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8b37c3f | 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 | """Run and overwrite the grouped classical/ensemble benchmark for one dataset."""
from __future__ import annotations
import argparse
from pathlib import Path
import sys
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from src.experiments.classical import run_grouped_tabular_benchmark
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("dataset", choices=("nasa", "calce", "oxford"))
parser.add_argument("--project-root", type=Path, default=PROJECT_ROOT)
parser.add_argument("--seeds", type=int, nargs="+", default=(17, 42, 2026))
args = parser.parse_args()
frame = pd.read_csv(
args.project_root / "artifacts" / "v3" / "features" / args.dataset / "features.csv"
)
metrics, predictions = run_grouped_tabular_benchmark(
frame,
dataset_name=args.dataset.upper() if args.dataset == "nasa" else args.dataset.title(),
n_splits=min(5, frame["battery_id"].nunique()),
seeds=tuple(args.seeds),
)
results = args.project_root / "artifacts" / "v3" / "results"
results.mkdir(parents=True, exist_ok=True)
metrics.to_csv(results / f"{args.dataset}_classical_fold_metrics.csv", index=False)
predictions.to_csv(results / f"{args.dataset}_classical_predictions.csv", index=False)
print(metrics.groupby("model")[["mae", "rmse", "r2", "within_5pp"]].mean().sort_values("mae"))
if __name__ == "__main__":
main()
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