Initial commit

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
2026-07-13 15:38:41 +08:00
commit 71db82393a
180 changed files with 170640 additions and 0 deletions
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+#!/usr/bin/env python3
+"""
+测试脚本：完整Pipeline - 数据加载→赋分→PCA→标准化→水平判定→统计分析
+"""
+import sys
+from pathlib import Path
+import json
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
+
+from app.engines.data_engine import DataEngine
+from app.engines.scoring_engine import ScoringEngine
+from app.engines.stats_engine import StatsEngine
+from app.config import DIMENSION_FRAMEWORK, LEVEL_THRESHOLDS
+import logging
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s")
+
+
+def main():
+    print("=" * 80)
+    print("完整Pipeline测试：Excel → 赋分 → PCA → 标准化 → 水平判定")
+    print("=" * 80)
+
+    # Step 1: 加载数据
+    print("\n[Step 1] 加载Excel数据...")
+    data_engine = DataEngine()
+    data_engine.load_all()
+
+    # Step 2: 赋分
+    print("\n[Step 2] 对所有学校进行赋分...")
+    scoring_engine = ScoringEngine(data_engine)
+    raw_scores = scoring_engine.score_all_schools()
+
+    # 打印赋分结果概要
+    print("\n📊 赋分结果概要:")
+    for school in data_engine.schools:
+        scores = raw_scores[school]
+        print(f"\n  🏫 {school}:")
+        for dim, vals in scores.items():
+            avg = sum(vals) / len(vals) if vals else 0
+            print(f"    {dim}: {len(vals)}个指标, 均值={avg:.2f}, 值={[round(v,1) for v in vals[:5]]}{'...' if len(vals) > 5 else ''}")
+
+    # Step 3: 统计分析
+    print("\n[Step 3] PCA合成 + 标准化...")
+    stats_engine = StatsEngine()
+    sub_scores = stats_engine.compute_dimension_scores(raw_scores)
+    print(f"\n三级维度标准化得分 (均值50, 标准差10):")
+    print(sub_scores.round(2).to_string())
+
+    # Step 4: 二级维度聚合
+    print("\n[Step 4] 二级维度聚合...")
+    dim_scores = stats_engine.compute_dimension_aggregates(sub_scores)
+    print(f"\n二级维度得分:")
+    print(dim_scores.round(2).to_string())
+
+    # Step 5: 水平判定
+    print("\n[Step 5] 水平判定...")
+    levels = stats_engine.compute_levels(sub_scores)
+    print(f"\n各学校各维度水平:")
+    print(levels.to_string())
+
+    # Step 6: 聚类分析
+    print("\n[Step 6] 聚类分析...")
+    dim_cols = [c for c in dim_scores.columns if c in DIMENSION_FRAMEWORK]
+    clusters = stats_engine.cluster_analysis(dim_scores[dim_cols])
+    print(f"\n学校聚类结果:")
+    for school, cluster in clusters["school_clusters"].items():
+        print(f"  {school}: {cluster}")
+
+    # Step 7: 相关性
+    print("\n[Step 7] 维度间相关性:")
+    corr = stats_engine.correlation_analysis(dim_scores)
+    print(corr.round(3).to_string())
+
+    # Step 8: 生成一所学校的完整报告数据
+    test_school = "延安中学"
+    print(f"\n[Step 8] 生成 {test_school} 的完整报告数据包...")
+    report_data = stats_engine.compute_school_report_data(test_school, sub_scores, dim_scores)
+
+    print(f"\n📋 {test_school} 报告数据概要:")
+    print(f"  总体得分: {report_data['overall']['score']}")
+    print(f"  区内排名: {report_data['overall']['rank_in_district']}/{report_data['overall']['total_schools']}")
+    print(f"  聚类类型: {report_data['overall']['cluster']}")
+
+    print(f"\n  二级维度得分:")
+    for dim, data in report_data['dimensions'].items():
+        print(f"    {dim}: {data['score']} (区均值{data['district_avg']}, 差异{data['diff_district']:+.2f})")
+
+    print(f"\n  三级维度水平:")
+    for sub_dim, data in report_data['sub_dimensions'].items():
+        print(f"    {sub_dim}: 得分{data['score']}, 水平{data['level']}, {data['level_description'][:30]}")
+
+    # 保存报告数据为JSON
+    output_dir = Path(__file__).parent.parent / "output"
+    output_dir.mkdir(exist_ok=True)
+
+    # 清理numpy类型以便JSON序列化
+    def clean_for_json(obj):
+        import numpy as np
+        if isinstance(obj, dict):
+            return {k: clean_for_json(v) for k, v in obj.items()}
+        elif isinstance(obj, list):
+            return [clean_for_json(v) for v in obj]
+        elif isinstance(obj, (np.integer,)):
+            return int(obj)
+        elif isinstance(obj, (np.floating,)):
+            return round(float(obj), 4)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, float):
+            return round(obj, 4)
+        return obj
+
+    report_clean = clean_for_json(report_data)
+    with open(output_dir / f"{test_school}_report_data.json", "w", encoding="utf-8") as f:
+        json.dump(report_clean, f, ensure_ascii=False, indent=2)
+    print(f"\n✅ 报告数据已保存到 output/{test_school}_report_data.json")
+
+    # 保存所有学校的得分汇总
+    all_scores = {
+        "sub_dimension_scores": clean_for_json(sub_scores.to_dict()),
+        "dimension_scores": clean_for_json(dim_scores.to_dict()),
+        "levels": clean_for_json(levels.to_dict()),
+        "clusters": clean_for_json(clusters),
+    }
+    with open(output_dir / "all_schools_scores.json", "w", encoding="utf-8") as f:
+        json.dump(all_scores, f, ensure_ascii=False, indent=2)
+    print(f"✅ 全校得分汇总已保存到 output/all_schools_scores.json")
+
+    print("\n" + "=" * 80)
+    print("✅ 完整Pipeline测试完成!")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()