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b9d2398 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #!/usr/bin/env python3
"""Regenerate the C1 coordination figures/tables from raw experiment CSVs.
Consumes the output of ../grite/scripts/run_experiments.sh (coordination.csv) and writes
PDFs into ../figures plus a LaTeX-ready summary table. Pure stdlib + matplotlib; no seaborn.
Usage:
python plot_coordination.py --raw _raw --out ../figures
"""
import argparse
import csv
import statistics
from collections import defaultdict
from pathlib import Path
ARMS = ["no-coord", "locks-only", "locks-plus-state"]
ARM_LABEL = {
"no-coord": "No coordination",
"locks-only": "Locks only",
"locks-plus-state": "Locks + shared state",
}
def load(raw_dir: Path):
rows = []
with open(raw_dir / "coordination.csv", newline="") as f:
for r in csv.DictReader(f):
rows.append(r)
return rows
def aggregate(rows):
"""-> agg[arm][n] = {metric: (mean, ci95)} over seeds."""
buckets = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
for r in rows:
arm, n = r["arm"], int(r["n_agents"])
buckets[arm][n]["dup"].append(float(r["duplicate_work_rate"]))
buckets[arm][n]["conf"].append(float(r["conflicting_edits"]))
buckets[arm][n]["good"].append(float(r["goodput"]))
buckets[arm][n]["deny"].append(float(r["lock_denials"]))
def stat(xs):
m = statistics.fmean(xs)
if len(xs) > 1:
sd = statistics.pstdev(xs)
ci = 1.96 * sd / (len(xs) ** 0.5)
else:
ci = 0.0
return m, ci
agg = defaultdict(lambda: defaultdict(dict))
for arm, ns in buckets.items():
for n, metrics in ns.items():
for k, xs in metrics.items():
agg[arm][n][k] = stat(xs)
return agg
def plot(agg, out_dir: Path):
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
out_dir.mkdir(parents=True, exist_ok=True)
ns_all = sorted({n for arm in agg for n in agg[arm]})
# Figure 1: duplicate-work rate vs N, per arm.
fig, ax = plt.subplots(figsize=(4.2, 3.0))
for arm in ARMS:
if arm not in agg:
continue
ns = sorted(agg[arm])
ys = [agg[arm][n]["dup"][0] for n in ns]
es = [agg[arm][n]["dup"][1] for n in ns]
ax.errorbar(ns, ys, yerr=es, marker="o", capsize=3, label=ARM_LABEL[arm])
ax.set_xscale("log", base=2)
ax.set_xticks(ns_all)
ax.set_xticklabels(ns_all)
ax.set_xlabel("Concurrent agents $N$")
ax.set_ylabel("Duplicate-work rate")
ax.set_ylim(-0.02, 1.0)
ax.legend(fontsize=7)
ax.grid(True, alpha=0.3)
fig.tight_layout()
fig.savefig(out_dir / "duplicate_work.pdf")
plt.close(fig)
# Figure 2 (money figure): coordination overhead (lock denials, proxy) vs duplicate-work
# AVOIDED relative to no-coord. One point per (arm, N): the Pareto trade-off.
fig, ax = plt.subplots(figsize=(4.2, 3.0))
for arm in ARMS:
if arm not in agg or arm == "no-coord":
continue
xs, ys = [], []
for n in sorted(agg[arm]):
base = agg["no-coord"][n]["dup"][0]
avoided = base - agg[arm][n]["dup"][0]
overhead = agg[arm][n]["deny"][0] # lock-wait/denial events as overhead proxy
xs.append(overhead)
ys.append(avoided)
ax.plot(xs, ys, marker="s", label=ARM_LABEL[arm])
ax.set_xlabel("Coordination overhead (lock denials)")
ax.set_ylabel("Duplicate work avoided vs. no-coord")
ax.legend(fontsize=7)
ax.grid(True, alpha=0.3)
fig.tight_layout()
fig.savefig(out_dir / "pareto.pdf")
plt.close(fig)
print(f"[plot_coordination] wrote {out_dir}/duplicate_work.pdf, {out_dir}/pareto.pdf")
def write_table(agg, out_dir: Path):
"""Emit a LaTeX booktabs summary at the largest N."""
n = max({n for arm in agg for n in agg[arm]})
lines = [
"% auto-generated by plot_coordination.py -- do not edit",
"\\begin{tabular}{lrrr}",
"\\toprule",
f"Arm ($N={n}$) & Dup-work rate & Conflicting edits & Goodput \\\\",
"\\midrule",
]
for arm in ARMS:
if arm not in agg or n not in agg[arm]:
continue
d = agg[arm][n]
lines.append(
f"{ARM_LABEL[arm]} & {d['dup'][0]:.2f} & {d['conf'][0]:.0f} & {d['good'][0]:.2f} \\\\"
)
lines += ["\\bottomrule", "\\end{tabular}"]
(out_dir / "coordination_table.tex").write_text("\n".join(lines) + "\n")
print(f"[plot_coordination] wrote {out_dir}/coordination_table.tex")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--raw", default="_raw", type=Path)
ap.add_argument("--out", default=Path("../figures"), type=Path)
args = ap.parse_args()
rows = load(args.raw)
agg = aggregate(rows)
plot(agg, args.out)
write_table(agg, args.out)
if __name__ == "__main__":
main()
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