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feat: add versioned DEB bias backtesting

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This commit is contained in:
2569718930@qq.com
2026-05-27 21:40:26 +08:00
parent 4d22191a2d
commit 8609376c59
20 changed files with 792 additions and 44 deletions
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web/analysis_service.py
+67 -16
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@@ -24,7 +24,7 @@ from web.core import (
_sf,
_weather,
)
from src.analysis.deb_algorithm import calculate_dynamic_weights
from src.analysis.deb_algorithm import calculate_deb_prediction
from src.analysis.settlement_rounding import apply_city_settlement
from src.data_collection.country_networks import build_country_network_snapshot
from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
@@ -1082,11 +1082,18 @@ def _analyze(
# ── 6. DEB fusion ──
deb_val, deb_weights = None, ""
deb_raw_val, deb_version = None, None
deb_bias_adjustment, deb_bias_samples = 0.0, 0
deb_intraday_adjustment = 0.0
if current_forecasts:
blended, winfo = calculate_dynamic_weights(city, current_forecasts)
if blended is not None:
deb_val = blended
deb_weights = winfo
deb_result = calculate_deb_prediction(city, current_forecasts)
if deb_result.get("prediction") is not None:
deb_val = deb_result.get("prediction")
deb_raw_val = deb_result.get("raw_prediction")
deb_version = deb_result.get("version")
deb_bias_adjustment = deb_result.get("bias_adjustment") or 0.0
deb_bias_samples = deb_result.get("bias_samples") or 0
deb_weights = deb_result.get("weights_info") or ""
# ── 7. Ensemble stats ──
ens_data = {
@@ -1230,6 +1237,10 @@ def _analyze(
# Use shared DEB if not already set
if deb_val is None and sd.get("deb_prediction") is not None:
deb_val = sd["deb_prediction"]
deb_raw_val = sd.get("deb_raw_prediction") or deb_val
deb_version = sd.get("deb_version")
deb_bias_adjustment = sd.get("deb_bias_adjustment") or 0.0
deb_bias_samples = sd.get("deb_bias_samples") or 0
deb_weights = sd.get("deb_weights", "")
except Exception as e:
@@ -1288,10 +1299,11 @@ def _analyze(
max_correction_clamped = max(-max_correction, min(max_correction, max_so_far_excess * max(0.3, weight)))
blended_correction = hourly_correction * 0.6 + max_correction_clamped * 0.4
deb_intraday_adjustment = round(blended_correction, 1)
deb_val = round(deb_val + blended_correction, 1)
if mu is not None:
mu = round(mu + blended_correction, 1)
deb_weights = f"{deb_weights or 'DEB'} + intraday_bias({blended_correction:+.1f})"
deb_weights = f"{deb_weights or 'DEB'} + intraday_bias({deb_intraday_adjustment:+.1f})"
# ── 12b. Next 48h hourly block for future-date analysis modal ──
next_48h_hourly = {
@@ -1490,6 +1502,10 @@ def _analyze(
if i == 0:
day_m = current_forecasts.copy()
d_val, d_winfo = deb_val, deb_weights
d_raw_val = deb_raw_val
d_version = deb_version
d_bias_adjustment = deb_bias_adjustment
d_bias_samples = deb_bias_samples
else:
day_m = mm_daily_raw.get(d_str, {}).copy()
if i < len(maxtemps) and maxtemps[i] is not None:
@@ -1505,14 +1521,20 @@ def _analyze(
}
d_val, d_winfo = None, ""
d_raw_val, d_version = None, None
d_bias_adjustment, d_bias_samples = 0.0, 0
d_probs = []
d_probs_all = []
if day_m:
try:
blended, winfo = calculate_dynamic_weights(city, day_m)
if blended is not None:
d_val = blended
d_winfo = winfo
deb_result = calculate_deb_prediction(city, day_m)
if deb_result.get("prediction") is not None:
d_val = deb_result.get("prediction")
d_raw_val = deb_result.get("raw_prediction")
d_version = deb_result.get("version")
d_bias_adjustment = deb_result.get("bias_adjustment") or 0.0
d_bias_samples = deb_result.get("bias_samples") or 0
d_winfo = deb_result.get("weights_info") or ""
# Calculate future probability based on model divergence
m_vals = [v for v in day_m.values() if v is not None]
@@ -1532,7 +1554,14 @@ def _analyze(
if day_m:
multi_model_daily[d_str] = {
"models": day_m,
"deb": {"prediction": d_val, "weights_info": d_winfo},
"deb": {
"prediction": d_val,
"raw_prediction": d_raw_val,
"version": d_version,
"weights_info": d_winfo,
"bias_adjustment": d_bias_adjustment,
"bias_samples": d_bias_samples,
},
"probabilities": d_probs if i > 0 else probabilities, # Use today's real prob for today
"probabilities_all": d_probs_all if i > 0 else probabilities_all,
}
@@ -1689,7 +1718,15 @@ def _analyze(
"forecasts": {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},
"deb": {
"prediction": deb_val,
"raw_prediction": deb_raw_val,
"version": deb_version,
"weights_info": deb_weights,
"bias_adjustment": deb_bias_adjustment,
"bias_samples": deb_bias_samples,
"intraday_adjustment": deb_intraday_adjustment,
},
"deviation_monitor": deviation_monitor,
"ensemble": ens_data,
"probabilities": {
@@ -1998,10 +2035,18 @@ def _analyze_summary(city: str, force_refresh: bool = False) -> Dict[str, Any]:
}
deb_val = None
deb_raw_val = None
deb_version = None
deb_bias_adjustment = 0.0
deb_bias_samples = 0
if current_forecasts:
blended, _weights_info = calculate_dynamic_weights(city, current_forecasts)
if blended is not None:
deb_val = blended
deb_result = calculate_deb_prediction(city, current_forecasts)
if deb_result.get("prediction") is not None:
deb_val = deb_result.get("prediction")
deb_raw_val = deb_result.get("raw_prediction")
deb_version = deb_result.get("version")
deb_bias_adjustment = deb_result.get("bias_adjustment") or 0.0
deb_bias_samples = deb_result.get("bias_samples") or 0
if deb_val is None:
deb_val = om_today
@@ -2071,7 +2116,13 @@ def _analyze_summary(city: str, force_refresh: bool = False) -> Dict[str, Any]:
"obs_age_min": obs_age_min,
"observation_status": "live" if cur_temp is not None else "missing",
},
"deb": {"prediction": _sf(deb_val)},
"deb": {
"prediction": _sf(deb_val),
"raw_prediction": _sf(deb_raw_val),
"version": deb_version,
"bias_adjustment": deb_bias_adjustment,
"bias_samples": deb_bias_samples,
},
"deviation_monitor": deviation_monitor or {},
"updated_at": datetime.now(timezone.utc).isoformat(),
}