new file: checkpoints_tree/task1_tr_tree/best_tr_tree.pkl

new file:   checkpoints_tree/task1_tr_tree/model_selection.csv
	new file:   evaluation_outputs/task1_tr_tree/evaluation_train_all_samples.csv
	new file:   evaluation_outputs/task1_tr_tree/evaluation_train_curve.png
	new file:   evaluation_outputs/task1_tr_tree/evaluation_val_all_samples.csv
	new file:   evaluation_outputs/task1_tr_tree/evaluation_val_curve.png
	new file:   evaluation_outputs/task1_tr_tree/harmonic_5mm_1.55Hz_prediction.png
	new file:   scripts_tree/__pycache__/config.cpython-310.pyc
	new file:   scripts_tree/config.py
	new file:   scripts_tree/evaluate.py
	new file:   scripts_tree/predict_single.py
	new file:   scripts_tree/train.py

scripts_tree/predict_single.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import argparse
+import pickle
+from pathlib import Path
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from scripts.dataset import build_record_from_file
+
+try:
+    from .config import FEATURE_NAMES, evaluation_dir, make_experiment_config
+except ImportError:
+    from config import FEATURE_NAMES, evaluation_dir, make_experiment_config
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Predict task1 RMS from a single waveform CSV with tree model.")
+    parser.add_argument("--file", type=str, required=True)
+    parser.add_argument("--checkpoint", type=str, default=None)
+    return parser.parse_args()
+
+
+def resolve_checkpoint_path(project_root: Path, checkpoint_arg: str | None) -> Path:
+    if checkpoint_arg:
+        return Path(checkpoint_arg).resolve()
+    return project_root / "checkpoints_tree" / "task1_tr_tree" / "best_tr_tree.pkl"
+
+
+def save_prediction_figure(file_path: Path, record, pred_rms: float, true_rms: float | None, output_dir: Path) -> Path:
+    time_middle = record.time_middle.detach().cpu().numpy().reshape(-1)
+    x_middle = record.x_middle.detach().cpu().numpy().reshape(-1)
+    sampling_rate = record.sampling_rate
+    fft_values = np.fft.rfft(x_middle)
+    freqs = np.fft.rfftfreq(x_middle.size, d=1.0 / sampling_rate)
+    amplitudes = np.abs(fft_values)
+
+    fig, axes = plt.subplots(3, 1, figsize=(12, 10))
+    fig.suptitle(f"Task1 TR Tree Single-File Prediction | {file_path.name}")
+
+    axes[0].plot(time_middle, x_middle, color="tab:blue")
+    axes[0].set_title("Input Base Excitation (Middle Segment)")
+    axes[0].set_xlabel("Time")
+    axes[0].set_ylabel("Acceleration")
+    axes[0].grid(True, alpha=0.3)
+
+    axes[1].plot(freqs, amplitudes, color="tab:purple")
+    axes[1].axvline(record.frequency_hz, color="tab:red", linestyle="--", label=f"Dominant freq = {record.frequency_hz:.4f} Hz")
+    axes[1].set_xlim(0.0, 5.0)
+    axes[1].set_title("Input Spectrum")
+    axes[1].set_xlabel("Frequency (Hz)")
+    axes[1].set_ylabel("Amplitude")
+    axes[1].grid(True, alpha=0.3)
+    axes[1].legend()
+
+    labels = ["Pred RMS"] if true_rms is None else ["True RMS", "Pred RMS"]
+    values = [pred_rms] if true_rms is None else [true_rms, pred_rms]
+    colors = ["tab:orange"] if true_rms is None else ["tab:green", "tab:orange"]
+    axes[2].bar(labels, values, color=colors)
+    title = f"Predicted RMS = {pred_rms:.6f}"
+    if true_rms is not None:
+        error_percent = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0
+        title = f"True RMS = {true_rms:.6f} | Pred RMS = {pred_rms:.6f} | Error = {error_percent:.2f}%"
+    axes[2].set_title(title)
+    axes[2].set_ylabel("RMS")
+    axes[2].grid(True, axis="y", alpha=0.3)
+
+    plt.tight_layout()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    figure_path = output_dir / f"{file_path.stem}_prediction.png"
+    plt.savefig(figure_path, dpi=180, bbox_inches="tight")
+    plt.close(fig)
+    return figure_path
+
+
+def main() -> None:
+    args = parse_args()
+    config = make_experiment_config()
+    ckpt_path = resolve_checkpoint_path(config.data.project_root, args.checkpoint)
+    with ckpt_path.open("rb") as handle:
+        bundle = pickle.load(handle)
+    model = bundle["model"]
+
+    file_path = Path(args.file).resolve()
+    record = build_record_from_file(file_path, split="predict", config=config.data)
+    pred_tr = max(float(model.predict([record.features.tolist()])[0]), 1e-6)
+    pred_rms = pred_tr * float(record.x_rms.item())
+    true_rms = float(record.y_rms.item()) if record.y_rms.numel() > 0 else None
+
+    out_dir = evaluation_dir(config)
+    fig_path = save_prediction_figure(file_path, record, pred_rms, true_rms, out_dir)
+
+    print(f"Checkpoint: {ckpt_path}")
+    print(f"Model: {bundle['model_name']}")
+    print(f"Input file: {file_path}")
+    print(f"Extracted frequency (Hz): {record.frequency_hz:.6f}")
+    for feature_name, feature_value in zip(FEATURE_NAMES, record.features.tolist()):
+        print(f"{feature_name}: {feature_value:.6f}")
+    print(f"Predicted TR: {pred_tr:.6f}")
+    print(f"Predicted RMS: {pred_rms:.6f}")
+    if true_rms is not None:
+        error_percent = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0
+        print(f"True RMS: {true_rms:.6f}")
+        print(f"Relative RMS Error (%): {error_percent:.4f}")
+    print(f"Figure saved to: {fig_path}")
+
+
+if __name__ == "__main__":
+    main()