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import argparse
from pathlib import Path
from typing import List, Dict
from difflib import SequenceMatcher
import numpy as np
from collections import defaultdict
class QueryVariantVerifier:
def __init__(self, min_similarity: float = 0.2, max_similarity: float = 0.8):
self.min_similarity = min_similarity
self.max_similarity = max_similarity
def compute_similarity(self, text1: str, text2: str) -> float:
return SequenceMatcher(None, text1.lower(), text2.lower()).ratio()
def verify_single_item(self, item: Dict) -> Dict:
result = {
'has_variants': False,
'num_variants': 0,
'similarities': [],
'issues': [],
'quality_score': 0.0,
}
if 'query_variants' not in item or not item['query_variants']:
result['issues'].append("缺少query_variants字段")
return result
result['has_variants'] = True
result['num_variants'] = len(item['query_variants'])
original = item.get('query_original', item.get('question', ''))
if not original:
result['issues'].append("缺少原始query")
return result
variants = item['query_variants']
for i, variant in enumerate(variants):
if not variant or not variant.strip():
result['issues'].append(f"Variant {i+1} 为空")
continue
sim = self.compute_similarity(original, variant)
result['similarities'].append(sim)
if sim < self.min_similarity:
result['issues'].append(
f"Variant {i+1} 相似度过低 ({sim:.2f}): 可能偏离原意"
)
elif sim > self.max_similarity:
result['issues'].append(
f"Variant {i+1} 相似度过高 ({sim:.2f}): 过于相似"
)
if variant.lower().strip() == original.lower().strip():
result['issues'].append(f"Variant {i+1} 与原query完全相同")
if result['similarities']:
avg_sim = np.mean(result['similarities'])
if 0.4 <= avg_sim <= 0.6:
result['quality_score'] = 100
elif 0.3 <= avg_sim < 0.4 or 0.6 < avg_sim <= 0.7:
result['quality_score'] = 80
elif 0.2 <= avg_sim < 0.3 or 0.7 < avg_sim <= 0.8:
result['quality_score'] = 60
else:
result['quality_score'] = 40
return result
def verify_dataset(self, data_path: str) -> Dict:
print(f"\n{'='*60}")
print(f"验证数据集: {data_path}")
print(f"{'='*60}\n")
with open(data_path, 'r', encoding='utf-8') as f:
if data_path.endswith('.jsonl'):
data = [json.loads(line) for line in f]
else:
data = json.load(f)
results = []
all_issues = defaultdict(int)
for idx, item in enumerate(data):
result = self.verify_single_item(item)
result['idx'] = idx
results.append(result)
for issue in result['issues']:
all_issues[issue] += 1
stats = self._compute_stats(results)
self._print_report(stats, all_issues, data_path)
self._print_examples(data, results)
return {
'stats': stats,
'issues': dict(all_issues),
'results': results,
}
def _compute_stats(self, results: List[Dict]) -> Dict:
total = len(results)
with_variants = sum(1 for r in results if r['has_variants'])
all_similarities = []
all_quality_scores = []
for r in results:
all_similarities.extend(r['similarities'])
if r['quality_score'] > 0:
all_quality_scores.append(r['quality_score'])
return {
'total_samples': total,
'with_variants': with_variants,
'without_variants': total - with_variants,
'avg_variants_per_query': np.mean([r['num_variants'] for r in results]),
'avg_similarity': np.mean(all_similarities) if all_similarities else 0,
'similarity_std': np.std(all_similarities) if all_similarities else 0,
'avg_quality_score': np.mean(all_quality_scores) if all_quality_scores else 0,
'samples_with_issues': sum(1 for r in results if r['issues']),
}
def _print_report(self, stats: Dict, issues: Dict, data_path: str):
print("📊 验证报告")
print("="*60)
print("\n基本统计:")
print(f" 总样本数: {stats['total_samples']}")
print(f" 包含variants: {stats['with_variants']} " +
f"({stats['with_variants']/stats['total_samples']*100:.1f}%)")
print(f" 缺失variants: {stats['without_variants']}")
print(f" 平均variants数/query: {stats['avg_variants_per_query']:.2f}")
print("\n质量统计:")
print(f" 平均相似度: {stats['avg_similarity']:.3f} " +
f"(± {stats['similarity_std']:.3f})")
print(f" 平均质量分数: {stats['avg_quality_score']:.1f}/100")
print(f" 存在问题的样本: {stats['samples_with_issues']} " +
f"({stats['samples_with_issues']/stats['total_samples']*100:.1f}%)")
print("\n质量评估:")
avg_sim = stats['avg_similarity']
if 0.4 <= avg_sim <= 0.6:
print(" ✅ 优秀 - 相似度在理想范围内")
elif 0.3 <= avg_sim < 0.4 or 0.6 < avg_sim <= 0.7:
print(" ✓ 良好 - 相似度可接受")
elif 0.2 <= avg_sim < 0.3 or 0.7 < avg_sim <= 0.8:
print(" ⚠️ 一般 - 相似度需要改进")
else:
print(" ❌ 差 - 相似度不合理，需要重新生成")
if issues:
print("\n常见问题:")
sorted_issues = sorted(issues.items(), key=lambda x: x[1], reverse=True)
for issue, count in sorted_issues[:5]:
print(f" • {issue}: {count} 次")
print("\n" + "="*60)
def _print_examples(self, data: List[Dict], results: List[Dict], num_examples: int = 3):
print("\n📝 示例")
print("="*60)
good_examples = [r for r in results if r['quality_score'] >= 80 and not r['issues']]
medium_examples = [r for r in results if 50 <= r['quality_score'] < 80]
bad_examples = [r for r in results if r['quality_score'] < 50 or r['issues']]
if good_examples:
print("\n✅ 优质示例:")
for i, result in enumerate(good_examples[:2], 1):
idx = result['idx']
item = data[idx]
original = item.get('query_original', item.get('question', ''))
print(f"\n示例 {i}:")
print(f" 原始: {original}")
for j, (variant, sim) in enumerate(zip(item['query_variants'], result['similarities']), 1):
print(f" 变体{j}: {variant} (相似度: {sim:.2f})")
if bad_examples:
print("\n⚠️ 需要改进的示例:")
for i, result in enumerate(bad_examples[:2], 1):
idx = result['idx']
item = data[idx]
original = item.get('query_original', item.get('question', ''))
print(f"\n示例 {i}:")
print(f" 原始: {original}")
if 'query_variants' in item:
for j, variant in enumerate(item['query_variants'], 1):
print(f" 变体{j}: {variant}")
print(f" 问题: {', '.join(result['issues'][:3])}")
print("\n" + "="*60)
def main():
parser = argparse.ArgumentParser(
description="验证生成的Query Variants质量"
)
parser.add_argument(
"--input",
type=str,
required=True,
help="输入数据文件路径"
)
parser.add_argument(
"--min_similarity",
type=float,
default=0.2,
help="最小相似度阈值 (默认: 0.2)"
)
parser.add_argument(
"--max_similarity",
type=float,
default=0.8,
help="最大相似度阈值 (默认: 0.8)"
)
parser.add_argument(
"--output_report",
type=str,
default=None,
help="输出报告文件路径 (JSON格式)"
)
args = parser.parse_args()
verifier = QueryVariantVerifier(
min_similarity=args.min_similarity,
max_similarity=args.max_similarity,
)
report = verifier.verify_dataset(args.input)
if args.output_report:
output_dir = Path(args.output_report).parent
output_dir.mkdir(parents=True, exist_ok=True)
with open(args.output_report, 'w', encoding='utf-8') as f:
json.dump(report, f, ensure_ascii=False, indent=2)
print(f"\n✓ 报告已保存到: {args.output_report}")
if __name__ == "__main__":
main()
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