gavindiaz/PolyWeather
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

删除 LGBM 全部代码和模型文件，EMOS 简化为纯 legacy 高斯分桶模式

Browse Source
This commit is contained in:
2569718930@qq.com
2026-05-18 22:05:55 +08:00
parent aec47adda1
commit 0e0aad3171
26 changed files with 11 additions and 149413 deletions
Download Patch File Download Diff File Expand all files Collapse all files
web/analysis_service.py
+1 -48
View File
@@ -29,7 +29,6 @@ from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
from src.data_collection.city_time import get_city_utc_offset_seconds
from src.data_collection.nmc_sources import NMC_CITY_REFERENCES
from src.database.runtime_state import IntradayPathSnapshotRepository
from src.models.lgbm_daily_high import predict_lgbm_daily_high
from web.services.groq_commentary import (
build_groq_commentary_context as _groq_context_builder,
clean_commentary_text as _groq_clean_text,
@@ -2187,24 +2186,6 @@ def _analyze(
else:
peak_status = "before"
lgbm_val = None
if current_forecasts and deb_val is not None:
lgbm_val, _ = predict_lgbm_daily_high(
city_name=city,
current_forecasts=current_forecasts,
deb_prediction=deb_val,
current_temp=cur_temp,
max_so_far=max_so_far,
humidity=_sf(primary_current.get("humidity")),
wind_speed_kt=_sf(primary_current.get("wind_speed_kt")),
visibility_mi=_sf(primary_current.get("visibility_mi")),
local_hour=local_hour,
local_date=local_date_str,
peak_status=peak_status,
)
# LGBM is kept as an independent reference (lgbm.prediction),
# not fed back into DEB to avoid circular dependency
deviation_monitor = _build_deviation_monitor(
current_temp=cur_temp,
deb_prediction=deb_val,
@@ -2225,33 +2206,14 @@ def _analyze(
probabilities = []
probabilities_all = []
shadow_probabilities = []
shadow_probabilities_all = []
mu = None
probability_engine = "legacy"
probability_calibration_mode = "legacy"
probability_calibration_version = None
probability_raw_mu = None
probability_raw_sigma = None
probability_calibrated_mu = None
probability_calibrated_sigma = None
dynamic_commentary = {"summary": "", "notes": []}
try:
_, _ai_context, sd = _trend_analyze(raw, sym, city)
# Use structured data from shared engine
mu = sd.get("mu")
probabilities = sd.get("probabilities", [])
probabilities_all = sd.get("probabilities_all", probabilities)
shadow_probabilities = sd.get("shadow_probabilities", [])
shadow_probabilities_all = sd.get("shadow_probabilities_all", shadow_probabilities)
probability_engine = sd.get("probability_engine", "legacy")
probability_calibration_mode = sd.get("probability_calibration_mode", "legacy")
probability_calibration_version = sd.get("probability_calibration_version")
probability_raw_mu = sd.get("probability_raw_mu")
probability_raw_sigma = sd.get("probability_raw_sigma")
probability_calibrated_mu = sd.get("probability_calibrated_mu")
probability_calibrated_sigma = sd.get("probability_calibrated_sigma")
dynamic_commentary = sd.get("dynamic_commentary") or dynamic_commentary
trend_info["is_dead_market"] = sd.get("trend_info", {}).get("is_dead_market", False)
trend_info["direction"] = sd.get("trend_info", {}).get("direction", trend_info.get("direction", "unknown"))
@@ -2632,22 +2594,13 @@ def _analyze(
"multi_model": {k: v for k, v in current_forecasts.items() if v is not None},
"multi_model_daily": multi_model_daily,
"deb": {"prediction": deb_val, "weights_info": deb_weights},
"lgbm": {"prediction": lgbm_val},
"deviation_monitor": deviation_monitor,
"ensemble": ens_data,
"probabilities": {
"mu": round(mu, 1) if mu is not None else None,
"distribution": probabilities,
"distribution_all": probabilities_all or probabilities,
"engine": probability_engine,
"calibration_mode": probability_calibration_mode,
"calibration_version": probability_calibration_version,
"raw_mu": probability_raw_mu,
"raw_sigma": probability_raw_sigma,
"calibrated_mu": probability_calibrated_mu,
"calibrated_sigma": probability_calibrated_sigma,
"shadow_distribution": shadow_probabilities,
"shadow_distribution_all": shadow_probabilities_all or shadow_probabilities,
"engine": "legacy",
},
"trend": trend_info,
"peak": {