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d6a8a693c5
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24387d0455
3 changed files with 29 additions and 44 deletions
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@ -246,8 +246,7 @@ def _clean_address_for_geocode(addr: str) -> str:
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"""Чистим address для геокодера.
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Cian отдаёт «улица Латвийская, 56/3 · р-н Чкаловский» — суффикс ' · ...'
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мешает Nominatim. Берём часть до ' · '. Остальные источники такого суффикса
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не используют — адрес остаётся без изменений.
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мешает Nominatim. Берём часть до ' · '. N1 отдаёт «Репина, 75/2 стр.» — ок.
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"""
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main = addr.split(" · ")[0].strip()
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return main or addr
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@ -261,7 +260,7 @@ async def geocode_missing(
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) -> dict:
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"""Геокодинг listings ИЛИ deals у которых нет lat/lon (используя address).
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target=listings (по умолч.) — объявления; target=deals — сделки Росреестра.
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target=listings (по умолч.) — объявления Cian/N1; target=deals — сделки Росреестра.
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Чанк-обработка с бюджетом по времени (~240с, заведомо меньше cron
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`curl -m 320`): за вызов геокодим сколько успеваем, остаток уходит в
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`remaining`, cron вызывает в цикле пока `remaining` > 0.
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@ -270,8 +269,12 @@ async def geocode_missing(
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адреса не выбираются повторно 7 дней → cron-loop завершается, не зацикливается.
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geom обновляется автоматически триггером.
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"""
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# Доп. фильтр для listings — у Avito встречаются плейсхолдер-адреса.
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extra_filter = "AND address NOT LIKE '%(Avito)%'" if target == "listings" else ""
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# Доп. фильтр для listings — у Avito/N1 встречаются плейсхолдер-адреса.
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extra_filter = (
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"AND address NOT LIKE '%(Avito)%' AND address NOT LIKE '%(N1)%'"
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if target == "listings"
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else ""
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)
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rows = (
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db.execute(
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text(
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@ -1,10 +1,9 @@
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"""Tests for _fetch_analogs per-address cap and per-source quota (source starvation fix).
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Regression: Монтёрская 8/2 — 91 Avito listings с distance=0 выдавливали
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Cian/Yandex из топ-50, т.к. pure-distance sort + LIMIT 50.
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Cian/Yandex/N1 из топ-50, т.к. pure-distance sort + LIMIT 50.
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Fix: MAX_ANALOGS_PER_ADDRESS cap в SQL + MIN_ANALOGS_PER_SOURCE quota в Python.
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"""
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import os
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# Settings requires DATABASE_URL at init time. Set dummy DSN before any app import.
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@ -16,7 +15,6 @@ from unittest.mock import MagicMock
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# ── Helpers ───────────────────────────────────────────────────────────────────
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def _make_listing(
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*,
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source: str,
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@ -60,7 +58,6 @@ def _make_db_mock(rows: list[dict[str, Any]]) -> MagicMock:
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# ── Test 1: per-address cap ───────────────────────────────────────────────────
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def test_address_cap_limits_per_address_listings() -> None:
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"""_fetch_analogs caps at MAX_ANALOGS_PER_ADDRESS listings from a single address.
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@ -94,7 +91,6 @@ def test_address_cap_limits_per_address_listings() -> None:
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# ── Test 2: source quota (regression for Cian starvation) ────────────────────
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def test_source_quota_prevents_cian_starvation() -> None:
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"""MIN_ANALOGS_PER_SOURCE guarantees Cian is represented despite Avito dominance.
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@ -107,28 +103,22 @@ def test_source_quota_prevents_cian_starvation() -> None:
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# SQL already applied address cap. Simulate SQL result after cap:
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# 5 avito (cap applied to large block), 8 cian (different address, 200m away).
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avito_rows = [
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_make_listing(
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source="avito",
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address="ул. Монтёрская, 8/2",
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distance_m=0.0,
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relevance_score=float(i) * 0.01,
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)
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_make_listing(source="avito", address="ул. Монтёрская, 8/2", distance_m=0.0,
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relevance_score=float(i) * 0.01)
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for i in range(5)
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]
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cian_rows = [
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_make_listing(
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source="cian",
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address="ул. Монтёрская, 1",
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distance_m=200.0,
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relevance_score=0.2 + float(i) * 0.01,
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)
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_make_listing(source="cian", address="ул. Монтёрская, 1", distance_m=200.0,
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relevance_score=0.2 + float(i) * 0.01)
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for i in range(8)
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]
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# SQL returns avito first (better relevance), then cian
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sql_rows = avito_rows + cian_rows
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db = _make_db_mock(sql_rows)
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result, _, _ = _fetch_analogs(db, lat=56.838, lon=60.595, rooms=1, area=38.0, radius_m=1000)
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result, _, _ = _fetch_analogs(
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db, lat=56.838, lon=60.595, rooms=1, area=38.0, radius_m=1000
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)
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cian_count = sum(1 for r in result if r["source"] == "cian")
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expected_min = min(8, MIN_ANALOGS_PER_SOURCE)
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@ -140,7 +130,6 @@ def test_source_quota_prevents_cian_starvation() -> None:
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# ── Test 3: no source starvation when quota > supply ─────────────────────────
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def test_source_quota_includes_all_when_supply_below_min() -> None:
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"""When a source has fewer listings than MIN_ANALOGS_PER_SOURCE, all are included.
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@ -149,38 +138,31 @@ def test_source_quota_includes_all_when_supply_below_min() -> None:
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from app.services.estimator import _fetch_analogs
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avito_rows = [
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_make_listing(
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source="avito",
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address="ул. Монтёрская, 8/2",
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distance_m=0.0,
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relevance_score=float(i) * 0.01,
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)
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_make_listing(source="avito", address="ул. Монтёрская, 8/2", distance_m=0.0,
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relevance_score=float(i) * 0.01)
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for i in range(5)
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]
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cian_rows = [
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_make_listing(
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source="cian",
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address="ул. Монтёрская, 3",
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distance_m=300.0,
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relevance_score=0.3 + float(i) * 0.01,
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)
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_make_listing(source="cian", address="ул. Монтёрская, 3", distance_m=300.0,
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relevance_score=0.3 + float(i) * 0.01)
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for i in range(3)
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]
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sql_rows = avito_rows + cian_rows
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db = _make_db_mock(sql_rows)
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result, _, _ = _fetch_analogs(db, lat=56.838, lon=60.595, rooms=1, area=38.0, radius_m=1000)
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result, _, _ = _fetch_analogs(
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db, lat=56.838, lon=60.595, rooms=1, area=38.0, radius_m=1000
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)
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cian_count = sum(1 for r in result if r["source"] == "cian")
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assert (
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cian_count == 3
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), f"All 3 Cian listings (below MIN quota) must be included, got {cian_count}"
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assert cian_count == 3, (
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f"All 3 Cian listings (below MIN quota) must be included, got {cian_count}"
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)
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assert len(result) == 8 # 5 avito + 3 cian
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# ── Test 4: fallback signal preserved ────────────────────────────────────────
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def test_fallback_signal_reflects_radius() -> None:
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"""_fetch_analogs returns correct fallback_used boolean based on radius_m.
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@ -190,9 +172,8 @@ def test_fallback_signal_reflects_radius() -> None:
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from app.services.estimator import DEFAULT_RADIUS_M, FALLBACK_RADIUS_M, _fetch_analogs
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rows = [
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_make_listing(
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source="avito", address="ул. Ленина, 1", distance_m=100.0, relevance_score=0.1
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),
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_make_listing(source="avito", address="ул. Ленина, 1", distance_m=100.0,
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relevance_score=0.1),
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]
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db_default = _make_db_mock(rows)
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@ -89,6 +89,7 @@ const SOURCE_COLORS: Record<string, string> = {
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cian: "#1baf7a",
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yandex: "#eda100",
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domklik: "#008300",
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n1: "#4a3aa7",
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};
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const FALLBACK_SOURCE_COLOR = "#6b7280"; // неизвестный/прочий листинговый источник
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const COLOR_TARGET = "#f59e0b"; // та же семантика, что и MapCard.COLOR_TARGET
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