RoyirinRosmontis
a10442d78c
- 全链路：多源JD接入/清洗交叉验证/结构化抽取(星火+离线双通道)/时序知识图谱/新岗位发现/演化分析/人岗匹配/学习路径 - WebUI 五视图：驾驶舱/图谱工作台/岗位演化/匹配(单人+批量)/学习路径 - 设置中心(config.json)/Token鉴权/SQLite状态库/语义向量通道(BGE-M3本地或联网API) - 71 个 pytest 用例；数据目录仅保留结构(.gitkeep)，数据由 scripts/generate_dataset.py 复现
252 lines
11 KiB
Python
252 lines
11 KiB
Python
"""指标复现脚本：真实“从文本抽取 → 与 gold 比对”流程，不硬编码任何答案。
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三项硬指标（合成测试集）：
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1. JD 解析准确率 —— 对 data/testset/jd_testset.jsonl 重新抽取，与 gold_answers.json
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算字段级 P/R/F1（必备技能/加分技能/职责/场景 micro-F1 + 岗位名称准确率）；
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2. 简历提取准确率 —— 对 data/resumes/*.txt 解析技能，与 gold.json 算 F1；
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3. 人岗匹配准确率 —— 对 data/testset/match_gold.json 标注对跑匹配引擎，
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判定“命中预期匹配/不匹配”的比例。
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两个真实数据维度（data/real/，与合成集分开报告、不混算）：
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4. 真实 JD（AutoWeakS 初标+抽检 silver gold）—— 对 jd_sample.jsonl 重新抽取，
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与 gold_answers.json 算技能/场景字段级 F1；
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5. 真实简历（AIStudio CC0）—— 对 50 份简历解析，姓名/电话命中率 + 技能 F1。
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输出 reports/metrics.json 与控制台报告。
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"""
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from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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from app.extract.offline import OfflineExtractor # noqa: E402
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from app.extract.base import SkillMatcher, load_lexicon # noqa: E402
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from app.graph.tkg import TemporalKG # noqa: E402
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from app.ingest.loader import load_jd_file # noqa: E402
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from app.match.engine import MatchEngine # noqa: E402
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from app.match.resume import parse_resume # noqa: E402
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from app.models import JDRecord # noqa: E402
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def prf(tp: int, pred: int, gold: int) -> tuple[float, float, float]:
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p = tp / pred if pred else 0.0
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r = tp / gold if gold else 0.0
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f1 = 2 * p * r / (p + r) if p + r else 0.0
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return round(p, 4), round(r, 4), round(f1, 4)
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def eval_jd_extraction() -> dict:
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"""JD 解析：从文本重新抽取，与标准答案做字段级比对。"""
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records, errors = load_jd_file(ROOT / "data" / "testset" / "jd_testset.jsonl")
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gold = json.loads((ROOT / "data" / "testset" / "gold_answers.json").read_text(encoding="utf-8"))
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extractor = OfflineExtractor(load_lexicon())
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fields = ["required_skills", "preferred_skills", "responsibilities", "scenarios"]
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tp = {f: 0 for f in fields}
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pred = {f: 0 for f in fields}
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gold_n = {f: 0 for f in fields}
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title_hit = 0
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for rec in records:
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g = gold[rec.jid]
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ext = extractor.extract(rec)
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title_hit += int(ext.job_title == g["job_title"])
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for f in fields:
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got, want = set(getattr(ext, f)), set(g[f])
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tp[f] += len(got & want)
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pred[f] += len(got)
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gold_n[f] += len(want)
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detail = {}
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f1s = []
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for f in fields:
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p, r, f1 = prf(tp[f], pred[f], gold_n[f])
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detail[f] = {"precision": p, "recall": r, "f1": f1}
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f1s.append(f1)
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title_acc = round(title_hit / len(records), 4)
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# 综合准确率：技能字段权重更高（技能是岗位图谱的核心）
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accuracy = round(0.6 * (f1s[0] + f1s[1]) / 2 + 0.2 * f1s[2] + 0.1 * f1s[3] + 0.1 * title_acc, 4)
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return {"accuracy": accuracy, "title_accuracy": title_acc, "fields": detail,
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"samples": len(records), "bad_lines": len(errors)}
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def eval_resume() -> dict:
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"""简历技能提取准确率（逐份 F1 取平均）。"""
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gold = json.loads((ROOT / "data" / "resumes" / "gold.json").read_text(encoding="utf-8"))
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matcher = SkillMatcher(load_lexicon())
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f1s, detail = [], {}
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for name, g in gold.items():
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text = (ROOT / "data" / "resumes" / name).read_text(encoding="utf-8")
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profile = parse_resume(text, matcher)
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got, want = set(profile.skills), set(g["skills"])
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p, r, f1 = prf(len(got & want), len(got), len(want))
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f1s.append(f1)
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detail[name] = {"precision": p, "recall": r, "f1": f1,
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"extra": sorted(got - want), "missing": sorted(want - got)}
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return {"accuracy": round(sum(f1s) / len(f1s), 4), "detail": detail}
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def eval_match() -> dict:
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"""人岗匹配准确率：命中预期匹配/不匹配。"""
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graph_path = ROOT / "data" / "graph.json"
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if not graph_path.exists():
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from app.services.pipeline import run_pipeline
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run_pipeline()
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kg = TemporalKG.load(graph_path)
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by_title = {j.title: j for j in kg.jobs.values()}
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pairs = json.loads((ROOT / "data" / "testset" / "match_gold.json").read_text(encoding="utf-8"))
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engine = MatchEngine(load_lexicon())
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from app.models import ResumeProfile
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hit, detail = 0, []
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for pair in pairs:
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job = by_title.get(pair["job_title"])
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if job is None:
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continue
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profile = ResumeProfile(skills=pair["skills"], years=pair["years"],
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scenarios=pair["scenarios"])
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result = engine.match(profile, job)
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ok = result.is_match == pair["expected_match"]
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hit += int(ok)
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detail.append({"job": pair["job_title"], "expected": pair["expected_match"],
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"score": result.total_score, "ok": ok})
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return {"accuracy": round(hit / len(pairs), 4), "pairs": len(pairs), "detail": detail}
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def eval_real_jd() -> dict:
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"""真实 JD（AutoWeakS）：从文本重新抽取，与 silver gold（初标+抽检）字段级比对。
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真实 JD 是自由文本、无编号版面，gold 的 responsibilities/preferred_skills 为空，
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口径 = 全文词表技能（required_skills）+ 应用场景 + 启发式岗位名，见 data/real/README.md。
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"""
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records, errors = load_jd_file(ROOT / "data" / "real" / "autoweaks_jd" / "jd_sample.jsonl")
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gold = json.loads(
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(ROOT / "data" / "real" / "autoweaks_jd" / "gold_answers.json").read_text(encoding="utf-8"))
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extractor = OfflineExtractor(load_lexicon())
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fields = ["required_skills", "preferred_skills", "scenarios"]
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tp = {f: 0 for f in fields}
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pred = {f: 0 for f in fields}
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gold_n = {f: 0 for f in fields}
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title_hit = 0
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for rec in records:
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g = gold[rec.jid]
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ext = extractor.extract(rec)
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title_hit += int(ext.job_title == g["job_title"])
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for f in fields:
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got, want = set(getattr(ext, f)), set(g[f])
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tp[f] += len(got & want)
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pred[f] += len(got)
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gold_n[f] += len(want)
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detail, active_f1 = {}, []
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for f in fields:
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if not gold_n[f] and not pred[f]:
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detail[f] = None # 该字段在真实语料中无标注口径，不参与
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continue
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p, r, f1 = prf(tp[f], pred[f], gold_n[f])
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detail[f] = {"precision": p, "recall": r, "f1": f1}
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active_f1.append((f, f1))
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weights = {"required_skills": 0.7, "preferred_skills": 0.2, "scenarios": 0.1}
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wsum = sum(weights[f] for f, _ in active_f1) or 1.0
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accuracy = round(sum(weights[f] * f1 for f, f1 in active_f1) / wsum, 4)
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return {"accuracy": accuracy,
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"title_accuracy": round(title_hit / len(records), 4),
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"fields": detail, "samples": len(records), "bad_lines": len(errors)}
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def eval_real_resume() -> dict:
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"""真实简历（AIStudio CC0）：姓名/电话命中率（脱敏形态比对）+ 技能 F1。"""
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gold = json.loads(
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(ROOT / "data" / "real" / "aistudio_resumes" / "gold.json").read_text(encoding="utf-8"))
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matcher = SkillMatcher(load_lexicon())
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f1s, name_hit, phone_hit, phone_total = [], 0, 0, 0
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empty_skills = 0
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for name, g in gold.items():
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text = (ROOT / "data" / "real" / "aistudio_resumes" / name).read_text(encoding="utf-8")
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profile = parse_resume(text, matcher)
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# gold 存明文，解析结果是脱敏形态（姓氏+* / 138****5678），按脱敏形态比对
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want_name = g["name"][0] + "*" * (len(g["name"]) - 1)
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name_hit += int(profile.name == want_name)
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if g.get("phone"):
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phone_total += 1
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want_phone = g["phone"][:3] + "****" + g["phone"][-2:]
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phone_hit += int(profile.phone == want_phone)
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got, want = set(profile.skills), set(g["skills"])
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if not got and not want:
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empty_skills += 1
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f1s.append(1.0) # 双方均无词表技能 = 完全一致（真实简历大量非技术内容）
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continue
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_, _, f1 = prf(len(got & want), len(got), len(want))
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f1s.append(f1)
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n = len(gold)
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return {"accuracy": round(sum(f1s) / n, 4),
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"name_accuracy": round(name_hit / n, 4),
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"phone_accuracy": round(phone_hit / phone_total, 4) if phone_total else 0.0,
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"samples": n, "phone_samples": phone_total,
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"empty_skill_resumes": empty_skills}
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def main() -> None:
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jd = eval_jd_extraction()
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resume = eval_resume()
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match = eval_match()
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real_jd = eval_real_jd()
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real_resume = eval_real_resume()
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metrics = {
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"jd_extraction_accuracy": jd["accuracy"],
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"resume_extraction_accuracy": resume["accuracy"],
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"match_accuracy": match["accuracy"],
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"jd_detail": jd,
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"resume_detail": resume["detail"],
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"match_detail": match["detail"],
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"real_jd": real_jd,
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"real_resume": real_resume,
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}
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out = ROOT / "reports"
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out.mkdir(exist_ok=True)
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(out / "metrics.json").write_text(
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json.dumps(metrics, ensure_ascii=False, indent=1), encoding="utf-8")
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print("=" * 56)
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print("CareerNexus 指标复现报告")
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print("=" * 56)
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print(f"JD 解析准确率 : {jd['accuracy']:.2%} (样本 {jd['samples']} 条)")
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for f, d in jd["fields"].items():
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print(f" - {f:<18} P={d['precision']:.2%} R={d['recall']:.2%} F1={d['f1']:.2%}")
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print(f" - 岗位名称准确率 {jd['title_accuracy']:.2%}")
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print(f"简历提取准确率 : {resume['accuracy']:.2%}")
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for name, d in resume["detail"].items():
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print(f" - {name:<22} F1={d['f1']:.2%}")
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print(f"人岗匹配准确率 : {match['accuracy']:.2%} (标注对 {match['pairs']} 对)")
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print("-" * 56)
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print("真实数据维度（data/real/，与合成集分开报告）")
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print(f"真实 JD（AutoWeakS 初标+抽检）: 综合 F1 {real_jd['accuracy']:.2%}"
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f" (样本 {real_jd['samples']} 条)")
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for f, d in real_jd["fields"].items():
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if d is None:
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print(f" - {f:<18} (真实语料无标注口径，不参与)")
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else:
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print(f" - {f:<18} P={d['precision']:.2%} R={d['recall']:.2%} F1={d['f1']:.2%}")
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print(f" - 岗位名称准确率 {real_jd['title_accuracy']:.2%} (启发式标注)")
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print(f"真实简历（AIStudio CC0） : 技能 F1 {real_resume['accuracy']:.2%}"
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f" 姓名命中 {real_resume['name_accuracy']:.2%}"
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f" 电话命中 {real_resume['phone_accuracy']:.2%} ({real_resume['samples']} 份)")
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print(f"\n指标已写入 {out / 'metrics.json'}")
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target = 0.90
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for name, v in [("JD解析", jd["accuracy"]), ("简历提取", resume["accuracy"]),
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("人岗匹配", match["accuracy"])]:
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status = "达标" if v >= target else "未达标"
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print(f"[{status}] {name} {v:.2%} (目标 ≥{target:.0%})")
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real_targets = [("真实JD技能F1", real_jd["fields"]["required_skills"]["f1"], 0.80),
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("真实姓名命中", real_resume["name_accuracy"], 1.00)]
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for name, v, t in real_targets:
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status = "达标" if v >= t else "未达标"
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print(f"[{status}] {name} {v:.2%} (目标 ≥{t:.0%})")
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if __name__ == "__main__":
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main()
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