add predict
This commit is contained in:
lekss361
parent
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commit
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3 changed files with 360 additions and 0 deletions
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@ -9,6 +9,7 @@ from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.orm import Session
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from app.core.db import get_db
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from app.schemas.recommend import RecommendMixInput, RecommendMixOutput
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from app.services import analytics_queries as q
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router = APIRouter()
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@ -180,3 +181,26 @@ def prinzip_objects(db: Annotated[Session, Depends(get_db)]) -> list[dict[str, A
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# Funnel-эндпойнты (CRM-данные) перенесены в backend/app/api/v1/admin_leads.py:
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# /api/v1/admin/leads/funnel/{monthly,by-source,by-object} с X-Admin-Token.
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# ---- Recommender (Уровень 1, rule-based) -----------------------------------
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@router.post("/recommend/mix", response_model=RecommendMixOutput)
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def recommend_mix(
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payload: RecommendMixInput,
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db: Annotated[Session, Depends(get_db)],
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) -> dict[str, Any]:
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"""Rule-based квартирография для участка в указанном районе ЕКБ.
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На вход — district_name (8 районов ЕКБ), опционально area_total_m2 и
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target_class. На выход — распределение по 5 бакетам комнатности с
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ожидаемыми ценами/площадями/выручкой и список comparable ЖК.
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"""
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return q.recommend_mix(
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db,
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district_name=payload.district_name,
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area_total_m2=payload.area_total_m2,
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target_class=payload.target_class,
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months_window=payload.months_window,
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)
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46
backend/app/schemas/recommend.py
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46
backend/app/schemas/recommend.py
Normal file
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@ -0,0 +1,46 @@
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"""IO contracts for the rule-based квартирография recommender.
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POST /api/v1/analytics/recommend/mix
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"""
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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ClassLiteral = Literal["Comfort", "Comfort+", "Business", "Elite"]
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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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class RecommendBucket(BaseModel):
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bucket: str
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share_pct: float
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deal_count: int
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area_avg_m2: float
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area_median_m2: float
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price_median_per_m2: float
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price_p25_per_m2: float
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price_p75_per_m2: float
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units_planned: int | None = None
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revenue_planned_rub: float | None = None
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class RecommendComparable(BaseModel):
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obj_id: int
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comm_name: str | None = None
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dev_name: str | None = None
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obj_class: str | None = None
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flat_count: int | None = None
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sold_perc: float | None = None
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class RecommendMixOutput(BaseModel):
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scope: dict[str, Any]
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buckets: list[RecommendBucket]
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summary: dict[str, Any]
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comparables: list[RecommendComparable]
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@ -955,3 +955,293 @@ def prinzip_objects_with_velocity(db: Session) -> list[dict[str, Any]]:
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}
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for r in rows
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]
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# ── Rule-based recommender (Уровень 1) ────────────────────────────────────────
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# Pretty-name map shared with quartirography_deals(). Keep IDs sortable so
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# bucket ordering is deterministic in the response.
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_BUCKET_PRETTY: dict[str, str] = {
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"1-Студия": "Студии 15-30",
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"2-1-к": "1-к 30-45",
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"3-2-к": "2-к 45-60",
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"4-3-к": "3-к 60-80",
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"5-80+ м²": "80+ м²",
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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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district_name: str,
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area_total_m2: float | None = None,
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target_class: str | None = None,
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months_window: int = 12,
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region_code: int = 66,
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) -> dict[str, Any]:
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"""Rule-based квартирография recommender.
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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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"""
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warnings: list[str] = []
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# 1) District lookup
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district_row = (
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db.execute(
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text(
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"""
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SELECT district_name, zk_count, flat_count,
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median_price_per_m2, mean_price_per_m2
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FROM ekb_districts
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WHERE district_name ILIKE :dn
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LIMIT 1
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"""
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),
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{"dn": district_name.strip()},
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)
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.mappings()
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.first()
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)
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if not district_row:
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return {
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"scope": {"district": district_name, "error": "district unknown"},
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"buckets": [],
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"summary": {
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"total_revenue_rub": None,
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"weighted_avg_price_per_m2": None,
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"warnings": [f"Район '{district_name}' не найден в ekb_districts"],
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},
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"comparables": [],
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}
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district_median = _f(district_row["median_price_per_m2"])
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if district_median is None:
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warnings.append(
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f"В ekb_districts нет median_price_per_m2 для '{district_row['district_name']}',"
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" коэффициент района = 1.0"
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)
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# 2) City-wide median baseline
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city_median = _f(
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db.execute(
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text(
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"""
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SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY median_price_per_m2)
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FROM ekb_districts
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WHERE median_price_per_m2 IS NOT NULL
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"""
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)
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).scalar()
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)
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district_factor = (
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district_median / city_median
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if (district_median and city_median and city_median > 0)
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else 1.0
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)
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# 3) Class multiplier from yandex_realty_zk price ranges (price_from)
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class_multiplier = 1.0
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if target_class:
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cls_row = (
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db.execute(
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text(
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"""
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SELECT
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AVG(price_from) FILTER (WHERE obj_class = :cls) AS class_avg,
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AVG(price_from) AS overall_avg
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FROM yandex_realty_zk
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WHERE price_from IS NOT NULL AND price_from > 0
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"""
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),
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{"cls": target_class},
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)
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.mappings()
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.first()
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)
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cavg = _f(cls_row["class_avg"]) if cls_row else None
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oavg = _f(cls_row["overall_avg"]) if cls_row else None
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if cavg and oavg and oavg > 0:
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class_multiplier = cavg / oavg
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else:
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warnings.append(
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f"Нет ценовых данных yandex_realty_zk для класса '{target_class}',"
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" коэффициент класса = 1.0"
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)
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# 4) Bucket distribution from rosreestr_deals — city-wide, last N months
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bucket_rows = (
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db.execute(
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text(
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"""
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WITH bucketed AS (
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SELECT CASE
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WHEN area < 30 THEN '1-Студия'
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WHEN area < 45 THEN '2-1-к'
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WHEN area < 60 THEN '3-2-к'
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WHEN area < 80 THEN '4-3-к'
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ELSE '5-80+ м²'
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END AS bucket,
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area,
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price_per_sqm
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FROM rosreestr_deals
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WHERE region_code = :rc
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AND doc_type = 'ДДУ'
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-- realestate_type_code 002001003000 = квартиры (жилые помещения).
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-- 001 = земельные участки, 002 = нежилые помещения.
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AND realestate_type_code = '002001003000'
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AND area > 10
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AND area <= 200 -- отсечь выбросы (коммерческие площади)
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AND price_per_sqm BETWEEN 30000 AND 1000000
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AND period_start_date >= NOW()
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- (:months_window || ' months')::INTERVAL
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)
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SELECT bucket,
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COUNT(*)::bigint AS deals,
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AVG(area) AS area_avg,
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PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY area) AS area_median,
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PERCENTILE_CONT(0.5) WITHIN GROUP
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(ORDER BY price_per_sqm) AS price_median,
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PERCENTILE_CONT(0.25) WITHIN GROUP
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(ORDER BY price_per_sqm) AS price_p25,
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PERCENTILE_CONT(0.75) WITHIN GROUP
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(ORDER BY price_per_sqm) AS price_p75
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FROM bucketed
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GROUP BY bucket
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ORDER BY bucket
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"""
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),
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{"rc": region_code, "months_window": months_window},
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)
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.mappings()
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.all()
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)
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total_deals = sum(int(r["deals"] or 0) for r in bucket_rows) or 1
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# 5) Build buckets with adjusted prices + optional allocation
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buckets: list[dict[str, Any]] = []
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weighted_num = 0.0 # Σ area_avg × share × price
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weighted_den = 0.0 # Σ area_avg × share
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total_revenue = 0.0
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have_revenue = False
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for r in bucket_rows:
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bid = r["bucket"]
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deals = int(r["deals"] or 0)
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share = round(deals * 100 / total_deals, 1)
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area_avg = _f(r["area_avg"]) or 0.0
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area_med = _f(r["area_median"]) or 0.0
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p_med_city = _f(r["price_median"]) or 0.0
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p25_city = _f(r["price_p25"]) or 0.0
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p75_city = _f(r["price_p75"]) or 0.0
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adj = district_factor * class_multiplier
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p_med = round(p_med_city * adj, 2)
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p25 = round(p25_city * adj, 2)
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p75 = round(p75_city * adj, 2)
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units_planned: int | None = None
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revenue_planned: float | None = None
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if area_total_m2 and area_avg > 0:
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allocated = area_total_m2 * (share / 100.0)
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units_planned = max(1, round(allocated / area_avg))
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revenue_planned = round(units_planned * area_avg * p_med, 2)
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total_revenue += revenue_planned
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have_revenue = True
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weighted_num += area_avg * share * p_med
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weighted_den += area_avg * share
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if deals < 30:
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warnings.append(
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f"Бакет '{_BUCKET_PRETTY.get(bid, bid)}': только {deals} сделок"
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f" за {months_window} мес — оценка с большой погрешностью"
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)
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buckets.append(
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{
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"bucket": _BUCKET_PRETTY.get(bid, bid),
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"share_pct": share,
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"deal_count": deals,
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"area_avg_m2": round(area_avg, 1),
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"area_median_m2": round(area_med, 1),
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"price_median_per_m2": p_med,
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"price_p25_per_m2": p25,
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"price_p75_per_m2": p75,
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"units_planned": units_planned,
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"revenue_planned_rub": revenue_planned,
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}
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)
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weighted_avg_price = round(weighted_num / weighted_den, 2) if weighted_den > 0 else None
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# 6) Comparable ЖК — same district (parsed from addr) and class
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cmp_rows = (
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db.execute(
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text(
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"""
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WITH latest_agg AS (
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SELECT obj_id, MAX(snapshot_date) AS snap
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FROM domrf_kn_sales_agg
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WHERE type = 'apartments'
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GROUP BY obj_id
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)
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SELECT o.obj_id, o.comm_name, o.dev_name, o.obj_class, o.flat_count,
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a.perc AS sold_perc
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FROM domrf_kn_objects o
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LEFT JOIN latest_agg la ON la.obj_id = o.obj_id
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LEFT JOIN domrf_kn_sales_agg a
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ON a.obj_id = la.obj_id
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AND a.snapshot_date = la.snap
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AND a.type = 'apartments'
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WHERE o.region_cd = :rc
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AND o.addr ILIKE '%' || :dn || '%'
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AND (:cls::text IS NULL OR o.obj_class = :cls)
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ORDER BY o.flat_count DESC NULLS LAST
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LIMIT 5
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"""
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),
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{"rc": region_code, "dn": district_row["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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"scope": {
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"district": district_row["district_name"],
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"district_zk_count": district_row["zk_count"],
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"district_median_price_per_m2": district_median,
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"district_factor": round(district_factor, 4),
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"class_multiplier": round(class_multiplier, 4),
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"target_class": target_class,
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"months_window": months_window,
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"region_code": region_code,
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"total_deals": total_deals if bucket_rows else 0,
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"data_caveat": (
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"MVP: bucket-распределение город-wide (регион 66). Район влияет"
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" только на ценовой коэффициент. v2 добавит per-district demand"
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" при заведении PostGIS-полигонов."
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),
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},
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"buckets": buckets,
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"summary": {
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"total_revenue_rub": round(total_revenue, 2) if have_revenue else None,
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"weighted_avg_price_per_m2": weighted_avg_price,
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"warnings": warnings,
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},
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"comparables": [
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{
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"obj_id": r["obj_id"],
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"comm_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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"flat_count": r["flat_count"],
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"sold_perc": _f(r["sold_perc"]),
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}
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for r in cmp_rows
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],
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}
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Reference in a new issue