File size: 1,332 Bytes
2532605 | 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 | import os
from pathlib import Path
from data import run_full_pipeline
from ml.train import train_model
def main():
print("Starting full data pipeline (2014-2024)...")
print("This may take some time as it fetches and caches historical telemetry.")
df = run_full_pipeline(
fastf1_start=2018,
fastf1_end=2026,
jolpica_start=2014,
jolpica_end=2017,
)
output_dir = Path("data_output")
output_dir.mkdir(parents=True, exist_ok=True)
out_path = output_dir / "fastf1_races.parquet"
df.to_parquet(out_path, index=False)
print(f"\nSaved full dataset to {out_path} ({len(df)} rows)")
print("\nTraining LightGBM model on full dataset...")
run_id = train_model(data_path=str(out_path))
print(f"Model trained successfully! MLflow Run ID: {run_id}")
# Update .env
env_path = Path(".env")
if env_path.exists():
with open(env_path, "r") as f:
lines = f.readlines()
with open(env_path, "w") as f:
for line in lines:
if line.startswith("KRONECTOR_MODEL_RUN_ID="):
f.write(f"KRONECTOR_MODEL_RUN_ID={run_id}\n")
else:
f.write(line)
print("Updated .env with new KRONECTOR_MODEL_RUN_ID")
if __name__ == "__main__":
main()
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