Archive probability snapshots and wire them into training
This commit is contained in:
2569718930@qq.com
@@ -33,11 +33,128 @@ def _load_json_if_exists(path):
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return data if isinstance(data, dict) else {}
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def _extract_samples(history, settlement_history=None):
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def _load_history_with_fallback(path):
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data = load_history(path)
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if data:
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return data
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return _load_json_if_exists(path)
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def _load_snapshot_rows(path):
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rows = []
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if not path or not os.path.exists(path):
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return rows
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with open(path, "r", encoding="utf-8") as fh:
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for line in fh:
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line = line.strip()
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if not line:
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continue
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try:
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row = json.loads(line)
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except Exception:
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continue
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if isinstance(row, dict):
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rows.append(row)
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return rows
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def _actual_high_for(history, settlement_history, city, date_str):
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city_rows = (history or {}).get(city) or {}
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record = city_rows.get(date_str) or {}
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actual_high = _sf(record.get("actual_high")) if isinstance(record, dict) else None
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filled = False
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if actual_high is None:
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actual_high = _sf(((settlement_history.get(city) or {}).get(date_str) or {}).get("max_temp"))
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filled = actual_high is not None
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return actual_high, filled
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def _extract_snapshot_samples(history, snapshot_rows, settlement_history=None):
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samples = []
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filled_actual_from_history = 0
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today = datetime.utcnow().strftime("%Y-%m-%d")
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settlement_history = settlement_history or {}
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for row in snapshot_rows or []:
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city = str(row.get("city") or "").strip().lower()
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date_str = str(row.get("date") or "").strip()
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if not city or not date_str or date_str == today:
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continue
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actual_high, filled = _actual_high_for(history, settlement_history, city, date_str)
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if actual_high is None:
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continue
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if filled:
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filled_actual_from_history += 1
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raw_mu = _sf(row.get("raw_mu"))
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raw_sigma = _sf(row.get("raw_sigma"))
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deb_prediction = _sf(row.get("deb_prediction"))
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ensemble = row.get("ensemble") or {}
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if not isinstance(ensemble, dict):
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ensemble = {}
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ens_median = _sf(ensemble.get("median"))
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ensemble_spread = None
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ens_p10 = _sf(ensemble.get("p10"))
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ens_p90 = _sf(ensemble.get("p90"))
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if ens_p10 is not None and ens_p90 is not None and ens_p90 >= ens_p10:
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ensemble_spread = max(0.1, (ens_p90 - ens_p10) / 2.56)
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multi_model = row.get("multi_model") or {}
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if not isinstance(multi_model, dict):
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multi_model = {}
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forecast_values = [val for val in (_sf(v) for v in multi_model.values()) if val is not None]
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forecast_values.sort()
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if ensemble_spread is None:
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if len(forecast_values) >= 2:
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ensemble_spread = max(0.6, (forecast_values[-1] - forecast_values[0]) / 2.0)
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elif raw_sigma is not None:
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ensemble_spread = raw_sigma
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else:
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ensemble_spread = 1.0
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if raw_sigma is None:
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raw_sigma = ensemble_spread
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peak_status = str(row.get("peak_status") or "before").strip().lower()
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peak_flag = 0.0
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if peak_status == "in_window":
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peak_flag = 0.5
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elif peak_status == "past":
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peak_flag = 1.0
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max_so_far = _sf(row.get("max_so_far"))
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max_so_far_gap = None
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if deb_prediction is not None and max_so_far is not None:
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max_so_far_gap = deb_prediction - max_so_far
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if raw_mu is None:
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continue
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samples.append(
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{
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"city": city,
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"date": date_str,
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"timestamp": row.get("timestamp"),
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"actual_high": actual_high,
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"raw_mu": raw_mu,
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"raw_sigma": raw_sigma or 1.0,
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"deb_prediction": deb_prediction,
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"ens_median": ens_median if ens_median is not None else raw_mu,
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"ensemble_spread": ensemble_spread,
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"max_so_far_gap": max_so_far_gap,
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"peak_flag": peak_flag,
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"sample_source": "snapshot",
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}
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)
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return samples, filled_actual_from_history
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def _extract_daily_record_samples(history, settlement_history=None, excluded_keys=None):
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samples = []
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filled_actual_from_history = 0
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today = datetime.utcnow().strftime("%Y-%m-%d")
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settlement_history = settlement_history or {}
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excluded_keys = excluded_keys or set()
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for city, city_rows in (history or {}).items():
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if not isinstance(city_rows, dict):
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continue
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@@ -45,6 +162,8 @@ def _extract_samples(history, settlement_history=None):
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for date_str, record in city_rows.items():
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if date_str == today or not isinstance(record, dict):
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continue
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if (city, date_str) in excluded_keys:
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continue
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actual_high = _sf(record.get("actual_high"))
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if actual_high is None:
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actual_high = _sf((city_settlement.get(date_str) or {}).get("max_temp"))
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@@ -99,11 +218,30 @@ def _extract_samples(history, settlement_history=None):
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"ensemble_spread": ensemble_spread,
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"max_so_far_gap": max_so_far_gap,
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"peak_flag": peak_flag,
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"sample_source": "daily_record",
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}
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)
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return samples, filled_actual_from_history
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def _extract_samples(history, settlement_history=None, snapshot_rows=None):
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snapshot_samples, snapshot_filled = _extract_snapshot_samples(
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history,
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snapshot_rows or [],
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settlement_history=settlement_history,
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)
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excluded_keys = {
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(sample["city"], sample["date"])
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for sample in snapshot_samples
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}
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daily_samples, daily_filled = _extract_daily_record_samples(
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history,
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settlement_history=settlement_history,
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excluded_keys=excluded_keys,
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)
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return snapshot_samples + daily_samples, snapshot_filled + daily_filled
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def main():
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parser = argparse.ArgumentParser(description="Fit PolyWeather probability calibration parameters.")
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parser.add_argument(
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@@ -126,6 +264,11 @@ def main():
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),
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help="Optional daily settlement history JSON built from historical CSV files.",
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)
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parser.add_argument(
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"--snapshot-file",
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default=os.path.join(PROJECT_ROOT, "data", "probability_training_snapshots.jsonl"),
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help="Optional JSONL file with archived probability snapshots.",
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)
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parser.add_argument(
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"--version",
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default=None,
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@@ -133,11 +276,13 @@ def main():
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)
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args = parser.parse_args()
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history = load_history(args.history_file)
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history = _load_history_with_fallback(args.history_file)
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settlement_history = _load_json_if_exists(args.settlement_history)
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snapshot_rows = _load_snapshot_rows(args.snapshot_file)
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samples, filled_actual_from_history = _extract_samples(
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history,
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settlement_history=settlement_history,
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snapshot_rows=snapshot_rows,
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)
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calibration = fit_calibration(samples, version=args.version)
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if not samples:
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