bot-backend
ed4335b932
The expected_sold_range_low/high were derived from the analog asking-IQR (× asking→sold ratio): only ~55% of actual sold prices fell inside the stated range — a 50%-ish interval mislabeled as the estimate's range. Replace with a calibrated band around the expected_sold POINT, gated behind estimate_calibrated_pi_enabled (default ON): range_low = expected_sold_price × 0.649 (empirical p10 of sold/expected_sold) range_high = expected_sold_price × 1.392 (empirical p90, n=2366 prod deals) This is a genuine ~80% prediction interval (verified 80.0% coverage on the 2366-deal sample). Headline asking range_low/high (market-asking IQR) untouched. Frozen backtest baseline regenerated: range_coverage.overall 55.23 → 80.14 (n_covered 153 → 222); expected_sold POINT mape/bias and headline unchanged (only the band moved). Tests asserting the old asking-IQR × ratio band updated to the calibrated band (documented behavior change).
199 lines
7.6 KiB
Python
199 lines
7.6 KiB
Python
"""Tests for Fix 4: clamp expected_sold <= asking (ratio cap 1.0).
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Smoke-диагноз: 3к/27.0М -> expected_sold 31.4М > asking.
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Причина: _get_asking_sold_ratio для high-price tier возвращает ratio > 1.0
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(product artefact) -> expected_sold = headline x ratio > headline.
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Проверяем:
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1. ratio=1.15 + флаг on -> expected_sold == headline (не выше); лог clamp.
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2. ratio=0.8 -> expected_sold = headline x 0.8 (норм, без clamp).
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3. ratio None -> expected_sold не выводится (graceful).
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4. Флаг off + ratio > 1 -> expected_sold > headline (старое поведение).
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"""
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from __future__ import annotations
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import logging
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import os
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from datetime import UTC, datetime
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from typing import Any
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from unittest.mock import AsyncMock, MagicMock, patch
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import anyio
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import pytest
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os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://test:test@localhost:5432/test")
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def _make_listing(*, price_per_m2: float, area_m2: float = 50.0) -> dict[str, Any]:
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return {
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"source": "cian",
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"source_url": "https://cian.ru/offer/1",
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"address": "ЕКБ, ул. Малышева, 30",
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"lat": 56.838,
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"lon": 60.595,
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"rooms": 3,
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"area_m2": area_m2,
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"floor": 10,
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"total_floors": 20,
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"price_rub": price_per_m2 * area_m2,
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"price_per_m2": price_per_m2,
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"listing_date": datetime(2026, 5, 1),
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"days_on_market": 5,
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"photo_urls": [],
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"scraped_at": datetime(2026, 5, 20, tzinfo=UTC),
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"distance_m": 100.0,
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"relevance_score": 0.1,
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}
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_ANALOGS: list[dict[str, Any]] = [
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_make_listing(price_per_m2=260_000.0),
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_make_listing(price_per_m2=270_000.0),
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_make_listing(price_per_m2=280_000.0),
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]
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def _make_geo():
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from app.services.geocoder import GeocodeResult
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return GeocodeResult(
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lat=56.838,
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lon=60.595,
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full_address="Свердловская обл., Екатеринбург, ул. Малышева, 30",
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provider="nominatim",
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)
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def _make_payload(rooms: int = 3):
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from app.schemas.trade_in import TradeInEstimateInput
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return TradeInEstimateInput(
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address="ЕКБ, ул. Малышева, 30", area_m2=50.0, rooms=rooms, floor=10, total_floors=20
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)
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def _run_estimate(
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ratio_tuple: tuple[float | None, str | None],
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*,
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clamp_enabled: bool = True,
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) -> Any:
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from app.services.estimator import estimate_quality
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db = MagicMock()
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payload = _make_payload()
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async def _run() -> Any:
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with (
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patch("app.core.config.settings.estimate_expected_sold_le_asking", new=clamp_enabled),
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patch("app.services.estimator.geocode", new=AsyncMock(return_value=_make_geo())),
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patch("app.services.estimator.dadata_clean_address", new=AsyncMock(return_value=None)),
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patch("app.services.estimator.match_house_readonly", return_value=None),
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patch(
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"app.services.estimator.get_house_metadata",
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new=AsyncMock(return_value=None),
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),
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patch(
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"app.services.estimator._fetch_analogs",
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return_value=(list(_ANALOGS), False, "S"),
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),
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patch("app.services.estimator._fetch_deals", return_value=[]),
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patch(
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"app.services.estimator._get_or_fetch_imv_cached",
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new=AsyncMock(return_value=None),
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),
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patch(
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"app.services.estimator._get_or_fetch_yandex_valuation_cached",
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new=AsyncMock(return_value=None),
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),
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patch(
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"app.services.estimator.estimate_via_cian_valuation",
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new=AsyncMock(return_value=None),
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),
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patch("app.services.estimator._fetch_dkp_corridor", return_value=None),
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patch("app.services.estimator._get_asking_sold_ratio", return_value=ratio_tuple),
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):
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return await estimate_quality(payload, db)
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return anyio.run(_run)
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def test_expected_sold_clamped_to_headline_when_ratio_above_1(
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caplog: pytest.LogCaptureFixture,
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) -> None:
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ratio = 1.15
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with caplog.at_level(logging.INFO, logger="app.services.estimator"):
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est = _run_estimate((ratio, "per_rooms"), clamp_enabled=True)
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assert est.median_price_rub > 0, "headline должен быть задан аналогами"
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assert est.expected_sold_price_rub is not None
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assert (
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est.expected_sold_price_rub <= est.median_price_rub
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), f"expected_sold {est.expected_sold_price_rub} > asking {est.median_price_rub}"
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assert est.expected_sold_per_m2 is not None
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assert (
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est.expected_sold_per_m2 <= est.median_price_per_m2
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), "expected_sold_per_m2 превышает median_price_per_m2"
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# #1966: expected_sold range is now a calibrated ~80% PI around the point
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# (point × [p10, p90] of sold/expected_sold), so it is NO LONGER bounded by the
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# asking-IQR band — the high arm (point × 1.392) legitimately exceeds range_high.
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# The invariant we keep is the calibrated band itself + ordering low ≤ point ≤ high.
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from app.core.config import settings
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assert est.expected_sold_range_high_rub is not None
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assert est.expected_sold_range_high_rub == round(
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est.expected_sold_price_rub * settings.estimate_pi_high_mult
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)
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assert est.expected_sold_range_low_rub is not None
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assert est.expected_sold_range_low_rub == round(
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est.expected_sold_price_rub * settings.estimate_pi_low_mult
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)
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assert (
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est.expected_sold_range_low_rub
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<= est.expected_sold_price_rub
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<= est.expected_sold_range_high_rub
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)
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assert est.expected_sold_price_rub == est.median_price_rub
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assert est.expected_sold_per_m2 == est.median_price_per_m2
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clamp_logs = [r for r in caplog.records if "clamped" in r.getMessage()]
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assert clamp_logs, "Ожидался log-message о clamp ratio"
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assert "1.150" in clamp_logs[0].getMessage() or "1.15" in clamp_logs[0].getMessage()
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def test_expected_sold_not_clamped_when_ratio_below_1() -> None:
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ratio = 0.8
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est = _run_estimate((ratio, "per_rooms"), clamp_enabled=True)
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assert est.median_price_rub > 0
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assert est.expected_sold_price_rub == round(est.median_price_rub * ratio)
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assert est.expected_sold_per_m2 == round(est.median_price_per_m2 * ratio)
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# #1966: calibrated ~80% PI around the expected_sold point (not asking-IQR × ratio).
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from app.core.config import settings
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assert est.expected_sold_range_low_rub == round(
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est.expected_sold_price_rub * settings.estimate_pi_low_mult
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)
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assert est.expected_sold_range_high_rub == round(
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est.expected_sold_price_rub * settings.estimate_pi_high_mult
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)
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assert est.expected_sold_price_rub < est.median_price_rub
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def test_expected_sold_none_when_ratio_none() -> None:
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est = _run_estimate((None, None), clamp_enabled=True)
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assert est.expected_sold_price_rub is None
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assert est.expected_sold_per_m2 is None
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assert est.expected_sold_range_low_rub is None
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assert est.expected_sold_range_high_rub is None
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assert est.asking_to_sold_ratio is None
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assert est.median_price_rub > 0
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def test_expected_sold_exceeds_asking_when_clamp_flag_off() -> None:
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ratio = 1.15
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est = _run_estimate((ratio, "per_rooms"), clamp_enabled=False)
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assert est.median_price_rub > 0
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assert est.expected_sold_price_rub is not None
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assert est.expected_sold_price_rub > est.median_price_rub, (
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f"Ожидалось expected_sold > asking при clamp=False, "
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f"но {est.expected_sold_price_rub} <= {est.median_price_rub}"
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)
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assert est.expected_sold_price_rub == round(est.median_price_rub * ratio)
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