cc03ffa32d
3 pre-push code-reviewer findings fixed:
1. TS union extended ('objective'|'sale_graph'|'rosreestr_fallback')
+ UI conditions handle both objective и sale_graph как valid sources.
2. COALESCE(deals_total_vol_m2, deals_total_count * 45.0) — NULL safety
for DKP-only rows (vol_m2 nullable, count > 0).
3. room_bucket parking filter — verified false positive (все 5 buckets
apartments: студия/1/2/3/4+).
Refs: PR #157 pre-push code-reviewer
327 lines
15 KiB
Python
327 lines
15 KiB
Python
"""Velocity-score — темп продаж конкурентов вокруг участка.
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Per #34 D2: утилизация objective_corpus_room_month (еженедельно обновляемые данные).
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Ранее использовался domrf_kn_sale_graph (последнее обновление 2026-01, устарел).
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Главный demand-сигнал «продастся ли» — среднемесячный объём продаж
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конкурирующих ЖК в радиусе radius_km от участка, нормированный к
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ЕКБ-медиане по данным Objective.
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Foundation: domrf_kn_objects (lat/lon, comm_name, obj_class, region_cd),
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objective_complex_mapping (domrf_obj_id ↔ objective_complex_name),
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objective_corpus_room_month (project_name, deals_total_vol_m2,
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deals_total_count, report_month).
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Linkage: domrf_kn_objects.obj_id
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→ objective_complex_mapping.domrf_obj_id
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→ objective_complex_mapping.objective_complex_name
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→ objective_corpus_room_month.project_name
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"""
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from __future__ import annotations
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import logging
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from dataclasses import dataclass
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from typing import Any, Literal
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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logger = logging.getLogger(__name__)
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# Fallback если в БД нет данных за окно months_window.
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# Эмпирика по ЕКБ: ~4 500 м²/мес на один ЖК (apartments, 2024-2025).
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_EKB_MEDIAN_FALLBACK_SQM_PER_MONTH: float = 4500.0
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@dataclass(frozen=True)
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class VelocityResult:
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"""Результат расчёта velocity-score для участка."""
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competitors_count: int
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monthly_velocity_sqm: float # avg м²/мес по конкурентам в радиусе
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ekb_median_sqm: float # benchmark ЕКБ для нормализации
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velocity_score: float # 0..1 — отношение к benchmark
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confidence: Literal["high", "medium", "low"]
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months_observed: int # фактически использованных месяцев
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period_start: str # YYYY-MM
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period_end: str # YYYY-MM
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sample_competitors: list[dict[str, Any]] # top-5 для UI
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def as_dict(self) -> dict[str, Any]:
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return {
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"competitors_count": self.competitors_count,
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"monthly_velocity_sqm": round(self.monthly_velocity_sqm, 1),
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"ekb_median_sqm": round(self.ekb_median_sqm, 1),
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"velocity_score": round(self.velocity_score, 3),
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"confidence": self.confidence,
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"months_observed": self.months_observed,
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"period": {"start": self.period_start, "end": self.period_end},
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"sample_competitors": self.sample_competitors,
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}
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def compute_velocity(
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db: Session,
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parcel_geom_wkt: str,
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radius_km: float = 3.0,
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obj_class: str | None = None,
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months_window: int = 6,
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) -> VelocityResult | None:
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"""Вычислить velocity-score для участка.
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Алгоритм:
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1. Найти ЖК-конкуренты в радиусе radius_km (через lat/lon ST_DWithin).
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2. Взять objective_corpus_room_month за последние months_window месяцев
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через objective_complex_mapping (domrf_obj_id → project_name).
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3. Посчитать суммарный объём deals_total_vol_m2.
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4. Нормировать на ЕКБ-медиану → score 0..1.
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Возвращает None если parcel_geom_wkt невалиден или конкурентов нет.
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"""
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# ── Step 1: конкуренты по lat/lon в радиусе ──────────────────────────────
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# DISTINCT ON (obj_id) ORDER BY snapshot_date DESC — latest snapshot only.
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# obj_class в domrf_kn_objects заполнен слабо (много NULL); фильтруем
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# только если явно передан.
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class_filter = "AND o.obj_class = :obj_class" if obj_class else ""
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# SAVEPOINT per query: failure rollbacks ТОЛЬКО savepoint, не outer tx.
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# db.rollback() здесь НЕЛЬЗЯ — он orphan'ит outer SessionTransaction
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# (см. PR #155 bot review — SQLAlchemy 2.0 begin_nested context cleanup).
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try:
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with db.begin_nested():
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comp_rows = (
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db.execute(
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text(
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f"""
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WITH latest_obj AS (
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SELECT DISTINCT ON (obj_id)
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obj_id,
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comm_name,
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dev_name,
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obj_class,
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latitude,
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longitude,
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district_name
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FROM domrf_kn_objects
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WHERE latitude IS NOT NULL
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AND longitude IS NOT NULL
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AND region_cd = 66
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{class_filter}
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ORDER BY obj_id, snapshot_date DESC NULLS LAST
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)
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SELECT
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o.obj_id,
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o.comm_name,
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o.dev_name,
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o.obj_class,
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o.district_name,
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ST_Distance(
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ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
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ST_Centroid(ST_GeomFromText(:parcel_wkt, 4326))::geography
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) AS distance_m
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FROM latest_obj o
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WHERE ST_DWithin(
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ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
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ST_Centroid(ST_GeomFromText(:parcel_wkt, 4326))::geography,
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:radius_m
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)
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ORDER BY distance_m ASC
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LIMIT 200
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"""
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),
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{
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"parcel_wkt": parcel_geom_wkt,
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"radius_m": radius_km * 1000.0,
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"obj_class": obj_class,
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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("velocity: competitor query failed for wkt=%s", parcel_geom_wkt[:80])
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# SAVEPOINT auto-rollbacks через __exit__ context manager.
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# Outer tx остаётся clean — caller продолжает работать без cascade.
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return None
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if not comp_rows:
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return None
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obj_ids: list[int] = [int(r["obj_id"]) for r in comp_rows]
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competitor_meta: dict[int, dict[str, Any]] = {
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int(r["obj_id"]): {
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"name": r["comm_name"],
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"dev_name": r["dev_name"],
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"obj_class": r["obj_class"],
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"district_name": r["district_name"],
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"distance_m": round(float(r["distance_m"]), 0),
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}
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for r in comp_rows
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}
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# ── Step 2: objective_corpus_room_month за последние N месяцев ───────────
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# Linkage: domrf_obj_id → objective_complex_mapping → project_name →
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# objective_corpus_room_month.
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# deals_total_vol_m2 = DDU + DKP м² за месяц (primary signal).
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# deals_total_count > 0 — фильтрует месяцы без сделок.
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# GROUP BY domrf_obj_id чтобы сохранить совместимость с caller (obj_id key).
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try:
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with db.begin_nested():
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sales_rows = (
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db.execute(
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text(
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"""
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WITH mapped AS (
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SELECT cm.domrf_obj_id AS obj_id,
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cm.objective_complex_name
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FROM objective_complex_mapping cm
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WHERE cm.domrf_obj_id = ANY(:obj_ids)
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)
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SELECT
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m.obj_id,
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SUM(COALESCE(crm.deals_total_vol_m2,
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crm.deals_total_count * 45.0)) AS total_sqm,
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COUNT(DISTINCT crm.report_month) AS months_with_data,
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MIN(crm.report_month) AS period_start,
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MAX(crm.report_month) AS period_end
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FROM objective_corpus_room_month crm
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JOIN mapped m
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ON m.objective_complex_name = crm.project_name
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WHERE crm.report_month >= (CURRENT_DATE - :window_interval::interval)
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AND crm.deals_total_count > 0
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GROUP BY m.obj_id
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"""
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),
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{
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"obj_ids": obj_ids,
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"window_interval": f"{months_window} months",
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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("velocity: objective sales query failed for obj_ids=%s", obj_ids[:5])
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# SAVEPOINT auto-rollback'нут — outer tx clean
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return None
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if not sales_rows:
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return None
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total_sqm = sum(float(r["total_sqm"] or 0.0) for r in sales_rows)
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months_observed = max((int(r["months_with_data"] or 0) for r in sales_rows), default=0)
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period_start_dates = [r["period_start"] for r in sales_rows if r["period_start"]]
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period_end_dates = [r["period_end"] for r in sales_rows if r["period_end"]]
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period_start = min(period_start_dates).strftime("%Y-%m") if period_start_dates else ""
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period_end = max(period_end_dates).strftime("%Y-%m") if period_end_dates else ""
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if months_observed == 0 or total_sqm <= 0:
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return None
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# Среднемесячный объём в расчёте: суммарный по всем конкурентам / месяцев.
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# Чем больше конкурентов с данными — тем весомее результат.
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monthly_velocity = total_sqm / months_observed
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# ── Step 3: ЕКБ-медиана ──────────────────────────────────────────────────
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ekb_median = (
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_get_ekb_median(db, months_window=months_window) or _EKB_MEDIAN_FALLBACK_SQM_PER_MONTH
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)
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# ── Step 4: нормализация → score 0..1 ────────────────────────────────────
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# Логика: сравниваем суммарный velocity радиуса с «нормой» одного ЖК.
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# Если в радиусе продаётся N × ekb_median → рынок горячий.
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# Нормируем: score = min(1.0, total_velocity / (n_competitors × ekb_median × 2))
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# Cap 2×median = «насыщен». Итоговый score 0..1.
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n_with_sales = len(sales_rows)
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denominator = n_with_sales * ekb_median * 2.0 if n_with_sales > 0 else ekb_median * 2.0
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velocity_score = min(1.0, max(0.0, monthly_velocity / denominator))
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# ── Step 5: confidence ───────────────────────────────────────────────────
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n_comps = len(comp_rows)
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if n_comps >= 10 and months_observed >= 5:
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confidence: Literal["high", "medium", "low"] = "high"
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elif n_comps >= 5 and months_observed >= 3:
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confidence = "medium"
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else:
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confidence = "low"
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# ── Step 6: top-5 конкурентов по объёму продаж ───────────────────────────
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sales_by_id: dict[int, float] = {
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int(r["obj_id"]): float(r["total_sqm"] or 0.0) for r in sales_rows
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}
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sample = sorted(
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[
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{
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"obj_id": oid,
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**competitor_meta[oid],
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"total_sqm_period": round(sales_by_id.get(oid, 0.0), 0),
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}
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for oid in obj_ids
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if oid in competitor_meta
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],
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key=lambda x: x["total_sqm_period"],
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reverse=True,
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)[:5]
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return VelocityResult(
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competitors_count=n_comps,
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monthly_velocity_sqm=monthly_velocity,
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ekb_median_sqm=ekb_median,
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velocity_score=velocity_score,
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confidence=confidence,
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months_observed=months_observed,
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period_start=period_start,
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period_end=period_end,
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sample_competitors=sample,
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)
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def _get_ekb_median(db: Session, months_window: int = 6) -> float | None:
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"""ЕКБ-wide медиана monthly velocity (м²/мес) per ЖК — benchmark.
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Источник: objective_corpus_room_month (актуальные данные, обновляется еженедельно).
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Ранее использовался domrf_kn_sale_graph с фильтром region_cd=66.
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objective_corpus_room_month не имеет region_cd — данные Objective'а
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покрывают primarily ЕКБ, что для baseline допустимо.
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Учитываются только ЖК с ≥3 месяцами данных за окно (стабильный сигнал).
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Fallback к _EKB_MEDIAN_FALLBACK_SQM_PER_MONTH если нет данных в БД.
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"""
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try:
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with db.begin_nested():
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row = (
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db.execute(
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text(
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"""
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WITH per_project AS (
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SELECT
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project_name,
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SUM(COALESCE(deals_total_vol_m2,
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deals_total_count * 45.0)) AS total_sqm,
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COUNT(DISTINCT report_month) AS months_data
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FROM objective_corpus_room_month
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WHERE report_month >= (CURRENT_DATE - :window_interval::interval)
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AND deals_total_count > 0
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GROUP BY project_name
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HAVING COUNT(DISTINCT report_month) >= 3
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),
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per_project_velocity AS (
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SELECT total_sqm / months_data AS velocity
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FROM per_project
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)
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SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY velocity) AS median
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FROM per_project_velocity
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"""
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),
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{"window_interval": f"{months_window} months"},
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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.warning("velocity: ekb_median query failed, using fallback")
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# SAVEPOINT auto-rollback'нут
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return None
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if row and row["median"] is not None:
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return float(row["median"])
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return None
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