feat(analytics): recommend_mix v3.1-v3.4 - noise + 2D competitors + 24m cap + success-driven
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lekss361
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6 changed files with 452 additions and 40 deletions
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@ -14,7 +14,7 @@ class RecommendMixInput(BaseModel):
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district_name: str = Field(..., min_length=2, max_length=80)
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area_total_m2: float | None = Field(default=None, ge=100, le=500_000)
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target_class: ClassLiteral | None = None
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months_window: int = Field(default=12, ge=3, le=36)
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months_window: int = Field(default=12, ge=3, le=24)
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# Velocity / pricing scenario knobs (live-tuned client-side; backend just
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# ships base coefficients so frontend can recompute without round-trips).
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# 0.01..3.0 = -99%..+200% к рынку. min=0.01 (а не 0) чтобы избежать
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@ -39,6 +39,8 @@ class RecommendBucket(BaseModel):
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# what-if recompute.
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velocity_per_month: float | None = None
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months_to_sellout: float | None = None
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# Success-driven mix flag (issue #25): bucket has top success_score in district
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is_top_success: bool = False
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class RecommendComparable(BaseModel):
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@ -6,12 +6,15 @@ Region 66 = Sverdlovskaya oblast. Developer 6208_0 = PRINZIP.
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from __future__ import annotations
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import logging
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from decimal import Decimal
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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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logger = logging.getLogger(__name__)
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def _f(value: Any) -> float | None:
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if value is None:
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@ -1445,12 +1448,17 @@ def _elasticity_coef(
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region_code: int,
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district_name: str,
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target_class: str | None,
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elasticity_window_months: int = 24,
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) -> dict[str, Any]:
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"""Fit log-log regression LN(realised) ~ LN(price_avg) on sale_graph
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observations for the same район+class. Returns elasticity (slope), R²,
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n. Falls back to FALLBACK_ELASTICITY if data thin or regression weak."""
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where_class = "AND o.obj_class = :cls" if target_class else ""
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params: dict[str, Any] = {"rc": region_code, "dn": district_name}
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params: dict[str, Any] = {
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"rc": region_code,
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"dn": district_name,
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"ew": elasticity_window_months,
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}
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if target_class:
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params["cls"] = target_class
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row = (
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@ -1472,7 +1480,7 @@ def _elasticity_coef(
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WHERE sg.type = 'apartments'
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AND sg.realised IS NOT NULL AND sg.realised > 0
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AND sg.price_avg IS NOT NULL AND sg.price_avg > 0
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AND sg.report_month >= NOW() - INTERVAL '36 months'
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AND sg.report_month >= NOW() - (:ew || ' months')::interval
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)
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SELECT
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regr_slope(y, x) AS slope,
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@ -1511,6 +1519,7 @@ def _elasticity_per_bucket_coef(
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district_name: str,
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target_class: str | None,
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fallback: dict[str, Any],
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elasticity_window_months: int = 24,
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) -> dict[str, dict[str, Any]]:
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"""Per-bucket эластичность (Tier 3): группируем sale_graph-наблюдения по
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«доминирующему bucket» каждого ЖК (mode total_area из domrf_kn_flats),
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@ -1522,7 +1531,11 @@ def _elasticity_per_bucket_coef(
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общую эластичность из `fallback` со source='fallback_global'.
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"""
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where_class = "AND o.obj_class = :cls" if target_class else ""
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params: dict[str, Any] = {"rc": region_code, "dn": district_name}
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params: dict[str, Any] = {
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"rc": region_code,
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"dn": district_name,
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"ew": elasticity_window_months,
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}
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if target_class:
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params["cls"] = target_class
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rows = (
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@ -1578,7 +1591,7 @@ def _elasticity_per_bucket_coef(
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WHERE sg.type = 'apartments'
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AND sg.realised IS NOT NULL AND sg.realised > 0
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AND sg.price_avg IS NOT NULL AND sg.price_avg > 0
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AND sg.report_month >= NOW() - INTERVAL '36 months'
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AND sg.report_month >= NOW() - (:ew || ' months')::interval
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)
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SELECT bucket,
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regr_slope(y, x) AS slope,
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@ -1619,6 +1632,198 @@ def _elasticity_per_bucket_coef(
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return out
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def _noise_penalty_factor(db: Session, district_name: str | None) -> tuple[float, dict]:
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"""Penalty к ценам исходя из плотности шумных объектов в районе.
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Returns: (factor in [0.90, 1.0], breakdown dict).
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Чем больше магистралей/жд/промзон — тем ниже factor (max -10%).
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"""
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if not district_name:
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return 1.0, {}
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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 district_noise AS (
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SELECT n.source_type, n.road_class, COUNT(*) AS n
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FROM osm_noise_sources_ekb n
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JOIN ekb_districts d ON ST_Intersects(n.geom, d.geom)
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WHERE d.district_name = :dn
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GROUP BY 1, 2
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)
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SELECT COALESCE(SUM(n), 0) AS total_sources,
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COALESCE(SUM(CASE WHEN source_type = 'railway' THEN n END), 0) AS railway_n,
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COALESCE(SUM(CASE WHEN source_type = 'industrial' THEN n END), 0)
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AS industrial_n,
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COALESCE(
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SUM(CASE WHEN road_class IN ('motorway', 'trunk') THEN n END), 0
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) AS magistral_n
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FROM district_noise
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"""
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),
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{"dn": district_name},
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)
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.mappings()
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.first()
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)
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if not row or not row["total_sources"]:
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return 1.0, {"district": district_name, "noise_sources": 0}
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score = (
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float(row["magistral_n"]) * 0.05
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+ float(row["railway_n"]) * 0.02
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+ float(row["industrial_n"]) * 0.03
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)
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penalty = min(0.10, max(0.0, score / 100))
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factor = 1.0 - penalty
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return round(factor, 4), {
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"district": district_name,
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"magistral_n": int(row["magistral_n"]),
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"railway_n": int(row["railway_n"]),
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"industrial_n": int(row["industrial_n"]),
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"total_sources": int(row["total_sources"]),
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"penalty_pct": round(penalty * 100, 1),
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}
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def _competitors_two_dim(
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db: Session,
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*,
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region_code: int,
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district_name: str,
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target_class: str | None,
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) -> tuple[int, int, float, str]:
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"""Двумерный подсчёт активных конкурентов:
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- radius_n: ЖК в радиусе 3км от центроида района
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- district_only_n: ЖК в районе, но вне 3км радиуса
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- total_weighted = radius_n * 1.0 + district_only_n * 0.6
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Returns (radius_n, district_only_n, total_weighted, scope).
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Если district_name не найден в ekb_districts — падает в старый
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_active_competitors_count с total_weighted = float(competitors).
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"""
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# Получаем центроид района для radius-фильтра
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centroid_row = (
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db.execute(
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text(
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"""
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SELECT ST_AsText(ST_Centroid(geom)) AS centroid_wkt
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FROM ekb_districts
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WHERE district_name = :dn
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LIMIT 1
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"""
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),
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{"dn": district_name},
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)
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.mappings()
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.first()
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)
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if not centroid_row or not centroid_row["centroid_wkt"]:
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# Fallback: используем старый одномерный счётчик
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n, scope = _active_competitors_count(
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db, region_code=region_code, district_name=district_name, target_class=target_class
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)
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return 0, n, float(n), scope
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class_filter = "AND obj_class = :cls" if target_class else ""
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params: dict[str, Any] = {
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"rc": region_code,
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"dn": district_name,
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"centroid": centroid_row["centroid_wkt"],
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}
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if target_class:
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params["cls"] = target_class
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row = (
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db.execute(
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text(
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f"""
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WITH active AS (
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SELECT DISTINCT ON (obj_id) obj_id, latitude, longitude, district_name
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FROM domrf_kn_objects
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WHERE region_cd = :rc
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AND site_status = 'Строящиеся'
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AND district_name = :dn
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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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centroid AS (
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SELECT ST_SetSRID(ST_GeomFromText(:centroid), 4326)::geography AS pt
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)
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SELECT
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COUNT(*) FILTER (
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WHERE ST_DWithin(
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ST_SetSRID(ST_MakePoint(a.longitude, a.latitude), 4326)::geography,
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c.pt,
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3000
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)
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) AS radius_n,
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COUNT(*) FILTER (
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WHERE NOT ST_DWithin(
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ST_SetSRID(ST_MakePoint(a.longitude, a.latitude), 4326)::geography,
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c.pt,
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3000
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)
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) AS district_only_n
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FROM active a, centroid c
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"""
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),
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params,
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)
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.mappings()
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.first()
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)
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radius_n = int(row["radius_n"] or 0) if row else 0
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district_only_n = int(row["district_only_n"] or 0) if row else 0
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total_weighted = radius_n * 1.0 + district_only_n * 0.6
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if total_weighted < 1.0:
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# Нет конкурентов в районе — fallback к старому счётчику (регион)
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n, scope = _active_competitors_count(
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db, region_code=region_code, district_name=district_name, target_class=target_class
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)
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return 0, n, float(max(n, 1)), scope
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return radius_n, district_only_n, max(total_weighted, 1.0), "district_2d"
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def _bucket_success_ranking(
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db: Session, district_name: str | None, target_class: str | None
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) -> list[dict]:
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"""Рейтинг bucket'ов по success_score из v_bucket_success_score.
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Возвращает список dict {bucket, success_score, n_deals, velocity_z,
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price_z, area_z}, sorted DESC by success_score. Пустой список если
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данных нет или district_name не передан.
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"""
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if not district_name:
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return []
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rows = (
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db.execute(
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text(
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"""
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SELECT bucket, success_score, n_deals, velocity_z, price_z, area_z
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FROM v_bucket_success_score
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WHERE district_name = :dn
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AND obj_class = COALESCE(:cls, 'Comfort')
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ORDER BY success_score DESC
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"""
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),
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{"dn": district_name, "cls": target_class},
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)
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.mappings()
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.all()
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)
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return [
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{
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"bucket": r["bucket"],
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"success_score": float(r["success_score"]) if r["success_score"] is not None else 0.0,
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"n_deals": int(r["n_deals"] or 0),
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"velocity_z": float(r["velocity_z"]) if r["velocity_z"] is not None else 0.0,
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"price_z": float(r["price_z"]) if r["price_z"] is not None else 0.0,
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"area_z": float(r["area_z"]) if r["area_z"] is not None else 0.0,
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}
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for r in rows
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]
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def recommend_mix(
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db: Session,
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*,
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@ -1630,16 +1835,25 @@ def recommend_mix(
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price_factor: float = 1.0,
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target_months: int | None = None,
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) -> dict[str, Any]:
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"""Rule-based квартирография recommender.
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"""Rule-based квартирография recommender v3.1-v3.4.
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City-wide bucket distribution from rosreestr_deals (последние N месяцев),
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скорректированная на район (через ekb_districts.median_price_per_m2) и
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класс (через yandex_realty_zk price-агрегаты per-class).
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See plan: C:/Users/user/.claude/plans/crispy-swinging-gadget.md
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v3.1: noise penalty (-10% max) по osm_noise_sources_ekb
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v3.2: hard-cap comparables по boundaries района
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v3.3: hard-cap 24 мес + elasticity_window_months = 24
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v3.4: success-driven mix из v_bucket_success_score
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"""
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warnings: list[str] = []
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# #24 Hard-cap: данные старше 24 мес нерелевантны (ставки ЦБ, ипотека менялись)
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if months_window > 24:
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logger.warning("recommend_mix: months_window=%d > 24, capped to 24", months_window)
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months_window = 24
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elasticity_window_months = 24 # синхронизировано с share_window (issue #24)
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# 1) District lookup
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district_row = (
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db.execute(
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@ -1858,6 +2072,7 @@ def recommend_mix(
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region_code=region_code,
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district_name=district_row["district_name"],
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target_class=target_class_for_geo,
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elasticity_window_months=elasticity_window_months,
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)
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elasticity = elast["elasticity"]
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if elast["source"] == "fallback":
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@ -1877,23 +2092,27 @@ def recommend_mix(
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district_name=district_row["district_name"],
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target_class=target_class_for_geo,
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fallback=elast,
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elasticity_window_months=elasticity_window_months,
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)
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# 5b-1) N активных конкурентов с каскадным fallback (район+класс →
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# район → регион). Используется как divisor в rosreestr-fallback ветке.
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competitors, competitors_scope = _active_competitors_count(
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db,
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region_code=region_code,
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district_name=district_row["district_name"],
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target_class=target_class_for_geo,
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# 5b-1) Двумерные конкуренты (#23): radius_n (3км) + district_only_n.
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# total_weighted используется как divisor в rosreestr-fallback.
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competitors_radius_n, competitors_district_only_n, competitors_weighted, competitors_scope = (
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_competitors_two_dim(
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db,
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region_code=region_code,
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district_name=district_row["district_name"],
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target_class=target_class_for_geo,
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)
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)
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# Обратная совместимость: одномерный счётчик для warnings
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competitors = round(competitors_weighted)
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if competitors_scope == "fallback_singleton":
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warnings.append(
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f"Не нашлось активно строящихся ЖК ни в районе {district_row['district_name']}"
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f" ни в регионе {region_code} — нормировка отключена (как для монополиста)."
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)
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elif competitors_scope != "district+class":
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# Информативное сообщение о расширении scope при недостатке локальных данных.
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elif competitors_scope not in ("district+class", "district_2d"):
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scope_label = {
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"district": f"районе {district_row['district_name']} (без класса)",
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"region": f"регионе {region_code} (вне района)",
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@ -1917,8 +2136,8 @@ def recommend_mix(
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" темп считается по rosreestr-сделкам ÷ конкуренты (грубее)."
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)
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market_vel_pm = (
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(total_deals / max(effective_window, 1) / max(competitors, 1))
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if total_deals and competitors
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(total_deals / max(effective_window, 1) / max(competitors_weighted, 1.0))
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if total_deals and competitors_weighted
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else 0.0
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)
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@ -1960,6 +2179,12 @@ def recommend_mix(
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mortgage_rate, mortgage_period = _current_mortgage_rate(db)
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# #22 Noise penalty: плотность шумных объектов района → штраф до -10% цены
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noise_penalty, noise_breakdown = _noise_penalty_factor(db, district_row["district_name"])
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# #25 Success-driven ranking из v_bucket_success_score
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success_ranking = _bucket_success_ranking(db, district_row["district_name"], target_class)
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# 5b-3) Per-bucket project velocity at price_factor=1.0:
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# bucket_market_v = market_vel_pm × bucket.share/100 — доля per-ЖК
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# темпа, аллоцированная на размерный сегмент.
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@ -1970,9 +2195,12 @@ def recommend_mix(
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# динамика (горит/остывает).
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# adjusted = project_velocity × price_factor^elasticity
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# months_to_sellout = units_planned / adjusted
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# Цена тоже корректируется на poi_factor (развитость района = премиум).
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# Цены корректируются на poi_factor (развитость района = премиум)
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# и noise_penalty (шумное окружение = дисконт).
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pf_pow = price_factor**elasticity if price_factor > 0 else 1.0
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macro_velocity_mult = sat_factor * trend_factor
|
||||
# Комбинированный ценовой коэффициент: POI-премиум × noise-дисконт
|
||||
combined_price_factor = poi_factor * noise_penalty
|
||||
total_units = 0
|
||||
for b in buckets:
|
||||
bucket_market_v = bucket_market_velocities.get(b["bucket"], 0.0)
|
||||
|
|
@ -1986,14 +2214,17 @@ def recommend_mix(
|
|||
b["elasticity_r2"] = be.get("r2", 0.0)
|
||||
b["elasticity_n"] = be.get("n", 0)
|
||||
b["elasticity_source"] = be.get("source", "fallback_global")
|
||||
# POI-корректировка на цену (на ВСЕ p25/median/p75)
|
||||
b["price_median_per_m2"] = round(b["price_median_per_m2"] * poi_factor, 2)
|
||||
b["price_p25_per_m2"] = round(b["price_p25_per_m2"] * poi_factor, 2)
|
||||
b["price_p75_per_m2"] = round(b["price_p75_per_m2"] * poi_factor, 2)
|
||||
# POI-корректировка + noise penalty на цены (ВСЕ p25/median/p75)
|
||||
b["price_median_per_m2"] = round(b["price_median_per_m2"] * combined_price_factor, 2)
|
||||
b["price_p25_per_m2"] = round(b["price_p25_per_m2"] * combined_price_factor, 2)
|
||||
b["price_p75_per_m2"] = round(b["price_p75_per_m2"] * combined_price_factor, 2)
|
||||
b["is_top_success"] = False
|
||||
if b["units_planned"] and bucket_velocity > 0:
|
||||
# Revenue тоже пересчитываем после POI-correction (linear scale).
|
||||
# Revenue тоже пересчитываем после combined-correction (linear scale).
|
||||
if b["revenue_planned_rub"] is not None:
|
||||
b["revenue_planned_rub"] = round(b["revenue_planned_rub"] * poi_factor, 2)
|
||||
b["revenue_planned_rub"] = round(
|
||||
b["revenue_planned_rub"] * combined_price_factor, 2
|
||||
)
|
||||
adjusted_velocity = bucket_velocity * bucket_pf_pow
|
||||
b["months_to_sellout"] = (
|
||||
round(b["units_planned"] / adjusted_velocity, 1) if adjusted_velocity > 0 else None
|
||||
|
|
@ -2001,11 +2232,34 @@ def recommend_mix(
|
|||
total_units += b["units_planned"]
|
||||
else:
|
||||
b["months_to_sellout"] = None
|
||||
# Итог revenue + weighted_avg_price после POI-correction (linear scale).
|
||||
# Итог revenue + weighted_avg_price после POI-correction + noise penalty.
|
||||
if have_revenue:
|
||||
total_revenue *= poi_factor
|
||||
total_revenue *= combined_price_factor
|
||||
if weighted_avg_price is not None:
|
||||
weighted_avg_price = round(weighted_avg_price * poi_factor, 2)
|
||||
weighted_avg_price = round(weighted_avg_price * combined_price_factor, 2)
|
||||
|
||||
# #25 Success-driven mix: поднимаем долю top-success bucket'а на 10%,
|
||||
# пропорционально уменьшаем остальные. Условие: success_score > 0 AND n_deals >= 30.
|
||||
if success_ranking:
|
||||
top = next(
|
||||
(r for r in success_ranking if r["success_score"] > 0 and r["n_deals"] >= 30),
|
||||
None,
|
||||
)
|
||||
if top:
|
||||
top_bucket_name = top["bucket"]
|
||||
# Найти bucket в списке по имени
|
||||
top_b = next((b for b in buckets if b["bucket"] == top_bucket_name), None)
|
||||
if top_b is not None:
|
||||
boost = top_b["share_pct"] * 0.10 # +10%
|
||||
top_b["share_pct"] = round(top_b["share_pct"] + boost, 1)
|
||||
top_b["is_top_success"] = True
|
||||
# Пропорционально уменьшаем остальные чтобы sum = 100
|
||||
other_sum = sum(b["share_pct"] for b in buckets if b["bucket"] != top_bucket_name)
|
||||
if other_sum > 0:
|
||||
scale = (100.0 - top_b["share_pct"]) / other_sum
|
||||
for b in buckets:
|
||||
if b["bucket"] != top_bucket_name:
|
||||
b["share_pct"] = round(b["share_pct"] * scale, 1)
|
||||
|
||||
# 5c) Inverse mode: target_months → required price_factor.
|
||||
# Tier 3: используем weighted-by-units эластичность (per-bucket эластичности
|
||||
|
|
@ -2067,7 +2321,11 @@ def recommend_mix(
|
|||
round(total_revenue / total_units, 2) if (have_revenue and total_units > 0) else None
|
||||
)
|
||||
|
||||
# 6) Comparable ЖК — same district (parsed from addr) and class
|
||||
# 6) Comparable ЖК — same district (parsed from addr) and class.
|
||||
# #22 Hard-cap по границам: фильтруем по ST_Within чтобы исключить ЖК
|
||||
# у границы района, формально в domrf по district_name, но реально за
|
||||
# пределами полигона (координаты из v_complex_full). ЖК без координат
|
||||
# (latitude/longitude NULL) — пропускаем через LEFT JOIN + фильтр.
|
||||
cmp_rows = (
|
||||
db.execute(
|
||||
text(
|
||||
|
|
@ -2088,7 +2346,10 @@ def recommend_mix(
|
|||
cad_buildings_n
|
||||
FROM v_complex_full
|
||||
ORDER BY lower(canonical_name), cad_buildings_n DESC NULLS LAST
|
||||
)
|
||||
),
|
||||
district_geom AS (
|
||||
SELECT geom FROM ekb_districts WHERE district_name = :dn LIMIT 1
|
||||
),
|
||||
latest_obj AS (
|
||||
-- domrf_kn_objects содержит ~3 snapshot'а на obj_id;
|
||||
-- берём только самый свежий, иначе comparables дублируются
|
||||
|
|
@ -2112,6 +2373,16 @@ def recommend_mix(
|
|||
AND a.snapshot_date = la.snap
|
||||
AND a.type = 'apartments'
|
||||
LEFT JOIN vcf_dedup c ON c.name_key = lower(o.comm_name)
|
||||
WHERE (
|
||||
-- hard-cap по границам района: только если координаты известны И
|
||||
-- точка внутри полигона. Без координат — включаем (нет данных для отсева)
|
||||
c.latitude IS NULL
|
||||
OR c.longitude IS NULL
|
||||
OR ST_Within(
|
||||
ST_SetSRID(ST_MakePoint(c.longitude, c.latitude), 4326),
|
||||
(SELECT geom FROM district_geom)
|
||||
)
|
||||
)
|
||||
ORDER BY o.flat_count DESC NULLS LAST
|
||||
LIMIT 5
|
||||
"""
|
||||
|
|
@ -2166,6 +2437,8 @@ def recommend_mix(
|
|||
"velocity_objects": vel["objects_count"],
|
||||
"competitors_count": competitors,
|
||||
"competitors_scope": competitors_scope,
|
||||
"competitors_radius_n": competitors_radius_n,
|
||||
"competitors_district_only_n": competitors_district_only_n,
|
||||
"saturation_median": sat_median,
|
||||
"saturation_n": sat_n,
|
||||
"sat_factor": round(sat_factor, 4),
|
||||
|
|
@ -2184,13 +2457,17 @@ def recommend_mix(
|
|||
"elasticity_source": elast["source"],
|
||||
"elasticity_weighted": (round(weighted_elasticity, 4) if total_units > 0 else None),
|
||||
"elasticity_per_bucket": elast_per_bucket,
|
||||
# Окна источников данных — для прозрачности и UI-tooltip:
|
||||
# Окна источников данных — для прозрачности и UI-tooltip.
|
||||
# share_window_months — окно по rosreestr_deals для bucket-shares
|
||||
# и market velocity (input months_window, может расшириться до 27).
|
||||
# elasticity_window_months — окно по domrf_kn_sale_graph (фиксировано
|
||||
# 36 мес — sale_graph есть с 2023г, шире окно даёт устойчивее регрессию).
|
||||
# elasticity_window_months — синхронизировано с share_window (issue #24).
|
||||
"share_window_months": effective_window,
|
||||
"elasticity_window_months": 36,
|
||||
"elasticity_window_months": elasticity_window_months,
|
||||
# Noise penalty (issue #22)
|
||||
"noise_penalty": noise_penalty,
|
||||
"noise_breakdown": noise_breakdown,
|
||||
# Success ranking (issue #25)
|
||||
"success_ranking": success_ranking,
|
||||
"cadastre_median_per_m2": (
|
||||
round(cadastre["median_per_m2"], 0)
|
||||
if cadastre["median_per_m2"] is not None
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ export function RecommendBucketsTable({
|
|||
<tr style={{ background: "#f6f7f9" }}>
|
||||
{[
|
||||
"Бакет",
|
||||
"Успех",
|
||||
"Доля",
|
||||
"Сделок",
|
||||
"Площадь ср., м²",
|
||||
|
|
@ -88,6 +89,16 @@ export function RecommendBucketsTable({
|
|||
<td style={td}>
|
||||
<strong>{r.bucket}</strong>
|
||||
</td>
|
||||
<td style={{ ...td, textAlign: "center" }}>
|
||||
{r.is_top_success === true ? (
|
||||
<span
|
||||
title="Топ-bucket в районе за 24м (быстрые продажи + высокая цена + компактная площадь)"
|
||||
style={{ cursor: "help", fontSize: 16 }}
|
||||
>
|
||||
⭐
|
||||
</span>
|
||||
) : null}
|
||||
</td>
|
||||
<td style={td}>{r.effective_share_pct.toFixed(1)}%</td>
|
||||
<td style={td}>{fmtInt(r.deal_count)}</td>
|
||||
<td style={td}>{r.area_avg_m2.toFixed(1)}</td>
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ import { useDistricts } from "@/lib/analytics-api";
|
|||
import type { RecommendClass, RecommendMixInput } from "@/types/analytics";
|
||||
|
||||
const CLASSES: RecommendClass[] = ["Comfort", "Comfort+", "Business", "Elite"];
|
||||
const MONTHS_OPTIONS = [12, 18, 24, 27];
|
||||
const MONTHS_OPTIONS = [12, 18, 24];
|
||||
|
||||
interface Props {
|
||||
value: RecommendMixInput;
|
||||
|
|
@ -130,7 +130,25 @@ export function RecommendForm({
|
|||
</label>
|
||||
|
||||
<label>
|
||||
<span style={labelStyle}>Окно сделок</span>
|
||||
<span style={labelStyle}>
|
||||
Окно сделок{" "}
|
||||
<span
|
||||
style={{
|
||||
background: "#eff6ff",
|
||||
color: "#1d4ed8",
|
||||
padding: "1px 6px",
|
||||
borderRadius: 4,
|
||||
fontSize: 10,
|
||||
fontWeight: 600,
|
||||
textTransform: "none",
|
||||
letterSpacing: 0,
|
||||
marginLeft: 4,
|
||||
}}
|
||||
title="ЦБ менял ключевую ставку, льготная ипотека, программы — данные >2 лет нерелевантны для прогноза"
|
||||
>
|
||||
📊 Данные: последние 24 месяца
|
||||
</span>
|
||||
</span>
|
||||
<select
|
||||
value={value.months_window}
|
||||
onChange={(e) =>
|
||||
|
|
@ -145,8 +163,7 @@ export function RecommendForm({
|
|||
))}
|
||||
</select>
|
||||
<span style={hintStyle}>
|
||||
При <30 сделок в любом бакете окно автоматически расширяется до 27
|
||||
мес (max доступный rosreestr-архив).
|
||||
При <30 сделок в любом бакете окно расширяется до max 24 мес.
|
||||
</span>
|
||||
</label>
|
||||
|
||||
|
|
|
|||
|
|
@ -148,6 +148,46 @@ export function RecommendVelocityPanel({
|
|||
/>
|
||||
</div>
|
||||
|
||||
{/* #22 Noise penalty badge */}
|
||||
{scope.noise_penalty != null && scope.noise_penalty < 0.98 ? (
|
||||
<div
|
||||
style={{
|
||||
display: "inline-flex",
|
||||
alignItems: "center",
|
||||
gap: 4,
|
||||
marginBottom: 12,
|
||||
}}
|
||||
>
|
||||
<span
|
||||
style={{
|
||||
background: "#fef2f2",
|
||||
color: "#b91c1c",
|
||||
padding: "2px 8px",
|
||||
borderRadius: 6,
|
||||
fontSize: 11,
|
||||
fontWeight: 600,
|
||||
cursor: "default",
|
||||
}}
|
||||
title={(() => {
|
||||
const nb = scope.noise_breakdown;
|
||||
if (!nb || !("magistral_n" in nb)) return "нет данных";
|
||||
return (
|
||||
`Магистрали: ${nb.magistral_n} · ` +
|
||||
`ЖД: ${nb.railway_n} · ` +
|
||||
`Промзоны: ${nb.industrial_n} · ` +
|
||||
`Итого источников: ${nb.total_sources}`
|
||||
);
|
||||
})()}
|
||||
>
|
||||
{"🔊"} −
|
||||
{scope.noise_breakdown && "penalty_pct" in scope.noise_breakdown
|
||||
? scope.noise_breakdown.penalty_pct
|
||||
: Math.round((1 - scope.noise_penalty) * 100)}
|
||||
% (магистрали/жд/промзоны в районе)
|
||||
</span>
|
||||
</div>
|
||||
) : null}
|
||||
|
||||
{/* Warning badge — fallback на rosreestr (нет sale_graph для контекста) */}
|
||||
{scope.velocity_source === "rosreestr_fallback" ? (
|
||||
<div
|
||||
|
|
@ -376,6 +416,29 @@ export function RecommendVelocityPanel({
|
|||
</div>
|
||||
) : null}
|
||||
|
||||
{/* #25 Success ranking banner */}
|
||||
{scope.success_ranking != null &&
|
||||
scope.success_ranking.length > 0 &&
|
||||
scope.success_ranking[0].success_score > 0 ? (
|
||||
<div
|
||||
style={{
|
||||
marginTop: 12,
|
||||
padding: "8px 12px",
|
||||
background: "#fefce8",
|
||||
border: "1px solid #fde68a",
|
||||
borderRadius: 8,
|
||||
fontSize: 13,
|
||||
color: "#78350f",
|
||||
lineHeight: 1.5,
|
||||
}}
|
||||
>
|
||||
💎 Рекомендация смещена в пользу{" "}
|
||||
<strong>{scope.success_ranking[0].bucket}</strong> — лучшая динамика в
|
||||
районе ({scope.success_ranking[0].n_deals} сделок, успех{" "}
|
||||
{scope.success_ranking[0].success_score.toFixed(2)})
|
||||
</div>
|
||||
) : null}
|
||||
|
||||
{/* Methodology note */}
|
||||
<div
|
||||
style={{
|
||||
|
|
@ -401,8 +464,24 @@ export function RecommendVelocityPanel({
|
|||
{scope.velocity_source === "sale_graph"
|
||||
? `sale_graph: ${scope.velocity_objects} ЖК / ${scope.velocity_observations} точек`
|
||||
: "fallback на rosreestr-сделки"}
|
||||
), нормирован на <strong>{scope.competitors_count}</strong> активных
|
||||
конкурентов в районе. Применены macro-факторы:{" "}
|
||||
),{" "}
|
||||
{scope.competitors_radius_n != null &&
|
||||
scope.competitors_district_only_n != null ? (
|
||||
<span
|
||||
title="Дальние конкуренты в районе тоже создают спрос, но меньше близких. Учитываются с весом 0.6 при нормировке velocity."
|
||||
style={{ cursor: "help" }}
|
||||
>
|
||||
нормирован: в радиусе 3км{" "}
|
||||
<strong>{scope.competitors_radius_n} ЖК</strong> · дальше по району{" "}
|
||||
<strong>{scope.competitors_district_only_n} ЖК</strong> (вес 0.6)
|
||||
</span>
|
||||
) : (
|
||||
<>
|
||||
нормирован на <strong>{scope.competitors_count}</strong> активных
|
||||
конкурентов в районе
|
||||
</>
|
||||
)}
|
||||
. Применены macro-факторы:{" "}
|
||||
<strong>sat ×{scope.sat_factor.toFixed(2)}</strong>
|
||||
{scope.saturation_median != null
|
||||
? ` (sold% ${scope.saturation_median.toFixed(0)})`
|
||||
|
|
|
|||
|
|
@ -236,6 +236,8 @@ export interface RecommendBucket {
|
|||
elasticity_r2?: number;
|
||||
elasticity_n?: number;
|
||||
elasticity_source?: "regression" | "fallback_global";
|
||||
/** #25 — top success bucket flag */
|
||||
is_top_success?: boolean;
|
||||
}
|
||||
|
||||
export interface ElasticityPerBucket {
|
||||
|
|
@ -303,6 +305,30 @@ export interface RecommendMixOutput {
|
|||
elasticity_per_bucket: ElasticityPerBucket;
|
||||
share_window_months: number;
|
||||
elasticity_window_months: number;
|
||||
/** #22 — noise penalty (0.9..1.0) */
|
||||
noise_penalty?: number;
|
||||
noise_breakdown?:
|
||||
| {
|
||||
district: string;
|
||||
magistral_n: number;
|
||||
railway_n: number;
|
||||
industrial_n: number;
|
||||
total_sources: number;
|
||||
penalty_pct: number;
|
||||
}
|
||||
| Record<string, never>;
|
||||
/** #23 — 2D competitors */
|
||||
competitors_radius_n?: number;
|
||||
competitors_district_only_n?: number;
|
||||
/** #25 — success ranking */
|
||||
success_ranking?: Array<{
|
||||
bucket: string;
|
||||
success_score: number;
|
||||
n_deals: number;
|
||||
velocity_z: number;
|
||||
price_z: number;
|
||||
area_z: number;
|
||||
}>;
|
||||
cadastre_median_per_m2: number | null;
|
||||
cadastre_buildings_n: number;
|
||||
cadastre_vs_market_pct: number | null;
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue