Three times in this log a verdict rested on a sample that could not have produced it: 38.19 (programme-house claimed at N=20, resolves at N=60), 38.21 (harbor at n=3 resolves nothing finer than ~15 fails, yet every recorded margin is smaller), 39.5/38.20 (a 10-fail cpsat margin inside a +-23-fail noise band). Each was found years later. experiments/ab_report.py makes it visible when the verdict is made: minimum detectable difference = t_crit(0.975, N-1) * sd / sqrt(N) A margin below the MDD is not a weak result but an absent one -- the experiment could not have distinguished it from zero however it came out. The report flags that, refuses to endorse a winner, and states the N needed. Validated against both datasets measured this session, reproducing the hand-computed figures exactly: programme-house N=60 +0.567 p=0.017 MDD 0.462 verdict supported programme-house N=20 +0.700 p=0.085 MDD 0.805 UNDERPOWERED, N~=26 harbor N=24 +1.208 p=0.502 MDD 3.669 UNDERPOWERED, N~=202 Harbor needing ~200 seeds means it cannot answer the collapse_insearch question at any N this project would realistically run. Fixed a defect in my own first version: with all-ties (sd=0) the MDD collapses to zero and the naive abs(mean) < mdd reported "margin exceeds the MDD -- verdict supported" for a margin of 0.000, with t=nan. A reporter that endorses a zero margin is worse than none. Degenerate cases are now explicit and distinguish all-ties from a constant non-zero difference. Separate correction found while validating: 38.19's published p-values for N=20 and N=40 were 0.069 and 0.045, from a normal approximation. The exact paired t-test gives 0.085 and 0.052 -- so N=40 did NOT reach significance either; it took N=60. The approximation was anti-conservative, making results look more significant than they are, the same direction of error this thread is about. Corrected at all four citing sites, and the "needs N >= 40" guidance raised to N >= 60. Closes homemaker-py-tco. Lint at parity (46); tests 384 passed, 0 failed. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
165 lines
6.8 KiB
Python
165 lines
6.8 KiB
Python
"""Fixed-budget search A/B for the crinkliness modes (`homemaker-py-ssz`).
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DESIGN.md §38.6 A/B'd the modes against the §38.2 *deletion test*, which has
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since been retracted, and it used the pre-§39.4 `type[:1] in ("C","O")` prefix
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rule that mislabels programme rooms as circulation. So the modes have never
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been measured against what `ssz`'s acceptance criteria actually asks for: a
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fixed-budget search, hard/soft fail split, on harbor-house and maple-court.
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**The scoring discipline is the point of this script.** `compact_ok`,
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`exempt_circulation` and `compact_ok` all return 1.0 for leaves that stock
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scores below FAIL_THRESHOLD, so scoring an arm under its own objective deletes
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a fail category for free and every arm "wins". Two numbers are therefore
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reported per arm:
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urb the arm's final layout re-scored under the STOCK objective. This is
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the comparable yardstick, and the one that answers "did optimising
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under this variant steer the search to a better building?"
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own the same layout under the arm's own objective. Lower than `urb` by
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construction for the permissive modes; it is reported only so the
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size of the definitional discount is visible, never as the result.
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A mode passes on `urb`, not on `own`.
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Usage::
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python experiments/ab_ssz_search.py --budget 3000 --seeds 3
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python experiments/ab_ssz_search.py --modes urb compact_ok --seeds 2
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"""
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from __future__ import annotations
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import argparse
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import collections
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import copy
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import csv
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import time
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from pathlib import Path
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from homemaker_layout import dom as dom_mod
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from homemaker_layout import driver, fitness
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CORPUS = ["examples/harbor-house", "examples/maple-court"]
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MODES = ["urb", "floor", "compact_ok", "exempt_circulation"]
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def _with_mode(mode: str):
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"""Patch `fitness.load_config` so every evaluator built during the run --
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the driver's, the inner loop's, the seeder's -- sees `crinkliness_mode`.
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`driver.search` has no parameter for it, and `driver._fitness_for` is
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lru_cached on its arguments, so the cache is cleared around the patch or a
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later arm would silently reuse the previous arm's evaluator.
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"""
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orig = fitness.load_config
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def patched(directory, overrides=None):
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ov = dict(overrides or {})
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ov["crinkliness_mode"] = mode
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return orig(directory, overrides=ov)
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return orig, patched
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def tiers(fails) -> tuple[int, int]:
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c = collections.Counter(fitness.classify_fail_tier(f) for f in fails)
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return c["hard"], c["soft"]
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def run_arm(progdir: str, seed: int, mode: str, budget: int,
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child_budget: int) -> dict:
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orig, patched = _with_mode(mode)
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fitness.load_config = patched
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driver._fitness_for.cache_clear()
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t0 = time.perf_counter()
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try:
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res = driver.search(
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dom_mod.load(f"{progdir}/init.dom"), progdir,
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budget=budget, seed=seed, child_budget=child_budget, n_workers=1)
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root = copy.deepcopy(res.best.root)
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own_conf, own_cost = patched(progdir, overrides={"leaf_sharing": True,
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"collapse_insearch": True})
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_, own_fails = fitness.Fitness(own_conf, own_cost).score_with_fails(
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copy.deepcopy(root))
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finally:
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fitness.load_config = orig
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driver._fitness_for.cache_clear()
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# the comparable yardstick: stock objective, same layout
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conf, cost = orig(progdir, overrides={"leaf_sharing": True,
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"collapse_insearch": True})
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_, urb_fails = fitness.Fitness(conf, cost).score_with_fails(copy.deepcopy(root))
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uh, us = tiers(urb_fails)
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oh, os_ = tiers(own_fails)
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return dict(programme=Path(progdir).name, seed=seed, mode=mode,
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urb_hard=uh, urb_soft=us, urb_total=uh + us,
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own_hard=oh, own_soft=os_, own_total=oh + os_,
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elapsed_s=round(time.perf_counter() - t0, 1))
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def main() -> None:
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--budget", type=int, default=3000)
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ap.add_argument("--child-budget", type=int, default=80)
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ap.add_argument("--seeds", type=int, default=3)
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ap.add_argument("--modes", nargs="+", default=MODES)
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ap.add_argument("--corpus", nargs="+", default=CORPUS)
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ap.add_argument("--out", default="experiments/results/ab_ssz_search.csv")
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args = ap.parse_args()
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rows = []
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out = Path(args.out)
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out.parent.mkdir(parents=True, exist_ok=True)
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for progdir in args.corpus:
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for mode in args.modes:
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for seed in range(args.seeds):
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r = run_arm(progdir, seed, mode, args.budget, args.child_budget)
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rows.append(r)
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print(f" {r['programme']:<14} {mode:<20} seed={seed} "
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f"urb {r['urb_hard']}h/{r['urb_soft']}s "
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f"(own {r['own_hard']}h/{r['own_soft']}s) "
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f"{r['elapsed_s']}s", flush=True)
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with out.open("w", newline="") as fh:
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w = csv.DictWriter(fh, fieldnames=list(rows[0]))
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w.writeheader()
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w.writerows(rows)
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# homemaker-py-tco: report what this N could actually resolve, beside the
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# result, so an underpowered verdict is visible when it is made. §38.19 and
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# §38.21 are both cases where that was only noticed years later.
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from ab_report import format_report, paired_report
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base = args.modes[0]
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for progdir in args.corpus:
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name = Path(progdir).name
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for mode in args.modes[1:]:
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by = {}
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for r in rows:
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if r["programme"] == name and r["mode"] in (base, mode):
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by.setdefault(r["seed"], {})[r["mode"]] = r["urb_hard"] + r["urb_soft"]
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seeds = sorted(s for s, v in by.items() if base in v and mode in v)
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if len(seeds) < 2:
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continue
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print(f"\n--- {name}: {mode} vs {base} (stock-scored total fails) ---")
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print(format_report(paired_report(
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[by[s][base] for s in seeds], [by[s][mode] for s in seeds], base, mode)))
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print(f"\n=== stock-objective (urb) fail counts, budget {args.budget} ===")
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print(f" {'programme':<14}{'mode':<22}{'hard':<14}{'soft':<14}total")
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print(" " + "-" * 70)
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for progdir in args.corpus:
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name = Path(progdir).name
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for mode in args.modes:
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sel = [r for r in rows if r["programme"] == name and r["mode"] == mode]
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if not sel:
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continue
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h = sum(r["urb_hard"] for r in sel) / len(sel)
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s = sum(r["urb_soft"] for r in sel) / len(sel)
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print(f" {name:<14}{mode:<22}{h:<14.1f}{s:<14.1f}{h + s:.1f}")
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print(f"\nwrote {out}")
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if __name__ == "__main__":
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main()
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