Sebastian Rust
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
"""Pairwise hypothesis tests across solutions.
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Means: Welch's t (two-sided + one-sided) and Mann-Whitney U.
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Variance: Brown-Forsythe (Levene, median-centered) and Fligner-Killeen.
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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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from scipy import stats
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PAIRS = [
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("no-tso", "tso"),
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("no-tso", "tso-pacing"),
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("no-tso", "cake"),
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("tso", "tso-pacing"),
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("tso", "cake"),
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("tso-pacing", "cake"),
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]
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ALPHA = 0.05
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def _fmt(x: float) -> str:
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return f"{x:.4g}"
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def _mean_tests(out: dict, base: str, a: np.ndarray, b: np.ndarray) -> None:
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_, p_t = stats.ttest_ind(a, b, equal_var=False)
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_, p_tl = stats.ttest_ind(a, b, equal_var=False, alternative="less")
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_, p_tg = stats.ttest_ind(a, b, equal_var=False, alternative="greater")
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_, p_u = stats.mannwhitneyu(a, b, alternative="two-sided")
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out[f"{base}/welch-p"] = _fmt(p_t)
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out[f"{base}/welch-less-p"] = _fmt(p_tl)
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out[f"{base}/welch-greater-p"] = _fmt(p_tg)
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out[f"{base}/mwu-p"] = _fmt(p_u)
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def _variance_tests(out: dict, base: str, a: np.ndarray, b: np.ndarray) -> None:
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bf = stats.levene(a, b, center="median")
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fl = stats.fligner(a, b)
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out[f"{base}/bf-p"] = _fmt(bf.pvalue)
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out[f"{base}/fligner-p"] = _fmt(fl.pvalue)
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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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runs_path = exp_dir / "runs.csv"
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rtts_path = exp_dir / "rtts.csv"
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if runs_path.exists():
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sources.append(runs_path)
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runs = pd.read_csv(runs_path)
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for metric, col in (("sender-cpu", "cpu_sender"),
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("receiver-cpu", "cpu_receiver")):
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vals = {sol: sub[col].to_numpy() for sol, sub in runs.groupby("solution")}
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for a, b in PAIRS:
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if a not in vals or b not in vals:
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continue
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base = f"{exp_dir.name}/{metric}/test/{a}-vs-{b}"
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_mean_tests(out, base, vals[a], vals[b])
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if rtts_path.exists():
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sources.append(rtts_path)
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rtts = pd.read_csv(rtts_path)
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vals = {sol: sub["rtt_us"].to_numpy() / 1000.0
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for sol, sub in rtts.groupby("solution")}
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for a, b in PAIRS:
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if a not in vals or b not in vals:
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continue
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base = f"{exp_dir.name}/rtt/test/{a}-vs-{b}"
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_mean_tests(out, base, vals[a], vals[b])
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_variance_tests(out, base, vals[a], vals[b])
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idts_path = exp_dir / "idts.csv"
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if idts_path.exists():
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sources.append(idts_path)
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idts = pd.read_csv(idts_path, usecols=["solution", "idt_us"])
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vals = {sol: sub["idt_us"].to_numpy()
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for sol, sub in idts.groupby("solution")}
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for a, b in PAIRS:
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if a not in vals or b not in vals:
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continue
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base = f"{exp_dir.name}/idt/test/{a}-vs-{b}"
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_mean_tests(out, base, vals[a], vals[b])
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_variance_tests(out, base, vals[a], vals[b])
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def n_with(in_pattern: str, suffix: str) -> int:
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return sum(1 for k in out if in_pattern in k and k.endswith(suffix))
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families = {
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"cpu": n_with("-cpu/test/", "/mwu-p"),
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"rtt-mean": n_with("/rtt/test/", "/mwu-p"),
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"rtt-variance": n_with("/rtt/test/", "/bf-p"),
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"idt-mean": n_with("/idt/test/", "/mwu-p"),
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"idt-variance": n_with("/idt/test/", "/bf-p"),
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}
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for name, n in families.items():
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if n > 0:
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out[f"bonferroni/{name}-n"] = str(n)
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out[f"bonferroni/{name}-alpha"] = _fmt(ALPHA / n)
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out["bonferroni/alpha-uncorrected"] = _fmt(ALPHA)
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return out, sources
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