# tests/test_dataset_integrity.py import os import pytest import glob def get_all_dataset_paths(): """获取所有三级目录下的数据集路径""" dataset_paths = [] # SRSD数据集 srsd_base = 'srsd' srsd_categories = [ 'srsd-feynman_easy', 'srsd-feynman_medium', 'srsd-feynman_hard', 'srsd-feynman_easy_dummy', 'srsd-feynman_medium_dummy', 'srsd-feynman_hard_dummy' ] for category in srsd_categories: category_path = os.path.join(srsd_base, category) if os.path.exists(category_path): for dataset_dir in os.listdir(category_path): dataset_path = os.path.join(category_path, dataset_dir) if os.path.isdir(dataset_path): dataset_paths.append(dataset_path) # SRBench1.0数据集 srbench_base = 'srbench1.0' srbench_categories = ['feynman', 'strogatz', 'blackbox'] for category in srbench_categories: category_path = os.path.join(srbench_base, category) if os.path.exists(category_path): for dataset_dir in os.listdir(category_path): dataset_path = os.path.join(category_path, dataset_dir) if os.path.isdir(dataset_path): dataset_paths.append(dataset_path) # LLM-SRBench数据集 llm_srbench_base = 'llm-srbench' llm_categories = ['chem_react', 'phys_osc', 'bio_pop_growth', 'matsci', 'lsrtransform'] for category in llm_categories: category_path = os.path.join(llm_srbench_base, category) if os.path.exists(category_path): for dataset_dir in os.listdir(category_path): dataset_path = os.path.join(category_path, dataset_dir) if os.path.isdir(dataset_path): dataset_paths.append(dataset_path) return dataset_paths def test_dataset_files_exist(): """测试所有三级目录下数据集必要文件是否存在""" dataset_paths = get_all_dataset_paths() print(f"\n{'='*80}") print(f"数据集完整性检查报告") print(f"{'='*80}") print(f"找到 {len(dataset_paths)} 个数据集目录") # 所有必需文件 required_files = ['formula.py', 'train.csv', 'id_test.csv', 'ood_test.csv', 'valid.csv', 'metadata.yaml'] # 统计信息 total_datasets = len(dataset_paths) complete_datasets = 0 incomplete_datasets = 0 # 按类别统计 category_stats = {} # 详细结果 detailed_results = [] for dataset_path in dataset_paths: dataset_name = os.path.basename(dataset_path) category = os.path.basename(os.path.dirname(dataset_path)) main_category = os.path.basename(os.path.dirname(os.path.dirname(dataset_path))) # 初始化类别统计 if main_category not in category_stats: category_stats[main_category] = {'total': 0, 'complete': 0, 'missing_files': {}} category_stats[main_category]['total'] += 1 # 检查文件 missing_files = [] present_files = [] # 对于srbench1.0的blackbox类别，不检查formula.py files_to_check = required_files if main_category == 'srbench1.0' and category == 'blackbox': files_to_check = [f for f in required_files if f != 'formula.py'] for required_file in files_to_check: file_path = os.path.join(dataset_path, required_file) if os.path.exists(file_path): present_files.append(required_file) else: missing_files.append(required_file) # 判断是否完整 is_complete = len(missing_files) == 0 if is_complete: complete_datasets += 1 category_stats[main_category]['complete'] += 1 else: incomplete_datasets += 1 # 记录缺失文件统计 for missing_file in missing_files: if missing_file not in category_stats[main_category]['missing_files']: category_stats[main_category]['missing_files'][missing_file] = 0 category_stats[main_category]['missing_files'][missing_file] += 1 # 添加到详细结果 detailed_results.append({ 'path': f"{main_category}/{category}/{dataset_name}", 'is_complete': is_complete, 'missing_files': missing_files, 'present_files': present_files }) # 打印总体统计 print(f"\n总体统计:") print(f" 总数据集数量: {total_datasets}") print(f" 完整数据集: {complete_datasets}") print(f" 不完整数据集: {incomplete_datasets}") print(f" 完整性比例: {(complete_datasets/total_datasets*100):.1f}%") # 打印按类别统计 print(f"\n按类别统计:") for category, stats in category_stats.items(): if stats['total'] > 0: completeness = (stats['complete'] / stats['total']) * 100 print(f" {category}:") print(f" 总数: {stats['total']}") print(f" 完整: {stats['complete']}") print(f" 完整性: {completeness:.1f}%") if stats['missing_files']: print(f" 缺失文件统计:") for file, count in sorted(stats['missing_files'].items()): print(f" {file}: {count} 个数据集缺失") # 打印详细结果 print(f"\n详细检查结果:") print(f"{'='*80}") # 先显示不完整的数据集 incomplete_results = [r for r in detailed_results if not r['is_complete']] if incomplete_results: print(f"\n不完整的数据集 ({len(incomplete_results)} 个):") for result in incomplete_results: print(f" ❌ {result['path']}") print(f" 缺失文件: {', '.join(result['missing_files'])}") print(f" 存在文件: {', '.join(result['present_files'])}") print() # 显示完整的数据集 complete_results = [r for r in detailed_results if r['is_complete']] if complete_results: print(f"\n完整的数据集 ({len(complete_results)} 个):") for result in complete_results[:10]: # 只显示前10个 print(f" ✅ {result['path']}") if len(complete_results) > 10: print(f" ... 还有 {len(complete_results) - 10} 个完整数据集") # 断言检查 if incomplete_datasets > 0: print(f"\n⚠️ 发现 {incomplete_datasets} 个不完整的数据集") assert False, f"发现 {incomplete_datasets} 个不完整的数据集" else: print(f"\n🎉 所有数据集完整性检查通过！") def test_dataset_statistics(): """生成数据集统计信息""" dataset_paths = get_all_dataset_paths() stats = { 'srsd': {'total': 0, 'complete': 0}, 'srbench1.0': {'total': 0, 'complete': 0}, 'llm-srbench': {'total': 0, 'complete': 0} } for dataset_path in dataset_paths: main_category = os.path.basename(os.path.dirname(os.path.dirname(dataset_path))) category = os.path.basename(os.path.dirname(dataset_path)) if main_category in stats: stats[main_category]['total'] += 1 # 检查是否完整（包含所有必需文件） required_files = ['formula.py', 'train.csv', 'valid.csv', 'id_test.csv', 'ood_test.csv', 'metadata.yaml'] # 对于srbench1.0的blackbox类别，不检查metadata.yaml files_to_check = required_files if main_category == 'srbench1.0' and category == 'blackbox': files_to_check = [f for f in required_files if f != 'formula.py'] is_complete = all(os.path.exists(os.path.join(dataset_path, f)) for f in files_to_check) if is_complete: stats[main_category]['complete'] += 1 print("\n数据集完整性统计:") for category, data in stats.items(): if data['total'] > 0: completeness = (data['complete'] / data['total']) * 100 print(f"{category}: {data['complete']}/{data['total']} ({completeness:.1f}%)") return stats