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import csv
from collections import defaultdict
from statistics import mean, stdev
import math
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def rename_method(label: str) -> str:
# Use the same short labels as the TikZ version
mapping = {
'Spectral': 'Spectral Clustering',
'Densest': 'Densest Subgraph',
'QuasiClique': 'QuasiClique',
'k-core': 'k-core',
'L-RMC': 'L-RMC (ours)',
}
return mapping.get(label, label)
def load_rows(path):
with open(path, 'r') as f:
reader = csv.DictReader(f)
return [row for row in reader]
def aggregate_f1_vs_pout(rows):
# Group by (Method, p_out) across k and p_in
group = defaultdict(list)
for row in rows:
method = row['Method']
try:
pout = float(row['ExternalDensity'])
f1 = float(row['F1'])
except (KeyError, ValueError):
continue
group[(method, pout)].append(f1)
summary = defaultdict(dict) # method -> pout -> (mean, ci95, n)
for (method, pout), vals in group.items():
n = len(vals)
mu = mean(vals)
if n > 1:
s = stdev(vals)
else:
s = 0.0
ci95 = 1.96 * s / math.sqrt(max(n, 1))
summary[method][pout] = (mu, ci95, n)
return summary
def plot(summary, out_path):
pouts = sorted({p for m in summary for p in summary[m].keys()})
methods = sorted(summary.keys())
plt.figure(figsize=(6.0, 3.4), dpi=200)
for method in methods:
means = []
ci = []
for p in pouts:
mu, ci95, _ = summary[method].get(p, (float('nan'), 0.0, 0))
means.append(mu)
ci.append(ci95)
label = rename_method(method)
plt.plot(pouts, means, marker='o', linewidth=2, markersize=4, label=label)
# Shaded 95% CI
lower = [m - c for m, c in zip(means, ci)]
upper = [m + c for m, c in zip(means, ci)]
plt.fill_between(pouts, lower, upper, alpha=0.15)
plt.xlabel(r"$p_{\text{out}}$")
plt.ylabel("F1")
plt.title("F1 vs $p_{\\text{out}}$ (avg over $k,p_{\\text{in}}$)")
plt.grid(True, alpha=0.3)
plt.legend(fontsize=7, frameon=False)
plt.tight_layout()
plt.savefig(out_path, bbox_inches='tight')
print(f"Saved {out_path}")
if __name__ == '__main__':
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('csv', help='Path to Table1 CSV (per-setting averages)')
ap.add_argument('--out', default='appendix_f1_vs_pout.png', help='Output PNG path')
args = ap.parse_args()
rows = load_rows(args.csv)
summary = aggregate_f1_vs_pout(rows)
plot(summary, args.out)
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