247c252b04
Symptom: sold_pct_of_supply = sum_deals_24mo / supply_count_snapshot * 100 yields >100% (e.g. 199%) for fast-selling small formats due to incompatible time windows (24-month deals vs point-in-time supply snapshot). Option A (hotfix): clamp to 100.0, expose is_oversold=True for UI badge. - best_layouts.py: compute sold_pct_raw, clamp with min(..., 100.0), set is_oversold = raw > 100% - parcel.py TopLayoutRow: add is_oversold: bool field - best-layouts.ts TopLayoutRow: add is_oversold: boolean - BestLayoutsBlock.tsx: show warn badge ">100%" when is_oversold=True - tests: two new cases — raw 199% clamps + is_oversold=True; raw 50% passes through + is_oversold=False Closes (epic part) #271 item 5
590 lines
23 KiB
Python
590 lines
23 KiB
Python
"""Анализ лучших планировок конкурентов по velocity (Issue #113 Phase 2.1).
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Источники:
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cad_parcels_geom / cad_quarters_geom — центроид участка
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domrf_kn_objects — ЖК в радиусе (latitude/longitude → geography)
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mv_layout_velocity — (obj_id, room_bucket) → агрегат продаж 24 мес
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domrf_kn_flats — supply count по (room_bucket, area_bin)
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Алгоритм:
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Step 1: центроид участка (cad_parcels_geom → cad_quarters_geom fallback).
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Step 2: obj_id конкурентов в радиусе (domrf_kn_objects + фильтры).
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Step 3: JOIN mv_layout_velocity GROUP BY room_bucket.
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Step 4: scale velocity по time_window.
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Step 5: supply side из domrf_kn_flats — один батч-запрос.
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Step 6: per-row signature + sold_pct.
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Step 7: фильтр min_velocity + sort + rank.
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Step 8: build recommendation_for_tz (unit-mix, price, rationale).
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Step 9: data_quality (coverage + confidence).
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"""
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from __future__ import annotations
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import datetime as dt
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import logging
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from typing import Any
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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from app.schemas.parcel import (
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BestLayoutsRequest,
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BestLayoutsResponse,
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LayoutDataQuality,
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LayoutTzMixRow,
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LayoutTzRecommendation,
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TopLayoutRow,
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)
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from app.services.site_finder.layout_signature import area_bin, layout_signature
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logger = logging.getLogger(__name__)
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# Confidence thresholds (per coverage % of objects with MV velocity data)
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# Tune via PR if business feedback требует.
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LAYOUT_CONFIDENCE_HIGH_PCT = 50.0
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LAYOUT_CONFIDENCE_MEDIUM_PCT = 20.0
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# Делители velocity: 24 мес → масштаб на указанный window
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_VELOCITY_DIVISORS: dict[str, float] = {
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"last_month": 24.0,
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"last_quarter": 8.0,
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"last_year": 2.0,
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}
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# ── SQL: центроид участка ─────────────────────────────────────────────────────
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_PARCEL_CENTROID_SQL = text("""
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SELECT ST_X(pt) AS center_lon,
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ST_Y(pt) AS center_lat
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FROM (
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SELECT ST_Centroid(geom) AS pt
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FROM cad_parcels_geom
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WHERE cad_num = :cad_num AND geom IS NOT NULL
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UNION ALL
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SELECT ST_Centroid(geom) AS pt
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FROM cad_quarters_geom
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WHERE cad_number = :quarter AND geom IS NOT NULL
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) sub
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LIMIT 1
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""")
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# ── SQL: obj_id конкурентов в радиусе ─────────────────────────────────────────
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# Геометрия domrf_kn_objects вычисляется on-the-fly из (latitude, longitude)
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# как ST_SetSRID(ST_MakePoint(longitude, latitude), 4326)::geography
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# (consistency с competitors.py).
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# obj_class_filter: NULL = все классы.
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# filter_competitor_obj_ids: NULL = не фильтровать по списку.
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_COMPETITORS_IN_RADIUS_SQL = text("""
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SELECT DISTINCT ON (obj_id) obj_id
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FROM domrf_kn_objects
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WHERE latitude IS NOT NULL AND longitude IS NOT NULL
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AND ST_DWithin(
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ST_SetSRID(ST_MakePoint(longitude, latitude), 4326)::geography,
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ST_SetSRID(
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ST_MakePoint(CAST(:center_lon AS float), CAST(:center_lat AS float)),
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4326
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)::geography,
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CAST(:radius_m AS float)
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)
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AND (
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CAST(:obj_class_filter AS text) IS NULL
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OR obj_class = CAST(:obj_class_filter AS text)
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)
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ORDER BY obj_id, snapshot_date DESC NULLS LAST
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""")
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# ── SQL: mv_layout_velocity GROUP BY room_bucket ─────────────────────────────
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_VELOCITY_BY_ROOM_SQL = text("""
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SELECT
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room_bucket,
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SUM(total_deals_24mo) AS sum_deals,
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AVG(avg_area_m2) AS avg_area_m2,
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AVG(avg_price_thousand_rub_per_m2) * 1000.0 AS avg_price_per_m2_rub,
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array_agg(DISTINCT obj_id) AS competitor_obj_ids,
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COUNT(DISTINCT obj_id) AS competitor_count,
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MIN(window_start) AS window_start,
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MAX(window_end) AS window_end
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FROM mv_layout_velocity
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WHERE obj_id = ANY(:obj_ids)
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GROUP BY room_bucket
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""")
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# ── SQL: supply по (room_bucket, area_bin) за последний снимок ───────────────
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# Один батч-запрос вместо N — возвращает map (rb, ab) → count.
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# room_bucket и area_bin вычисляются в SQL аналогично layout_signature.py.
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_SUPPLY_BATCH_SQL = text("""
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SELECT
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CASE
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WHEN f.is_studio = TRUE OR f.flat_type = 'Квартира-студия' THEN 'studio'
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WHEN f.rooms = 0 THEN 'studio'
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WHEN f.rooms IN (1, 2, 3) THEN f.rooms::text
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WHEN f.rooms >= 4 THEN '4+'
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ELSE '1'
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END AS rb,
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CASE
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WHEN f.total_area < 25 THEN '<25'
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WHEN f.total_area < 40 THEN '25-40'
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WHEN f.total_area < 60 THEN '40-60'
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WHEN f.total_area < 80 THEN '60-80'
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WHEN f.total_area < 100 THEN '80-100'
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ELSE '100+'
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END AS ab,
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COUNT(*) AS units
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FROM domrf_kn_flats f
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JOIN domrf_kn_objects o ON f.obj_id = o.obj_id
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WHERE o.latitude IS NOT NULL AND o.longitude IS NOT NULL
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AND ST_DWithin(
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ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
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ST_SetSRID(
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ST_MakePoint(CAST(:center_lon AS float), CAST(:center_lat AS float)),
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4326
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)::geography,
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CAST(:radius_m AS float)
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)
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AND f.snapshot_date = CAST(:latest_snap AS date)
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GROUP BY rb, ab
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""")
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# ── Вспомогательные функции ───────────────────────────────────────────────────
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def _quarter_from_cad(cad_num: str) -> str:
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"""Извлечь кадастровый квартал: '66:41:0303161:123' → '66:41:0303161'."""
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parts = cad_num.split(":")
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if len(parts) >= 3:
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return ":".join(parts[:3])
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return cad_num
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def _normalize_pct(buckets: dict[str, float]) -> dict[str, int]:
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"""Нормировать доли до целых процентов с суммой ровно 100.
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Алгоритм largest-remainder (Hamilton method):
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1. Floor каждого значения.
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2. Остаток 100 − sum_floors распределить в top-bucket по дробной части.
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"""
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if not buckets:
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return {}
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total = sum(buckets.values())
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if total <= 0:
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n = len(buckets)
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base = 100 // n
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result = {k: base for k in buckets}
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# распределить остаток
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remainder = 100 - base * n
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for k in list(buckets.keys())[:remainder]:
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result[k] += 1
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return result
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raw = {k: v / total * 100.0 for k, v in buckets.items()}
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floors = {k: int(v) for k, v in raw.items()}
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remainder = 100 - sum(floors.values())
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# sort by fractional part desc
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fracs = sorted(buckets.keys(), key=lambda k: -(raw[k] - floors[k]))
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for k in fracs[:remainder]:
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floors[k] += 1
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return floors
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# ── Главная функция ───────────────────────────────────────────────────────────
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def get_best_layouts(
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db: Session,
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cad_num: str,
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request: BestLayoutsRequest,
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) -> BestLayoutsResponse:
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"""Top layouts (rooms × area_bin) конкурентов с рейтингом по velocity.
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Raises:
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ValueError: если центроид участка не найден (caller → HTTP 404).
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"""
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quarter = _quarter_from_cad(cad_num)
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radius_m = request.radius_km * 1000.0
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# ── Step 1: центроид участка ─────────────────────────────────────────────
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try:
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coord_row = (
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db.execute(
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_PARCEL_CENTROID_SQL,
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{"cad_num": cad_num, "quarter": quarter},
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)
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.mappings()
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.first()
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)
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except Exception:
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logger.exception("best_layouts: centroid query failed for cad_num=%s", cad_num)
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raise
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if not coord_row:
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raise ValueError(f"Геометрия для {cad_num} не найдена")
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center_lon = float(coord_row["center_lon"])
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center_lat = float(coord_row["center_lat"])
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# ── Step 2: obj_id конкурентов в радиусе ────────────────────────────────
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try:
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id_rows = (
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db.execute(
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_COMPETITORS_IN_RADIUS_SQL,
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{
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"center_lon": center_lon,
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"center_lat": center_lat,
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"radius_m": radius_m,
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"obj_class_filter": request.obj_class_filter,
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},
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)
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.mappings()
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.all()
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)
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except Exception:
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logger.exception("best_layouts: competitors-in-radius query failed for cad_num=%s", cad_num)
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raise
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all_obj_ids: list[int] = [int(r["obj_id"]) for r in id_rows]
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objects_total_in_radius = len(all_obj_ids)
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# Применить exclude / filter из request
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exclude_set = set(request.exclude_competitor_obj_ids)
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if exclude_set:
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all_obj_ids = [oid for oid in all_obj_ids if oid not in exclude_set]
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if request.filter_competitor_obj_ids is not None:
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filter_set = set(request.filter_competitor_obj_ids)
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all_obj_ids = [oid for oid in all_obj_ids if oid in filter_set]
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if not all_obj_ids:
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return _empty_response(
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radius_km=request.radius_km,
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time_window=request.time_window,
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objects_total_in_radius=objects_total_in_radius,
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)
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# ── Step 3: mv_layout_velocity GROUP BY room_bucket ─────────────────────
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try:
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vel_rows = db.execute(_VELOCITY_BY_ROOM_SQL, {"obj_ids": all_obj_ids}).mappings().all()
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except Exception:
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logger.exception(
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"best_layouts: velocity query failed for cad_num=%s obj_count=%d",
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cad_num,
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len(all_obj_ids),
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)
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raise
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if not vel_rows:
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return _empty_response(
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radius_km=request.radius_km,
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time_window=request.time_window,
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objects_total_in_radius=objects_total_in_radius,
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)
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# ── Step 5: supply side (батч-запрос) ────────────────────────────────────
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# Pre-compute последний snapshot_date один раз — избегаем subquery на каждый scan.
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latest_snap: dt.date | None = db.scalar(text("SELECT MAX(snapshot_date) FROM domrf_kn_flats"))
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if latest_snap is None:
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logger.warning("best_layouts: domrf_kn_flats пустой (нет snapshot_date), supply=0 fallback")
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supply_rows = []
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else:
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try:
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supply_rows = (
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db.execute(
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_SUPPLY_BATCH_SQL,
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{
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"center_lon": center_lon,
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"center_lat": center_lat,
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"radius_m": radius_m,
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"latest_snap": latest_snap,
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},
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)
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.mappings()
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.all()
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)
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except Exception:
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logger.warning("best_layouts: supply query failed, supply=0 fallback")
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supply_rows = []
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supply_map: dict[tuple[str, str], int] = {
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(str(r["rb"]), str(r["ab"])): int(r["units"]) for r in supply_rows
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}
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# ── Step 4 + 6: scale velocity и enrichment per row ──────────────────────
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divisor = _VELOCITY_DIVISORS[request.time_window]
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enriched: list[dict[str, Any]] = []
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window_start: dt.date | None = None
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window_end: dt.date | None = None
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# Собираем obj_ids с данными в MV (для data_quality)
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obj_ids_with_data: set[int] = set()
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for r in vel_rows:
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room_bucket = str(r["room_bucket"])
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sum_deals = float(r["sum_deals"]) if r["sum_deals"] is not None else 0.0
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avg_area = float(r["avg_area_m2"]) if r["avg_area_m2"] is not None else 0.0
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price_rub = (
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float(r["avg_price_per_m2_rub"]) if r["avg_price_per_m2_rub"] is not None else None
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)
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competitor_obj_ids: list[int] = (
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[int(oid) for oid in r["competitor_obj_ids"]] if r["competitor_obj_ids"] else []
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)
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competitor_count = int(r["competitor_count"])
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obj_ids_with_data.update(competitor_obj_ids)
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# Step 4: scale
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velocity_per_month = round(sum_deals / divisor, 2)
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# Step 6: area_bin по avg_area (layout_signature.area_bin)
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ab = area_bin(avg_area) if avg_area > 0 else "<25"
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sig = layout_signature(room_bucket, ab) # type: ignore[arg-type]
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supply_count = supply_map.get((room_bucket, ab), 0)
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sold_pct: float | None = None
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is_oversold = False
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if supply_count > 0:
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sold_pct_raw = sum_deals / supply_count * 100.0
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is_oversold = sold_pct_raw > 100.0
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# Clamp at 100%: сделки за 24 мес / текущий snapshot supply несопоставимы.
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# Значения >100% артефакт окна, не реальная «распроданность».
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sold_pct = round(min(sold_pct_raw, 100.0), 1)
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# data window
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if r["window_start"] is not None:
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ws = r["window_start"]
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if isinstance(ws, str):
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ws = dt.date.fromisoformat(ws)
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elif isinstance(ws, dt.datetime):
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ws = ws.date()
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window_start = ws if window_start is None else min(window_start, ws)
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if r["window_end"] is not None:
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we = r["window_end"]
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if isinstance(we, str):
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we = dt.date.fromisoformat(we)
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elif isinstance(we, dt.datetime):
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we = we.date()
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window_end = we if window_end is None else max(window_end, we)
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enriched.append(
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{
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"room_bucket": room_bucket,
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"area_bin": ab,
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"signature": sig,
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"competitor_obj_ids": competitor_obj_ids,
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"competitor_count": competitor_count,
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"sum_deals": sum_deals,
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"velocity_per_month": velocity_per_month,
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"avg_price_per_m2_rub": price_rub,
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"avg_area_m2": avg_area,
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"supply_units_in_radius": supply_count,
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"sold_pct_of_supply": sold_pct,
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"is_oversold": is_oversold,
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}
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)
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# ── Step 7: фильтр min_velocity + sort + rank ────────────────────────────
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filtered = [
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row for row in enriched if row["velocity_per_month"] >= request.min_velocity_per_month
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]
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filtered.sort(key=lambda r: r["velocity_per_month"], reverse=True)
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top_layouts: list[TopLayoutRow] = []
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for rank_idx, row in enumerate(filtered, start=1):
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top_layouts.append(
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TopLayoutRow(
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rank=rank_idx,
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room_bucket=row["room_bucket"],
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area_bin=row["area_bin"],
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signature=row["signature"],
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competitor_obj_ids=row["competitor_obj_ids"],
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competitor_count=row["competitor_count"],
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total_sold_in_window=int(row["sum_deals"]),
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velocity_per_month=row["velocity_per_month"],
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avg_price_per_m2_rub=row["avg_price_per_m2_rub"],
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avg_area_m2=round(row["avg_area_m2"], 1),
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supply_units_in_radius=row["supply_units_in_radius"],
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sold_pct_of_supply=row["sold_pct_of_supply"],
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is_oversold=row["is_oversold"],
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)
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)
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# ── Step 8: build recommendation_for_tz ─────────────────────────────────
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# Используем filtered (только > min_velocity) для recommendation.
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# Если после фильтрации всё пустое — используем enriched (все данные без фильтра).
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rec_source = filtered if filtered else enriched
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today = dt.date.today()
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ws_date = window_start if window_start is not None else today
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we_date = window_end if window_end is not None else today
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recommendation = _build_recommendation(
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rows=rec_source,
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radius_km=request.radius_km,
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time_window=request.time_window,
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target_total_flats=request.target_total_flats,
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window_start=ws_date,
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window_end=we_date,
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all_enriched=enriched,
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)
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# ── Step 9: data_quality ─────────────────────────────────────────────────
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# Denominator = post-filter set (effective consideration set после exclude/filter).
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objects_total_after_filter = len(all_obj_ids)
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objects_with_data = len(obj_ids_with_data & set(all_obj_ids))
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coverage_pct = (
|
||
round(objects_with_data / objects_total_after_filter * 100.0, 1)
|
||
if objects_total_after_filter > 0
|
||
else 0.0
|
||
)
|
||
if coverage_pct >= LAYOUT_CONFIDENCE_HIGH_PCT:
|
||
confidence: str = "high"
|
||
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_after_filter,
|
||
velocity_coverage_pct=coverage_pct,
|
||
confidence=confidence, # type: ignore[arg-type]
|
||
)
|
||
|
||
return BestLayoutsResponse(
|
||
top_layouts=top_layouts,
|
||
recommendation_for_tz=recommendation,
|
||
data_quality=data_quality,
|
||
)
|
||
|
||
|
||
def _build_recommendation(
|
||
rows: list[dict[str, Any]],
|
||
radius_km: float,
|
||
time_window: str,
|
||
target_total_flats: int | None,
|
||
window_start: dt.date,
|
||
window_end: dt.date,
|
||
all_enriched: list[dict[str, Any]],
|
||
) -> LayoutTzRecommendation:
|
||
"""Собрать LayoutTzRecommendation из enriched rows."""
|
||
if not rows:
|
||
return LayoutTzRecommendation(
|
||
rationale_text=(
|
||
f"В радиусе {radius_km}км: нет layout-паттернов с достаточной velocity."
|
||
),
|
||
mix=[],
|
||
weighted_avg_price_per_m2_rub=None,
|
||
based_on_obj_count=0,
|
||
based_on_total_deals=0,
|
||
data_window_start=window_start,
|
||
data_window_end=window_end,
|
||
)
|
||
|
||
# Группировка по room_bucket (строки уже могут быть per-bucket из MV GROUP BY)
|
||
rb_deals: dict[str, float] = {}
|
||
rb_area_weighted: dict[str, float] = {}
|
||
rb_price_weighted: dict[str, float] = {}
|
||
rb_price_total_deals: dict[str, float] = {}
|
||
all_competitor_ids: set[int] = set()
|
||
|
||
for row in rows:
|
||
rb = row["room_bucket"]
|
||
sd = float(row["sum_deals"])
|
||
rb_deals[rb] = rb_deals.get(rb, 0.0) + sd
|
||
rb_area_weighted[rb] = rb_area_weighted.get(rb, 0.0) + row["avg_area_m2"] * sd
|
||
all_competitor_ids.update(row["competitor_obj_ids"])
|
||
if row["avg_price_per_m2_rub"] is not None:
|
||
rb_price_weighted[rb] = rb_price_weighted.get(rb, 0.0) + (
|
||
row["avg_price_per_m2_rub"] * sd
|
||
)
|
||
rb_price_total_deals[rb] = rb_price_total_deals.get(rb, 0.0) + sd
|
||
|
||
total_deals = sum(rb_deals.values())
|
||
pct_map = _normalize_pct(rb_deals)
|
||
|
||
mix: list[LayoutTzMixRow] = []
|
||
for rb, pct in sorted(pct_map.items(), key=lambda x: -x[1]):
|
||
avg_area = (
|
||
round(rb_area_weighted[rb] / rb_deals[rb], 1) if rb_deals.get(rb, 0) > 0 else None
|
||
)
|
||
abs_units: int | None = None
|
||
if target_total_flats is not None:
|
||
abs_units = round(pct / 100.0 * target_total_flats)
|
||
mix.append(
|
||
LayoutTzMixRow(
|
||
room_bucket=rb,
|
||
pct=pct,
|
||
abs_units=abs_units,
|
||
avg_target_area_m2=avg_area,
|
||
)
|
||
)
|
||
|
||
# Weighted avg price across all room_buckets
|
||
total_price_deals = sum(rb_price_total_deals.values())
|
||
weighted_price: float | None = None
|
||
if total_price_deals > 0:
|
||
weighted_price = round(sum(rb_price_weighted.values()) / total_price_deals, 0)
|
||
|
||
# Rationale
|
||
competitor_count = len(all_competitor_ids)
|
||
tw_label = {"last_month": "1 мес", "last_quarter": "квартал", "last_year": "год"}.get(
|
||
time_window, time_window
|
||
)
|
||
rationale_text = (
|
||
f"В радиусе {radius_km}км за {tw_label}: "
|
||
f"{len(rows)} активных layout-паттернов, "
|
||
f"total {int(total_deals)} продаж в {competitor_count} ЖК"
|
||
)
|
||
|
||
# based_on_obj_count из all_enriched (уникальные obj_id с данными MV)
|
||
all_mv_obj_ids: set[int] = set()
|
||
for row in all_enriched:
|
||
all_mv_obj_ids.update(row["competitor_obj_ids"])
|
||
|
||
return LayoutTzRecommendation(
|
||
rationale_text=rationale_text,
|
||
mix=mix,
|
||
weighted_avg_price_per_m2_rub=weighted_price,
|
||
based_on_obj_count=len(all_mv_obj_ids),
|
||
based_on_total_deals=int(total_deals),
|
||
data_window_start=window_start,
|
||
data_window_end=window_end,
|
||
)
|
||
|
||
|
||
def _empty_response(
|
||
radius_km: float,
|
||
time_window: str,
|
||
objects_total_in_radius: int,
|
||
) -> BestLayoutsResponse:
|
||
"""Ответ когда нет конкурентов или нет MV данных."""
|
||
today = dt.date.today()
|
||
tw_label = {"last_month": "1 мес", "last_quarter": "квартал", "last_year": "год"}.get(
|
||
time_window, time_window
|
||
)
|
||
return BestLayoutsResponse(
|
||
top_layouts=[],
|
||
recommendation_for_tz=LayoutTzRecommendation(
|
||
rationale_text=(
|
||
f"В радиусе {radius_km}км за {tw_label}: "
|
||
f"конкуренты не найдены или нет данных velocity."
|
||
),
|
||
mix=[],
|
||
weighted_avg_price_per_m2_rub=None,
|
||
based_on_obj_count=0,
|
||
based_on_total_deals=0,
|
||
data_window_start=today,
|
||
data_window_end=today,
|
||
),
|
||
data_quality=LayoutDataQuality(
|
||
objects_with_velocity_data=0,
|
||
objects_total_in_radius=objects_total_in_radius,
|
||
velocity_coverage_pct=0.0,
|
||
confidence="low",
|
||
),
|
||
)
|