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lofyer
co-authored by
factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
commit
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#!/usr/bin/env python3
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"""
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覆盖率审计脚本
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检查：
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1. 二期ETL输出的字段名称，赋分引擎实际用到了多少
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2. 赋分引擎里按关键词匹配的字段，在二期数据中能否命中
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3. 每个维度的赋分数据源覆盖情况
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"""
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import sys
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import re
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import inspect
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from pathlib import Path
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from collections import defaultdict
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import pandas as pd
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent))
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "backend"))
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from data_engine_era2 import DataEngineEra2
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from app.engines.scoring_engine import ScoringEngine
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from config_era2 import DIMENSION_FRAMEWORK, SUBJECTS
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def section(title):
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print(f"\n{'=' * 80}")
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print(f" {title}")
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print(f"{'=' * 80}")
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def main():
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# ===== 加载二期全量数据（不分区，看全貌） =====
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section("1. 加载二期全量数据")
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engine = DataEngineEra2(district_filter=None)
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engine.load_all()
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print(f" 学校数: {len(engine.schools)}")
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# 获取全量字段名称
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course_df = engine._course_impl
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subject_df = engine._subject_impl
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basic_df = engine._basic_info
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course_fields = sorted(course_df["字段名称"].dropna().unique())
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subject_fields = sorted(subject_df["字段名称"].dropna().unique())
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basic_fields = sorted(basic_df["字段名称"].dropna().unique())
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print(f" 课程实施表字段名称数: {len(course_fields)}")
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print(f" 学科课程表字段名称数: {len(subject_fields)}")
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print(f" 基础信息表字段名称数: {len(basic_fields)}")
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# ===== 2. 提取赋分引擎中所有用到的字段名称和关键词 =====
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section("2. 赋分引擎字段引用分析")
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# 从 ScoringEngine 源码中提取所有字符串常量（字段名称/关键词）
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src = inspect.getsource(ScoringEngine)
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# 提取所有中文字符串（字段名称）
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# 匹配双引号和单引号中的中文字符串
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field_refs = set()
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keyword_refs = set()
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# 精确字段引用：df["字段名称"] == "xxx" 或 字段名称 == field
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exact_patterns = re.findall(r'["\']([^"\']*[\u4e00-\u9fff][^"\']*)["\']', src)
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for p in exact_patterns:
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# 跳过注释性文字
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if len(p) > 30 or '：' in p or '。' in p or '赋分' in p:
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continue
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field_refs.add(p)
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# 关键词引用：str.contains("xxx")
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contains_patterns = re.findall(r'\.str\.contains\(["\']([^"\']+)["\']', src)
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for p in contains_patterns:
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# 这些是用 | 分隔的关键词
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for kw in p.split('|'):
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keyword_refs.add(kw.strip())
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# 其他关键词引用（in循环中的列表）
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keyword_lists = re.findall(r'for (?:keyword|field|hw_type|resource) in \[([^\]]+)\]', src)
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for kl in keyword_lists:
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items = re.findall(r'["\']([^"\']+)["\']', kl)
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for item in items:
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if any('\u4e00' <= c <= '\u9fff' for c in item):
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field_refs.add(item)
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print(f"\n 赋分引擎中精确引用的字段名称: {len(field_refs)}个")
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print(f" 赋分引擎中关键词引用: {len(keyword_refs)}个")
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# ===== 3. 逐一检查精确字段在二期数据中的命中情况 =====
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section("3. 精确字段匹配检查")
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all_data_fields = set(course_fields) | set(subject_fields) | set(basic_fields)
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matched = []
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missing = []
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for f in sorted(field_refs):
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if f in all_data_fields:
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matched.append(f)
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else:
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missing.append(f)
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print(f"\n ✅ 命中: {len(matched)}/{len(field_refs)}")
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print(f" ❌ 未命中: {len(missing)}/{len(field_refs)}")
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if missing:
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print(f"\n 未命中的字段（赋分引擎引用但二期数据中不存在）:")
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for f in missing:
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# 尝试模糊匹配
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fuzzy = [df for df in all_data_fields if f.replace('_', '') in df.replace('_', '') or df.replace('_', '') in f.replace('_', '')]
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if fuzzy:
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print(f" ❌ {f}")
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print(f" → 可能对应: {fuzzy[:3]}")
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else:
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print(f" ❌ {f} (无近似匹配)")
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# ===== 4. 关键词匹配检查 =====
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section("4. 关键词匹配检查")
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for kw in sorted(keyword_refs):
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course_hits = course_df[course_df["字段名称"].str.contains(kw, na=False)]["字段名称"].unique()
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subject_hits = subject_df[subject_df["字段名称"].str.contains(kw, na=False)]["字段名称"].unique()
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total = len(course_hits) + len(subject_hits)
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status = "✅" if total > 0 else "❌"
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print(f" {status} '{kw}': 课程表{len(course_hits)}个, 学科表{len(subject_hits)}个")
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if total == 0:
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# 看看有没有相近的
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all_names = list(course_fields) + list(subject_fields)
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similar = [n for n in all_names if kw[:2] in n][:3]
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if similar:
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print(f" → 近似: {similar}")
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# ===== 5. 按维度逐一检查赋分数据覆盖 =====
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section("5. 按维度检查赋分数据覆盖（抽样一所学校）")
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# 取一所数据较完整的学校
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test_school = engine.schools[0]
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print(f" 测试学校: {test_school}")
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scoring = ScoringEngine(engine)
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scores = scoring._score_school(test_school)
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print(f"\n {'维度':<20} {'赋分项数':>8} {'均值':>8} {'是否有效':>8}")
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print(f" {'─' * 50}")
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for dim, vals in scores.items():
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n = len(vals)
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avg = np.mean(vals) if vals else 0
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# 判断是否有效：是否全是默认值
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is_default = (n <= 1 and abs(avg - 1.0) < 0.01) or (n <= 1 and abs(avg - 1.5) < 0.01) or (n <= 1 and abs(avg - 2.0) < 0.01) or (n <= 1 and abs(avg - 0.5) < 0.01)
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status = "⚠️ 默认值" if is_default else "✅"
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print(f" {dim:<20} {n:>8} {avg:>8.2f} {status:>8}")
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# ===== 6. 多校抽样统计 =====
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section("6. 多校统计：各维度赋分项数分布")
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# 取前20所学校统计
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sample_schools = engine.schools[:20]
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dim_stats = defaultdict(list)
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for school in sample_schools:
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s = scoring._score_school(school)
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for dim, vals in s.items():
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dim_stats[dim].append(len(vals))
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print(f"\n 抽样学校数: {len(sample_schools)}")
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print(f"\n {'维度':<20} {'最小':>6} {'最大':>6} {'均值':>6} {'全为1':>8}")
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print(f" {'─' * 50}")
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for dim in DIMENSION_FRAMEWORK:
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for sub in DIMENSION_FRAMEWORK[dim]["sub_dimensions"]:
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vals = dim_stats.get(sub, [0])
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min_v = min(vals)
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max_v = max(vals)
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avg_v = np.mean(vals)
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all_one = sum(1 for v in vals if v <= 1)
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warn = "⚠️" if all_one > len(vals) * 0.5 else ""
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print(f" {sub:<20} {min_v:>6} {max_v:>6} {avg_v:>6.1f} {all_one:>4}/{len(vals)} {warn}")
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# ===== 7. 二期新增但赋分引擎未使用的字段 =====
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section("7. 二期数据中存在但赋分引擎未引用的高频字段（Top 30）")
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# 统计二期中每个字段名称出现的学校数
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course_field_school_count = course_df.groupby("字段名称")["学校名称"].nunique().sort_values(ascending=False)
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subject_field_school_count = subject_df.groupby("字段名称")["学校名称"].nunique().sort_values(ascending=False)
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# 过滤掉已被赋分引擎引用的
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unused_course = course_field_school_count[~course_field_school_count.index.isin(field_refs)]
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unused_subject = subject_field_school_count[~subject_field_school_count.index.isin(field_refs)]
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# 进一步过滤：去掉被关键词匹配可能命中的
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def is_keyword_matched(field_name):
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for kw in keyword_refs:
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if kw in str(field_name):
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return True
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return False
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unused_course_strict = unused_course[~unused_course.index.map(is_keyword_matched)]
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unused_subject_strict = unused_subject[~unused_subject.index.map(is_keyword_matched)]
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print(f"\n [课程实施表] 未被引用的字段 (按学校覆盖率排序, Top 20):")
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for field, cnt in unused_course_strict.head(20).items():
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print(f" {field}: {cnt}所学校有数据")
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print(f"\n [学科课程表] 未被引用的字段 (Top 20):")
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for field, cnt in unused_subject_strict.head(20).items():
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print(f" {field}: {cnt}所学校有数据")
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print(f"\n✅ 覆盖率审计完成!")
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if __name__ == "__main__":
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main()
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