fix(best-layouts): address review-bot minor items (#113 PR C)

3 carryover 🟡 items из re-review verdict на a88917f:

1. Coverage % denominator now post-filter (was mixing pre-filter count
   с post-filter intersection → artificially low confidence when
   user passed exclude/filter list)
2. MAX(snapshot_date) pre-computed via db.scalar() + bind :latest_snap
   instead of subquery in WHERE (avoid re-execution per spatial scan)
3. LAYOUT_CONFIDENCE_HIGH_PCT / LAYOUT_CONFIDENCE_MEDIUM_PCT as
   module-level constants instead of hardcoded literals

Integration test gap (#4 в re-review) — отдельный follow-up issue,
не в scope этого PR.

Tests: 10/10 best-layouts pass, 43/43 regression OK.

backend/app/services/site_finder/best_layouts.py

@@ -39,6 +39,11 @@ from app.services.site_finder.layout_signature import area_bin, layout_signature
 
 logger = logging.getLogger(__name__)
 
+# Confidence thresholds (per coverage % of objects with MV velocity data)
+# Tune via PR if business feedback требует.
+LAYOUT_CONFIDENCE_HIGH_PCT = 50.0
+LAYOUT_CONFIDENCE_MEDIUM_PCT = 20.0
+
 # Делители velocity: 24 мес → масштаб на указанный window
 _VELOCITY_DIVISORS: dict[str, float] = {
     "last_month": 24.0,
@@ -139,7 +144,7 @@ _SUPPLY_BATCH_SQL = text("""
           )::geography,
           CAST(:radius_m AS float)
       )
-      AND f.snapshot_date = (SELECT MAX(snapshot_date) FROM domrf_kn_flats)
+      AND f.snapshot_date = CAST(:latest_snap AS date)
     GROUP BY rb, ab
 """)
 
@@ -279,18 +284,29 @@ def get_best_layouts(
         )
 
     # ── Step 5: supply side (батч-запрос) ────────────────────────────────────
-    try:
+    # Pre-compute последний snapshot_date один раз — избегаем subquery на каждый scan.
-        supply_rows = (
+    latest_snap: dt.date | None = db.scalar(text("SELECT MAX(snapshot_date) FROM domrf_kn_flats"))
-            db.execute(
+    if latest_snap is None:
-                _SUPPLY_BATCH_SQL,
+        logger.warning("best_layouts: domrf_kn_flats пустой (нет snapshot_date), supply=0 fallback")
-                {"center_lon": center_lon, "center_lat": center_lat, "radius_m": radius_m},
-            )
-            .mappings()
-            .all()
-        )
-    except Exception:
-        logger.warning("best_layouts: supply query failed, supply=0 fallback")
         supply_rows = []
+    else:
+        try:
+            supply_rows = (
+                db.execute(
+                    _SUPPLY_BATCH_SQL,
+                    {
+                        "center_lon": center_lon,
+                        "center_lat": center_lat,
+                        "radius_m": radius_m,
+                        "latest_snap": latest_snap,
+                    },
+                )
+                .mappings()
+                .all()
+            )
+        except Exception:
+            logger.warning("best_layouts: supply query failed, supply=0 fallback")
+            supply_rows = []
 
     supply_map: dict[tuple[str, str], int] = {
         (str(r["rb"]), str(r["ab"])): int(r["units"]) for r in supply_rows
@@ -410,22 +426,24 @@ def get_best_layouts(
     )
 
     # ── Step 9: data_quality ─────────────────────────────────────────────────
+    # Denominator = post-filter set (effective consideration set после exclude/filter).
+    objects_total_after_filter = len(all_obj_ids)
     objects_with_data = len(obj_ids_with_data & set(all_obj_ids))
     coverage_pct = (
-        round(objects_with_data / objects_total_in_radius * 100.0, 1)
+        round(objects_with_data / objects_total_after_filter * 100.0, 1)
-        if objects_total_in_radius > 0
+        if objects_total_after_filter > 0
         else 0.0
     )
-    if coverage_pct >= 50.0:
+    if coverage_pct >= LAYOUT_CONFIDENCE_HIGH_PCT:
         confidence: str = "high"
-    elif coverage_pct >= 20.0:
+    elif coverage_pct >= LAYOUT_CONFIDENCE_MEDIUM_PCT:
         confidence = "medium"
     else:
         confidence = "low"
 
     data_quality = LayoutDataQuality(
         objects_with_velocity_data=objects_with_data,
-        objects_total_in_radius=objects_total_in_radius,
+        objects_total_in_radius=objects_total_after_filter,
         velocity_coverage_pct=coverage_pct,
         confidence=confidence,  # type: ignore[arg-type]
     )

backend/tests/api/v1/test_parcel_best_layouts.py

@@ -3,11 +3,13 @@
 Mock-based — не требуют живой БД.
 Паттерн mock DB: аналогично test_parcel_competitors.py — dependency_overrides[get_db].
 
-Порядок вызовов db.execute в get_best_layouts:
+Порядок вызовов в get_best_layouts:
+  db.scalar()  → MAX(snapshot_date)            (только когда vel_rows non-empty)
+  db.execute() calls:
   1. _PARCEL_CENTROID_SQL       → .mappings().first()
   2. _COMPETITORS_IN_RADIUS_SQL → .mappings().all()
   3. _VELOCITY_BY_ROOM_SQL      → .mappings().all()
-  4. _SUPPLY_BATCH_SQL          → .mappings().all()
+  4. _SUPPLY_BATCH_SQL          → .mappings().all()  (пропускается если latest_snap is None)
 """
 
 from __future__ import annotations
@@ -83,17 +85,22 @@ def _make_db(
     id_rows: list[MagicMock] | None = None,
     vel_rows: list[MagicMock] | None = None,
     supply_rows: list[MagicMock] | None = None,
+    latest_snap: dt.date | None = None,
 ) -> MagicMock:
     """Сконструировать mock Session.
 
-    Порядок execute:
+    db.scalar() возвращает latest_snap (MAX snapshot_date) — вызывается перед supply.
+    Порядок db.execute():
     1. centroid → .mappings().first()
     2. competitors-in-radius → .mappings().all()
     3. velocity → .mappings().all()
-    4. supply → .mappings().all()
+    4. supply → .mappings().all()  (только если latest_snap is not None)
     """
     db = MagicMock()
 
+    # db.scalar — pre-computed MAX(snapshot_date) для supply query
+    db.scalar.return_value = latest_snap if latest_snap is not None else _TODAY
+
     results: list[MagicMock] = []
 
     # 1: centroid