fix(forecasting): deal_count confidence note carries «за N мес» window (#1637) #1684

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lekss361 merged 2 commits from fix/confidence-deal-count-window-1637 into main 2026-06-17 17:57:41 +00:00
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@ -189,10 +189,10 @@ def _domrf_coverage(analyze: dict[str, Any], supply_layers: dict[str, Any] | Non
"""
if supply_layers is not None:
coverage = supply_layers.get("domrf_coverage")
if isinstance(coverage, (int, float)) and not isinstance(coverage, bool):
if isinstance(coverage, int | float) and not isinstance(coverage, bool):
return _clamp_fraction(float(coverage))
pct = analyze.get("market_data_coverage_pct")
if isinstance(pct, (int, float)) and not isinstance(pct, bool):
if isinstance(pct, int | float) and not isinstance(pct, bool):
return _clamp_fraction(float(pct) / 100.0)
return None
@ -223,6 +223,19 @@ def _history_months(
return None
def _deal_count_months(market_metrics: dict[str, Any] | None) -> int | None:
"""Окно наблюдения для deal_count (мес) — для deal_count_months #990. PURE.
Читает тот же `market_metrics.window_months` (§9.2), что и `_history_months`
именно за это окно считается n_sold. Нет None (#990 пропускает суффикс «за N мес»).
"""
if market_metrics is not None:
window = market_metrics.get("window_months")
if isinstance(window, int) and window > 0:
return window
return None
def _confounded(forecasts: Sequence[dict[str, Any]]) -> bool:
"""Пересекает ли окно прогноза шок-период — для confounded #990. PURE.
@ -293,10 +306,10 @@ def _primary_deficit_index(forecasts: Sequence[dict[str, Any]]) -> float | None:
)
if primary is not None and primary.get("deficit_index") is not None:
di = primary["deficit_index"]
return float(di) if isinstance(di, (int, float)) and not isinstance(di, bool) else None
return float(di) if isinstance(di, int | float) and not isinstance(di, bool) else None
for f in forecasts:
di = f.get("deficit_index")
if isinstance(di, (int, float)) and not isinstance(di, bool):
if isinstance(di, int | float) and not isinstance(di, bool):
return float(di)
return None
@ -314,10 +327,10 @@ def _primary_months_of_inventory(forecasts: Sequence[dict[str, Any]]) -> float |
)
if primary is not None and primary.get("months_of_inventory") is not None:
moi = primary["months_of_inventory"]
return float(moi) if isinstance(moi, (int, float)) and not isinstance(moi, bool) else None
return float(moi) if isinstance(moi, int | float) and not isinstance(moi, bool) else None
for f in forecasts:
moi = f.get("months_of_inventory")
if isinstance(moi, (int, float)) and not isinstance(moi, bool):
if isinstance(moi, int | float) and not isinstance(moi, bool):
return float(moi)
return None
@ -342,7 +355,7 @@ def _overall_score(product_scores: dict[str, Any] | None) -> float | None:
if not isinstance(product_scores, dict):
return None
overall = product_scores.get("overall")
if isinstance(overall, (int, float)) and not isinstance(overall, bool):
if isinstance(overall, int | float) and not isinstance(overall, bool):
return float(overall)
return None
@ -388,10 +401,10 @@ def _market_now_summary(
parts: list[str] = []
if market_metrics is not None:
velocity = market_metrics.get("unit_velocity")
if isinstance(velocity, (int, float)) and not isinstance(velocity, bool):
if isinstance(velocity, int | float) and not isinstance(velocity, bool):
parts.append(f"абсорбция ~{round(float(velocity), 1)} ед./мес")
avg_price = analyze.get("market_avg_price_per_m2")
if isinstance(avg_price, (int, float)) and not isinstance(avg_price, bool):
if isinstance(avg_price, int | float) and not isinstance(avg_price, bool):
parts.append(f"средняя цена ~{round(float(avg_price)):,} ₽/м²".replace(",", " "))
# #1634: НЕ через _analog_count — он отдаёт market_metrics.obj_count (число ЖК во
# всей district-wide/микрорайонной выборке §9.2), что НЕ равно «конкурентов рядом».
@ -618,6 +631,7 @@ def _build_confidence(
market_metrics, future_supply, forecasts, product_scores, special_indices
),
deal_count=_deal_count(analyze, market_metrics),
deal_count_months=_deal_count_months(market_metrics),
analog_count=_analog_count(analyze, market_metrics),
domrf_coverage=_domrf_coverage(analyze, supply_layers),
history_months=_history_months(market_metrics, forecasts),