Sebastian Rust
61 lines
2.5 KiB
Python
61 lines
2.5 KiB
Python
"""RTT per (experiment, solution): mean, SD, quantiles, spread.
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Also emits tso-pacing's variance-reduction ratios vs the other solutions.
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"""
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from pathlib import Path
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import numpy as np
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import pandas as pd
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def _stats(x: np.ndarray) -> dict[str, str]:
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q05, q25, q50, q75, q95 = np.percentile(x, [5, 25, 50, 75, 95])
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return {
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"mean-ms": f"{x.mean():.2f}",
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"sd-ms": f"{x.std(ddof=1):.2f}",
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"median-ms": f"{q50:.2f}",
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"iqr-ms": f"{q75 - q25:.2f}",
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"p05-ms": f"{q05:.2f}",
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"p95-ms": f"{q95:.2f}",
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"spread-5-95-ms": f"{q95 - q05:.2f}",
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"n-samples": str(x.size),
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}
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def compute(derived: Path) -> tuple[dict[str, str], list[Path]]:
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out: dict[str, str] = {}
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sources: list[Path] = []
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for exp_dir in sorted(p for p in derived.iterdir() if p.is_dir()):
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rtts = exp_dir / "rtts.csv"
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if not rtts.exists():
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continue
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sources.append(rtts)
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df = pd.read_csv(rtts)
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per_sol: dict[str, dict[str, str]] = {}
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for sol, sub in df.groupby("solution", sort=True):
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x = sub["rtt_us"].to_numpy() / 1000.0
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stats = _stats(x)
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per_sol[sol] = stats
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for k, v in stats.items():
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out[f"{exp_dir.name}/rtt/{sol}/{k}"] = v
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if "tso-pacing" in per_sol:
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pac_sd = float(per_sol["tso-pacing"]["sd-ms"])
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pac_spread = float(per_sol["tso-pacing"]["spread-5-95-ms"])
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pac_iqr = float(per_sol["tso-pacing"]["iqr-ms"])
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for other in ("no-tso", "tso", "cake"):
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if other not in per_sol:
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continue
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o = per_sol[other]
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o_sd = float(o["sd-ms"])
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o_spread = float(o["spread-5-95-ms"])
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o_iqr = float(o["iqr-ms"])
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base = f"{exp_dir.name}/rtt/tso-pacing-vs-{other}"
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out[f"{base}/sd-ratio-pct"] = f"{100 * pac_sd / o_sd:.1f}"
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out[f"{base}/spread-ratio-pct"] = f"{100 * pac_spread / o_spread:.1f}"
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out[f"{base}/iqr-ratio-pct"] = f"{100 * pac_iqr / o_iqr:.1f}"
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out[f"{base}/sd-reduction-pct"] = f"{100 * (1 - pac_sd / o_sd):.1f}"
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out[f"{base}/spread-reduction-pct"] = f"{100 * (1 - pac_spread / o_spread):.1f}"
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out[f"{base}/iqr-reduction-pct"] = f"{100 * (1 - pac_iqr / o_iqr):.1f}"
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return out, sources
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