check(samples: List[Dict]):
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all_cats = {f"R{i}" for i in range(1, 11)}
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all_fines = VALID_C_FINE
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cat_cnt = Counter(s.get("c_primary") for s in samples if s.get("y_risk") == 1)
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fine_cnt = Counter(t for s in samples for t in s.get("c_fine", []))
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print("\n覆盖率检查（合并后全集）：")
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print(" 一级类别（≥50条）：")
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for cat in sorted(all_cats):
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n = cat_cnt.get(cat, 0)
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ok = "✓" if n >= 50 else "✗"
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print(f" {cat}: {n:5d} {ok}")
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print(" 细粒度标签（≥30条）：")
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for tag in sorted(all_fines):
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n = fine_cnt.get(tag, 0)
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ok = "✓" if n >= 30 else "✗"
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print(f" {tag}: {n:5d} {ok}")
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# ── 主入口 ────────────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--v4", default="data/processed/CompanionRisk-Bench-v4-backup/all.jsonl")
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parser.add_argument("--en-core", default="data/raw/generated_english_core.jsonl")
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parser.add_argument("--en-targeted", default="data/raw/generated_english_targeted.jsonl")
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parser.add_argument("--out-dir", default="data/processed/CompanionRisk-Bench-v5")
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args = parser.parse_args()
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out_dir = Path(args.out_dir)
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print(f"\n{'='*56}")
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print(f" CompanionRisk-Bench v5 构建")
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print(f"{'='*56}")
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# 1. 加载
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print("\n[1/5] 加载数据...")
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zh_v4 = load_jsonl(Path(args.v4))
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en_core = load_jsonl(Path(args.en_core))
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en_tgt = load_jsonl(Path(args.en_targeted))
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print(f" 中文 v4 (已质检) : {len(zh_v4):6d} 条")
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print(f" 英文 core : {len(en_core):6d} 条")
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print(f" 英文 targeted : {len(en_tgt):6d} 条")
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print(f" 合计（过滤前） : {len(zh_v4)+len(en_core)+len(en_tgt):6d} 条")
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# 2. 标记语言字段（确保一致）
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for s in zh_v4:
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s.setdefault("lang", "zh")
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for s in en_core + en_tgt:
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s.setdefault("lang", "en")
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# 3. 质量过滤（仅对新英文数据）
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print("\n[2/5] 质量过滤（英文数据）...")
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en_all = en_core + en_tgt
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en_filtered, reasons = quality_filter(en_all)
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dropped = len(en_all) - len(en_filtered)
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print(f" 英文过滤前: {len(en_all)} → 过滤后: {len(en_filtered)} (丢弃 {dropped} 条)")
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if reasons:
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for k, v in sorted(reasons.items(), key=lambda x: -x[1]):
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print(f" {k}: {v}")
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# 4. 合并 + 全局去重
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print("\n[3/5] 合并 + 全局去重...")
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merged = zh_v4 + en_filtered
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unique, dups = deduplicate(merged)
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print(f" 合并后: {len(merged)} → 去重后: {len(unique)} (去除 {dups} 条重复)")
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# 5. 分层划分（按 y_risk × lang）
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print("\n[4/5] 分层划分 (train:dev:test ≈ 70:15:15)...")
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train, dev, test = stratified_split(unique)
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print(f" train: {len(train)}")
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print(f" dev : {len(dev)}")
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print(f" test : {len(test)}")
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# 6. 保存
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print(f"\n[5/5] 保存到 {out_dir}/...")
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save_jsonl(train, out_dir / "train.jsonl")
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save_jsonl(dev, out_dir / "dev.jsonl")
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save_jsonl(test, out_dir / "test.jsonl")
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all_samples = train + dev + test
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for i, s in enumerate(all_samples):
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s["final_id"] = f"crb-v5-{i:05d}"
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save_jsonl(all_samples, out_dir / "all.jsonl")
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print(f" 保存完成：train / dev / test / all")
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# 7. 统计报告
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print(f"\n{'='*56}")
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print(f" 数据集统计报告")
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print(f"{'='*56}")
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print_stats("ALL (v5)", all_samples)
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print_stats("TRAIN", train)
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print_stats("DEV", dev)
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print_stats("TEST", test)
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coverage_check(all_samples)
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# 8. 语言 × 分割矩阵
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print("\n 语言 × 分割分布：")
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print(f" {'':12} {'train':>8} {'dev':>8} {'test':>8} {'total':>8}")
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for lang in ("zh", "en"):
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row = [sum(1 for s in split if s.get("lang") == lang)
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for split in (train, dev, test)]
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print(f" {lang:12} {row[0]:>8} {row[1]:>8} {row[2]:>8} {sum(row):>8}")
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print(f"\n{'='*56}")
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print(f" 构建完成！总样本数: {len(all_samples)}")
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print(f" 输出目录: {out_dir.resolve()}")
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print(f"{'='*56}\n")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user