Initial commit
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
This commit is contained in:
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
71db82393a
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#!/usr/bin/env python3
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"""
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测试脚本：验证数据引擎能正确读取和解析Excel数据
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"""
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import sys
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from pathlib import Path
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# 添加项目路径
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sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
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from app.engines.data_engine import DataEngine
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from app.config import DIMENSION_FRAMEWORK, SCHOOL_TYPE_MAP
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import json
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def main():
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print("=" * 80)
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print("数据引擎测试")
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print("=" * 80)
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engine = DataEngine()
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engine.load_all()
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# 1. 基本信息
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summary = engine.summary()
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print(f"\n📊 数据摘要:")
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print(f" 学校数量: {summary['school_count']}")
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print(f" 学校列表: {summary['schools']}")
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print(f" 基础信息行数: {summary['basic_info_rows']}")
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print(f" 课程实施行数: {summary['course_impl_rows']}")
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print(f" 学科课程行数: {summary['subject_impl_rows']}")
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# 2. 测试单校数据
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test_school = "延安中学"
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print(f"\n🏫 {test_school} 基本信息:")
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info = engine.get_school_basic_info(test_school)
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for k, v in info.items():
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print(f" {k}: {v}")
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# 3. 测试课程数据
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print(f"\n📚 {test_school} 课程实施数据:")
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course_df = engine.get_school_course_data(test_school)
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print(f" 总行数: {len(course_df)}")
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print(f" 涉及学科: {sorted(course_df['学科'].unique().tolist())}")
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print(f" 涉及题号: {sorted(course_df['题号'].unique().tolist())[:20]}...")
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# 4. 测试学科数据
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print(f"\n🔬 {test_school} 学科课程数据:")
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subject_df = engine.get_school_subject_data(test_school)
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print(f" 总行数: {len(subject_df)}")
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print(f" 涉及学科: {sorted(subject_df['学科'].unique().tolist())}")
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# 5. 测试周课时数据
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print(f"\n⏰ {test_school} 周课时数据（前10行）:")
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hours = engine.get_weekly_hours(test_school)
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if len(hours) > 0:
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print(hours[["学科", "年级", "学期", "字段名称", "字段取值"]].head(10).to_string())
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else:
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print(" 无课时数据")
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# 6. 测试课程规范数据
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print(f"\n📋 {test_school} 课程规范数据（前5条）:")
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norms = engine.get_course_norms(test_school)
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for n in norms[:5]:
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print(f" {n['字段名称']}: {n['字段取值']}")
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# 7. 所有学校类型
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print(f"\n🏷️ 学校类型:")
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for school in engine.schools:
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info = SCHOOL_TYPE_MAP.get(school, {})
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print(f" {school}: {info.get('type', '未知')} ({info.get('nature', '')}) [{info.get('feature', '')}]")
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print("\n✅ 数据引擎测试完成!")
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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"""
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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
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"""
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import sys
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from pathlib import Path
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import json
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sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
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from app.engines.data_engine import DataEngine
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from app.engines.scoring_engine import ScoringEngine
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from app.engines.stats_engine import StatsEngine
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from app.config import DIMENSION_FRAMEWORK, LEVEL_THRESHOLDS
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import logging
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s")
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def main():
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print("=" * 80)
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print("完整Pipeline测试：Excel → 赋分 → PCA → 标准化 → 水平判定")
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print("=" * 80)
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# Step 1: 加载数据
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print("\n[Step 1] 加载Excel数据...")
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data_engine = DataEngine()
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data_engine.load_all()
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# Step 2: 赋分
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print("\n[Step 2] 对所有学校进行赋分...")
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scoring_engine = ScoringEngine(data_engine)
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raw_scores = scoring_engine.score_all_schools()
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# 打印赋分结果概要
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print("\n📊 赋分结果概要:")
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for school in data_engine.schools:
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scores = raw_scores[school]
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print(f"\n 🏫 {school}:")
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for dim, vals in scores.items():
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avg = sum(vals) / len(vals) if vals else 0
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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 ''}")
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# Step 3: 统计分析
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print("\n[Step 3] PCA合成 + 标准化...")
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stats_engine = StatsEngine()
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sub_scores = stats_engine.compute_dimension_scores(raw_scores)
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print(f"\n三级维度标准化得分 (均值50, 标准差10):")
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print(sub_scores.round(2).to_string())
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# Step 4: 二级维度聚合
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print("\n[Step 4] 二级维度聚合...")
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dim_scores = stats_engine.compute_dimension_aggregates(sub_scores)
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print(f"\n二级维度得分:")
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print(dim_scores.round(2).to_string())
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# Step 5: 水平判定
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print("\n[Step 5] 水平判定...")
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levels = stats_engine.compute_levels(sub_scores)
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print(f"\n各学校各维度水平:")
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print(levels.to_string())
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# Step 6: 聚类分析
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print("\n[Step 6] 聚类分析...")
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dim_cols = [c for c in dim_scores.columns if c in DIMENSION_FRAMEWORK]
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clusters = stats_engine.cluster_analysis(dim_scores[dim_cols])
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print(f"\n学校聚类结果:")
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for school, cluster in clusters["school_clusters"].items():
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print(f" {school}: {cluster}")
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# Step 7: 相关性
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print("\n[Step 7] 维度间相关性:")
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corr = stats_engine.correlation_analysis(dim_scores)
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print(corr.round(3).to_string())
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# Step 8: 生成一所学校的完整报告数据
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test_school = "延安中学"
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print(f"\n[Step 8] 生成 {test_school} 的完整报告数据包...")
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report_data = stats_engine.compute_school_report_data(test_school, sub_scores, dim_scores)
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print(f"\n📋 {test_school} 报告数据概要:")
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print(f" 总体得分: {report_data['overall']['score']}")
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print(f" 区内排名: {report_data['overall']['rank_in_district']}/{report_data['overall']['total_schools']}")
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print(f" 聚类类型: {report_data['overall']['cluster']}")
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print(f"\n 二级维度得分:")
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for dim, data in report_data['dimensions'].items():
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print(f" {dim}: {data['score']} (区均值{data['district_avg']}, 差异{data['diff_district']:+.2f})")
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print(f"\n 三级维度水平:")
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for sub_dim, data in report_data['sub_dimensions'].items():
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print(f" {sub_dim}: 得分{data['score']}, 水平{data['level']}, {data['level_description'][:30]}")
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# 保存报告数据为JSON
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output_dir = Path(__file__).parent.parent / "output"
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output_dir.mkdir(exist_ok=True)
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# 清理numpy类型以便JSON序列化
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def clean_for_json(obj):
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import numpy as np
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if isinstance(obj, dict):
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return {k: clean_for_json(v) for k, v in obj.items()}
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elif isinstance(obj, list):
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return [clean_for_json(v) for v in obj]
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elif isinstance(obj, (np.integer,)):
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return int(obj)
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elif isinstance(obj, (np.floating,)):
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return round(float(obj), 4)
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elif isinstance(obj, np.ndarray):
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return obj.tolist()
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elif isinstance(obj, float):
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return round(obj, 4)
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return obj
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report_clean = clean_for_json(report_data)
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with open(output_dir / f"{test_school}_report_data.json", "w", encoding="utf-8") as f:
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json.dump(report_clean, f, ensure_ascii=False, indent=2)
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print(f"\n✅ 报告数据已保存到 output/{test_school}_report_data.json")
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# 保存所有学校的得分汇总
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all_scores = {
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"sub_dimension_scores": clean_for_json(sub_scores.to_dict()),
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"dimension_scores": clean_for_json(dim_scores.to_dict()),
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"levels": clean_for_json(levels.to_dict()),
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"clusters": clean_for_json(clusters),
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}
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with open(output_dir / "all_schools_scores.json", "w", encoding="utf-8") as f:
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json.dump(all_scores, f, ensure_ascii=False, indent=2)
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print(f"✅ 全校得分汇总已保存到 output/all_schools_scores.json")
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print("\n" + "=" * 80)
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print("✅ 完整Pipeline测试完成!")
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print("=" * 80)
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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"""
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完整报告生成脚本：Excel → 赋分 → 统计 → LLM并行生成 → HTML报告
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"""
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import sys
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import time
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import argparse
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import logging
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
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from app.engines.data_engine import DataEngine
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from app.engines.scoring_engine import ScoringEngine
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from app.engines.stats_engine import StatsEngine
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from app.engines.llm_engine import LLMEngine
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from app.engines.report_renderer import ReportRenderer
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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def progress_callback(completed, total, segment_id):
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"""进度回调"""
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pct = completed / total * 100
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bar = "█" * int(pct / 5) + "░" * (20 - int(pct / 5))
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print(f"\r [{bar}] {pct:.0f}% ({completed}/{total}) {segment_id:<40}", end="", flush=True)
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def main():
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parser = argparse.ArgumentParser(description="生成学校课程实施监测报告")
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parser.add_argument("--school", type=str, default="延安中学", help="学校名称")
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parser.add_argument("--no-cache", action="store_true", help="不使用LLM缓存")
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parser.add_argument("--no-llm", action="store_true", help="跳过LLM生成（仅图表+数据）")
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parser.add_argument("--list-schools", action="store_true", help="列出所有可用学校")
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args = parser.parse_args()
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start_time = time.time()
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# ===== Step 1: 加载数据 =====
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print("=" * 70)
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print("📊 课程实施监测报告生成系统")
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print("=" * 70)
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logger.info("[1/5] 加载Excel数据...")
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data_engine = DataEngine()
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data_engine.load_all()
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if args.list_schools:
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print("\n可用学校:")
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for s in data_engine.schools:
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from app.config import SCHOOL_TYPE_MAP
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info = SCHOOL_TYPE_MAP.get(s, {})
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print(f" {s} ({info.get('type', '')})")
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return
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school = args.school
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if school not in data_engine.schools:
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print(f"\n❌ 学校 '{school}' 不在数据中。可用学校:")
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for s in data_engine.schools:
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print(f" - {s}")
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return
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print(f"\n🏫 目标学校: {school}")
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# ===== Step 2: 赋分 =====
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logger.info("[2/5] 全区赋分计算...")
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scoring_engine = ScoringEngine(data_engine)
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raw_scores = scoring_engine.score_all_schools()
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# ===== Step 3: 统计分析 =====
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logger.info("[3/5] PCA合成 + 标准化 + 统计分析...")
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stats_engine = StatsEngine()
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sub_scores = stats_engine.compute_dimension_scores(raw_scores)
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dim_scores = stats_engine.compute_dimension_aggregates(sub_scores)
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report_data = stats_engine.compute_school_report_data(school, sub_scores, dim_scores)
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# ===== Step 4: LLM生成 =====
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if args.no_llm:
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logger.info("[4/5] 跳过LLM生成（--no-llm模式）")
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llm_sections = {}
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else:
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logger.info("[4/5] LLM并行生成报告文字...")
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llm_engine = LLMEngine()
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llm_engine.set_progress_callback(progress_callback)
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llm_sections = llm_engine.generate_report_segments(
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report_data,
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use_cache=not args.no_cache,
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)
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print() # 换行
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# ===== Step 5: 渲染HTML =====
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logger.info("[5/5] 渲染HTML报告...")
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renderer = ReportRenderer()
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output_dir = Path(__file__).parent.parent / "output"
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output_dir.mkdir(parents=True, exist_ok=True)
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output_path = output_dir / f"{school}_报告.html"
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renderer.render_to_file(report_data, llm_sections, output_path)
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elapsed = time.time() - start_time
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print(f"\n{'=' * 70}")
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print(f"✅ 报告生成完成!")
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print(f" 学校: {school}")
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print(f" 总体得分: {report_data['overall']['score']}分 (区内第{report_data['overall']['rank_in_district']}名)")
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print(f" LLM段落: {len(llm_sections)}个")
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print(f" 耗时: {elapsed:.1f}秒")
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print(f" 输出: {output_path}")
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print(f"{'=' * 70}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,207 @@
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#!/usr/bin/env python3
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"""
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批量报告生成脚本：为长宁区全部9所学校生成课程实施监测报告
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数据加载和赋分/统计只做一次，LLM和渲染对每校独立执行
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"""
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import sys
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import time
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import argparse
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import logging
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
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from app.engines.data_engine import DataEngine
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from app.engines.scoring_engine import ScoringEngine
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from app.engines.stats_engine import StatsEngine
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from app.engines.llm_engine import LLMEngine
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from app.engines.report_renderer import ReportRenderer
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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def progress_callback(completed, total, segment_id):
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"""进度回调"""
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pct = completed / total * 100
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bar = "█" * int(pct / 5) + "░" * (20 - int(pct / 5))
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print(f"\r [{bar}] {pct:.0f}% ({completed}/{total}) {segment_id:<40}", end="", flush=True)
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def main():
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parser = argparse.ArgumentParser(description="批量生成全部学校课程实施监测报告")
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parser.add_argument("--no-cache", action="store_true", help="不使用LLM缓存")
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parser.add_argument("--no-llm", action="store_true", help="跳过LLM生成（仅图表+数据）")
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parser.add_argument("--schools", nargs="*", help="指定学校列表（默认全部9所）")
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args = parser.parse_args()
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total_start = time.time()
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print("=" * 70)
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print("📊 课程实施监测报告 — 批量生成系统")
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print("=" * 70)
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# ===== Step 1: 加载数据（只做一次） =====
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logger.info("[全局 1/3] 加载Excel数据...")
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data_engine = DataEngine()
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data_engine.load_all()
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# ===== Step 2: 全区赋分（只做一次） =====
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logger.info("[全局 2/3] 全区赋分计算...")
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scoring_engine = ScoringEngine(data_engine)
|
||||
raw_scores = scoring_engine.score_all_schools()
|
||||
|
||||
# ===== Step 3: 统计分析（只做一次） =====
|
||||
logger.info("[全局 3/3] PCA合成 + 标准化...")
|
||||
stats_engine = StatsEngine()
|
||||
sub_scores = stats_engine.compute_dimension_scores(raw_scores)
|
||||
dim_scores = stats_engine.compute_dimension_aggregates(sub_scores)
|
||||
|
||||
# 确定生成哪些学校
|
||||
all_schools = list(data_engine.schools)
|
||||
if args.schools:
|
||||
schools = []
|
||||
for s in args.schools:
|
||||
if s in all_schools:
|
||||
schools.append(s)
|
||||
else:
|
||||
print(f" ⚠️ 学校 '{s}' 不在数据中，跳过")
|
||||
if not schools:
|
||||
print("❌ 没有有效的学校，退出")
|
||||
return
|
||||
else:
|
||||
schools = all_schools
|
||||
|
||||
print(f"\n🏫 将为以下 {len(schools)} 所学校生成报告：")
|
||||
for i, s in enumerate(schools, 1):
|
||||
from app.config import SCHOOL_TYPE_MAP
|
||||
info = SCHOOL_TYPE_MAP.get(s, {})
|
||||
print(f" {i}. {s} ({info.get('type', '未知类型')})")
|
||||
|
||||
# ===== 初始化引擎 =====
|
||||
renderer = ReportRenderer()
|
||||
llm_engine = None
|
||||
if not args.no_llm:
|
||||
llm_engine = LLMEngine()
|
||||
llm_engine.set_progress_callback(progress_callback)
|
||||
|
||||
output_dir = Path(__file__).parent.parent / "output"
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# ===== 逐校生成 =====
|
||||
results = []
|
||||
for idx, school in enumerate(schools, 1):
|
||||
school_start = time.time()
|
||||
print(f"\n{'─' * 70}")
|
||||
print(f"🔄 [{idx}/{len(schools)}] 正在生成: {school}")
|
||||
print(f"{'─' * 70}")
|
||||
|
||||
try:
|
||||
# 统计分析（学校专属数据包）
|
||||
report_data = stats_engine.compute_school_report_data(school, sub_scores, dim_scores)
|
||||
|
||||
# LLM生成
|
||||
if args.no_llm:
|
||||
print(" ⏭️ 跳过LLM生成（--no-llm模式）")
|
||||
llm_sections = {}
|
||||
else:
|
||||
print(" 🤖 LLM并行生成报告文字...")
|
||||
llm_sections = llm_engine.generate_report_segments(
|
||||
report_data,
|
||||
use_cache=not args.no_cache,
|
||||
)
|
||||
print() # 换行（progress bar之后）
|
||||
|
||||
# 渲染HTML
|
||||
output_path = output_dir / f"{school}_报告.html"
|
||||
renderer.render_to_file(report_data, llm_sections, output_path)
|
||||
|
||||
# 保存数据JSON
|
||||
import json
|
||||
|
||||
def _json_safe(obj):
|
||||
"""处理numpy类型"""
|
||||
import numpy as np
|
||||
if isinstance(obj, (np.integer,)):
|
||||
return int(obj)
|
||||
if isinstance(obj, (np.floating,)):
|
||||
return float(obj)
|
||||
if isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
|
||||
|
||||
json_path = output_dir / f"{school}_report_data.json"
|
||||
with open(json_path, "w", encoding="utf-8") as f:
|
||||
json.dump(report_data, f, ensure_ascii=False, indent=2, default=_json_safe)
|
||||
|
||||
elapsed = time.time() - school_start
|
||||
score = report_data["overall"]["score"]
|
||||
rank = report_data["overall"]["rank_in_district"]
|
||||
cluster = report_data["overall"]["cluster"]
|
||||
llm_count = len(llm_sections)
|
||||
|
||||
results.append({
|
||||
"school": school,
|
||||
"status": "✅",
|
||||
"score": score,
|
||||
"rank": rank,
|
||||
"cluster": cluster,
|
||||
"llm_segments": llm_count,
|
||||
"time": elapsed,
|
||||
"output": str(output_path),
|
||||
})
|
||||
|
||||
print(f" ✅ 完成! 得分={score}分 排名={rank}/9 类型={cluster} LLM={llm_count}段 耗时={elapsed:.1f}s")
|
||||
|
||||
except Exception as e:
|
||||
elapsed = time.time() - school_start
|
||||
logger.error(f" ❌ 生成失败: {e}", exc_info=True)
|
||||
results.append({
|
||||
"school": school,
|
||||
"status": "❌",
|
||||
"error": str(e),
|
||||
"time": elapsed,
|
||||
})
|
||||
|
||||
# ===== 汇总报告 =====
|
||||
total_elapsed = time.time() - total_start
|
||||
|
||||
print(f"\n{'=' * 70}")
|
||||
print(f"📋 批量生成结果汇总")
|
||||
print(f"{'=' * 70}")
|
||||
print(f"{'学校':<10} {'状态':<4} {'得分':<8} {'排名':<8} {'类型':<8} {'LLM段':<8} {'耗时':<8}")
|
||||
print(f"{'─' * 62}")
|
||||
|
||||
success_count = 0
|
||||
for r in results:
|
||||
if r["status"] == "✅":
|
||||
success_count += 1
|
||||
print(f"{r['school']:<10} {r['status']:<4} {r['score']:<8.2f} {r['rank']}/9{'':<5} {r['cluster']:<8} {r['llm_segments']:<8} {r['time']:.1f}s")
|
||||
else:
|
||||
print(f"{r['school']:<10} {r['status']:<4} {'失败: ' + r.get('error', '未知')}")
|
||||
|
||||
print(f"{'─' * 62}")
|
||||
print(f"成功: {success_count}/{len(schools)} | 总耗时: {total_elapsed:.1f}s")
|
||||
print(f"输出目录: {output_dir}")
|
||||
print(f"{'=' * 70}")
|
||||
|
||||
# 保存汇总JSON
|
||||
import json
|
||||
summary_path = output_dir / "batch_summary.json"
|
||||
with open(summary_path, "w", encoding="utf-8") as f:
|
||||
json.dump({
|
||||
"generated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"total_schools": len(schools),
|
||||
"success_count": success_count,
|
||||
"total_time": round(total_elapsed, 1),
|
||||
"results": results,
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
print(f"汇总文件: {summary_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user