gahusb
4097c95286
- get_market_deals_for에 optional conn 파라미터 추가(재사용 시 open/close 안 함, 기존 단건 호출부는 불변) - bulk_upsert_listing_matches 추가(listing_matches 다건 upsert, 단일 connection) - run_listing_matching이 listings 조회+매물별 market_deals 조회+최종 upsert를 단일 _conn()으로 처리 (기존: 269건 매물마다 개별 _conn() 3회 → 병목) - POST /api/realestate/listings/rematch: MOLIT 재수집 없이 기존 listings+market_deals로 즉시 재판정 (알림 미발송, 즉시 피드백용) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EqCYBhvTcdeCTUDX3RhWx9
80 lines
4.4 KiB
Python
80 lines
4.4 KiB
Python
import statistics
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def _deals(vals, key="deposit", dt="전세"):
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return [{key: v, "deal_type": dt} for v in vals]
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def test_compute_safety_tiers():
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from app.listing_matcher import compute_safety
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deals = _deals([40000, 42000, 44000, 43000]) # median ~42500
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assert compute_safety(28000, "전세", deals)["tier"] == "안전" # 0.66
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assert compute_safety(32000, "전세", deals)["tier"] == "주의" # 0.75
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assert compute_safety(38000, "전세", deals)["tier"] == "위험" # 0.89
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assert compute_safety(28000, "전세", _deals([40000, 41000]))["tier"] == "보류" # 표본<3
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def test_compute_valuation_tiers():
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from app.listing_matcher import compute_valuation
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deals = _deals([80000, 88000, 90000, 87000], key="sale_amount", dt="매매") # median ~87500
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assert compute_valuation(83000, deals)["tier"] == "저평가" # 0.95
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assert compute_valuation(89000, deals)["tier"] == "시세" # 1.02
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assert compute_valuation(95000, deals)["tier"] == "고가" # 1.09
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def test_match_listing_rent_deposit_cap():
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from app.listing_matcher import match_listing
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crit = {"dongs": ["신대방동"], "deal_types": ["전세"], "max_deposit": 32000,
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"min_area": 40.0, "house_types": ["아파트"]}
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ok = match_listing(crit, {"deal_type": "전세", "deposit": 29000, "area_exclusive": 42.0,
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"dong": "신대방동"}, budget={"max_deposit": 45000})
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assert ok["passed"] is True
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over = match_listing(crit, {"deal_type": "전세", "deposit": 40000, "area_exclusive": 42.0,
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"dong": "신대방동"}, budget={"max_deposit": 45000})
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assert over["passed"] is False
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# ── Task 2: run_listing_matching 단일 conn 통합 테스트 ───────────────────────
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def test_run_listing_matching_end_to_end_safe_rent_and_overpriced_sale():
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"""listings+market_deals+criteria를 시드하고 run_listing_matching()을 돌려
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listing_matches가 실제로 생성되고 tier/passed가 기대대로인지 확인한다
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(단일 connection 리팩터 후에도 판정 로직이 보존됐는지 검증하는 회귀 테스트)."""
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from app import db
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from app.listing_matcher import run_listing_matching
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# criteria: 신대방동 전세/매매, 보증금 상한 32000, 최소면적 40
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db.update_listing_criteria({
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"dongs": ["신대방동"], "deal_types": ["전세", "매매"],
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"max_deposit": 32000, "min_area": 40.0,
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"house_types": ["아파트"], "equity": 20000, "annual_income": 6000,
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})
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# 전세 실거래 시세(median ~42000) — 안전 매물(28000, ratio 0.67)
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for i, v in enumerate((40000, 42000, 44000)):
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db.upsert_market_deal({"house_type": "아파트", "dong_code": "11590", "dong": "신대방동",
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"complex_name": "OO", "area": 42.0, "deal_type": "전세",
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"deposit": v, "sale_amount": None,
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"deal_ym": f"2026{i+1:02d}", "floor": "5", "source": "molit"})
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l_rent, _ = db.upsert_listing({"article_no": "R1", "source": "naver", "deal_type": "전세",
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"deposit": 28000, "area_exclusive": 42.0,
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"complex_name": "OO", "dong": "신대방동", "dong_code": "11590"})
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# 매매 실거래 시세(median ~87500) — 고가 매물(95000, ratio 1.09)
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for i, v in enumerate((80000, 88000, 90000, 87000)):
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db.upsert_market_deal({"house_type": "아파트", "dong_code": "11590", "dong": "신대방동",
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"complex_name": "PP", "area": 59.0, "deal_type": "매매",
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"deposit": None, "sale_amount": v,
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"deal_ym": f"2026{i+1:02d}", "floor": "10", "source": "molit"})
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l_sale, _ = db.upsert_listing({"article_no": "R2", "source": "naver", "deal_type": "매매",
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"sale_price": 95000, "area_exclusive": 59.0,
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"complex_name": "PP", "dong": "신대방동", "dong_code": "11590"})
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run_listing_matching()
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matches = {m["listing_id"]: m for m in db.get_listing_matches()}
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assert len(matches) == 2
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rent_m = matches[l_rent["id"]]
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assert rent_m["passed"] == 1
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assert rent_m["safety_tier"] == "안전"
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assert rent_m["sample_size"] == 3
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sale_m = matches[l_sale["id"]]
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assert sale_m["valuation_tier"] == "고가"
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assert sale_m["sample_size"] == 4
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