modified: .DS_Store
modified: checkpoints_final/task1_final/task1_final_model.pkl new file: checkpoints_final/task1_final/task1_tmd_adapter.pkl new file: scripts_4/README.md new file: scripts_4/__init__.py new file: scripts_4/__pycache__/adapter.cpython-314.pyc new file: scripts_4/adapter.py new file: scripts_4/predict_tmd_single.py new file: scripts_4/train_tmd_adapter.py
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CrbnsCat10n
164
scripts_4/train_tmd_adapter.py
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164
scripts_4/train_tmd_adapter.py
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from __future__ import annotations
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import argparse
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import json
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import pickle
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from pathlib import Path
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import sys
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(PROJECT_ROOT / "scripts"))
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from config import make_experiment_config
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from dataset import build_record_from_file
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try:
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from .adapter import FrequencyScaleAdapter, save_adapter
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except ImportError:
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from adapter import FrequencyScaleAdapter, save_adapter
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Fit a tiny-sample TMD adapter on top of the Non_TMD baseline .pkl model.")
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parser.add_argument("--checkpoint", type=str, default=None, help="Path to baseline model .pkl.")
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parser.add_argument("--tmd-dir", type=str, default="downloads/TMD", help="Directory that contains TMD harmonic csv files.")
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parser.add_argument("--output", type=str, default="checkpoints_final/task1_final/task1_tmd_adapter.pkl")
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parser.add_argument("--report-json", type=str, default="evaluation_outputs/task1_final/tmd_adapter_report.json")
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parser.add_argument("--shrinkage", type=float, default=0.20, help="Scale shrinkage toward 1.0.")
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parser.add_argument("--extrapolation-decay-hz", type=float, default=0.25)
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parser.add_argument("--min-scale", type=float, default=0.40)
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parser.add_argument("--max-scale", type=float, default=2.50)
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return parser.parse_args()
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def resolve_checkpoint_path(checkpoint_arg: str | None) -> Path:
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if checkpoint_arg:
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return Path(checkpoint_arg).resolve()
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return PROJECT_ROOT / "checkpoints_final" / "task1_final" / "task1_final_model.pkl"
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def load_baseline_model(model_path: Path):
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with model_path.open("rb") as handle:
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payload = pickle.load(handle)
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return payload["model"], payload.get("summary", {})
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def base_predict_rms(model, features: np.ndarray, x_rms: float, eps: float) -> float:
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pred_tr = max(float(model.predict([features.tolist()])[0]), eps)
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return pred_tr * float(x_rms)
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def collect_tmd_rows(model, tmd_dir: Path, eps: float) -> list[dict]:
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config = make_experiment_config().data
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config.data_dir = str(Path(tmd_dir).relative_to(PROJECT_ROOT))
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config.__post_init__()
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rows: list[dict] = []
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for file_path in sorted(config.data_root.rglob("harmonic_5mm_*Hz_TMD.csv")):
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record = build_record_from_file(file_path, config)
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base_pred = base_predict_rms(model, record.features, record.x_rms, eps)
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scale = float(record.y_rms / max(base_pred, eps))
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rows.append(
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{
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"file_name": file_path.name,
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"frequency_hz": float(record.frequency_hz),
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"true_y_rms": float(record.y_rms),
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"base_pred_y_rms": float(base_pred),
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"scale_true_over_base": float(scale),
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}
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)
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if not rows:
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raise RuntimeError("No TMD harmonic files found.")
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return rows
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def loocv_error(rows: list[dict], shrinkage: float, extrapolation_decay_hz: float, min_scale: float, max_scale: float) -> list[float]:
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errors: list[float] = []
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eps = 1e-12
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for holdout in range(len(rows)):
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train_rows = [row for i, row in enumerate(rows) if i != holdout]
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adapter = FrequencyScaleAdapter(
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frequencies_hz=np.asarray([row["frequency_hz"] for row in train_rows], dtype=np.float64),
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scale_values=np.asarray([row["scale_true_over_base"] for row in train_rows], dtype=np.float64),
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shrinkage=shrinkage,
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extrapolation_decay_hz=extrapolation_decay_hz,
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min_scale=min_scale,
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max_scale=max_scale,
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)
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sample = rows[holdout]
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pred = adapter.predict(sample["base_pred_y_rms"], sample["frequency_hz"])
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err = abs(pred - sample["true_y_rms"]) / max(abs(sample["true_y_rms"]), eps) * 100.0
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errors.append(float(err))
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return errors
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def main() -> None:
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args = parse_args()
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ckpt_path = resolve_checkpoint_path(args.checkpoint)
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model, baseline_summary = load_baseline_model(ckpt_path)
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rows = collect_tmd_rows(model, (PROJECT_ROOT / args.tmd_dir).resolve(), eps=1e-6)
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rows = sorted(rows, key=lambda x: x["frequency_hz"])
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adapter = FrequencyScaleAdapter(
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frequencies_hz=np.asarray([row["frequency_hz"] for row in rows], dtype=np.float64),
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scale_values=np.asarray([row["scale_true_over_base"] for row in rows], dtype=np.float64),
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shrinkage=args.shrinkage,
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extrapolation_decay_hz=args.extrapolation_decay_hz,
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min_scale=args.min_scale,
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max_scale=args.max_scale,
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)
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adapted_rows = []
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for row in rows:
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adapted_pred = adapter.predict(row["base_pred_y_rms"], row["frequency_hz"])
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adapted_err = abs(adapted_pred - row["true_y_rms"]) / max(abs(row["true_y_rms"]), 1e-12) * 100.0
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adapted_rows.append({**row, "adapted_pred_y_rms": adapted_pred, "adapted_err_pct": adapted_err})
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loocv_errors = loocv_error(
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rows=rows,
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shrinkage=args.shrinkage,
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extrapolation_decay_hz=args.extrapolation_decay_hz,
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min_scale=args.min_scale,
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max_scale=args.max_scale,
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)
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report = {
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"checkpoint": str(ckpt_path),
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"baseline_summary": baseline_summary,
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"samples": adapted_rows,
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"adapter_scales_after_shrinkage": [
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{"frequency_hz": float(f), "scale": float(s)}
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for f, s in zip(adapter.frequencies_hz.tolist(), adapter.scale_values.tolist())
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],
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"metrics": {
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"base_mean_err_pct": float(
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np.mean(
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[
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abs(row["base_pred_y_rms"] - row["true_y_rms"]) / max(abs(row["true_y_rms"]), 1e-12) * 100.0
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for row in rows
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]
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)
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),
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"adapted_mean_err_pct": float(np.mean([row["adapted_err_pct"] for row in adapted_rows])),
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"loocv_mean_err_pct": float(np.mean(loocv_errors)),
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"loocv_max_err_pct": float(np.max(loocv_errors)),
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},
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}
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output_path = (PROJECT_ROOT / args.output).resolve()
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save_adapter(adapter, output_path, extra={"report_metrics": report["metrics"], "checkpoint": str(ckpt_path)})
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report_path = (PROJECT_ROOT / args.report_json).resolve()
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report_path.parent.mkdir(parents=True, exist_ok=True)
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report_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
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print(f"Adapter saved to: {output_path}")
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print(f"Report saved to: {report_path}")
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print(pd.DataFrame(adapted_rows).to_string(index=False))
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print(f"LOOCV mean error (%): {report['metrics']['loocv_mean_err_pct']:.4f}")
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print(f"LOOCV max error (%): {report['metrics']['loocv_max_err_pct']:.4f}")
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if __name__ == "__main__":
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main()
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