2569718930@qq.com
154 lines
4.8 KiB
Python
154 lines
4.8 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.analysis.deb_algorithm import load_history, save_history # noqa: E402
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from src.analysis.probability_calibration import ( # noqa: E402
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ENGINE_MODE_EMOS_SHADOW,
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apply_probability_calibration,
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build_probability_features,
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)
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from scripts.fit_probability_calibration import _default_history_arg # noqa: E402
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def _sample_to_features(sample):
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peak_flag = sample.get("peak_flag")
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if peak_flag == 1.0:
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peak_status = "past"
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elif peak_flag == 0.5:
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peak_status = "in_window"
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else:
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peak_status = "before"
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return build_probability_features(
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city_name=sample.get("city") or "",
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raw_mu=sample.get("raw_mu"),
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raw_sigma=sample.get("raw_sigma"),
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deb_prediction=sample.get("deb_prediction"),
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ens_data={
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"median": sample.get("ens_median"),
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"p10": None,
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"p90": None,
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},
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current_forecasts={},
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max_so_far=None,
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peak_status=peak_status,
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local_hour_frac=None,
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)
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def main():
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parser = argparse.ArgumentParser(description="Backfill shadow probability snapshots into daily records.")
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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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)
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parser.add_argument(
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"--training-samples",
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default=os.path.join(
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PROJECT_ROOT,
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"artifacts",
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"probability_calibration",
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"training_samples.json",
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),
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)
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parser.add_argument(
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"--calibration-file",
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default=os.path.join(
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PROJECT_ROOT,
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"artifacts",
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"probability_calibration",
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"default.json",
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),
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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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with open(args.training_samples, "r", encoding="utf-8") as fh:
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training_payload = json.load(fh)
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updated = 0
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touched = 0
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for sample in training_payload.get("samples") or []:
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city = str(sample.get("city") or "").strip().lower()
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date_str = str(sample.get("date") or "").strip()
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if not city or not date_str:
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continue
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record = ((history.get(city) or {}).get(date_str) or {})
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if not isinstance(record, dict) or not record:
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continue
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legacy_distribution = [
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{"value": row.get("v"), "probability": row.get("p")}
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for row in (record.get("prob_snapshot") or [])
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if isinstance(row, dict) and row.get("v") is not None
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]
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if not legacy_distribution:
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continue
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calibration = apply_probability_calibration(
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city_name=city,
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temp_symbol="°F" if city in {"atlanta", "chicago", "dallas", "miami", "new york", "seattle"} else "°C",
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raw_mu=sample.get("raw_mu"),
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raw_sigma=sample.get("raw_sigma"),
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max_so_far=None,
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legacy_distribution=legacy_distribution,
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features=_sample_to_features(sample),
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calibration_path=args.calibration_file,
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mode=ENGINE_MODE_EMOS_SHADOW,
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)
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shadow_distribution = calibration.get("shadow_distribution") or []
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compact_shadow = [
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{
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"v": int(row.get("value")),
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"p": round(float(row.get("probability") or 0.0), 3),
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}
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for row in shadow_distribution[:4]
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if row.get("value") is not None
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]
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compact_calibration = {
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"mode": calibration.get("mode"),
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"engine": calibration.get("engine"),
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"version": calibration.get("calibration_version"),
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"source": calibration.get("calibration_source"),
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"raw_mu": calibration.get("raw_mu"),
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"raw_sigma": calibration.get("raw_sigma"),
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"calibrated_mu": calibration.get("calibrated_mu"),
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"calibrated_sigma": calibration.get("calibrated_sigma"),
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}
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touched += 1
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if (
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record.get("shadow_prob_snapshot") == compact_shadow
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and record.get("probability_calibration") == compact_calibration
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):
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continue
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record["shadow_prob_snapshot"] = compact_shadow
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record["probability_calibration"] = compact_calibration
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history[city][date_str] = record
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updated += 1
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save_history(args.history_file, history)
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print(
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json.dumps(
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{
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"samples_seen": len(training_payload.get("samples") or []),
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"records_considered": touched,
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"records_updated": updated,
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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 __name__ == "__main__":
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main()
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