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#!/usr/bin/env python3
from __future__ import annotations
import argparse
import csv
import math
from collections import defaultdict
from pathlib import Path
import matplotlib.pyplot as plt
DEFAULT_BACKBONES = [
"gin",
"gine",
"gcn",
"graphsage",
"gatv2",
"graphconv",
"transformer",
"pna",
"gen",
"film",
"resgated",
"tag",
"sgc",
"cheb",
"arma",
"mf",
"appnp",
]
def mean(xs: list[float]) -> float:
return sum(xs) / len(xs) if xs else math.nan
def parse_list(value: str) -> list[str]:
return [x.strip() for x in value.replace(",", " ").split() if x.strip()]
def load_cells(path: Path, compute_label: str, datasets: list[str]) -> dict[tuple[str, str], float]:
requested = {x.lower() for x in datasets}
values: dict[tuple[str, str], list[float]] = defaultdict(list)
with path.open() as f:
for row in csv.DictReader(f):
dataset = row["dataset"].lower()
if requested and dataset not in requested:
continue
if row["compute_label"] != compute_label:
continue
values[(dataset, row["view"])].append(float(row["test_delta"]))
return {key: mean(xs) for key, xs in values.items()}
def render_table(
cells: dict[tuple[str, str], float],
datasets: list[str],
backbones: list[str],
title: str,
out_path: Path,
digits: int,
) -> None:
n_rows = max(1, len(datasets))
n_cols = max(1, len(backbones))
fig_w = max(12.0, 1.05 * n_cols + 2.8)
fig_h = max(2.4, 0.72 * n_rows + 1.8)
fig, ax = plt.subplots(figsize=(fig_w, fig_h), dpi=220)
ax.axis("off")
data = []
for dataset in datasets:
row = []
for backbone in backbones:
value = cells.get((dataset.lower(), backbone))
row.append("" if value is None or math.isnan(value) else f"{value:+.{digits}f}")
data.append(row)
table = ax.table(
cellText=data,
rowLabels=datasets,
colLabels=backbones,
cellLoc="center",
rowLoc="center",
loc="center",
)
table.auto_set_font_size(False)
table.set_fontsize(8.5)
table.scale(1.0, 1.65)
header_bg = "#f1f5f9"
edge = "#cbd5e1"
positive = "#15803d"
negative = "#b91c1c"
missing = "#64748b"
for (row_idx, col_idx), cell in table.get_celld().items():
cell.set_edgecolor(edge)
cell.set_linewidth(0.7)
text = cell.get_text()
if row_idx == 0 or col_idx == -1:
cell.set_facecolor(header_bg)
text.set_fontweight("bold")
text.set_color("#0f172a")
continue
raw = text.get_text()
if not raw:
text.set_text("-")
text.set_color(missing)
continue
value = float(raw)
if value > 0:
text.set_color(positive)
text.set_fontweight("bold")
elif value < 0:
text.set_color(negative)
else:
text.set_color("#334155")
ax.set_title(title, fontsize=13, fontweight="bold", pad=14)
fig.tight_layout(pad=0.8)
out_path.parent.mkdir(parents=True, exist_ok=True)
fig.savefig(out_path, bbox_inches="tight", facecolor="white")
plt.close(fig)
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--deltas", default="analysis/paired_deltas.csv")
ap.add_argument("--compute-label", default="fixed-rrog-T1-ns3+trace")
ap.add_argument("--datasets", default="zinc-cycle56")
ap.add_argument("--backbones", default=" ".join(DEFAULT_BACKBONES))
ap.add_argument("--out", default="analysis/zinc_delta_table.png")
ap.add_argument("--title", default="")
ap.add_argument("--digits", type=int, default=3)
args = ap.parse_args()
datasets = parse_list(args.datasets)
backbones = parse_list(args.backbones)
cells = load_cells(Path(args.deltas), args.compute_label, datasets)
title = args.title or f"Test Delta vs Classic: {args.compute_label}"
render_table(cells, datasets, backbones, title, Path(args.out), args.digits)
print(f"wrote {args.out}")
if __name__ == "__main__":
main()
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