tests/test_dataset_integrity.py

# 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