gendesign/tradein-mvp/backend/app/tasks/asking_to_sold_ratio.py
bot-backend ac99886201
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fix(tradein/estimator): remove dead tier-aware-ratio path — truncation artifact + footgun (#2002)
The tier_aware_ratio_enabled path (#928) binned SOLD deals by sold-ppm² against
ASKING-derived percentile bounds, then divided within-tier medians. That is a
ratio-of-truncated-medians ARTIFACT: a Monte-Carlo with a CONSTANT true
sold/asking=0.84 reproduced the prod tier values (0.94/0.99/0.96) and the skewed
deal split (57/27/15%) exactly — the "premium sells closer to asking" gradient is
100% spurious. Flipping the flag ON made prod WORSE (overall MAPE 14.6→17.2,
эконом bias +5.8→+18.1). It shipped dark (default False) but is a latent footgun.

A valid within-price-tier sold/asking is uncomputable from asking-less ДКП deals;
the per-rooms blend + the shipped hedonic (year+area) are the correct conditioning.
So the dead path is removed entirely, not just disabled.

- estimator._get_asking_sold_ratio: drop the tier branch (bounds read, t33/t66,
  asking_to_sold_ratios_tiered read, empirical-Bayes shrink) + the _legacy/tier
  cache-key split; keep only the legacy per-rooms → global -1 lookup. Cache key
  collapses to rooms bucket. anchor_ppm2 param retained for call-site compat (now
  unused).
- config: remove tier_aware_ratio_enabled + tier_ratio_shrink_k settings.
- tasks/asking_to_sold_ratio: drop the tiered refresh (DELETE/re-derive bounds +
  tier rows, tiered counters); the daily task no longer touches the dead tier
  tables. Legacy asking_to_sold_ratios DELETE+re-derive intact.
- tests: delete the tier-ratio unit test file (legacy path covered by
  test_estimator_expected_sold.py Layer 1); fix the daily-refresh fake-db
  (8→3 execute calls, 3 counter keys); drop the two segment-guard tests that
  asserted the removed tiered SQL; clean a dead per_rooms_tier basis in fixtures.

Tier tables asking_to_sold_ratios_tiered / asking_to_sold_tier_bounds + migration
098 are left in place (inert once nothing reads/writes them); a DROP migration is
a separate, optional follow-up.

Regression gate stays byte-identical (flag was False in prod → active path
unchanged). Refs #2002
2026-06-27 20:31:49 +03:00

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"""Daily recompute of the asking→sold correction ratios (#648 Stage 4).
ПРОБЛЕМА: asking_to_sold_ratios (migration 080) засеяна один раз derivation-CTE
(медиана SOLD ДКП за 12 мес vs медиана ASKING активных listings, per-rooms + global -1
fallback). Оценщик (estimator.py, Stage 3) читает её per-estimate (cache 300s) и домножает
asking-median прогноз на sold/asking. По мере ночного импорта новых ДКП-сделок
(rosreestr_dkp_import) ratio устаревает — нужен периодический пересчёт по тому же derivation.
TRUE-MIRROR REFRESH (флаг database-expert): 080-seed использовал ON CONFLICT DO UPDATE,
который при повторном прогоне ОБНОВЛЯЕТ существующие строки, но НЕ удаляет per-rooms строки
бакетов, упавших ниже порога 30/30 на более позднем прогоне (StaLE rows). Поэтому refresh
сначала DELETE FROM asking_to_sold_ratios WHERE district = '' (все строки #648), затем
заново гоняет ту же 080-derivation INSERT...SELECT. DELETE+INSERT в ОДНОЙ транзакции
(атомарно — таблица никогда не пуста mid-refresh). После commit таблица == свежий re-seed.
Задача синхронная (DB-only, никаких внешних HTTP-вызовов) — запускается in-app
scheduler'ом через trigger_asking_to_sold_ratio_run() (scheduler.py), по образцу
snapshot_listing_sources / import_rosreestr_dkp (sync task в run_in_executor).
Окно расписания 06:00-07:00 UTC — ПОСЛЕ rosreestr_dkp_import (04:00-06:00 UTC), чтобы
refresh потреблял свежие ДКП-сделки того же дня.
SQL derivation ниже — БАЙТ-В-БАЙТ та же логика, что seed в data/sql/080_asking_to_sold_ratios.sql
(deal_side / ask_side / per_bucket + deal_global / ask_global / global_row: трейлинг-12мес
окно, ppm²-полоса [_PPM2_MIN, settings.asking_ratio_ppm2_max] (default [30000,1200000]),
бакет LEAST(GREATEST(rooms,0),4), порог n_deals>=30 AND n_listings>=30 для per_rooms,
global -1 строка всегда). ON CONFLICT убран — DELETE идёт первым,
конфликтов нет (повторный прогон в одной tx невозможен, refresh = re-seed по семантике).
"""
from __future__ import annotations
import logging
from sqlalchemy import text
from sqlalchemy.orm import Session
from app.core.config import settings
from app.services import scrape_runs as runs_mod
# Нижняя граница ppm² — отсекает нежилые/технические сделки; не меняется.
_PPM2_MIN: int = 30_000
# Верхняя граница берётся из settings.asking_ratio_ppm2_max (default 1_200_000).
# QA-note: точное значение сверить с `SELECT max(price_per_m2) FROM deals
# WHERE source='rosreestr'` на проде — ceiling должен быть > max(ppm²) premium-сделок.
logger = logging.getLogger(__name__)
# ── True-mirror cleanup: drop all #648 rows before re-derivation ──────────────
# district = '' — все строки #648 (district зарезервирован под #647, пока всегда '').
# Удаляем ПЕРЕД re-derive, чтобы бакеты, упавшие ниже порога 30/30, не оставались
# stale (ON CONFLICT DO UPDATE такие строки бы не тронул). В одной транзакции с INSERT.
_DELETE_SQL = text(
"""
DELETE FROM asking_to_sold_ratios
WHERE district = ''
"""
)
# ── Derivation + re-seed (БАЙТ-В-БАЙТ из 080, ON CONFLICT убран — DELETE идёт первым) ──
# deal_side / ask_side / per_bucket + deal_global / ask_global / global_row:
# sold_median = percentile_cont(0.5) по deals.price_per_m2 (source='rosreestr',
# ppm² ∈ [_PPM2_MIN, settings.asking_ratio_ppm2_max], deal_date >= CURRENT_DATE 12 months),
# бакет LEAST(GREATEST(rooms,0),4).
# ask_median = percentile_cont(0.5) по listings.price_per_m2
# (is_active, та же ppm²-полоса [_PPM2_MIN, asking_ratio_ppm2_max]).
# per_rooms строки — только при n_deals>=30 AND n_listings>=30 AND ask>0 AND sold>0.
# global -1 строка (basis='global_fallback') — всегда (если ask>0 AND sold>0). window_months=12.
# Порог/окно — литералы; ppm²-полоса передаётся bind-параметрами :ppm2_min/:ppm2_max
# (безопасно от SQL-инъекций; CAST не нужен — psycopg v3 передаёт int напрямую).
_REDERIVE_SQL = text(
"""
WITH
-- SOLD медианы по бакетам комнат за трейлинг-12мес (ДКП Росреестра).
deal_side AS (
SELECT
LEAST(GREATEST(rooms, 0), 4) AS rooms_bucket,
percentile_cont(0.5) WITHIN GROUP (ORDER BY price_per_m2) AS sold_median,
COUNT(*) AS n_deals
FROM deals
WHERE source = 'rosreestr'
AND rooms IS NOT NULL
AND price_per_m2 BETWEEN :ppm2_min AND :ppm2_max
AND deal_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY LEAST(GREATEST(rooms, 0), 4)
),
-- ASKING медианы по бакетам комнат среди ТЕКУЩИХ активных объявлений.
ask_side AS (
SELECT
LEAST(GREATEST(rooms, 0), 4) AS rooms_bucket,
percentile_cont(0.5) WITHIN GROUP (ORDER BY price_per_m2) AS ask_median,
COUNT(*) AS n_listings
FROM listings
WHERE is_active
AND rooms IS NOT NULL
AND price_per_m2 BETWEEN :ppm2_min AND :ppm2_max
-- novostroyki guard (#1186): NULL = legacy вторичка до м.011
AND (listing_segment IS NULL OR listing_segment = 'vtorichka')
GROUP BY LEAST(GREATEST(rooms, 0), 4)
),
-- Per-rooms строки: только бакеты с обеими сторонами, прошедшие порог 30/30 и ask>0.
-- Тонкие бакеты (n<30) сюда НЕ попадают → estimator делает fallback на -1.
per_bucket AS (
SELECT
d.rooms_bucket,
''::text AS district,
(d.sold_median / a.ask_median)::numeric AS ratio,
round(d.sold_median)::bigint AS sold_median,
round(a.ask_median)::bigint AS ask_median,
d.n_deals::int AS n_deals,
a.n_listings::int AS n_listings,
12 AS window_months,
'per_rooms'::text AS basis
FROM deal_side d
JOIN ask_side a USING (rooms_bucket)
WHERE d.n_deals >= 30
AND a.n_listings >= 30
AND a.ask_median IS NOT NULL
AND a.ask_median > 0
AND d.sold_median IS NOT NULL -- divide-safety (порог n_deals>=30 гарантирует)
AND d.sold_median > 0
),
-- SOLD медиана по ВСЕМ комнатам (без бакет-фильтра) за трейлинг-12мес — для global row.
deal_global AS (
SELECT
percentile_cont(0.5) WITHIN GROUP (ORDER BY price_per_m2) AS sold_median,
COUNT(*) AS n_deals
FROM deals
WHERE source = 'rosreestr'
AND rooms IS NOT NULL
AND price_per_m2 BETWEEN :ppm2_min AND :ppm2_max
AND deal_date >= CURRENT_DATE - INTERVAL '12 months'
),
-- ASKING медиана по ВСЕМ активным listings (без бакет-фильтра) — для global row.
ask_global AS (
SELECT
percentile_cont(0.5) WITHIN GROUP (ORDER BY price_per_m2) AS ask_median,
COUNT(*) AS n_listings
FROM listings
WHERE is_active
AND rooms IS NOT NULL
AND price_per_m2 BETWEEN :ppm2_min AND :ppm2_max
-- novostroyki guard (#1186): NULL = legacy вторичка до м.011
AND (listing_segment IS NULL OR listing_segment = 'vtorichka')
),
-- Global fallback строка rooms_bucket=-1 (пишется всегда, если ask>0).
global_row AS (
SELECT
-1 AS rooms_bucket,
''::text AS district,
(d.sold_median / a.ask_median)::numeric AS ratio,
round(d.sold_median)::bigint AS sold_median,
round(a.ask_median)::bigint AS ask_median,
d.n_deals::int AS n_deals,
a.n_listings::int AS n_listings,
12 AS window_months,
'global_fallback'::text AS basis
FROM deal_global d
CROSS JOIN ask_global a
WHERE a.ask_median IS NOT NULL
AND a.ask_median > 0
AND d.sold_median IS NOT NULL -- защита от пустого окна сделок (иначе ratio=NULL)
AND d.sold_median > 0
)
INSERT INTO asking_to_sold_ratios (
rooms_bucket, district, ratio, sold_median, ask_median,
n_deals, n_listings, window_months, basis
)
SELECT rooms_bucket, district, ratio, sold_median, ask_median,
n_deals, n_listings, window_months, basis FROM global_row
UNION ALL
SELECT rooms_bucket, district, ratio, sold_median, ask_median,
n_deals, n_listings, window_months, basis FROM per_bucket
"""
)
# ── Post-insert counters ──────────────────────────────────────────────────────
# Считываем итог из таблицы (всё ещё в той же транзакции — до commit): сколько строк
# записано всего, сколько per_rooms, был ли использован global -1 fallback.
_COUNTERS_SQL = text(
"""
SELECT
COUNT(*) AS rows_written,
COUNT(*) FILTER (WHERE basis = 'per_rooms') AS per_rooms_rows,
COUNT(*) FILTER (WHERE rooms_bucket = -1) AS used_global_fallback
FROM asking_to_sold_ratios
WHERE district = ''
"""
)
def recompute_asking_to_sold_ratios(db: Session, run_id: int) -> dict[str, int]:
"""Пересчитать asking_to_sold_ratios (TRUE-MIRROR refresh, #648 Stage 4).
Sync (вызывается scheduler-триггером в executor, как snapshot_listing_sources).
В ОДНОЙ транзакции (атомарно — таблица никогда не пуста mid-refresh):
1. DELETE FROM asking_to_sold_ratios WHERE district = '' — снести stale-строки.
2. Заново прогнать 080-derivation INSERT...SELECT (per_rooms при 30/30 + global -1).
Затем counters из таблицы, commit, mark_done. Семантика == re-seed миграции 080.
Финализирует scrape_runs (mark_done / mark_failed) и пишет counters.
Returns {"rows_written": N, "per_rooms_rows": M, "used_global_fallback": 0|1}.
"""
counters: dict[str, int] = {
"rows_written": 0,
"per_rooms_rows": 0,
"used_global_fallback": 0,
}
try:
# DELETE + re-derive INSERT в одной транзакции (НЕ коммитим между ними —
# таблица не должна остаться пустой, если INSERT упадёт).
db.execute(_DELETE_SQL)
db.execute(
_REDERIVE_SQL,
{"ppm2_min": _PPM2_MIN, "ppm2_max": settings.asking_ratio_ppm2_max},
)
row = db.execute(_COUNTERS_SQL).mappings().first()
if row is not None:
counters["rows_written"] = int(row["rows_written"] or 0)
counters["per_rooms_rows"] = int(row["per_rooms_rows"] or 0)
counters["used_global_fallback"] = int(row["used_global_fallback"] or 0)
db.commit()
runs_mod.mark_done(db, run_id, counters)
logger.info(
"recompute_asking_to_sold_ratios run_id=%d done: "
"rows_written=%d per_rooms_rows=%d used_global_fallback=%d",
run_id,
counters["rows_written"],
counters["per_rooms_rows"],
counters["used_global_fallback"],
)
return counters
except Exception as exc:
logger.exception("recompute_asking_to_sold_ratios run_id=%d failed", run_id)
db.rollback()
runs_mod.mark_failed(db, run_id, str(exc)[:1000], counters)
raise