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| """Generate calibrated and governance post-processed predictions.""" | |
| from __future__ import annotations | |
| import argparse | |
| from pathlib import Path | |
| try: | |
| from .evaluate_model import predict_for_model_run | |
| except ImportError: # pragma: no cover - direct script execution | |
| from evaluate_model import predict_for_model_run | |
| def main(argv: list[str] | None = None) -> int: | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument("--model-run", type=Path, required=True) | |
| parser.add_argument("--features", type=Path, required=True) | |
| parser.add_argument("--output", type=Path, required=True) | |
| args = parser.parse_args(argv) | |
| _, predictions = predict_for_model_run(args.model_run, args.features) | |
| args.output.parent.mkdir(parents=True, exist_ok=True) | |
| predictions.to_csv(args.output, index=False) | |
| print(f"wrote_predictions: {args.output}") | |
| print(f"rows: {len(predictions)}") | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |