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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()
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