1 files changed, 139 insertions, 0 deletions
diff --git a/files/analysis/plot_mvp.py b/files/analysis/plot_mvp.py
new file mode 100644
index 0000000..495a9c2
--- /dev/null
+++ b/files/analysis/plot_mvp.py
@@ -0,0 +1,139 @@
+import argparse
+import csv
+import json
+import os
+from glob import glob
+from typing import Dict, List, Tuple
+
+import matplotlib.pyplot as plt
+
+
+def load_run(run_dir: str) -> Dict:
+    args_path = os.path.join(run_dir, "args.json")
+    metrics_path = os.path.join(run_dir, "metrics.csv")
+    if not (os.path.exists(args_path) and os.path.exists(metrics_path)):
+        return {}
+    with open(args_path, "r") as f:
+        args = json.load(f)
+    epochs = []
+    loss = []
+    acc = []
+    lyap = []
+    with open(metrics_path, "r") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            if row.get("step", "") != "epoch":
+                continue
+            try:
+                e = int(row.get("epoch", "0"))
+                l = float(row.get("loss", "nan"))
+                a = float(row.get("acc", "nan"))
+                y = row.get("lyap", "nan")
+                y = float("nan") if (y is None or y == "" or str(y).lower() == "nan") else float(y)
+            except Exception:
+                continue
+            epochs.append(e)
+            loss.append(l)
+            acc.append(a)
+            lyap.append(y)
+    if not epochs:
+        return {}
+    return {
+        "args": args,
+        "epochs": epochs,
+        "loss": loss,
+        "acc": acc,
+        "lyap": lyap,
+        "run_dir": run_dir,
+    }
+
+
+def label_for_run(run: Dict) -> str:
+    args = run["args"]
+    if args.get("lyapunov", False):
+        lam = args.get("lambda_reg", None)
+        tgt = args.get("lambda_target", None)
+        hid = args.get("hidden", None)
+        return f"Lyap λ={lam}, tgt={tgt}, H={hid}"
+    else:
+        hid = args.get("hidden", None)
+        return f"Baseline H={hid}"
+
+
+def gather_runs(base_dir: str) -> List[Dict]:
+    cand = sorted(glob(os.path.join(base_dir, "*")))
+    runs = []
+    for rd in cand:
+        data = load_run(rd)
+        if data:
+            runs.append(data)
+    return runs
+
+
+def plot_runs(runs: List[Dict], out_path: str):
+    if not runs:
+        raise SystemExit("No valid runs found (expected args.json and metrics.csv under base_dir/*)")
+    # Split baseline vs lyapunov
+    base = [r for r in runs if not r["args"].get("lyapunov", False)]
+    lyap = [r for r in runs if r["args"].get("lyapunov", False)]
+
+    fig, axes = plt.subplots(1, 3, figsize=(14, 4.5))
+
+    # Loss
+    ax = axes[0]
+    for r in base:
+        ax.plot(r["epochs"], r["loss"], label=label_for_run(r), alpha=0.9)
+    for r in lyap:
+        ax.plot(r["epochs"], r["loss"], label=label_for_run(r), linestyle="--", alpha=0.9)
+    ax.set_title("Training loss")
+    ax.set_xlabel("Epoch")
+    ax.set_ylabel("Loss")
+    ax.grid(True, alpha=0.3)
+    ax.legend(fontsize=8)
+
+    # Accuracy
+    ax = axes[1]
+    for r in base:
+        ax.plot(r["epochs"], r["acc"], label=label_for_run(r), alpha=0.9)
+    for r in lyap:
+        ax.plot(r["epochs"], r["acc"], label=label_for_run(r), linestyle="--", alpha=0.9)
+    ax.set_title("Training accuracy")
+    ax.set_xlabel("Epoch")
+    ax.set_ylabel("Accuracy")
+    ax.grid(True, alpha=0.3)
+    ax.legend(fontsize=8)
+
+    # Lyapunov estimate (only lyap runs)
+    ax = axes[2]
+    if lyap:
+        for r in lyap:
+            ax.plot(r["epochs"], r["lyap"], label=label_for_run(r), alpha=0.9)
+        ax.set_title("Surrogate Lyapunov estimate")
+        ax.set_xlabel("Epoch")
+        ax.set_ylabel("Avg log growth")
+        ax.grid(True, alpha=0.3)
+        ax.legend(fontsize=8)
+    else:
+        ax.text(0.5, 0.5, "No Lyapunov runs found", ha="center", va="center", transform=ax.transAxes)
+        ax.set_axis_off()
+
+    fig.tight_layout()
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    fig.savefig(out_path, dpi=150)
+    print(f"Saved figure to {out_path}")
+
+
+def main():
+    ap = argparse.ArgumentParser(description="Plot MVP runs: baseline vs Lyapunov comparison")
+    ap.add_argument("--base_dir", type=str, default="runs/mvp", help="Directory containing run subfolders")
+    ap.add_argument("--out", type=str, default="runs/mvp_summary.png", help="Output figure path")
+    args = ap.parse_args()
+
+    runs = gather_runs(args.base_dir)
+    plot_runs(runs, args.out)
+
+
+if __name__ == "__main__":
+    main()
+
+