tradein-mvp/backend/app/services/estimator.py

@@ -107,8 +107,9 @@ def _target_cohort_range(year_built: int | None) -> tuple[int, int] | None:
     return None
 
 
-# Маппинг наших house_type → словарь Avito-IMV (внешний source). НЕ путать с
+# Поправочные коэффициенты на состояние ремонта. Аналоги в выборке — микс
-# _REPAIR_COEF (heuristic-множитель ниже).
+# состояний (≈ "стандартный/косметический"), коэффициент сдвигает медиану под
+# конкретный ремонт целевой квартиры. Встреча Птицы: ремонт влияет на цену.
 _IMV_HOUSE_TYPE_MAP: dict[str | None, str | None] = {
     "panel": "panel",
     "brick": "brick",
@@ -127,22 +128,11 @@ _IMV_REPAIR_MAP: dict[str | None, str | None] = {
     None: None,
 }
 
-# Множители к медиане по состоянию ремонта. Аналоги в выборке — микс состояний;
-# коэффициент сдвигает оценку под ремонт целевой квартиры (встреча Птицы: ремонт
-# влияет на цену).
-#
-# WARNING: tunable МАРКЕТ-ЭВРИСТИКА, НЕ data-derived (issue #7). Вывести из данных пока
-# нельзя: listings.repair_state покрыт только ~2%, хранит un-normalized исходные
-# значения (cosmetic/euro/fine/without/designer/rough — НЕ целевой enum
-# needs_repair/standard/good/excellent), а медианы по нему confounded by area
-# (немонотонны). Baseline = standard = 1.00 (no-op: было 0.98, срезало каждую
-# «стандартную» оценку на 2% — пофикшено). Пересмотреть, когда покрытие
-# repair_state вырастет и появится нормализация.
 _REPAIR_COEF: dict[str, float] = {
-    "needs_repair": 0.94,  # требует ремонта — ниже рынка
+    "needs_repair": 0.92,  # требует ремонта — ниже рынка
-    "standard": 1.00,  # baseline
+    "standard": 0.98,
-    "good": 1.05,
+    "good": 1.03,
-    "excellent": 1.10,  # евроремонт — выше рынка
+    "excellent": 1.08,  # евроремонт — выше рынка
 }
 _REPAIR_LABEL: dict[str | None, str] = {
     "needs_repair": "требует ремонта",

tradein-mvp/backend/tests/test_estimator_repair_coef.py

@@ -1,182 +0,0 @@
-"""Regression tests for the repair-state coefficient (issue #7).
-
-Verifies the `_REPAIR_COEF` heuristic multiplier in `estimate_quality`:
-- excellent / needs_repair median ratio == coef ratio (within int rounding)
-- standard (baseline 1.0) is a true no-op vs the unadjusted market median
-- repair_state=None is a no-op (coef 1.0)
-- coef != 1.0 emits the repair note into confidence_explanation
-
-Coefficient values are read DYNAMICALLY from `_REPAIR_COEF` so the assertions
-survive future re-calibration. The full `estimate_quality` flow is exercised with
-all I/O stubbed (geocode / house-meta / _fetch_analogs / _fetch_deals / IMV /
-Yandex / Cian / DB) so the test isolates the multiplier — no DB, no network.
-"""
-
-from __future__ import annotations
-
-import os
-from datetime import UTC, datetime
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import anyio
-
-# Settings requires DATABASE_URL at init time. Set dummy DSN before any app import.
-os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://test:test@localhost/test_db")
-
-
-# ── Fixtures / helpers ──────────────────────────────────────────────────────
-
-
-def _make_listing(*, price_per_m2: float, area_m2: float = 40.0) -> dict[str, Any]:
-    """Minimal listing dict matching the keys the aggregation block reads."""
-    price_rub = price_per_m2 * area_m2
-    return {
-        "source": "cian",
-        "source_url": "https://cian.ru/offer/1",
-        "address": "ЕКБ, ул. Учителей, 18",
-        "lat": 56.838,
-        "lon": 60.595,
-        "rooms": 1,
-        "area_m2": area_m2,
-        "floor": 4,
-        "total_floors": 16,
-        "price_rub": price_rub,
-        "price_per_m2": price_per_m2,
-        "listing_date": datetime(2026, 5, 1),
-        "days_on_market": 10,
-        "photo_urls": [],
-        "scraped_at": datetime(2026, 5, 20, tzinfo=UTC),
-        "distance_m": 100.0,
-        "relevance_score": 0.1,
-    }
-
-
-# Three fixed analogs → deterministic median ppm2 = 150_000 (< 5 ⇒ no outlier drop).
-_ANALOGS: list[dict[str, Any]] = [
-    _make_listing(price_per_m2=140_000.0),
-    _make_listing(price_per_m2=150_000.0),
-    _make_listing(price_per_m2=160_000.0),
-]
-
-
-def _make_fake_geo():
-    from app.services.geocoder import GeocodeResult
-
-    return GeocodeResult(
-        lat=56.838,
-        lon=60.595,
-        full_address="Свердловская обл., Екатеринбург, ул. Учителей, 18",
-        provider="nominatim",
-    )
-
-
-def _make_payload(repair_state: str | None):
-    from app.schemas.trade_in import TradeInEstimateInput
-
-    return TradeInEstimateInput(
-        address="ЕКБ, ул. Учителей, 18",
-        area_m2=40.0,
-        rooms=1,
-        floor=4,
-        total_floors=16,
-        repair_state=repair_state,
-    )
-
-
-def _run_estimate(repair_state: str | None):
-    """Invoke estimate_quality with every external dependency stubbed."""
-    from app.services.estimator import estimate_quality
-
-    db = MagicMock()
-    payload = _make_payload(repair_state)
-
-    async def _run():
-        with (
-            patch("app.services.estimator.geocode", new=AsyncMock(return_value=_make_fake_geo())),
-            patch("app.services.estimator.dadata_clean_address",
-                  new=AsyncMock(return_value=None)),
-            patch("app.services.estimator.match_house_readonly", return_value=None),
-            patch("app.services.estimator.get_house_metadata", new=AsyncMock(return_value=None)),
-            # 3-tuple: (listings, fallback_used, analog_tier). Same analogs every call so
-            # all fallback tiers are equivalent and the median is stable.
-            patch("app.services.estimator._fetch_analogs",
-                  return_value=(list(_ANALOGS), False, "S")),
-            patch("app.services.estimator._fetch_deals", return_value=[]),
-            patch("app.services.estimator._get_or_fetch_imv_cached",
-                  new=AsyncMock(return_value=None)),
-            patch("app.services.estimator._get_or_fetch_yandex_valuation_cached",
-                  new=AsyncMock(return_value=None)),
-            patch("app.services.estimator.estimate_via_cian_valuation",
-                  new=AsyncMock(return_value=None)),
-        ):
-            return await estimate_quality(payload, db)
-
-    return anyio.run(_run)
-
-
-# ── Tests ───────────────────────────────────────────────────────────────────
-
-
-def test_excellent_to_needs_repair_ratio_matches_coef() -> None:
-    """excellent ÷ needs_repair median ≈ _REPAIR_COEF["excellent"] / [...]["needs_repair"]."""
-    from app.services.estimator import _REPAIR_COEF
-
-    excellent = _run_estimate("excellent")
-    needs_repair = _run_estimate("needs_repair")
-
-    expected_ratio = _REPAIR_COEF["excellent"] / _REPAIR_COEF["needs_repair"]
-    actual_ratio = excellent.median_price_rub / needs_repair.median_price_rub
-
-    # int() truncation on both medians ⇒ allow a small tolerance.
-    assert abs(actual_ratio - expected_ratio) < 0.005, (
-        f"excellent/needs_repair ratio {actual_ratio:.5f} != coef ratio {expected_ratio:.5f}"
-    )
-
-
-def test_standard_is_baseline_noop() -> None:
-    """repair_state='standard' → median equals the unadjusted market median (coef 1.0)."""
-    from app.services.estimator import _REPAIR_COEF
-
-    assert _REPAIR_COEF["standard"] == 1.0, "baseline invariant: standard must be 1.0"
-
-    standard = _run_estimate("standard")
-    baseline = _run_estimate(None)
-
-    # Unadjusted median: median ppm2 (150_000) × area (40.0) = 6_000_000.
-    expected_median = int(150_000.0 * 40.0)
-    assert standard.median_price_rub == expected_median
-    assert standard.median_price_rub == baseline.median_price_rub
-
-
-def test_none_repair_state_is_noop() -> None:
-    """repair_state=None → coef 1.0, no adjustment, no repair note."""
-    baseline = _run_estimate(None)
-
-    expected_median = int(150_000.0 * 40.0)
-    assert baseline.median_price_rub == expected_median
-    assert "скорректирована на состояние ремонта" not in (
-        baseline.confidence_explanation or ""
-    )
-
-
-def test_repair_note_present_when_coef_differs() -> None:
-    """coef != 1.0 (excellent) → confidence_explanation contains the repair note."""
-    from app.services.estimator import _REPAIR_COEF
-
-    assert _REPAIR_COEF["excellent"] != 1.0, "fixture invariant: excellent must adjust"
-
-    excellent = _run_estimate("excellent")
-    explanation = excellent.confidence_explanation or ""
-    assert "скорректирована на состояние ремонта" in explanation
-    # Note reports the signed percentage derived from the coefficient.
-    pct = round((_REPAIR_COEF["excellent"] - 1.0) * 100)
-    assert f"{pct:+d}%" in explanation
-
-
-def test_standard_emits_no_repair_note() -> None:
-    """Baseline standard (1.0) is a true no-op — no repair note appended."""
-    standard = _run_estimate("standard")
-    assert "скорректирована на состояние ремонта" not in (
-        standard.confidence_explanation or ""
-    )