cheliangzhao
a2de8c5078
WeChat cross-batch refund reconciliation: - Add OriginalAmount field to CleanedBill for accurate cumulative refund math - DeduplicateRawFile detects WeChat status-update rows (已退款/已全额退款) and emits WechatRefundUpdates for Go-side reconciliation (Scenario 1) - WechatPy cleaner surfaces -退款 income rows with no same-batch expense match as unresolved_refunds for Go ReconcileRefund (Scenario 2) - Add ReconcileWechatRefund to repository interface and MongoDB implementation - upload.go step 15 iterates WechatRefundUpdates and reconciles against bills_cleaned Bug fixes: - ReviewStats: add nil repo check to prevent panic when DB is not connected - JWT: remove hardcoded fallback secret; return 500/401 if JWTSecret not configured - Remove unused parsePageParam dead code and its strconv import - BillDetailDrawer: show 不计收支 amount in muted gray instead of red - test_jd_cleaner.py: replace hardcoded D:\Projects\BillAI path with dynamic __file__ resolution Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
118 lines
5.4 KiB
Python
118 lines
5.4 KiB
Python
"""
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测试京东账单清洗器
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"""
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import zipfile
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import tempfile
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import os
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import csv
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import sys
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# 确保输出使用 UTF-8
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sys.stdout.reconfigure(encoding='utf-8')
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def test_jd_cleaner():
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base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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zip_path = os.path.join(base_dir, 'mock_data', '京东交易流水(申请时间2026年01月26日13时29分47秒)(密码683263)_209.zip')
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with zipfile.ZipFile(zip_path, 'r') as zf:
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with tempfile.TemporaryDirectory() as tmpdir:
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zf.extractall(tmpdir, pwd=b'683263')
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# Find CSV file
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for f in os.listdir(tmpdir):
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if f.endswith('.csv'):
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input_file = os.path.join(tmpdir, f)
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output_file = os.path.join(tmpdir, 'output.csv')
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print(f"Input file: {f}")
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print("-" * 60)
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# Run cleaner
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from cleaners.jd import JDCleaner
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cleaner = JDCleaner(input_file, output_file)
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cleaner.clean()
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# Read output and show review levels
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print("\n" + "=" * 60)
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print("OUTPUT REVIEW LEVELS")
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print("=" * 60)
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with open(output_file, 'r', encoding='utf-8') as of:
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reader = csv.reader(of)
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header = next(reader)
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review_idx = header.index('复核等级') if '复核等级' in header else -1
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cat_idx = header.index('交易分类') if '交易分类' in header else -1
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merchant_idx = header.index('交易对方') if '交易对方' in header else -1
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desc_idx = header.index('商品说明') if '商品说明' in header else -1
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stats = {'': 0, 'LOW': 0, 'HIGH': 0}
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rows_needing_review = []
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for row in reader:
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review = row[review_idx] if review_idx >= 0 else ''
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stats[review] = stats.get(review, 0) + 1
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if review: # Collect rows that need review
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cat = row[cat_idx] if cat_idx >= 0 else ''
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merchant = row[merchant_idx][:20] if merchant_idx >= 0 else ''
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desc = row[desc_idx][:25] if desc_idx >= 0 else ''
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rows_needing_review.append((review, cat, merchant, desc))
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# Print rows needing review
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print(f"{'Level':<5} | {'Category':<12} | {'Merchant':<20} | Description")
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print("-" * 70)
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for review, cat, merchant, desc in rows_needing_review:
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print(f"{review:<5} | {cat:<12} | {merchant:<20} | {desc}")
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print("\n" + "=" * 60)
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print("STATISTICS")
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print("=" * 60)
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print(f"No review (confident): {stats['']}")
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print(f"LOW (keyword match): {stats['LOW']}")
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print(f"HIGH (needs manual): {stats['HIGH']}")
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print(f"Total: {sum(stats.values())}")
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def test_infer_jd_category():
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"""测试分类推断逻辑"""
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from cleaners.jd import infer_jd_category
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print("\n" + "=" * 60)
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print("INFER_JD_CATEGORY TESTS")
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print("=" * 60)
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tests = [
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# (商户, 商品, 原分类, 预期等级, 说明)
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('京东外卖', '火鸡面', '', 0, '商户映射'),
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('京东平台商户', 'xxx', '食品酒饮', 0, '原分类映射'),
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('京东平台商户', 'xxx', '数码电器', 0, '原分类映射'),
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('京东平台商户', 'xxx', '日用百货', 0, '原分类映射'),
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('京东平台商户', 'xxx', '图书文娱', 0, '原分类映射'),
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('京东平台商户', '猫粮', '其他', 1, '空映射+关键词成功'),
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('京东平台商户', '咖啡', '其他网购', 1, '空映射+关键词成功'),
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('京东平台商户', 'xxx', '其他', 2, '空映射+关键词失败'),
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('京东平台商户', 'xxx', '家居用品', 2, '未知分类'),
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('京东平台商户', 'xxx', '母婴', 2, '未知分类'),
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('京东平台商户', 'xxx', '', 2, '无原分类+关键词失败'),
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]
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level_map = {0: 'NONE', 1: 'LOW', 2: 'HIGH'}
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print(f"{'Merchant':<15} | {'Product':<8} | {'OrigCat':<10} | {'Result':<12} | {'Level':<5} | {'Expected':<5} | Note")
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print("-" * 90)
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all_pass = True
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for merchant, product, orig_cat, expected_level, note in tests:
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cat, certain, level = infer_jd_category(merchant, product, orig_cat)
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status = "✓" if level == expected_level else "✗"
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if level != expected_level:
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all_pass = False
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print(f"{merchant:<15} | {product:<8} | {orig_cat or '(empty)':<10} | {cat:<12} | {level_map[level]:<5} | {level_map[expected_level]:<5} | {note} {status}")
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print("\n" + ("All tests passed!" if all_pass else "Some tests FAILED!"))
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if __name__ == '__main__':
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test_infer_jd_category()
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print("\n")
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test_jd_cleaner()
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