Add LightGBM daily high forecasting pipeline
This commit is contained in:
2569718930@qq.com
@@ -24,6 +24,162 @@ from src.analysis.deb_algorithm import calculate_dynamic_weights
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from src.analysis.settlement_rounding import apply_city_settlement
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from src.analysis.metar_narrator import describe_metar_report
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from src.data_collection.city_registry import ALIASES
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from src.models.lgbm_daily_high import predict_lgbm_daily_high
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def _interpolate_hourly_value(
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times: list,
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values: list,
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local_date: str,
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target_hour_frac: float,
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) -> Optional[float]:
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points = []
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for ts, raw_value in zip(times or [], values or []):
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if not str(ts).startswith(local_date):
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continue
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value = _sf(raw_value)
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if value is None:
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continue
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try:
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hh_mm = str(ts).split("T")[1]
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hour = int(hh_mm[:2])
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minute = int(hh_mm[3:5]) if len(hh_mm) >= 5 else 0
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except Exception:
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continue
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points.append((hour + minute / 60.0, value))
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if not points:
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return None
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points.sort(key=lambda item: item[0])
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if target_hour_frac <= points[0][0]:
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return float(points[0][1])
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if target_hour_frac >= points[-1][0]:
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return float(points[-1][1])
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for idx in range(1, len(points)):
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left_hour, left_value = points[idx - 1]
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right_hour, right_value = points[idx]
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if target_hour_frac > right_hour:
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continue
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if right_hour == left_hour:
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return float(right_value)
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ratio = (target_hour_frac - left_hour) / (right_hour - left_hour)
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return float(left_value + (right_value - left_value) * ratio)
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return float(points[-1][1])
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def _build_deviation_monitor(
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*,
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current_temp: Optional[float],
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deb_prediction: Optional[float],
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om_today: Optional[float],
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hourly_times: list,
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hourly_temps: list,
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local_date: str,
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local_hour_frac: float,
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observation_points: list,
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) -> Dict[str, Any]:
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if current_temp is None or deb_prediction is None or om_today is None:
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return {}
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offset = _sf(deb_prediction) - _sf(om_today)
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if offset is None:
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return {}
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expected_now = _interpolate_hourly_value(
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hourly_times,
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[(_sf(value) + offset) if _sf(value) is not None else None for value in hourly_temps],
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local_date,
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local_hour_frac,
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)
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if expected_now is None:
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return {}
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delta = float(current_temp) - expected_now
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abs_delta = abs(delta)
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if abs_delta < 0.8:
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direction = "normal"
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severity = "normal"
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elif delta <= -1.8:
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direction = "cold"
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severity = "strong"
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elif delta >= 1.8:
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direction = "hot"
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severity = "strong"
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elif delta < 0:
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direction = "cold"
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severity = "light"
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else:
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direction = "hot"
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severity = "light"
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deviation_series = []
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for item in observation_points or []:
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if not isinstance(item, dict):
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continue
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obs_temp = _sf(item.get("temp"))
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raw_time = str(item.get("time") or "").strip()
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if obs_temp is None:
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continue
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match = re.search(r"(\d{1,2}):(\d{2})", raw_time)
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if not match:
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continue
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obs_hour_frac = int(match.group(1)) + int(match.group(2)) / 60.0
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ref_temp = _interpolate_hourly_value(
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hourly_times,
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[(_sf(value) + offset) if _sf(value) is not None else None for value in hourly_temps],
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local_date,
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obs_hour_frac,
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)
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if ref_temp is None:
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continue
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deviation_series.append(float(obs_temp) - ref_temp)
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trend = "stable"
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if len(deviation_series) >= 2:
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latest = deviation_series[-1]
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previous = deviation_series[-2]
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if latest * previous > 0:
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if abs(latest) - abs(previous) >= 0.3:
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trend = "expanding"
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elif abs(previous) - abs(latest) >= 0.3:
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trend = "contracting"
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if direction == "normal":
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label_zh = f"正常 ±{abs_delta:.1f}°C"
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label_en = f"Normal ±{abs_delta:.1f}°C"
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elif direction == "cold":
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label_zh = f"偏冷 {delta:.1f}°C"
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label_en = f"Cool bias {delta:.1f}°C"
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else:
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label_zh = f"偏热 +{abs_delta:.1f}°C"
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label_en = f"Warm bias +{abs_delta:.1f}°C"
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trend_zh = {
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"contracting": "收敛中",
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"expanding": "扩大中",
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"stable": "稳定",
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}.get(trend, "稳定")
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trend_en = {
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"contracting": "contracting",
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"expanding": "expanding",
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"stable": "stable",
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}.get(trend, "stable")
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return {
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"available": True,
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"current_delta": round(delta, 1),
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"reference_temp": round(expected_now, 1),
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"direction": direction,
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"severity": severity,
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"trend": trend,
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"label_zh": label_zh,
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"label_en": label_en,
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"trend_label_zh": trend_zh,
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"trend_label_en": trend_en,
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}
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def _wind_components(speed: Optional[float], direction: Optional[float]) -> tuple[Optional[float], Optional[float]]:
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if speed is None or direction is None:
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@@ -959,6 +1115,40 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
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else:
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peak_status = "before"
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if current_forecasts and deb_val is not None:
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lgbm_val, _ = predict_lgbm_daily_high(
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city_name=city,
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current_forecasts=current_forecasts,
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deb_prediction=deb_val,
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current_temp=cur_temp,
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max_so_far=max_so_far,
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humidity=_sf(primary_current.get("humidity")),
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wind_speed_kt=_sf(primary_current.get("wind_speed_kt")),
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visibility_mi=_sf(primary_current.get("visibility_mi")),
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local_hour=local_hour,
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local_date=local_date_str,
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peak_status=peak_status,
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)
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if lgbm_val is not None:
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current_forecasts["LGBM"] = lgbm_val
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blended, winfo = calculate_dynamic_weights(city, current_forecasts)
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if blended is not None:
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deb_val = blended
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deb_weights = winfo
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deviation_monitor = _build_deviation_monitor(
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current_temp=cur_temp,
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deb_prediction=deb_val,
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om_today=om_today,
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hourly_times=h_times,
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hourly_temps=h_temps,
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local_date=local_date_str,
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local_hour_frac=local_hour_frac,
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observation_points=(
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settlement_today_obs if settlement_today_obs else metar_today_obs_payload
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),
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)
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# ── 10. Shared analysis (probability, trend, AI) via trend_engine ──
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# This single call replaces the duplicate probability engine, dead market
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# detection, forecast bust grading, and AI context building.
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@@ -1335,6 +1525,7 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
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"multi_model": {k: v for k, v in current_forecasts.items() if v is not None},
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"multi_model_daily": multi_model_daily,
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"deb": {"prediction": deb_val, "weights_info": deb_weights},
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"deviation_monitor": deviation_monitor,
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"ensemble": ens_data,
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"probabilities": {
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"mu": round(mu, 1) if mu is not None else None,
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@@ -1398,6 +1589,7 @@ def _build_city_summary_payload(data: Dict[str, Any]) -> Dict[str, Any]:
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"settlement_source_label": data.get("current", {}).get("settlement_source_label"),
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},
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"deb": {"prediction": data.get("deb", {}).get("prediction")},
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"deviation_monitor": data.get("deviation_monitor") or {},
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"risk": {
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"level": data.get("risk", {}).get("level"),
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"warning": data.get("risk", {}).get("warning"),
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