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6eff894 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | #!/usr/bin/env python3
import json, os, argparse
from pathlib import Path
from datetime import datetime, timedelta
import pandas as pd, numpy as np, subprocess
META = Path("models/rain_xgb_tuned_meta.json")
LOGS = Path("logs")
PRED_LOG = LOGS / "predictions.csv"
def ensure_hourly(lat, lon, past_days=120):
env = os.environ.copy()
env["LAT"], env["LON"], env["PAST_DAYS"] = str(lat), str(lon), str(past_days)
subprocess.run(["bash", "scripts/fetch_weather.sh"], check=True, env=env)
subprocess.run(["python3", "scripts/export_hourly.py"], check=True, env=env)
return pd.read_csv("results/hourly.csv", parse_dates=["time"])
def label_from_df(df, ts_pred, horizon_h, event_mm):
# find the row with time == ts_pred, then sum next H hours of precip_mm
# allow slight mismatch by nearest timestamp within 1 hour
idx = (df["time"] - ts_pred).abs().idxmin()
if abs((df.loc[idx, "time"] - ts_pred).total_seconds()) > 3600:
return None # can't align
end_idx = min(idx + horizon_h, len(df)-1)
total = float(np.nansum(df.loc[idx+1:end_idx, "precip_mm"]))
return 1 if total >= event_mm else 0
def main():
if not PRED_LOG.exists():
print("No predictions.csv found.")
return
meta = json.loads(Path(META).read_text())
H = int(meta["horizon_hours"]); event_mm = float(meta["event_mm"])
df = pd.read_csv(PRED_LOG, parse_dates=["ts_pred","logged_at"])
updated = 0
for i, row in df[df["y_true"].isna() | (df["y_true"]=="")].iterrows():
ts_pred = row["ts_pred"]
if datetime.now() < ts_pred + timedelta(hours=H):
continue # horizon not passed yet
# fetch enough history to cover that timestamp
hdf = ensure_hourly(row["lat"], row["lon"], past_days=120)
y = label_from_df(hdf, ts_pred, H, event_mm)
if y is not None:
df.at[i, "y_true"] = int(y)
updated += 1
df.to_csv(PRED_LOG, index=False)
print(f"Backfilled {updated} rows into {PRED_LOG}")
if __name__ == "__main__":
main()
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