Light1YT
574ee43577
Recreate the recommendation.py bridge module + RecommendMixInput
horizon_months/cad_num fields + RecommendForecast{Segment,Overlay} models +
endpoint passthrough + test_recommendation.py — completing the #982 opt-in
whose analytics_queries.recommend_mix wire-in landed in the prior commit.
build_forecast_overlay(db,*,district,cad_num,horizon_months,target_class,
market_buckets) matches the committed call. demand_supply via rank_segments
(cad_num) / demand_only pace-proxy (no cad_num, no fabricated supply, warning).
Pure room/class bridges drift-guarded vs _BUCKET_PRETTY; local §9.x imports
avoid the analytics_queries↔forecasting cycle. 55 tests green.
102 lines
4.3 KiB
Python
102 lines
4.3 KiB
Python
"""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=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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# деления на 0 / pf^elasticity = ∞ при «бесплатной» цене.
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price_factor: float = Field(default=1.0, ge=0.01, le=3.0)
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target_months: int | None = Field(default=None, ge=3, le=120)
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# #982 (953-A) forecast-overlay opt-in. Оба None по умолчанию → живой микс
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# БАЙТ-в-БАЙТ как раньше (overlay не добавляется). horizon_months задан →
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# advisory overlay под scope["forecast"]; cad_num задан → demand_supply-режим
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# (с supply/конкурентами участка), иначе demand_only (только темп спроса).
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horizon_months: int | None = Field(default=None, ge=3, le=24)
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cad_num: str | None = Field(default=None, max_length=40)
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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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# Velocity baseline (units/month for THIS project allocated to this bucket
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# at price_factor=1.0). Frontend scales by price_factor^elasticity for live
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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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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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cad_quarter: str | None = None
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lat: float | None = None
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lon: float | None = None
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buildings_n: int | None = None
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class RecommendForecastSegment(BaseModel):
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"""Одна ранжированная ячейка §9.7 forecast-overlay (#982). ADVISORY.
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`deficit_index`: в demand_supply-режиме ∈ [−1,+1] (>0 строить / <0 затоварка,
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supply-based); в demand_only-режиме ∈ (0,1] = ПРОКСИ относительной силы спроса
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(NOT supply-based — геометрии участка нет). `balance_units` (demand−supply)
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None в demand_only (предложение неизмеримо без cad_num).
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"""
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bucket: str
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obj_class: str | None = None
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deficit_index: float
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balance_units: float | None = None
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confidence: Literal["high", "medium", "low"]
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class RecommendForecastOverlay(BaseModel):
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"""§9.7 СОВЕТУЮЩИЙ forecast-overlay поверх живого микса (#982, 953-A).
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Кладётся ТОЛЬКО под scope["forecast"] при заданном horizon_months; живой микс
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(4 поля RecommendMixOutput) НЕ затрагивается. `advisory` ВСЕГДА True (наследует
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advisory-статус §9.x — не основание для инвест-решения). `mode`: demand_supply
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(cad_num задан — supply/конкуренты учтены) или demand_only (cad_num=None — только
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темп спроса; см. warnings).
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"""
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horizon_months: int
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mode: Literal["demand_supply", "demand_only"]
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advisory: bool
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ranked_segments: list[RecommendForecastSegment]
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warnings: list[str]
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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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