| """Appendix A1 — LightGCN hyperparameter sweep (dim x layers) F1 heatmap.""" |
| from pathlib import Path |
| import re |
| import pandas as pd |
| import numpy as np |
| import matplotlib.pyplot as plt |
| import seaborn as sns |
| from style import apply, save, PALETTE as C, COL2 |
|
|
| KEY = "figA1_hyperparam" |
| TITLE = "Appendix Figure A1. LightGCN hyperparameter sweep" |
|
|
|
|
| def make(root, out): |
| apply() |
| csv = root / "validation_runs" / "dynamic_summary.csv" |
| df = pd.read_csv(csv) |
| df = df[df.split == "dynamic_seed202"].copy() |
|
|
| def parse(run): |
| m = re.search(r"l(\d)d(\d+)", run) |
| return pd.Series([int(m.group(1)), int(m.group(2))] if m else [np.nan, np.nan]) |
|
|
| df[["layers", "dim"]] = df.run.apply(parse) |
| df = df.dropna(subset=["layers"]) |
| piv = df.groupby(["dim", "layers"]).f1.max().unstack() |
|
|
| fig, ax = plt.subplots(figsize=(COL2 * 0.62, 3.4)) |
| sns.heatmap(piv, annot=True, fmt=".5f", cmap="viridis", linewidths=0.5, linecolor="white", |
| cbar_kws={"label": "best validation F1"}, ax=ax) |
| try: |
| col = list(piv.columns).index(2) |
| row = list(piv.index).index(512) |
| ax.add_patch(plt.Rectangle((col, row), 1, 1, fill=False, edgecolor=C[3], lw=2.5)) |
| except ValueError: |
| pass |
| ax.set_xlabel("propagation layers"); ax.set_ylabel("embedding dim") |
| ax.set_title("LightGCN sweep — chosen config: 2 layers, dim 512", fontsize=9) |
| save(fig, KEY, out) |
| return dict(key=KEY, title=TITLE, status="ok", files=[f"{KEY}.pdf", f"{KEY}.png", f"{KEY}.svg"], |
| sources=[str(csv)], |
| caption=( |
| "LightGCN hyperparameter sweep (best validation F1 per dim×layers cell, seed=202). " |
| "Two propagation layers with a 512-d embedding is optimal (0.93858); a single layer " |
| "under-fits and very large embeddings over-fit. This is the configuration used throughout.")) |
|
|
|
|
| if __name__ == "__main__": |
| from style import ensure_dirs |
| r = make(Path("."), ensure_dirs(Path("."))) |
| print(r["key"], r["status"]) |
|
|
