2569718930@qq.com
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
import argparse
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import json
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import os
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import sys
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if PROJECT_ROOT not in sys.path:
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sys.path.insert(0, PROJECT_ROOT)
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from src.database.runtime_state import ( # noqa: E402
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ProbabilitySnapshotRepository,
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TrainingFeatureRecordRepository,
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get_state_storage_mode,
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)
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def _load_legacy_snapshot_rows(path: str):
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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 _spread_from_ensemble(ensemble: dict):
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if not isinstance(ensemble, dict):
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return None
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try:
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p10 = float(ensemble.get("p10"))
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p90 = float(ensemble.get("p90"))
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except Exception:
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return None
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if p90 < p10:
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return None
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return max(0.1, round((p90 - p10) / 2.56, 3))
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def main():
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parser = argparse.ArgumentParser(
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description="Restore permanent training feature history from snapshot archives."
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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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)
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args = parser.parse_args()
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rows = []
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if get_state_storage_mode() == "sqlite":
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rows.extend(ProbabilitySnapshotRepository().load_all_rows())
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rows.extend(_load_legacy_snapshot_rows(args.snapshot_file))
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latest = {}
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for row in rows:
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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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ts = str(row.get("timestamp") or "")
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if not city or not date_str:
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continue
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key = (city, date_str)
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current = latest.get(key)
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if current is None or ts >= str(current.get("timestamp") or ""):
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latest[key] = row
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repo = TrainingFeatureRecordRepository()
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restored = 0
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for (city, date_str), row in latest.items():
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repo.upsert_record(
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city,
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date_str,
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{
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"forecasts": row.get("multi_model") or {},
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"deb_prediction": row.get("deb_prediction"),
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"mu": row.get("raw_mu"),
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"probability_features": {
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"raw_mu": row.get("raw_mu"),
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"raw_sigma": row.get("raw_sigma"),
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"deb_prediction": row.get("deb_prediction"),
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"ens_median": ((row.get("ensemble") or {}).get("median")),
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"ensemble_spread": _spread_from_ensemble(row.get("ensemble") or {}),
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"max_so_far": row.get("max_so_far"),
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"peak_status": row.get("peak_status"),
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},
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"prob_snapshot": row.get("prob_snapshot") or [],
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"shadow_prob_snapshot": row.get("shadow_prob_snapshot") or [],
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"probability_calibration": {
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"engine": row.get("probability_engine"),
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"mode": row.get("probability_mode"),
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"calibration_version": row.get("calibration_version"),
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"calibration_source": row.get("calibration_source"),
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"calibrated_mu": row.get("calibrated_mu"),
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"calibrated_sigma": row.get("calibrated_sigma"),
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},
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"observation": row.get("observation") or {},
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"snapshot_timestamp": row.get("timestamp"),
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},
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)
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restored += 1
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print(json.dumps({"restored_feature_records": restored}, ensure_ascii=False))
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if __name__ == "__main__":
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main()
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