2569718930@qq.com
146 lines
4.8 KiB
Python
146 lines
4.8 KiB
Python
from __future__ import annotations
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import argparse
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import json
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import sys
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from collections import Counter
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from pathlib import Path
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from typing import Any, Dict
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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if str(PROJECT_ROOT) not in sys.path:
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.analysis.deb_algorithm import load_history, save_history # noqa: E402
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from src.analysis.probability_snapshot_archive import ( # noqa: E402
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load_snapshot_rows_for_day,
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)
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from src.database.runtime_state import STATE_STORAGE_FILE, get_state_storage_mode # noqa: E402
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from scripts.fit_probability_calibration import _default_history_arg # noqa: E402
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def _load_daily_records(path: Path) -> Dict[str, Dict[str, Dict[str, Any]]]:
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data = load_history(str(path))
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return data if isinstance(data, dict) else {}
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def _pick_model_value_from_snapshots(
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city: str,
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target_date: str,
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model_name: str,
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) -> float | None:
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rows = load_snapshot_rows_for_day(city, target_date)
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values = []
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for row in rows:
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mm = row.get("multi_model") or {}
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if not isinstance(mm, dict):
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continue
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value = mm.get(model_name)
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if value is None:
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continue
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try:
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values.append(float(value))
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except Exception:
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continue
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if not values:
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return None
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counts = Counter(values)
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return counts.most_common(1)[0][0]
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def main() -> int:
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parser = argparse.ArgumentParser(
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description="Backfill missing model forecasts in daily_records from archived probability snapshots."
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)
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parser.add_argument(
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"--history-file",
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default=_default_history_arg(),
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help="Optional legacy daily_records.json path. In sqlite mode this defaults to the runtime database.",
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)
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parser.add_argument("--city", help="Optional city filter, e.g. ankara")
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parser.add_argument("--date", help="Optional YYYY-MM-DD filter")
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parser.add_argument(
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"--model",
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default="MGM",
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help="Model name to backfill from snapshot multi_model payloads",
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)
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parser.add_argument(
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"--write",
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action="store_true",
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help="Write recovered values back to history file / runtime state",
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)
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args = parser.parse_args()
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history_path = Path(args.history_file) if args.history_file else None
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data = _load_daily_records(history_path or Path())
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model_name = str(args.model or "").strip()
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city_filter = str(args.city or "").strip().lower() or None
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date_filter = str(args.date or "").strip() or None
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recovered = []
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missing = []
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changed = False
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for city, city_rows in sorted(data.items()):
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if city_filter and city != city_filter:
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continue
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if not isinstance(city_rows, dict):
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continue
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for target_date, record in sorted(city_rows.items()):
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if date_filter and target_date != date_filter:
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continue
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if not isinstance(record, dict):
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continue
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forecasts = record.get("forecasts") or {}
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if not isinstance(forecasts, dict):
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forecasts = {}
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if forecasts.get(model_name) is not None:
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continue
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recovered_value = _pick_model_value_from_snapshots(city, target_date, model_name)
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if recovered_value is None:
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missing.append((city, target_date))
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continue
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recovered.append((city, target_date, recovered_value))
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if args.write:
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next_forecasts = dict(forecasts)
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next_forecasts[model_name] = recovered_value
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record["forecasts"] = next_forecasts
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changed = True
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print(
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json.dumps(
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{
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"model": model_name,
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"recovered_count": len(recovered),
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"missing_count": len(missing),
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"recovered": [
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{"city": city, "date": date_str, "value": value}
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for city, date_str, value in recovered
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],
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"missing": [
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{"city": city, "date": date_str}
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for city, date_str in missing
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],
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"write_requested": bool(args.write),
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"storage_mode": get_state_storage_mode(),
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},
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ensure_ascii=False,
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indent=2,
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)
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)
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if changed:
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previous_mode = get_state_storage_mode()
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# Reuse existing save path semantics. In sqlite-only mode, save_history would skip file write.
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save_history(str(history_path or ""), data)
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if previous_mode == STATE_STORAGE_FILE and (history_path is None or not history_path.exists()):
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raise FileNotFoundError(history_path)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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