97e1ddf138
Phase2 complete ablation (added missing variants): - Coverage-only: 20% contamination rate (confirms Gate is critical) - Gate-only: +5.2 tokens vs Full (coverage optimization marginal on clean data) - -Recency: 0 effect on clean data - -IDF: 0 effect on clean data Phase4 end-to-end quality evaluation: - CGK vs Last-5 across 5 queries: * CGK: 42.2 tok, purity=1.000, anchor_recall=0.638, term_cov=0.380, contamination=0 * Last-5: 67.6 tok, purity=0.280, anchor_recall=0.066, term_cov=0.080, contamination=5 - All quality metrics CGK >> Last-5 on synthetic clean data Known honest limitations: - Still no real dialogue data (synthetic 4-topic only) - No real LLM calls (quality is rule-estimated) - Parameter sensitivity only on clean data, not noisy real data
357 lines
15 KiB
Python
357 lines
15 KiB
Python
#!/usr/bin/env python3
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"""Phase 2 补全: 完整的 Ablation Study（包含缺失的 -recency, -coverage, -gate 变体）"""
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import sys, os, json
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sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
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from src.gatekeeper import ContextGatekeeper
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def estimate_tokens(text):
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if not text: return 0
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chinese = sum(1 for c in text if '\u4e00' <= c <= '\u9fff')
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english = len([w for w in text.split() if w.isascii()])
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return int(chinese * 0.4 + english * 1.3 + len(text) * 0.05)
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def measure_prompt_tokens(selected, query):
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ctx = "".join(f"用户: {i['user']}\n助手: {i['assistant']}\n\n" for i in selected)
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context_tok = estimate_tokens(ctx)
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fmt_overhead = int(context_tok * 0.08)
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return context_tok + fmt_overhead + estimate_tokens(query)
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def evaluate_contamination(selected, target):
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text = " ".join(i['user'] + i['assistant'] for i in selected)
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found = [t for t in ['Redis', 'asyncio', 'PostgreSQL', 'Git']
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if t.lower() in text.lower() and t.lower() != target.lower()]
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return len(found) > 0, found
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def evaluate_answer_quality(gate, query, target_topic):
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"""
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端到端答案质量评估：模拟 LLM 在不同上下文下的回答质量
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评估指标:
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1. 上下文正确性: 选中的块是否都与目标话题相关
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2. 上下文完整性: 选中的块是否覆盖了回答所需的关键信息
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3. 回答引用正确性: 如果用这些块让 LLM 回答，答案是否会引用错误话题
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由于没有真实 LLM 调用，用规则模拟：
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- 相关块比例 = 目标话题块数 / 总块数
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- 锚点覆盖率 = query 锚点在选中块中的出现率
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"""
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sel = gate.select(query)
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# 统计
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total_blocks = len(sel)
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topic_blocks = 0
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other_topic_texts = []
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for item in sel:
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text = item['user'] + ' ' + item['assistant']
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found_topics = [t for t in ['Redis', 'asyncio', 'PostgreSQL', 'Git']
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if t.lower() in text.lower() and t.lower() != target_topic.lower()]
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if found_topics:
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other_topic_texts.extend(found_topics)
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else:
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topic_blocks += 1
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# query 锚点覆盖率
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q_anchors, _ = gate.anchor_extractor.extract_with_deictic(query)
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covered_anchors = 0
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context_text = ' '.join(i['user'] + i['assistant'] for i in sel)
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for a in q_anchors:
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if a.lower() in context_text.lower():
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covered_anchors += 1
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anchor_coverage = covered_anchors / len(q_anchors) if q_anchors else 0
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return {
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'total_blocks': total_blocks,
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'topic_blocks': topic_blocks,
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'purity': topic_blocks / total_blocks if total_blocks > 0 else 0,
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'other_topics': list(set(other_topic_texts)),
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'anchor_coverage': anchor_coverage,
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'is_contaminated': len(other_topic_texts) > 0
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}
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redis_qa = [
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("Redis 分布式锁和 RedLock 算法有什么区别？", "RedLock是..."),
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("Redis 集群环境下怎么做分布式锁？", "集群下..."),
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("Redis 惰性删除和定期删除有什么区别？", "惰性删除..."),
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("Redis 的过期 key 对 RDB 快照有什么影响？", "过期key..."),
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("Redis 主从复制断线后如何增量同步？", "PSYNC..."),
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]
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asyncio_qa = [
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("asyncio.Task 的 cancel 方法怎么工作的？", "cancel..."),
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("asyncio.gather 和 asyncio.wait 的返回结果有什么区别？", "gather..."),
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("asyncio 的事件循环怎么启动和停止？", "事件循环..."),
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("asyncio.sleep 和 time.sleep 的区别是什么？", "sleep..."),
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("asyncio 的 Future 对象怎么获取结果？", "Future..."),
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]
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pg_qa = [
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("PostgreSQL 的 MVCC 机制是怎么保证读不阻塞写的？", "MVCC..."),
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("PostgreSQL 的 VACUUM 为什么要定期运行？", "VACUUM..."),
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("EXPLAIN ANALYZE 怎么看执行计划？", "EXPLAIN..."),
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("PostgreSQL B-tree 索引和 Hash 索引的区别是什么？", "B-tree..."),
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("PostgreSQL 的 TOAST 机制是什么？", "TOAST..."),
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]
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git_qa = [
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("Git 的 rebase 和 merge 的区别是什么？", "rebase..."),
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("Git reset 的 --soft、--mixed、--hard 有什么区别？", "reset..."),
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("Git stash 暂存区和工作目录的区别是什么？", "stash..."),
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("Git 的 bisect 怎么用来快速定位 bug？", "bisect..."),
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("Git 的 reflog 怎么用来恢复误删的提交？", "reflog..."),
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]
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TEST_SEQ = [
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("问PG", "EXPLAIN ANALYZE 怎么看执行计划？", "PostgreSQL"),
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("问Git", "Git 的 rebase 和 merge 有什么区别？", "Git"),
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("问Redis", "Redis 惰性删除和定期删除有什么区别？", "Redis"),
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("问asyncio", "asyncio.Task 的 cancel 方法怎么工作的？", "asyncio"),
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("再问Git", "Git 的 reset 和 revert 的应用场景有什么区别？", "Git"),
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]
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def build_gate():
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g = ContextGatekeeper(token_budget=4000)
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for i in range(5):
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g.add_turn(redis_qa[i][0], redis_qa[i][1])
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g.add_turn(asyncio_qa[i][0], asyncio_qa[i][1])
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g.add_turn(pg_qa[i][0], pg_qa[i][1])
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g.add_turn(git_qa[i][0], git_qa[i][1])
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return g
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def run_gate_only(gate, query):
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"""Gate-only: 只做话题过滤，不做覆盖优化（直接返回所有召回块）"""
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q_anchors, has_deictic = gate.anchor_extractor.extract_with_deictic(query)
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switched = gate.topic_gate.is_topic_switch(query, gate._active_topic)
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idf_cache = gate.anchor_extractor._idf_cache
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if switched:
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candidates = gate.blocks[-15:]
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else:
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candidates = gate.blocks
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retrieved = gate.retriever.retrieve(
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candidates, q_anchors, top_m=20, idf_cache=idf_cache,
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active_topic_anchors=gate._active_topic[0] if gate._active_topic else None,
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topic_switched=switched, query_text=query
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)
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return [{"user": b.user_text, "assistant": b.assistant_text, "turn_id": b.turn_id} for b, _ in retrieved]
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def run_coverage_only(gate, query):
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"""Coverage-only: 不做话题过滤，直接对所有块做覆盖优化"""
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q_anchors, _ = gate.anchor_extractor.extract_with_deictic(query)
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# Bypass gate: 强制 switched=False，全部块作为候选
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orig_switched = gate.topic_gate.is_topic_switch
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def fake_switch(*args, **kwargs):
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return False # 强制不做话题过滤
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gate.topic_gate.is_topic_switch = fake_switch
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# Bypass content-word filter by setting topic_switched=False in retrieve
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orig_retrieve = gate.retriever.retrieve
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def no_filter_retrieve(blocks, qa, top_m=20, **kwargs):
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# 把所有块都放进来，不做内容词过滤
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scored = []
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idf_cache = kwargs.get('idf_cache', {})
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for block in blocks:
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s = gate.retriever.score(block, qa, 0.0, idf_cache)
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scored.append((block, s))
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scored.sort(key=lambda x: x[1], reverse=True)
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return scored[:top_m]
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gate.retriever.retrieve = no_filter_retrieve
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sel = gate.select(query)
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gate.topic_gate.is_topic_switch = orig_switched
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gate.retriever.retrieve = orig_retrieve
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return sel
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def run_no_recency(gate, query):
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"""-Recency: 移除 recency 权重"""
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orig_WEIGHT_RECENT = gate.retriever.WEIGHT_RECENT
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gate.retriever.WEIGHT_RECENT = 0.0
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sel = gate.select(query)
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gate.retriever.WEIGHT_RECENT = orig_WEIGHT_RECENT
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return sel
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def run_no_idf(gate, query):
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"""-IDF: 移除 IDF 加权（所有词 IDF=1.0）"""
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orig_idf_cache = gate.anchor_extractor._idf_cache.copy() if hasattr(gate.anchor_extractor, '_idf_cache') else {}
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def fake_idf(anchor):
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return 1.0 # 固定 IDF=1.0，取消区分度
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orig_idf_fn = gate.anchor_extractor.idf
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gate.anchor_extractor.idf = fake_idf
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# 清空 idf_cache 让所有词都用默认值
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gate.anchor_extractor._idf_cache.clear()
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sel = gate.select(query)
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gate.anchor_extractor.idf = orig_idf_fn
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gate.anchor_extractor._idf_cache = orig_idf_cache
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return sel
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def main():
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results = {
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'Full CGK': [],
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'Gate-only': [], # 无覆盖优化（ChatGPT指出的缺失）
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'Coverage-only': [], # 无门控过滤（ChatGPT指出的缺失）
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'-Recency': [], # 无近期偏好
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'-IDF': [], # 无IDF加权
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'-Deictic': [],
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'-Exact Match': [],
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'-Trim': [],
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'Last-5 (baseline)': [],
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}
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print("="*70)
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print("Phase 2 COMPLETE Ablation Study (补全)")
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print("="*70)
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for label, query, target in TEST_SEQ:
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print(f"\n[{label}] {query[:45]}...")
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# Full CGK
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g = build_gate()
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sel = g.select(query)
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pt = measure_prompt_tokens(sel, query)
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cont, _ = evaluate_contamination(sel, target)
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aq = evaluate_answer_quality(g, query, target)
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results['Full CGK'].append({'pt': pt, 'cont': cont, 'aq': aq})
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print(f" Full CGK: {pt:5.0f} tok, 污染={cont}, 纯度={aq['purity']:.2f}, 锚点覆盖={aq['anchor_coverage']:.2f}")
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# Gate-only
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g2 = build_gate()
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sel2 = run_gate_only(g2, query)
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pt2 = measure_prompt_tokens(sel2, query)
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cont2, _ = evaluate_contamination(sel2, target)
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results['Gate-only'].append({'pt': pt2, 'cont': cont2})
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print(f" Gate-only: {pt2:5.0f} tok, 污染={cont2}")
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# Coverage-only
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g3 = build_gate()
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sel3 = run_coverage_only(g3, query)
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pt3 = measure_prompt_tokens(sel3, query)
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cont3, _ = evaluate_contamination(sel3, target)
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results['Coverage-only'].append({'pt': pt3, 'cont': cont3})
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print(f" Coverage-only:{pt3:5.0f} tok, 污染={cont3}")
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# -Recency
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g4 = build_gate()
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sel4 = run_no_recency(g4, query)
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pt4 = measure_prompt_tokens(sel4, query)
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cont4, _ = evaluate_contamination(sel4, target)
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results['-Recency'].append({'pt': pt4, 'cont': cont4})
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print(f" -Recency: {pt4:5.0f} tok, 污染={cont4}")
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# -IDF
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g5 = build_gate()
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sel5 = run_no_idf(g5, query)
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pt5 = measure_prompt_tokens(sel5, query)
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cont5, _ = evaluate_contamination(sel5, target)
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results['-IDF'].append({'pt': pt5, 'cont': cont5})
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print(f" -IDF: {pt5:5.0f} tok, 污染={cont5}")
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# -Deictic
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g6 = build_gate()
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orig = g6.anchor_extractor.extract_with_deictic
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def no_d(text):
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a, _ = orig(text)
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return a, False
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g6.anchor_extractor.extract_with_deictic = no_d
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sel6 = g6.select(query)
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pt6 = measure_prompt_tokens(sel6, query)
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cont6, _ = evaluate_contamination(sel6, target)
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results['-Deictic'].append({'pt': pt6, 'cont': cont6})
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print(f" -Deictic: {pt6:5.0f} tok, 污染={cont6}")
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# -Exact Match
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g7 = build_gate()
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orig_exact = g7.retriever._exact_match
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g7.retriever._exact_match = lambda b, qa: 0.0
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sel7 = g7.select(query)
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pt7 = measure_prompt_tokens(sel7, query)
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cont7, _ = evaluate_contamination(sel7, target)
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results['-Exact Match'].append({'pt': pt7, 'cont': cont7})
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print(f" -Exact Match: {pt7:5.0f} tok, 污染={cont7}")
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# -Trim
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g8 = build_gate()
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g8._trim_blocks_to_query = lambda blocks, qa: blocks
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sel8 = g8.select(query)
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pt8 = measure_prompt_tokens(sel8, query)
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cont8, _ = evaluate_contamination(sel8, target)
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results['-Trim'].append({'pt': pt8, 'cont': cont8})
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print(f" -Trim: {pt8:5.0f} tok, 污染={cont8}")
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# Last-5 baseline
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conv = [{'user': redis_qa[i][0], 'assistant': redis_qa[i][1]} for i in range(5)] + \
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[{'user': asyncio_qa[i][0], 'assistant': asyncio_qa[i][1]} for i in range(5)] + \
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[{'user': pg_qa[i][0], 'assistant': pg_qa[i][1]} for i in range(5)] + \
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[{'user': git_qa[i][0], 'assistant': git_qa[i][1]} for i in range(5)]
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sel9 = conv[-5:]
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pt9 = measure_prompt_tokens(sel9, query)
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cont9, _ = evaluate_contamination(sel9, target)
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results['Last-5 (baseline)'].append({'pt': pt9, 'cont': cont9})
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print(f" Last-5: {pt9:5.0f} tok, 污染={cont9}")
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# Summary
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print("\n" + "="*70)
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print("Ablation Summary (avg over {} queries)".format(len(TEST_SEQ)))
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print("="*70)
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full_avg = sum(d['pt'] for d in results['Full CGK']) / len(results['Full CGK'])
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full_cont = sum(1 for d in results['Full CGK'] if d['cont']) / len(results['Full CGK']) * 100
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print(f"\n{'Method':<20} {'Avg Tokens':>12} {'Cont%':>8} {'ΔTokens':>10} {'Notes':<30}")
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print("-"*80)
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print(f"{'Full CGK':<20} {full_avg:>12.1f} {full_cont:>8.1f} {'—':>10} {'baseline':<30}")
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for name in ['Gate-only', 'Coverage-only', '-Recency', '-IDF', '-Deictic', '-Exact Match', '-Trim', 'Last-5 (baseline)']:
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data = results[name]
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avg_tok = sum(d['pt'] for d in data) / len(data)
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cont_pct = sum(1 for d in data if d['cont']) / len(data) * 100
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diff = avg_tok - full_avg
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notes = ""
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if name == 'Gate-only':
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notes = "← 关键模块"
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elif name == 'Coverage-only':
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notes = "← 无门控"
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elif name == '-Recency':
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notes = "← recency权重→0"
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elif name == '-IDF':
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notes = "← IDF=1.0固定"
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print(f"{name:<20} {avg_tok:>12.1f} {cont_pct:>8.1f} {diff:>+10.1f} {notes:<30}")
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# Key findings
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print("\n" + "="*70)
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print("Key Findings")
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print("="*70)
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# Compare Gate-only vs Coverage-only
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go_avg = sum(d['pt'] for d in results['Gate-only']) / len(results['Gate-only'])
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co_avg = sum(d['pt'] for d in results['Coverage-only']) / len(results['Coverage-only'])
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go_cont = sum(1 for d in results['Gate-only'] if d['cont']) / len(results['Gate-only']) * 100
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co_cont = sum(1 for d in results['Coverage-only'] if d['cont']) / len(results['Coverage-only']) * 100
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print(f"\n1. Gate-only vs Coverage-only (最关键的两个 ablated variants):")
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print(f" Gate-only: {go_avg:.1f} tokens, 污染率 {go_cont:.0f}%")
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print(f" Coverage-only: {co_avg:.1f} tokens, 污染率 {co_cont:.0f}%")
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print(f" 结论: 门控过滤(Gate)对污染率的影响{'远大于' if co_cont > go_cont else '相当'}覆盖优化(Coverage)")
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# -Recency effect
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rec_avg = sum(d['pt'] for d in results['-Recency']) / len(results['-Recency'])
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rec_cont = sum(1 for d in results['-Recency'] if d['cont']) / len(results['-Recency']) * 100
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print(f"\n2. -Recency (移除 recency 权重):")
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print(f" -Recency: {rec_avg:.1f} tokens, 污染率 {rec_cont:.0f}% (vs Full {full_cont:.0f}%)")
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# -IDF effect
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idf_avg = sum(d['pt'] for d in results['-IDF']) / len(results['-IDF'])
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idf_cont = sum(1 for d in results['-IDF'] if d['cont']) / len(results['-IDF']) * 100
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print(f"\n3. -IDF (固定所有词 IDF=1.0):")
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print(f" -IDF: {idf_avg:.1f} tokens, 污染率 {idf_cont:.0f}% (vs Full {full_cont:.0f}%)")
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print(f" 结论: IDF 加权对 token 消耗的影响 {'明显' if abs(idf_avg - full_avg) > 5 else '较小'}")
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out_path = os.path.join(os.path.dirname(__file__), 'phase2_complete_ablation_results.json')
|
||
with open(out_path, 'w') as f:
|
||
json.dump(results, f, indent=2, ensure_ascii=False)
|
||
print(f"\nSaved to: {out_path}")
|
||
|
||
if __name__ == '__main__':
|
||
main() |